JP2016104984A - Methods and systems for enhancing control of power plant generating units - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide methods in which generating units include either an on-condition or an off-condition during a selected operating period.SOLUTION: The method includes: defining competing operating modes which include different possible combinations regarding which of a plurality of generating units have an on-condition and which have an off-condition during a selected operating period; defining multiple cases for each of the competing operating modes, where the multiple cases include varying a value of an operating parameter; receiving performance objectives that include a cost function; receiving an ambient conditions forecast; for the multiple cases of the competing operating modes, simulating the turndown operation with a power plant model given the value of the operating parameter and the ambient conditions forecast; and evaluating a simulation result pursuant to the cost function to select therefrom a preferred case.SELECTED DRAWING: Figure 1

Description

  The invention of this application relates generally to power generation, and more particularly to methods and systems related to optimizing and / or enhancing the economics and performance of power plants having thermal power generation units.

  In a power system, a large number of participants or power plants generate electricity, which is then distributed to residential and business customers through a common transmission line. As will be recognized, thermal power generation units, such as gas turbines, steam turbines, and combined cycle plants, are still utilized to generate a significant portion of the power required by such systems. Has been. Each of the power plants in such a system includes one or more power generation units, each of which typically includes a control system that controls operation, and two or more power generation units. In the case of a power plant with units, it includes a control system that controls the performance of the entire power plant. As an example, one of the responsibilities of the plant operator is the generation of an offer curve that represents the cost of power production. The offer curve typically includes an incremental variable cost curve, an average variable cost curve, or another suitable indication of variable generation costs, which is typically expressed in dollars per megawatt hour for megawatt output. Is done. The average variable cost curve can represent the cumulative cost divided by the cumulative power output for a given point, and the incremental variable cost curve represents the change in cost divided by the change in power output. It will be recognized that this is possible. An incremental variable cost curve can be obtained, for example, by taking the first derivative of a power plant input-output curve that represents the cost per hour for the generated power. In combined cycle power plants where waste heat from a fuel burning generator is used to produce steam to power an auxiliary steam turbine, incremental variable cost curves can also be obtained using known techniques. Is possible, but its derivation can be more complex.

  In most power systems, a competitive process, commonly referred to as economic power supply, is used to split the system load between power plants over future periods. As part of this process, the power plant periodically generates an offer curve and sends the offer curve to a power system authority or a power supply command center. Such an offer curve represents a bid from a power plant to generate a portion of the electricity required by the power system over future market periods. The power supply command authority should receive offer curves from the power plants in the system, evaluate those offer curves, and engage each power plant to most efficiently meet the expected load requirements of the system. Determine the level. In doing so, the power supply command authority analyzes the offer curve and establishes a shutdown schedule that describes the limits that each of the power plants will be involved over the relevant period in order to find the lowest power generation costs for the system. produce.

  Once the startup / shutdown schedule is communicated to the power plant, each power plant can determine the most efficient and cost-effective way to satisfy its load startup / shutdown plan. It will be appreciated that the power generation unit of the power plant includes a control system that monitors and controls operation. Such control system dominates the combustion system and other aspects of operation when the power generation unit includes a thermal power generator. (For illustrative purposes, both gas turbines and combined cycle power plants are described herein. However, certain embodiments of the invention may be applied to other types of power generation units, or others. It will be appreciated that the control system can provide fuel flow, inlet guide vanes, and other control inputs to ensure efficient operation of the engine. It is possible to run a scheduling algorithm that adjusts. However, the actual power output and efficiency of the power plant is affected by external factors such as variable ambient conditions that cannot be fully envisaged. As will be appreciated, the complexity of such systems and the variability of operating conditions makes it difficult to predict and control performance, which often results in inefficient operation.

  Mechanical degradation that occurs over time is another problem that makes it difficult to quantify the facts, which can have a significant impact on the performance of the power generation unit. The rate of degradation, replacement of worn components, timing of maintenance routines, and other factors affect the short-term performance of the plant, and therefore when generating a cost curve during the power supply process, as well as the plant It will be recognized that this needs to be taken into account when assessing the long-term cost effectiveness. As an example, the life of a gas turbine typically includes a limit value expressed in both time of operation and number of starts. If the gas turbine or its components reach their start-up limit before its time limit, the gas turbine or its component must be repaired or replaced even if the time-based lifetime remains. Although the time-based lifetime in a gas turbine can be extended by reducing the combustion temperature, this reduces the efficiency of the gas turbine, which increases the cost of operation. Conversely, increasing the combustion temperature increases efficiency but shortens gas turbine life and increases maintenance and / or replacement costs. As will be appreciated, the life cycle cost of a heat engine depends on many complex factors and at the same time represents an important consideration in the economic efficiency of a power plant.

  Given the complexity of modern power plants, especially power plants with multiple power generation units, and the markets in which power plants compete, power plant operators continue to struggle to maximize economic benefits I have been doing it. For example, grid compliance and power planning for power plants use static control profiles such as heat rate curves derived in an excessively static manner, i.e. collected and derived from periodic performance tests only Thus, it is adversely affected by controlling the thermal power generation unit. During these periodic updates, turbine engine performance can change (eg, from degradation), which can affect startup and load performance. Moreover, changes in external factors that occur within a day can lead to inefficient operation if this is not taken into account in the turbine control profile. To compensate for this type of variability, power plant operators often become overly conservative when planning for future operations, which results in power generation units that are not fully utilized. At other times, the plant operator may be forced to operate the unit inefficiently in order to satisfy an excessive startup / shutdown plan.

  Unless short-term inefficiencies and / or long-term degradation are identified as each is realized, conventional control systems for power plants must be frequently retuned, an expensive process. Or must be operated conservatively to proactively respond to component degradation. An alternative is to take the risk of violating operating limits that lead to excessive fatigue or failure. Similarly, conventional power plant control systems lack the ability to most cost effectively respond to changing conditions. As will be appreciated, this results in power plant utilization that is often far from optimal. Thus, there is a need for improved methods and systems for monitoring, modeling and controlling the operation of power plants, especially the myriad operating modes available to complex modern power plant operators, and each There is a need for methods and systems that allow a more complete understanding of the economic tradeoffs associated with operating modes.

U.S. Pat. No. 8816531

  The present application thus describes a control method for optimizing the turndown operation for a power plant having a plurality of power generation units over a selected operating period. The plurality of power generation units may each include either an on condition or an off condition during a selected operating period. The method is a step of defining competing operating modes, wherein the competing operating modes include which of the plurality of power generation units has an on condition and which has an off condition during a selected operating period. In that regard, defining a plurality of cases for each of the steps and competing modes of operation, including combinations that may differ, the plurality of cases varying the value of the operating parameter over a range Competing with a step of receiving a performance goal including a cost function for evaluating a turndown operation during a selected operation period, and receiving an ambient condition estimate for the selected operation period For each of the multiple operating mode cases, it is assumed that the operating parameters and ambient conditions are expected. Using the power plant model to simulate the power plant turndown behavior for the selected operating period, and according to the cost function, evaluate the simulation results from each of the simulations, and then the competing operating modes Selecting a preferred case from a plurality of cases for each of.

  These and other features of the present application will become more apparent when the following detailed description of the preferred embodiment is considered in conjunction with the drawings and appended claims.

1 is a schematic diagram of a power system according to an aspect of the present invention. 1 is a schematic diagram of an exemplary thermal power generation unit as may be used in a power plant according to an embodiment of the present invention. 1 is a schematic diagram of an exemplary power plant having multiple gas turbines, according to an embodiment of the present invention. FIG. 3 illustrates an exemplary system configuration for a plant controller and optimizer, according to aspects of the present invention. 1 is a schematic diagram of a power plant with a plant controller and optimizer having a system configuration, according to certain aspects of the invention. FIG. 2 illustrates a computer system having an exemplary user interface in accordance with certain aspects of the present invention. FIG. 3 illustrates an example incremental heat rate curve and the effect that errors can have on an economic power supply process. 1 is a schematic diagram illustrating an exemplary plant controller with a power system, according to aspects of the present invention. 2 is a flow diagram of a power plant control method according to an aspect of the present invention. 2 is a data flow diagram illustrating an architecture for a plant optimization system for a combined cycle power plant according to an aspect of the present invention. 1 is a simplified block diagram of a computer system as may be used with a real-time optimization system in accordance with aspects of the present invention. 2 is a flow diagram of an exemplary method for solving parameterized simultaneous equations and constraints according to the present invention. FIG. 3 is a diagram illustrating a simplified configuration of a computer system according to a control methodology of an embodiment of the present invention. FIG. 6 illustrates an alternative configuration of a computer system according to a control methodology of an embodiment of the present invention. 2 is a flow diagram of an exemplary control methodology, according to an exemplary aspect of the invention. 6 is a flow diagram of an alternative control methodology, according to an exemplary aspect of the invention. 6 is a flow diagram of an alternative control methodology, according to an exemplary aspect of the invention. FIG. 6 illustrates a flow diagram in which an alternative embodiment of the present invention related to optimization of turndown operation is provided. FIG. 6 illustrates a flow diagram in which an alternative embodiment of the present invention related to optimizing between turndown and shutdown operations is provided. 2 is a diagram illustrating available modes of operation of a gas turbine during a selected period of operation having a defined interval, according to aspects of an exemplary embodiment of the invention. 6 is a diagram illustrating available modes of operation of a gas turbine during a selected period of operation having a defined interval, in accordance with aspects of an alternative embodiment of the present invention. 6 is a flow diagram according to a power plant fleet optimization process according to an alternative embodiment of the present invention. 1 is a schematic diagram of a power plant fleet optimization system according to an aspect of the present invention. 2 is a schematic diagram of a power plant fleet optimization system according to an alternative aspect of the present invention. 2 is a schematic diagram of a power plant fleet optimization system according to an alternative aspect of the present invention. 1 is a schematic diagram of a power block optimization system including a block controller. 2 is a schematic diagram of an alternative power block optimization system including a block controller. 2 is a flowchart illustrating an embodiment of a process for optimizing a combined cycle power plant shutdown. FIG. 2 illustrates an exemplary control system in which a model-free adaptive controller is used in accordance with aspects of the present invention.

  Exemplary embodiments of the invention will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; Rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Similar numbers may represent similar elements throughout.

  In accordance with aspects of the present invention, systems and methods are disclosed that can be used to optimize the performance of power systems, power plants, and / or thermal power units. In an exemplary embodiment, this optimization includes economic optimization, which allows power plant operators to use one of the alternative modes of operation to enhance profitability. To decide. Embodiments can be utilized within a particular power system to provide a competitive advantage in obtaining an advantageous economic start-up plan during the powering process. The advisor function can allow the operator to choose between operating modes based on accurate economic comparisons and expectations. As another feature, the process of prospectively purchasing fuel for future power generation periods can be improved so that fuel inventory is minimized without increasing the risk of shortage. Other configurations of the present invention provide a computer-implemented method and apparatus for modeling a power system and a power plant having a plurality of thermal power generation units, as described below. Technical effects of some configurations of the present invention include energy system model generation and solutions that predict performance under various physical, operating, and / or economic conditions. An exemplary embodiment of the present invention combines a power plant model that predicts performance under various ambient and operating conditions with an economic model that includes economic constraints, goals, and market conditions to increase profitability. It comes to optimize. In doing so, the optimization system of the present invention relates to ambient conditions, operating conditions, contractual conditions, regulatory conditions, legal conditions, and / or certain combinations of economic and market conditions. It is possible to predict an optimized set point that maximizes profitability.

  FIG. 1 illustrates a schematic diagram of a power system 10 that includes aspects of the present invention and an exemplary environment in which embodiments may operate. The power system 10 can include a generator or plant 12, such as, for example, the illustrated wind and thermal power plants. It will be appreciated that a thermal power plant can include power generation units such as gas turbines, coal fired steam turbines, and / or combined cycle plants. In addition, the power system 10 may include other power sources such as solar power generation facilities, hydropower sources, geothermal power sources, nuclear power sources, and / or any other suitable power source now known or discovered in the future. It is possible to include a type of power plant (not shown). The transmission line 14 can connect various power plants 12 to customers or loads 16 of the power system 10. It should be understood that the transmission line 14 represents a grid or distribution network for a power system and can include multiple sections and / or substations as desired or required. is there. The power generated from the power plant 12 can be delivered to the load 16 via the transmission line 14, and the load 16 can include, for example, a municipal customer, a residential customer, or a corporate customer. Is possible. The power system 10 can also include a storage device 18 that is connected to the transmission line 14 and stores energy during periods of excessive power generation.

  The power system 10 also includes a control system or controller 22, 23, 25 that manages or controls the operation of some of the components contained within the power system 10. . For example, the plant controller 22 can control each operation of the power plant 12. The load controller 23 can control the operation of different loads 16 that are part of the power system 10. For example, the load controller 23 can manage the customer's power purchase mode or timing. The power supply command authority 24 can manage certain aspects of the operation of the power system 10 and can include a power system controller 25 that controls the economic power supply procedure. The load start / stop plan is distributed among participating power plants by the economic power supply procedure. The controllers 22, 23, 25 are represented by rectangular blocks, and the controllers 22, 23, 25 can be connected to the communication network 20 via a communication line or communication connection 21, and the communication network 20 exchanges data. Is done. Connection 21 can be wired or wireless. It will be appreciated that the communication network 20 can be connected to or part of a larger communication system or network, such as the Internet or a private computer network. In addition, the controllers 22, 23, 25 receive information, data, and instructions from the data library and data resources and / or to the data library and data resources through the communication network 20. Instructions can be sent and the data library and data resource can be generally referred to herein as “data resource 26” or alternatively, one or more such Data repositories can be stored or stored locally. Data resource 26 may include several types of data including, but not limited to, market data, operational data, and ambient data. Market data includes information about market conditions, such as energy sales prices, fuel costs, labor costs, regulations, and the like. The operational data includes information related to operating conditions of the power plant or its power generation units, such as temperature or pressure measurements in the power plant, air flow, fuel flow, and the like. Ambient data includes information related to ambient conditions in the plant, such as ambient air temperature, humidity, and / or pressure. Market data, operational data, and ambient data can each include historical records, current condition data, and / or data related to forecasts. For example, data resource 26 may include current and forecast weather / climate information, current and forecast market conditions, usage and performance history records for power plant operations, and / or similar components and / or configurations. Can include parameters measured for the operation of other power plants having as well as other data as needed and / or desired. In operation, for example, the power system controller 25 of the power supply command authority 24 receives data from other controllers 22, 23 in the power system 10 and issues instructions to the other controllers 22, 23 in the power system 10. Is possible. Each of the plant controller and the load controller then controls the system components for which it is responsible and relays information about the system components to the power system controller 25 and receives instructions from the power system controller 25.

  FIG. 2 is a schematic diagram of an exemplary thermal power generation unit or gas turbine system 30 that may be used in a power plant according to the present invention. As shown, the gas turbine system 30 includes a compressor 32, a combustor 34, a turbine 36 that is drivably coupled to the compressor 32, and a component controller 31. The component controller 31 can be connected to the plant controller 22, and the plant controller 22 can be connected to a user input device for receiving communications from an operator 39. Alternatively, it will be appreciated that the component controller 31 and the plant controller 22 can be combined into a single controller. The intake duct 40 guides ambient air to the compressor 32. As discussed in FIG. 3, injected water and / or other humidifiers may be directed through the intake duct 40 to the compressor. The intake duct 40 may have filters, screens, and sound absorbing devices that contribute to the pressure loss of ambient air that flows through the intake duct 40 and into the inlet guide vanes 41 of the compressor 32. The exhaust duct 42 directs combustion gas from the outlet of the turbine 36 through, for example, emission control and sound absorption devices. The sound absorbing material and the emission control device can apply back pressure to the turbine 36. Turbine 36 may drive a generator 44 that produces electrical power, which may then be distributed through power system 10 via transmission line 14.

  The operation of the gas turbine system 30 can be monitored by a number of sensors 46 that detect various operating conditions or parameters throughout the gas turbine system 30, for example, , Compressor 32, combustor 34, turbine 36, generator 44, and ambient environment 33. For example, temperature sensor 46 can monitor ambient temperature, compressor discharge temperature, turbine exhaust temperature, and other temperatures in the flow path of gas turbine system 30. Similarly, the pressure sensor 46 can monitor ambient pressure, compressor inlet, compressor outlet, turbine exhaust, and static and dynamic pressure levels at other suitable locations in the gas turbine system. . A humidity sensor 46, such as a wet bulb thermometer and a dry bulb thermometer, can measure the ambient humidity in the compressor intake duct. The sensor 46 also typically measures flow parameters, speed sensors, flame detector sensors, valve position sensors, guide vane angle sensors, and various operating parameters and operating conditions related to the operation of the gas turbine system 30. It is possible to include other sensors used. As used herein, the term “parameter” is used to define operating conditions within a system, such as the gas turbine system 30 or other power generation system described herein. Represents the measurable physical properties of the motion that can be used. Operating parameters include temperature, pressure, humidity, and gas flow characteristics at locations defined along the working fluid path, as well as other conditions that may be appropriate without limitation, ambient conditions, fuel characteristics, and so on. It is possible to include characteristics. It will also be appreciated that the control system 31 includes several actuators 47 by which the control system 31 mechanically controls the operation of the gas turbine system 30. Actuator 47 is a variable that allows manipulation of specific process inputs (ie, manipulated variables) for control of process outputs (ie, controlled variables) according to the desired operational result or mode. It is possible to include an electromechanical device having a set point or set point. For example, a command generated by the component controller 31 may be a fuel supply that allows one or more actuators 47 in the turbine system 30 to adjust the flow level, fuel split, and / or the type of fuel being burned. It is possible to cause the valve between the combustion chamber 34 to be adjusted. As another example, a command generated by the control system 31 can cause one or more actuators to adjust an inlet guide vane setting that changes the orientation angle of the inlet guide vane.

  The component controller 31 can be a computer system having a processor that uses a sensor measurement and instructions from a user or plant operator (hereinafter “operator 39”) to produce a gas turbine. Program code is executed to control the operation of the system 30. As discussed in more detail below, the software executed by the controller 31 may include a scheduling algorithm for coordinating any of the subsystems described herein. The component controller 31 can adjust the gas turbine system 30 based in part on algorithms stored in its digital memory. These algorithms, for example, allow the component controller 31 to maintain NOx and CO emissions in the turbine exhaust within certain predetermined emission limits, or in other cases combustor combustion. It may be possible to maintain the temperature within a predetermined limit. It is recognized that the algorithm may include inputs for parameter variables such as compressor pressure ratio, ambient humidity, inlet pressure loss, turbine exhaust back pressure, etc., as well as any other suitable parameters. Will be. The schedule and algorithm executed by the component controller 31 is adapted to changes in ambient conditions, which affect emissions, combustor dynamics, and combustion temperature limit values in full and partial load operating conditions, etc. Effect. As discussed in more detail below, the component controller 31 sets the desired turbine exhaust temperature and combustor fuel split with the goal of meeting performance goals while adhering to the operability limits of the gas turbine system. It is possible to apply an algorithm for scheduling a gas turbine, such as For example, the component controller 31 can determine the combustor temperature rise and NOx during part load operation, increasing the operating margin to the combustion dynamics limit, thereby improving the operability, reliability of the power generation unit. , And to improve operability.

  Turning to FIG. 3, a schematic diagram of an exemplary power plant 12 having a plurality of power generation units or plant components 49 according to aspects of the present invention is provided. The illustrated power plant 12 of FIG. 3 is a common configuration and will therefore be used to discuss some of the exemplary embodiments of the present invention presented below. However, as will be appreciated, the methods and systems described herein are more widely applicable to power plants having more power generation units than those shown in FIG. While it may be expandable, it is still applicable to power plants having a single power generation component such as that illustrated in FIG. It will be appreciated that the power plant 12 of FIG. 3 is a combined cycle plant, and the combined cycle plant includes several plant components 49, including a gas turbine system 30 and a steam turbine system 50. Power generation may be enhanced by other plant components 49, such as an intake conditioning system 51 and / or a heat recovery steam generator having a duct firing system (hereinafter “HRSG duct firing system 52”). . Each of gas turbine system 30, steam turbine system 50 including HRSG duct firing system 52, and intake conditioning system 51 is a control system or component controller that is in electronic communication with sensors 46 and actuators 47 dedicated to the respective plant components. It will be recognized that 31 is included. As used herein, the intake conditioning system 51 can represent the components used to condition the air prior to entering the compressor, unless otherwise stated, Inlet chilling systems or chillers, evaporators, nebulizers, irrigation systems, and / or in some alternative cases can include heating elements.

  In operation, the intake conditioning system 51 cools the air entering the gas turbine system 30 and enhances the power generation capacity of the unit. The HRSG duct firing system 52 is adapted to burn fuel, provide additional heat, and increase the supply of steam that is expanded through the turbine 53. Thus, the HRSG duct firing system 52 enhances the energy supplied by the hot exhaust gas 55 from the gas turbine system, thereby increasing the power generation capacity of the steam turbine system.

  As an exemplary operation, the power plant 12 of FIG. 3 directs the flow of fuel to the combustor 34 of the gas turbine system 30 for combustion. Turbine 36 is powered by combustion gases and drives compressor 32 and generator 44, which delivers electrical energy to transmission line 14 of power system 10. The component controller 31 of the gas turbine system 30 sets commands for the gas turbine system with respect to fuel flow rate, and the gas turbine, such as air inlet temperature, humidity, power output, shaft speed, and exhaust gas temperature. It is possible to receive sensor data from the system. The component controller 31 can also collect other operational data from pressure and temperature sensors, flow control devices, and other devices that monitor the operation of the gas turbine system. The component controller 31 can send data regarding the operation of the gas turbine system and receive instructions from the plant controller 22 regarding set points for actuators that control process inputs.

  During certain modes of operation, air entering the gas turbine system 30 may be cooled or otherwise conditioned by the intake conditioning system 51 to enhance the power generation capacity of the gas turbine system. . The intake air conditioning system 51 can include a refrigeration system 65 for cooling water and a component controller 31 that controls the operation of the intake air conditioning system 51. In this case, the component controller 31 can receive information regarding the temperature of the cooling water, as well as instructions regarding the desired level of injection, which can come from the plant controller 22. Also, the component controller 31 of the intake conditioning system 51 can issue commands that cause the refrigeration system 65 to create cooling water having a specific temperature and flow rate. The component controller 31 of the intake air conditioning system 51 can send data related to the operation of the intake air conditioning system 51.

  The steam turbine system 50 may include a turbine 53 and an HRSG duct firing system 52 and a component controller 31 dedicated to controlling the operation of the steam turbine system 50, as shown. Hot exhaust gas 55 from the exhaust duct of the gas turbine system 30 is directed into the steam turbine system 50 and can produce steam, which is expanded through the turbine 53. As will be appreciated, HRSG duct firing systems are constantly used to provide additional energy for the production of steam to increase the power generation capacity of the steam turbine system. The rotation induced in the turbine 53 by the steam drives the generator 44 to produce electrical energy, which is then sold in the power system 10 across the transmission line 14. Will be recognized. The component controller 31 of the steam turbine system 50 can set the flow rate of fuel burned by the duct firing device 52, thereby increasing steam generation beyond the amount that can be produced by the exhaust gas 55 alone. It is. The component controller 31 of the steam turbine system 50 may send data regarding the operation of the plant component 49 and receive instructions from that data regarding how the steam turbine system 50 should operate.

  The plant controller 22 of FIG. 3 is connected to each of the component controllers 31, as shown, and can communicate with sensors 46 and actuators 47 of several plant components 49 via these connections. is there. As part of controlling the power plant 12, the plant controller 22 can simulate the operation of the power plant 12. More specifically, the plant controller 22 includes or can communicate with a digital model (or simply “model”), which simulates the operation of each plant component 49. To do. The model can include an algorithm that correlates process input variables to process output variables. Algorithms can include sets of instructions, logic, mathematical formulas, functional relationship descriptions, schedules, data collection, and the like. In this case, the plant controller 22 includes a gas turbine model 60 that models the operation of the gas turbine system 30, an intake conditioning system model 61 that models the operation of the intake conditioning system 51, the steam turbine system 50, and the HRSG duct. A steam turbine model 62 that models the operation of the firing system 52. As a general note, the system and its associated model, and the separate steps of the methods provided herein, can be subdivided in various ways without substantially departing from the scope of the present invention, It will be appreciated that and / or can be combined, and that the manner in which each is described is exemplary unless specifically stated or claimed. Using these models, the plant controller 22 can simulate the operation of the power plant 12, such as, for example, parameters describing thermodynamic performance or operation.

  The plant controller 22 can then use the results from the simulation to determine an optimized mode of operation. Such an optimized operating mode may be described by a parameter set, which includes a plurality of operating parameters and / or set points for actuators, and / or other operating conditions. As used herein, an optimized mode of operation is at least a preferred mode of operation over at least one alternative mode of operation with defined criteria or performance metrics, Alternatively, performance indicators can be selected by an operator to evaluate plant operation. More specifically, an optimized mode of operation is preferred over one or more other possible modes of operation as used herein and simulated by a plant model. Is an operation mode evaluated as The optimized mode of operation is determined by evaluating how the model predicts that the power plant will operate under each mode of operation. As discussed below, the optimizer 64, eg, a digital software optimization program, is preferred or optimized by running a digital power plant model according to various parameter sets and then evaluating the results. It is possible to specify the mode of operation. The change in set point can be generated by a perturbation applied around the set point selected for analysis. These can be based in part on historical behavior. It will be appreciated that the optimized mode of operation can be determined by the optimizer 64 based on one or more defined cost functions. Such a cost function can take into account, for example, the cost of generating power, profitability, efficiency, or some other criteria as defined by the operator 39.

  To determine cost and profitability, the plant controller 22 includes or can communicate with an economic model 63, which can be, for example, a gas turbine system, an intake conditioning system, And track the price of power and certain other variable costs, such as the cost of fuel used in the HRSG duct firing system. The economic model 63 provides the data used by the plant controller 22 and the proposed setpoint (ie, the selected setpoint, the operation on which is determined to determine the optimized setpoint. It is possible to determine which of the (modelled) represents the minimum production cost or maximum profitability. According to other embodiments, as discussed in more detail in FIG. 4, the optimizer 64 of the plant controller 22 includes a filter, such as a Kalman filter, or operates with such a filter, It is possible to assist in tuning, adjusting, and calibrating the digital model, such that the model accurately simulates the operation of the power plant 12. As discussed below, the model can be a dynamic model, which includes a learning mode, where the actual operation (ie, the actual operation of the power plant 12). The model is tuned or matched through comparisons made between the predicted behavior (ie, the value for the same behavior parameter predicted by the model) (Reconciled). Also, as part of the control system, the filter can be used to adjust or calibrate the model in real time or in near real time, eg, every few minutes or every hour, or as specified.

  The optimized set point generated by the plant controller 22 represents the recommended mode of operation, for example, fuel and air settings for the gas turbine system, temperature and water mass flow for the intake conditioning system, The level of duct firing in the steam turbine system 50 can be included. According to certain embodiments, these suggested operating set points may be provided to operator 39 via an interface device, such as a computer display screen, printer, or sound speaker. By knowing the optimized set point, the operator can then enter the set point into the plant controller 22 and / or component controller 31, where the plant controller 22 and / or component controller 31 Next, control information for realizing a recommended mode of operation is generated. In embodiments where the optimized set point does not include specific control information for realizing the mode of operation, the component controller can provide the control information necessary for this, and more As discussed below, the component controller can continue to control the plant components in a closed loop manner according to the recommended operating mode until the next optimization cycle. Also, depending on the operator's choice, the plant controller 22 can directly or automatically implement the optimized set point without operator involvement.

  As an exemplary operation, the power plant 12 of FIG. 3 directs the flow of fuel to the combustor 34 of the gas turbine system 30 for combustion. Turbine 36 is powered by combustion gases and drives compressor 32 and generator 44, which delivers electrical energy to power line 14 of power system 10. The component controller 31 sets commands for the gas turbine system 30 with respect to fuel flow and from the gas turbine system 30 such as air inlet temperature and humidity, power output, shaft speed, and exhaust gas temperature. It is possible to receive sensor data. In addition, the component controller 31 can collect other operational data from pressure and temperature sensors, flow control devices, and other devices that monitor the gas turbine system 30. The component controller 31 of the gas turbine system 30 can send data regarding the operation of the system and receive instructions from the plant controller 22 regarding set points for actuators that control process inputs.

  During certain modes of operation, the air entering the gas turbine system 30 may be cooled by cold water supplied to the inlet air duct 40 from the intake conditioning system 51. It will be appreciated that cooling the air entering the gas turbine can be done to enhance the ability of the gas turbine engine to generate power. The intake air conditioning system 51 can include a refrigeration system or refrigerator 65 for cooling water and a component controller 31. In this case, the component controller 31 receives information regarding the temperature of the cooling water and commands regarding the desired cooling of the intake air. These commands can come from the plant controller 22. Also, the component controller 31 of the intake conditioning system 51 can issue commands that cause the refrigeration system 65 to create cooling water having a specific temperature and flow rate. The component controller 31 of the intake conditioning system 51 can send data related to the operation of the intake conditioning system 51 and receive instructions from the controller 22.

  The steam turbine system 50 may include an HRSG 52 comprising a duct firing device, a steam turbine 53, and a component controller 31 that may be dedicated to the operation of the steam turbine system 50. Hot exhaust gas 55 from the exhaust duct 42 of the gas turbine system 30 is directed into the steam turbine system 50 and produces steam that drives the steam turbine system 50. The HRSG duct firing system 52 is used to provide additional thermal energy, is capable of producing steam, and increases the power generation capacity of the steam turbine system 50. The steam turbine 53 drives a generator 44 to produce electrical energy that is delivered to the power system 10 via the transmission line 14. The component controller 31 of the steam turbine system 50 can set the flow rate of the fuel burned by the duct firing device 52. The heat generated by the duct firing device exceeds the amount produced by the exhaust gas 55 from the turbine 36 alone, increasing the generation of steam. The component controller 31 of the steam turbine system 50 can send data regarding the operation of the system and receive instructions from the plant controller 22.

  The plant controller 22 can communicate with the operator 39 and the data resource 26 and receive data about market conditions such as, for example, prices and demands for delivered power. According to certain embodiments, plant controller 22 issues recommendations to operator 39 regarding desired operating set points for gas turbine system 30, intake conditioning system 51, and steam turbine system 50. The plant controller 22 can receive and store data about the operation of the components and subsystems of the power plant 12. The plant controller 22 can be a computer system having a processor and memory for storing data, digital models 60, 61, 62, 63, an optimizer 64, and other computer programs. The computer system may be embodied in a single physical or virtual computing device or distributed across local or remote computing devices. The digital models 60, 61, 62, 63 may be embodied as a set of algorithms, such as transfer functions, related to the respective operating parameters of the system. The model can include a physics-based aerothermodynamic computer model, regression fit model, or other suitable computer-implemented model. According to a preferred embodiment, the models 60, 61, 62, 63 are tuned, adjusted, calibrated or predicted periodically, automatically and in real time or near real time. Can be tuned according to an ongoing comparison between measured motion and measured parameters of actual motion. The models 60, 61, 62, 63 can include a filter that receives data input regarding the actual physical and thermodynamic operating conditions of the combined cycle power plant. These data inputs may be supplied to the filter during operation of the power plant 12 in real time or periodically, such as every 5 minutes, every 15 minutes, every hour, every day, etc. The data input can be compared with the data predicted by the digital models 60, 61, 62, 63, and based on the comparison, the model can be continuously improved.

  FIG. 4 illustrates a schematic system configuration of the plant controller 22, which includes a filter 70, an artificial neural network configuration 71 (“neural network 71”), and an optimizer 64 according to aspects of the present invention. . The filter 70 can be, for example, a Kalman filter, which simulates the operation of the power plant 12 with actual data 72 of measured operating parameters from the sensor 46 of the power plant 12. It is possible to compare with the data 73 of the same operating parameters predicted by the models 60, 61, 62, 63 and the neural network 71. The difference between the actual and predicted data can then be used by the filter 70 to tune the power plant model simulated by the neural network 71 and the digital model.

  Although certain aspects of the invention have been described herein with reference to models in the form of neural network based models, the invention is not limited thereto, but is based on physics based models, data driven Including models, models developed empirically, models based on heuristics, support vector machine models, models developed by linear regression, models developed using "first principle" knowledge, etc. It should be understood that it can be expected to be implemented using this type of model. Additionally, in order to properly capture the relationship between the manipulated / disturbance variable and the controlled variable, according to certain preferred embodiments, the power plant model has one of the following characteristics: It is possible to have one or more. 1) Non-linearity (non-linear model can represent a curve rather than a straight line between manipulated / disturbance variables and controlled variables); 2) multiple inputs / multiple Output (the model can capture the relationship between multiple inputs (manipulated variables and disturbance variables) and multiple outputs (controlled variables)); 3) being dynamic (input A change does not instantaneously affect the output, but rather there can be a time delay, followed by a dynamic response to the change, e.g. an input change across the system It can take a few minutes to propagate well, since the optimization system runs at a predetermined frequency, the model represents the effect of these changes over time and must be taken into account 4) Suitable (The model can be updated at the start of each optimization to reflect current operating conditions); and 5) derived from empirical data (each power plant Is unique, the model can be derived from empirical data obtained from the power generation unit). Given the above requirements, a neural network based approach is a preferred technique for implementing the required plant model. Neural networks can be developed based on empirical data using advanced regression algorithms. As will be appreciated, neural networks can capture non-linearities that are commonly shown in the operation of power plant components. A neural network can also be used to represent a system with multiple inputs and outputs. In addition, the neural network can be updated using either feedback bias or online adaptive learning. The dynamic model can also be implemented in a neural network based structure. A variety of different types of model architectures have been used for dynamic neural network implementations. Many neural network model architectures require large amounts of data to successfully train dynamic neural networks. Assuming a robust power plant model, it is possible to calculate the effect that changes in manipulated variables have on controlled variables. Moreover, since the plant model is dynamic, it is possible to calculate the effect of changes in the manipulated variables over the future planning period.

  The filter 70 can generate a performance multiplier that is applied to the input or output of the digital model and neural network, or a weight applied to the logic unit, and an algorithm used by the digital model and neural network. Can be corrected. These effects by the filter reduce the difference between actual condition data and predicted data. The filter continues to operate to further reduce differences or to handle possible variations. By way of example, the filter 70 may include compressor discharge pressure and temperature in a gas turbine, gas turbine and steam turbine efficiency, gas turbine system, intake conditioning system, and flow of fuel to the HRSG duct firing system, and / or It is possible to generate a performance multiplier for the predicted data for other suitable parameters. It will be appreciated that these categories of operational data reflect operational parameters subject to performance degradation over time. By providing performance multipliers for these types of data, the filter 70 may be particularly useful in adjusting models and neural networks to account for degradation of power plant performance.

  As shown in FIG. 4, according to a particular embodiment of the invention, each of the digital models 60, 61, 62, 63 of several plant components 49 of the power plant of FIG. The algorithm is represented by several graphs, which are used to model the corresponding system. It will be appreciated that the model interacts and communicates with the neural network 71 and in doing so the neural network 71 forms an overall model of the combined cycle power plant 12. Thus, the neural network simulates the thermodynamic and economic operation of the plant. As shown by the solid arrows in FIG. 4, the neural network 71 collects data output by the models 60, 61, 62, 63 and is to be used as input by the digital model. I will provide a.

  4 also includes an optimizer 64 such as a computer program that interacts with the neural network 71 to provide a gas turbine system, an intake conditioning system, a steam turbine system, and an HRSG duct. Search for optimal set points for firing systems and achieve defined performance goals. The performance goal can be, for example, to maximize the profitability of the power plant. The optimizer 64 can cause the neural network 71 to run the digital models 60, 61, 62, 63 at various operation set points. The optimizer 64 can have a perturbation algorithm that assists in changing the behavioral set point of the model. The perturbation algorithm causes the combined cycle power plant simulation provided by the digital model and the neural network to operate at a set point different from the current operating set point for the plant. By simulating the operation of the power plant at different set points, the optimizer 64 will cause the plant to operate more economically, or some other that may be defined by the operator 39. Search for action set points that will improve performance according to the criteria.

  According to an exemplary embodiment, the economic model 63 provides data that is used by the optimizer 64 to determine which setpoint will be the most profitable. The economic model 63 may receive and store formatted fuel cost data, such as a chart 630 that correlates fuel costs over a period of time, such as during each season of the year, for example. Another chart 631 may correlate prices received for power at different times of the day, week or month. The economic model 63 can provide data regarding the price received for electricity and the cost of the fuel used to produce the electricity (gas turbine fuel, duct firing fuel, and intake conditioning system fuel). It is. Data from the economic model 63 can be used by the optimizer 64 to evaluate each of the operating conditions of the power plant according to performance targets defined by the operator. The optimizer 64 is optimal for any of the operating conditions of the power plant 12 assuming a performance goal as defined by the operator 39 (which is an alternative as used herein). It means that it is at least preferable to the operating state). As described, the digital model is used to simulate the operation of the plant components 49 of the power plant 12, such as modeling the thermodynamic behavior of a gas turbine system, an intake conditioning system, or a steam turbine system. Can be done. The model can include algorithms, such as mathematical equations and look-up tables, which are stored locally, periodically updated, or acquired remotely via data resources 26. It simulates the response of the plant component 49 to specific input conditions. Such a look-up table can include measured operating parameters that describe the same type of operation of components operating in a remote power plant facility.

  The thermal model 60 of the gas turbine system 30 includes, for example, an algorithm 600 that correlates the effect of inlet air temperature to power output. It will be appreciated that this algorithm can indicate that the power output decreases from the maximum value 601 when the inlet air temperature increases above the threshold 602 temperature. The model 60 may also include an algorithm 603 that correlates gas turbine heat rates at different power output levels of the engine. As discussed, the heat rate represents the efficiency of a gas turbine engine or other power generation unit and is inversely proportional to the efficiency. A lower heat rate indicates a higher thermodynamic performance efficiency. The digital model 61 can simulate the thermodynamic operation of the intake conditioning system 51. In this case, for example, the digital model 61 includes an algorithm 610, which correlates chilling capabilities based on the energy applied to run the refrigeration system 65 of the intake conditioning system 51 and calculates the chilling capability. Shows the amount of cooling applied to the air entering the gas turbine. There may be a maximum chilling capability value 611 that can be achieved by the refrigeration system 65. In another case, the associated algorithm 612 can correlate the energy applied to run the refrigeration system 65 to the temperature of the cold air entering the compressor 32 of the gas turbine system 30. The model 61 shows that, for example, reducing the temperature of the air entering the gas turbine below the dew point 613 of the ambient air dramatically increases the power required to run the intake conditioning system. It is possible to show. In the case of the steam turbine system 50, the digital model 62 may include an algorithm 620, which determines the power output of the steam turbine system, the amount of fuel consumed by duct firing, etc. Correlate to the energy added by the HRSG duct firing system 52. The model 62 can show that an upper threshold level 621 can exist for an increase in steam turbine system power that can be achieved, for example, by an HRSG duct firing system, which is included in the algorithm 620. May be included.

  According to a particular embodiment of the invention, as illustrated in FIG. 4, neural network 71 interacts with each of the digital models of several plant components 49 of power plant 12 of FIG. It is possible to provide communication between The interaction can include collecting output data from the model and generating input data that is used by the model to generate further output data. The neural network 71 can be a digital network connected to logic elements. The logic elements can each embody an algorithm that accepts data input and generates one or more data outputs. A simple logic element can sum the values of inputs and produce output data. Other logic elements can multiply the value of the input or apply other mathematical relationships to the input data. Data inputs to each of the logic elements of the neural network 71 can be assigned a weight such as a multiplier between 1 and zero. The weighting can be modified during the learning mode, which adjusts the neural network to better model the power plant performance. The weighting can also be adjusted based on the command provided by the filter. Adjusting the weighting of data input to logic units in a neural network is one example of how a neural network can be dynamically modified during the operation of a combined cycle power plant. Another example is to modify the weighting of the data input to an algorithm (which is an example of a logic unit) in each of a thermodynamic digital model for a steam turbine system, an intake conditioning system, and a gas turbine including. The plant controller 22 may be modified in other ways, such as adjustments made to the logic units and algorithms based on data provided by the optimizer and / or filters.

  The plant controller 22 can generate an output of a recommended set point or an optimized set point 74 for the combined cycle power plant 12, the output of which is shown in FIG. Before being transmitted to and implemented by the power plant actuator 47, it is possible to pass the operator 39 for approval. As shown, the optimized setpoint 74 may include input from an operator 39 via a computer system such as that described below in connection with FIG. 39 can be approved. The optimized set point 74 is generated by, for example, an intake conditioning system and used to cool the air entering the gas turbine system, the temperature and mass flow for the cooling water; the fuel flow to the gas turbine system As well as a duct firing rate. Also, the optimized setpoint 74 can then be used by the neural network 71 and models 60, 61, 62, 63 so that ongoing plant simulations can predict operational data and It will be recognized that the data can later be compared with actual operational data so that the plant model can be continuously improved.

  FIG. 5 illustrates a simplified system configuration of the plant controller 22 that includes the optimizer 64 and the power plant model 75. In this exemplary embodiment, the plant controller 22 includes an optimizer 64 and a power plant model 75 (for example, the neural network 71 and models 60, 61, 62, 63 discussed above in connection with FIG. 4). System). The power plant model 75 can simulate the overall operation of the power plant 12. According to the illustrated embodiment, the power plant 12 includes a plurality of power generation units or plant components 49. The plant component 49 can include, for example, a thermal power generation unit, or other plant subsystem as previously described, each of which can include a corresponding component controller 31. . The plant controller 22 can communicate with the component controller 31, and controls the operation of the power plant 12 through the component controller 31 and by the component controller 31 through connections to sensors 46 and actuators 47. Is possible.

  It will be appreciated that a power plant has a number of variables that affect its operation. Each of these variables can generally be classified as either an input variable or an output variable. Input variables represent process inputs and include variables that can be manipulated by the plant operator, such as air and fuel flow rates. Input variables also include variables that cannot be manipulated, such as ambient conditions. An output variable is a variable such as a power output that is controlled by manipulating an input variable that can be manipulated. A power plant model is configured to represent an algorithmic relationship between input variables and output variables or controlled variables, which are variables that can be manipulated or "variables to be manipulated" and variables that cannot be manipulated. Alternatively, an output variable or controlled variable including a “disturbance variable” will be referred to as a “controlled variable”. More specifically, the manipulated variable is a variable that can be changed by the plant controller 22 to affect the controlled variable. Variables that are manipulated include things such as valve set points that control fuel and air flow. Disturbance variables represent variables that affect a controlled variable but cannot be manipulated or controlled. Disturbance variables include ambient conditions, fuel characteristics, and the like. The optimizer 64 (1) power plant performance objectives (eg, satisfying load requirements while maximizing profitability), and (2) constraints associated with operating the power plant (eg, emissions) And set the optimal set point values for the manipulated variables.

  In accordance with the present invention, an “optimization cycle” can be initiated at a predetermined frequency (eg, every 5 to 60 seconds, or every 1 to 30 minutes). At the start of the optimization cycle, the plant controller 22 receives current data regarding manipulated, controlled and disturbance variables from the component controller 31 and / or directly from the respective sensors 46 of the plant component 49. It is possible to obtain The plant controller 22 can then use the power plant model 75 to determine an optimal set point value for the manipulated variable based on current data. In doing so, the plant controller 22 can run the plant model 75 at various operation set points, and which set of operation set points is most preferred given the performance goals of the power plant. This can be referred to as a “simulation run”. For example, the performance goal may be to maximize profitability. By simulating the operation of the power plant at different set points, the optimizer 64 will determine the set point that the plant model 75 predicts will cause the plant to operate optimally (or at least in a preferred manner). Explore a set. As stated, this optimal setpoint set can be referred to as an “optimized setpoint” or “optimized mode of operation”. Typically, in reaching the optimized setpoint, the optimizer 64 will compare multiple sets of setpoints, which are premised on operator-defined performance goals. It will then be found that it has an advantage over each of the other sets. The operator 39 of the power plant 12 can have an option to approve the optimized set point, or the optimized set point can be automatically approved. The plant controller 22 can send optimized setpoints to the component controller 31 or, alternatively, directly to the actuator 47 of the plant component 49, where the setpoints are optimized settings. It can be adjusted according to the point. The plant controller 22 can be run in a closed loop and is operated at a predetermined frequency (eg, every 10-30 seconds or more frequently) based on measured current operating conditions. The value of the set point of the variable to be adjusted is adjusted.

  The optimizer 64 can be used to minimize the “cost function” that will be subject to a set of constraints. The cost function is essentially a mathematical representation of the plant performance goal, and the constraint is the limit within which the power plant must operate. Such limits can represent legal constraints, regulatory constraints, environmental constraints, equipment constraints, or physical constraints. For example, to minimize NOx, the cost function includes a term that decreases as the level of NOx decreases. One common method for minimizing such a cost function is known, for example, as “gradient descent optimization”. Gradient descent is an optimization algorithm that approaches the local minimum of a function by taking a step proportional to the negative number of the gradient (or approximate gradient) of the function at the current point. It should be understood that multiple different optimization techniques can be used depending on the form of the model, as well as cost and constraints. For example, it is anticipated that the present invention may be implemented using a variety of different types of optimization approaches, either individually or in combination. These optimization approaches include, but are not limited to, linear programming, quadratic programming, mixed integer nonlinear programming, stochastic programming, global nonlinear programming, genetic algorithms, and particle swarm optimization techniques. Including. Additionally, the plant model 75 can be dynamic, so that the effects of changes are taken into account over future planning periods. Thus, the cost function includes terms over the future planning period. Since the model is used to predict over the planning period, this approach is called model predictive control, which is described by S. Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, "Nonlinear model predictive control using neural networks, "IEEE Control Systems Magazine, vol. 20, no. 2, pp. 53-62, 2000, which is fully incorporated herein by reference.

  Constraints can be imposed on both power plant process inputs (which include manipulated variables) and process outputs (which include controlled variables) over future planned time periods. Typically, constraints are imposed on the manipulated variables that match the limit values associated with the plant controller. The constraints on the output can be determined by the problem to be solved. According to embodiments of the present invention and as a step in the optimization cycle, the optimizer 64 calculates a complete trajectory of the movement of the manipulated variable over a future planning period, eg, 1 hour. It is possible. Thus, for an optimization system that runs every 30 seconds, 120 values can be calculated over a one hour future planning period for each manipulated variable. Since the plant model, or performance goal, or constraints can change before the next optimization cycle, the plant controller 22 / optimizer 64 is used as an optimized set point for each manipulated variable. It is possible to output only the first value in the planning target period for each manipulated variable to the component controller 31. In the next optimization cycle, the plant model 75 may be updated based on current conditions. Also, cost functions and constraints can be updated if they have changed. The optimizer 64 can then be used to recalculate the set of values for the manipulated variable over the planning period, with the first value in the planning period being the respective value for each manipulated variable. It is output to the component controller 31 as a set point value for the variable being manipulated. The optimizer 64 can repeat this process for each optimization cycle so that the power plant 12 is affected by unexpected changes in items such as load, ambient conditions, fuel characteristics, and the like. When used, it maintains constant optimal performance.

  Turning to FIG. 6, an exemplary environment and user input device for a plant controller and control program are illustrated in accordance with an exemplary embodiment. Although other configurations are possible, the embodiment includes a computer system 80 having a display 81, a processor 82, a user input device 83, and a memory 84. Aspects of computer system 80 may be located at power plant 12, while other aspects may be remote and connected via communication network 20. As discussed, the computer system 80 may be connected to each power generation unit or other plant component 49 of the power plant 12. The power plant component 49 may be a gas turbine system 30, a steam turbine system 50, an intake conditioning system 51, an HRSG duct firing system 52, and / or any subsystem or subcomponent associated therewith, or any of them Combinations can be included. The computer system 80 may also be connected to one or more sensors 46 and actuators 47 as needed or desired. As stated, sensor 46 may be configured to sense component operating conditions and parameters and relay signals to computer system 80 regarding these conditions. The computer system 80 can be configured to receive these signals and use them in the manner described herein, which sends the signals to one or more of the actuators 47. Can be included. However, unless otherwise required, the present invention may include embodiments that are not configured to directly control the power plant 12 and / or sense operating conditions. In the configuration of the present invention that controls the power plant 12 and / or senses operating conditions, such input or control interacts more directly with the physical components of the power plant and its sensors and actuators. It may be provided by receiving and / or transmitting signals from / to one or more separate software or hardware systems. The computer system 80 can include a power plant control program (“control program”) that manages data in the plant controller by performing the processes described herein. As such, the computer system 80 is made operable.

  Generally, the processor 82 executes program code that defines a control program, which is at least partially fixed in the memory 84. During execution of the program code, the processor 82 can process the data, which reads the converted data from the memory 84 and / or writes the converted data to the memory 84. As a result. Display 81 and input device 83 allow a human user to interact with computer system 80 and / or one or more communication devices, such that system user can use any type of communication link with computer system 80. It may be possible to communicate. In an embodiment, a communication network, such as networking hardware / software, may allow the computer system 80 to communicate with other devices inside and outside the node in which the communication network is installed. To this extent, the control program of the present invention can manage a set of interfaces that allow human users and / or system users to interact with the control program. In addition, the control program uses any solution to manage data such as control data, etc. (e.g., store, retrieve, generate, manipulate, organize, etc.) as discussed below. Etc.).

  The computer system 80 can include one or more general-purpose computing products capable of executing program code, such as a control program as defined herein, which program code is stored in the computer system 80. Installed on top of. As used herein, “program code” means any collection of instructions written in any language, code, or notation, and the instructions are computing with information processing capabilities. The device may be directly or after any combination of (a) conversion to another language, code or notation, (b) reproduction in a different material form, and / or (c) restoration. It is understood that it causes a specific action to be performed. Additionally, the computer code can include object code, source code, and / or executable code, and forms part of a computer program product when it is on at least one computer-readable medium. Is possible. The term “computer-readable medium” can include one or more of any type of tangible medium currently known or later developed, and a copy of the program code is tangible It is understood that from a representation medium, it can be perceived, reproduced, or otherwise transmitted by a computing device. When a computer executes computer program code, the computer becomes a device for practicing the present invention, and on a general-purpose microprocessor, specific logic circuits are generated by the configuration of the microprocessor with computer code segments. . The technical effect of the executable instructions is to implement a power plant control method and / or system and / or computer program product, which may be assumed ambient and / or market conditions, performance parameters, and / or Given the life cycle costs associated with it, models are used to enhance, enhance, or optimize power plant operating characteristics to make more efficient use of the power plant economic benefits . In addition to using current information, historical information and / or forecast information can be used, and the feedback loop allows the plant to operate dynamically more efficiently during changing conditions. Can be established as follows. The computer code of the control program can be written in computer instructions that can be executed by the plant controller 22. To this extent, the control program executed by computer system 80 can be embodied as any combination of system software and / or application software. Furthermore, the control program can be implemented using a set of modules. In this case, the module may allow the computer system 80 to perform a set of tasks used by the control program, and the module may be developed separately and / or of the control program. It can be implemented away from other parts. As used herein, the term “component” means any configuration of hardware, with or without software, that is described in conjunction with components using any solution. The term "module" means program code that allows a computer system to implement the actions described in conjunction with the module using any solution doing. When fixed in a memory 84 of a computer system 80 that includes a processor 82, a module is a substantial part of a component that implements an action. Nevertheless, it is understood that two or more components, modules, and / or systems may share some / all of their respective hardware and / or software. Further, it will be appreciated that some of the functionality discussed herein may not be implemented, or that additional functionality may be included as part of computer system 80. . When the computer system 80 includes multiple computing devices, each computing device may have only a portion of a control program (eg, one or more modules) that is fixed on the computing device. It is. Regardless, when the computer system 80 includes multiple computing devices, the computing devices can communicate via any type of communication link. Further, while performing the processes described herein, computer system 80 can communicate with one or more other computer systems using any type of communication link. .

  As discussed herein, the control program allows the computer system 80 to implement a power plant control product and / or method. The computer system 80 can obtain power plant control data using any solution. For example, the computer system 80 can generate power plant control data and / or is used to generate power plant control data and can generate power from one or more data stores, repositories, or sources. It is possible to read plant control data and receive power plant control data from another internal or external system or device such as a power plant, plant controller, component controller, and the like. In another embodiment, the present invention provides a method for providing a copy of a program code for a power plant control program, for example, wherein the program code includes some or all of the processes described herein. It is possible to implement. It will be appreciated that aspects of the present invention may be implemented as part of a business method for performing the processes described herein on a subscription basis, advertisement basis, and / or fee basis. A service provider may propose to implement a power plant control program and / or method as described herein. In this case, the service provider manages (eg, creates, maintains) a computer system, such as computer system 80, that performs the processes described herein for one or more customers. Support, etc.).

  A computer model of the power plant can be constructed and then used to control and optimize the power plant operation. Such a plant model can be dynamic and through an ongoing comparison between actual (ie, measured) operating parameters and the same parameters predicted by the plant model. Can be updated repeatedly. In preparing and maintaining such a model, instructions are provided to instruct the processor 82 of the computer system 80 to generate a library of energy system power generation units and components (“component library”) in response to user input. Written or otherwise provided. In some configurations, the user input and generated library include the characteristics of the component comprising the library, as well as rules that generate the script according to the action value and the characteristic value. These characteristic values can be compiled from data stored locally in memory 84 and / or obtained from a central data repository maintained at a remote location. A library of components can include non-physical components, such as economic or legal components. An example of an economic component is the purchase and sale of fuel, and examples of legal components are emission limits and credits. These non-physical components can be modeled using mathematical rules, just as components that represent physical equipment can be modeled using mathematical rules. The instructions can be configured to assemble a configuration of energy system components from the library, as can be configured by an operator. A library of energy system components can be provided to allow the user to select a component from which to replicate an actual power plant or to generate a virtual power plant. Each component can have several characteristics that can be used by the user to enter specific values that match the operating conditions of the actual or virtual power plant being modeled Will be recognized. Scripts can be generated for the assembled energy system components and their configuration. Generated scripts, when used in an energy system component configuration, include economic components and / or legal components, and / or mathematical relationships within and / or between energy system components Can be included. The computer system 80 can then solve the mathematical relationship and show the solved results on the display 81. In configurations where the signal can be transmitted from the computer 80, the signal can be used to control the energy system according to the solved results. Otherwise, the results can be displayed or printed, and the results can be used to set physical instrument parameters and / or non-physical parameters such as fuel purchase and / or sales etc. And / or use the determined non-physical parameters, and thus a suitable or optimized mode of operation is realized. A library of plant components can contain a central repository of data, which is a collection of data related to how each plant component operates under different parameters and conditions. Represents ongoing accumulation. A central repository of data can be used, for example, to provide “plug data” when it is determined that the sensor data is not reliable.

  Turning to FIGS. 7-9, a more detailed discussion of the economic power supply process is provided, which is that the control system discussed above is such a system, regardless of which case applies. Including schemes that can be used to optimize such power delivery procedures from the perspective of both the power system central authority participating in the plant or individual power plants. From the point of view of a central authority powering directive, the objectives of an economical powering process are to dynamically respond to changing variables, including changing load requirements or ambient conditions, while still minimizing the costs incurred in the system. Will be recognized. With respect to participating power plants, it will generally be recognized that the goal is to utilize available capacity while minimizing the costs incurred and maximizing economic benefits. Given the complexity of the power system, the economical power supply process typically involves frequently adjusting the load of the participating power plants by the power supply command center. When successful, the process results in available power plants operating at loads where their incremental power generation costs are approximately the same, which results in minimizing power generation costs. While complying with system constraints such as maximum and minimum allowable loads, system stability, etc. It will be appreciated that accurate incremental cost data is necessary for economical power supply to function optimally. Such incremental cost data has key components including fuel cost and incremental fuel consumption. Incremental fuel consumption data is usually given as an incremental heat rate versus power output curve. Specifically, the incremental heat rate IHR of a thermal power unit is defined as the slope of the heat rate curve, where the unit heat rate is the ratio of the heat input plotted against the electrical output at any load. This data error will result in the unit being fed at a load that does not minimize the total power generation cost.

  Multiple items can introduce errors into the incremental heat rate curve. These can be grouped into two categories. The first category includes items that create errors that exist when data is provided to a power dispatch center. For example, if data is collected by testing, errors due to instrument inaccuracy will be included in all calculations performed using it. As discussed in more detail below, certain aspects of the present invention provide a method for verifying sensor accuracy during data collection, and that the collected data may be unreliable due to sensor malfunction. Includes a method to identify instances in a timely manner. The second category of errors includes items that cause the data to become inaccurate over time. For example, if the performance of the power generation unit changes due to equipment deterioration or repair, or changes in ambient conditions, the incremental heat rate data used for power supply is updated with such data. Until it becomes an error. One aspect of the present invention is to identify those parameters of the thermal power unit that can greatly affect the incremental heat rate calculation. Knowledge of such parameters and their relative importance is then used to determine how often the feed data should be updated to reflect true plant performance Can be done.

Incremental heat rate data errors lead to situations where the power plant is mispowered, which typically results in increased power generation costs for the power system. For example, referring to the graph of FIG. 7, a situation is provided where the true incremental heat rate is different from the incremental heat rate used in the power supply process. When powering the unit, the power supply command authority uses incremental heat rate data that is in error by "E" as shown. (FIG. 7 assumes that the incremental heat rate of the power system is not affected by the load imposed on a given unit, which is compared to the size of a given power generation unit. It should be noted that the power generation unit will be powered at L ₁ , as shown, L ₁ being used Based on possible information, the load at which the unit and system incremental heat rates are equal. If the correct incremental heat rate information is used, the unit is must have been fed at L _2, L ₂ is the load true incremental heat rate of the plant is equal to the incremental heat rate of the power system. As will be recognized, errors result in underutilization of the power plant. In another case, i.e. where the correct incremental heat rate plot position is opposite to the true incremental heat rate plot, the error results in the unit being over-allocated. It may require that the unit operate inefficiently in order to satisfy its powered load start-up shutdown plan. From the perspective of the central power supply command authority of the power system, reducing errors in the data used in the power supply process reduces total system fuel costs, increases system efficiency, and / or loads It will be recognized that the risk of not meeting the requirements will be reduced. For power plant operators in the system, reducing such errors should facilitate full utilization of the plant and improve economic benefits.

  8 and 9 illustrate a schematic diagram of the plant controller 22 and a flow diagram 169 of the control method, respectively, according to aspects of the present invention. In these examples, a method is provided that illustrates economic optimization in a power system that uses economical power supply and distributes the load among potential providers. The basic process of economical power supply is a process that can be used in any manner between any two levels defined in a multi-layered hierarchy common to many power systems. In some cases, for example, an economic power supply process may be used as part of the competition process, which causes a central government authority or industry cooperative to divide the load among several competitors. Alternatively, the same principle of economical power supply can be used to share loads among co-owned power plants, minimizing power generation costs for plant owners. This principle can also be used at the plant level so that an operator or plant controller can share its load requirements between the different local power generation units available. Unless otherwise stated, it will be appreciated that the systems and methods of the present invention are generally applicable to any of these possible manifestations of an economic power supply process.

  In general, the power supply process seeks to minimize power generation costs in the power system through generation of power supply schedules, where the incremental power generation costs for each participating power plant or power generation unit are approximately the same. As will be appreciated, a number of terms are often used to describe an economical power supply process and are therefore defined as follows: The “forecast plan target period” is a predetermined period during which optimization is performed. For example, a typical forecast planning period can be several hours to several days. The “interval” in the forecast plan period is the predetermined time resolution of the optimization, that is, the “optimization cycle” described above, which is how often the optimization is performed during the forecast plan period. Describe what will be done. For example, a typical time interval for an optimization cycle can be from a few seconds to a few minutes. Finally, the “predicted length” is the number of time intervals at which optimization is to be performed, and can be obtained by dividing the prediction plan target period by the time interval. Therefore, in the prediction plan target period of 12 hours and the time interval of 5 minutes, the prediction length is 144 time intervals.

  Aspects of the invention provide methods and / or controllers for control for power plants and methods and systems for optimizing performance, cost effectiveness, and efficiency. For example, according to the present invention, a minimum variable operating cost can be realized for a thermal power generation unit or power plant, which includes variable performance characteristics and cost parameters (ie, fuel cost, ambient conditions, market conditions, etc.) and life cycle cost. (Ie, variable operation and its impact on maintenance schedule, parts replacement, etc.). By changing one or more parameters of a thermal power unit taking into account such factors, it is possible to obtain more economic benefits from the unit over its useful life. For example, in a power plant including a gas turbine, the combustion temperature provides a more economically desired load level based on operating profiles, ambient conditions, market conditions, expectations, power plant performance, and / or other factors. Can be changed as follows. As a result, the disposal of parts having a time-based remaining life remaining in a unit with a limited number of activations can be reduced. In addition, a power plant control system that includes a feedback loop that is updated with substantially real-time data from sensors that are regularly tested and validated as operating correctly allows for further plant optimization. Become. That is, according to a particular embodiment of the present invention, by introducing a real-time feedback loop between the power plant control system and the power supply command authority, the target load and unit startup / shutdown plans are in a very accurate offer curve. The offer curve is constructed based on real-time engine performance parameters.

  FIG. 8 illustrates a schematic design of an exemplary plant controller 22 according to an aspect of the present invention. It will be appreciated that the plant controller 22 may be particularly suitable for implementing the method 169 of FIG. Due to this, FIG. 8 and FIG. 9 will be discussed together, but it will be recognized that each can have aspects applicable to more general usage situations. It will be. The power system 10 shown in FIG. 8 includes “a power plant 12a” and “another power plant 12b”, the plant controller 22 is dedicated to the “power plant 12a”, and “the other power plant 12b” It is possible to represent a power plant in a power system that competes with the power plant 12a. Also, as shown, the power system 10 includes a feed command authority 24 that feeds power between all participating power plants 12a, 12b in the system through a dedicated system controller 25. Manage processes.

  The power plant 12a can include a number of sensors 46 and actuators 47, by which the plant controller 22 monitors operating conditions and controls the operation of the plant. The plant controller 22 can communicate with a number of data resources 26, which can be located remotely from the plant controller 22 and can be accessed by a communication network and / or locally. Contained and accessible by the local network. As shown, the schematic of the plant controller 22 includes a number of subsystems that are separated from each other by a number of boxes. These subsystems or “boxes” are primarily separated by function to assist in the explanation. However, unless otherwise stated, separate boxes may or may not represent individual chips or processors or other individual hardware elements, and run in the plant controller. It will be appreciated that separate sections of computer program code may or may not be represented. Similarly, method 169 is divided into two main sections or blocks, for convenience and to aid in the explanation. As with any or all of the separate blocks or steps shown in FIG. 9, any or all of the separate boxes shown in FIG. It will be recognized that they can be combined.

  The method 169 of FIG. 9 may begin, for example, from the control section 170, which receives or collects current information and data for use (in step 171), the data being market data , Operational data, and / or ambient data. Within the plant controller 22, a corresponding control module 110 may be arranged to request / receive this type of data from the data resource 26 or any other suitable source. Also, the control module 110 may be configured to receive a target load 128 from the power supply command authority 24 (but during an initial run, such a target load is not available and a predetermined initial target load may be used. ). Ambient data may be received from a remote or local data repository and / or prediction service and may be included as a component of the data resource 26. Also, ambient data can be collected via ambient sensors deployed around the power plant 12a and can be received via a communication link with the power supply command authority 24. According to aspects of the present invention, ambient data includes historical data, current data, and / or forecast data describing ambient conditions for power plant 12a, which includes, for example, air temperature, relative humidity, pressure, and the like. It is possible. Market data may be received from remote or local data repositories and / or forecasting services and may be included as a component of data resource 26. Market data may also be received via a communication link with the power supply command authority 24. In accordance with aspects of the present invention, market data includes historical data, current data, and / or forecast data describing market conditions for power plant 12a, such as energy sales prices, fuel costs, labor costs, etc. including. Also, operational data may be received from a data repository and / or prediction service and included as a component of data resource 26. The operational data can include data collected from a plurality of sensors 46, which are deployed in the power plant 12 and its plant components 49 and are physically associated with plant operations. Measure various parameters. The operational data can include historical data, current data, and / or expected data, as well as various process inputs and outputs.

  As can be seen in FIG. 9, the initial set point for the power plant 12 may be determined, for example, by the control model 111 in the plant controller 22 of FIG. For example, the control model 111 uses additional information such as thermodynamic and / or physical details of the power plant 12 and ambient data, or market data, or process data (see FIG. 9). In step 172, it may be configured to determine the value of the operating parameter for the power plant 12. In some cases, for example, the value of the operating parameter can be a value that would be required to achieve sufficient power output to meet the target load. The determined value may also be used as an initial set point for each operating parameter of the power plant 12 (also in step 172 of FIG. 9). It will be appreciated that examples of such operating parameters can include fuel flow, combustion temperature, location with respect to the inlet guide vanes (if guide vanes are present), steam pressure, steam temperature, and steam flow. The Rukoto. The performance index can then be determined using the performance model 112 of the plant controller 22 (in step 173 of FIG. 9). The performance index can provide operating characteristics such as the efficiency of the power plant 12. The performance model 112 may be configured to use thermodynamic and / or physical details of the power plant 12 and set points determined by the control model 111 to determine values for operating characteristics of the power plant 12. It is supposed to be. The performance model 112 may be configured to take into account additional information, such as ambient conditions, market conditions, process conditions, and / or other relevant information.

  In addition, according to certain aspects of the present invention, the power plant 12 may be used (in step 174 of FIG. 9) using, for example, a life cycle cost (LCC) model 113 included in the plant controller 22 of FIG. An estimate of the LCC of can be determined. The LCC model 113 can be a computer program or the like, and the LCC model 113 uses physical information and / or cost information about the power plant 12 and set points from the control model 111 to generate power. The plant 12 may be configured to determine an estimated life cycle cost. The life cycle cost can include, for example, the total cost, maintenance cost, and / or operating cost of the power plant 12 over its useful life. The LCC model 113 may be configured to consider the results of the performance model 112 for enhanced accuracy. Accordingly, the LCC model 113 uses the determined set point of the control model 111 and the operating characteristics from the performance model 112 and other information to estimate the useful life of the power plant 12 as desired. It is possible to estimate how much it may cost to operate and / or maintain the power plant 12 during its useful life. As described above, the useful life of a power plant can be expressed in terms of time of operation and / or number of activations, and a given power plant has an expected life that can be provided by the manufacturer of the power plant. Accordingly, the predetermined value of expected useful life can be used at least as a starting point for LCC model 113 and / or enhancement module 114.

  Using information from other embodiments of the present invention, such as initial set points, performance indicators, and results from determining an estimated life cycle, as described below (step 175 The optimization problem for the power plant 12 can be solved. Such optimization problems can include multiple equations and variables, depending on the depth of analysis desired, and can include a goal function, which in the embodiment is , An LCC-based target function. The solution may include providing an enhancement or enhancement of the operating parameters of the power plant 12, such as by minimizing an LCC-based objective function (again, step 175). In an embodiment, the solution to the optimization problem may be implemented by the enhancement module 114 of the plant controller 22 of FIG.

  As is known from optimization theory, an objective function represents a property or parameter that is to be optimized, and depending on how the optimization problem is defined, many variables and / or parameters Can be taken into account. In an optimization problem, depending on the particular problem and / or the parameters represented by the objective function, the objective function can be maximized or minimized. For example, as indicated above, the target function representing the LCC according to the embodiment is at least one operating parameter that can be used to run the power plant 12 to keep the LCC as low as possible. Will be minimized to produce Optimization problems for power plant 12, or at least the target function, include power plant characteristics, site parameters, customer specifications, results from control model 111, performance model 112, and / or LCC model 113, ambient conditions, market conditions Factors such as, and / or process conditions, and any additional information that may be appropriate and / or desirable may be taken into account. Such factors can be collected into the goal function term, for example, an LCC-based goal function includes maintenance and operating costs expressed over time, and time is estimated component lifetime This is the forecast plan period based on the number of years. In implementations of the invention, complex goal functions and / or optimization problems may be used, each of which may include many or all of the various functions and / or factors described herein. It will be recognized that there is.

  For example, maintenance costs can be determined by modeling parts of the power plant 12 and estimating wear based on various parameters such as those already discussed. It will be appreciated that any part of the power plant 12 can be modeled for these purposes. However, in practical applications, parts associated with fewer, larger parts of power plant 12 or fewer selected parts may be modeled and / or instead of modeling, with respect to some parts, A constant or plug value can be used. Whatever level of detail is used, minimization of such an LCC-based objective function is part of the optimization problem, which is much like the one provided above. As a result of these factors, it may vary for a given power plant and may include at least one enhanced or enhanced power plant 12 operating parameter, such as, for example, by minimizing LCC. Is possible. In addition, predetermined operating and / or downtime, predetermined upper and / or lower temperature at various locations of the power plant 12, predetermined torque, predetermined power output, and / or as desired and / or Alternatively, those skilled in the art will recognize that at least one constraint may be imposed on the optimization problem, such as other constraints as needed. To determine what constraints should be applied, and in what manner they should be applied for a given optimization problem, unless otherwise stated. Within the management authority of the vendor. In addition, those skilled in the art will recognize situations where additional optimization theory techniques can be applied, such as adding slack variables to enable a feasible solution to the optimization problem.

  Known techniques can be used, for example, by the enhancement module 114 (FIG. 8) to solve optimization problems related to the operation of the power plant 12. For example, integer programming, linear methods, mixed integer linear methods, mixed integer nonlinear methods, and / or other techniques may be used as needed and / or desired. In addition, as can be seen in the exemplary objective function, the optimization problem can be solved over the forecast planning period and can provide an array of values for at least one operating parameter of the power plant 12. is there. The enhancement or enhancement can be performed over a relatively short forecast planning period, such as, for example, on the order of 24 hours or even minutes, but the enhancement module 114 (FIG. 8) depends on the depth of analysis desired. Thus, it is possible to use a longer forecast planning target period, for example, up to the estimated useful life of the power plant 12. In an embodiment, for example, an initial set point determined by control model 111 (FIG. 8) or the like is adjusted and enhanced in response to optimization problem resolution and / or as part of optimization problem resolution. It is possible to produce a set point that is enhanced, enhanced, or optimized. In addition, determining an initial set point (in steps 172 to 175 of FIG. 9) to improve results and / or further enhance or enhance the control set point of the power plant 12, values of performance indicators Iterating can be used in conjunction with determining the estimated LCC cost, and enhancing or enhancing.

  As will be described, the offer curve section 180 can generate an offer curve or a set of offer curves, examples of which were shown earlier in connection with FIG. At the plant controller 22, control information 115 from the control module 110 and / or the data resource 26 may be received by the offer curve module 120 (at step 181 of FIG. 9). According to certain embodiments, the control information 115 includes control set points, performance, ambient conditions, and / or market conditions. This information may also be known as “as run” information. In addition, ambient condition forecast 121 and / or market condition forecast 122 may be received (at step 182). According to certain embodiments, a database 123 may be included, which may store current information, “time of run” information, and / or historical information locally, Including any or all of conditions, market conditions, power plant performance information, offer curves, control set points, and / or any other information that may be appropriate. The database 123 may be used to provide information for simulating the operation of the power plant 12 (at step 183), such as using an offline model 124 of the power plant 12.

  The offline model 124 can include a model similar to the control model 111, but can also include additional modeling information. For example, offline model 124 may incorporate some or all of control model 111, performance model 112, LCC model 113, and / or additional modeling information. By running the offline model 124 using the set points and / or information from enhancing or enhancing the LCC, the output of the offline model 124 can be used to calculate the power for each time interval within the forecast planning period. Generate one or more offer curves 125 (in step 184) that can be used to determine an estimate for the cost of production and an estimate for various values of the power output of the power plant 12 And the one or more offer curves 125 may be sent (at step 185) to the feed command authority 24 or otherwise provided. The offline model 124 may use any suitable information, such as historical information, current information, and / or forecast information, in determining the estimated operating cost and / or condition of the power plant 12. Is possible. In addition, the offline model 124 in the embodiment may be tuned (at step 186), such as by the model tuning module 126, for example. Tuning, for example, periodically adjusts parameters for the off-line model 124 based on information received and / or provided by other parts of the plant controller 22 to improve the actual operation of the power plant 12. Reflecting can be included to better simulate the operation of the power plant 12. Thus, for a given set of operating parameters, if the plant controller 22 observes actual process conditions that are different from those predicted by the offline model 124, the plant controller 22 will accordingly configure the offline model 124. It is possible to change.

  In addition to the offer curve 125 from the power plant 12a, as shown, the feed command authority 24 can receive the offer curve 125 from another power plant 12b under its control. The power supply command authority 24 can access the offer curve 125 and can generate a power supply schedule to accommodate the load on the power system 10. The power supply command authority 24 may additionally take into account expected ambient conditions, load expectations, and / or other information as needed and / or desired, It can be received from various local or remote data resources 26 that are accessed by the authority 24. As shown, the power supply schedule created by the power supply command authority 24 includes control signals for the power plant 12 that includes the target load 128, and as described above, the plant controller 22 controls its control. It is possible to respond to the signal.

  Inclusion of life cycle cost considerations increases the scope and accuracy of the plant model used in the optimization process, as described herein, and in this way strengthens the procedure. It will be recognized that it is possible to play a role as it makes possible. The offer curve 125 can represent a variable cost (measured in dollars per megawatt hour for a megawatt power plant output) as described above. Offer curve 125 may include an incremental variable cost offer curve and an average variable cost offer curve. As can be seen, embodiments of the present invention can provide an accurate assessment of variable costs via their generated offer curve 125. Using embodiments of the present invention, the incremental variable cost offer curve is shown to predict the actual incremental variable cost curve very closely, while the average variable cost offer curve is the actual average It is shown to predict the variable cost curve very closely. The accuracy of the offer curve generated by embodiments of the present invention indicates that the various models used in the plant controller 22 of FIG. 8 provide a representative model suitable for overview purposes. ing.

  Turning now to FIGS. 10-12, other aspects of the present invention are described with reference to and including the specific systems and methods provided above. FIG. 10 is a data flow diagram demonstrating an architecture for a plant optimization system 200 that may be used in a combined cycle power plant having a gas and steam turbine system. In the provided embodiment, system 200 includes monitoring and control instrument 202 associated with each of gas turbine (202) and steam turbine system (204), such as the sensors and actuators discussed above. 204. Each of the monitoring and control instruments 202, 204 can send a signal indicative of the measured operating parameter to the plant controller 208. The plant controller 208 receives the signal, processes the signal according to a predetermined algorithm, and transmits control signals to the monitoring and control instruments 202, 204 to affect changes to plant operation.

  The plant controller 208 is interfaced with the data collection module 210. The data collection model 210 may be communicatively coupled to a database / historian 212 that maintains archived data for future reference and analysis. The heat balance module 214 can receive data from the data collection model 210 and the database / historian 212 on demand, and the power plant mass to fit as closely as possible to the measured data. Process algorithms to tune the balance model and energy balance model. Inconsistencies between the model and the measured data can indicate errors in the data. As will be appreciated, the performance module 216 can use the plant equipment model to predict the expected performance of key plant components and equipment. Differences between expected performance and current performance can represent degradation of plant equipment, parts, and component conditions such as, but not limited to, dirt, scaling corrosion, and failure. In accordance with aspects of the present invention, the performance module 216 can track degradation over time so that performance problems with the most significant impact on plant performance are identified.

  As shown, an optimizer module 218 may be included. The optimizer module 218 may include a methodology for optimizing the economic power supply of the plant. For example, according to an embodiment, the power plant may be powered based on a heat rate or incremental heat rate, according to the assumption that the heat rate is equivalent to a financial source. The power plant includes an additional manufacturing process (not shown), and for the additional manufacturing process, the steam is used directly (i.e., the steam produced is separate from the power generation in the steam turbine). In an alternative scenario, it is recognized that the optimizer module 218 can solve the optimization problem and components with higher heat rates can be powered. It will be. For example, in certain situations, the demand for steam can exceed the demand for electricity, or the electrical output can be constrained by electrical system requirements. In such cases, powering the lower efficiency gas turbine engine may allow greater heat to be recovered without exceeding the limit and increasing the electrical output. In such a scenario, supplying components with higher heat rates is an economically optimized alternative.

  The optimizer module 218 may be selectable between an online (automatic) mode and an offline (manual) mode. In the online mode, the optimizer 218 generates the cost of electricity generated, the incremental cost at each power generation level, the cost of process steam, and a predetermined periodicity, eg, in real time or once every 5 minutes. Automatically calculate current plant economic parameters such as plant operating profit at Offline mode simulates steady state performance, analyzes “what-if” scenarios, analyzes budgets and upgrade options, and provides current plant operation to guarantee current power generation capacity, target heat rate, and conditions. It can be used to predict corrections, operational constraints and effects of maintenance actions, and fuel consumption. Optimizer 218 does not calculate output based on efficiency by combining plant heat balance and plant financial model, but generates power based on real-time economic cost data, output price, load level, and equipment degradation. Calculate the profit-optimized output for the plant. The optimizer 218 can be tuned to individually adapt to the degradation of each component, can produce an advisory output 220, and / or can produce a closed feedback loop control output 222. The advisory output 220 recommends to the operator where to set the controllable parameters of the power plant and facilitates optimizing each plant component to maximize profitability. In the exemplary embodiment, advisory output 220 is a computer display screen that is communicatively coupled to computer-executable optimizer module 218. In an alternative embodiment, the advisory output is a remote workstation display screen, which accesses the optimizer module 218 over the network. The closed feedback loop control output 222 can receive data from the optimizer module 218 and provides optimized setpoints and / or bias setpoints for the modules of the system 200 to implement real-time feedback control. calculate.

  FIG. 11 is a simplified block diagram of a real-time thermal power plant optimization system 230 that includes a server system 231 and a plurality of client subsystems according to aspects of the invention. The plurality of client subsystems is also referred to as a client system 234 and is communicatively coupled to the server system 231. As used herein, real-time represents a result that occurs in a substantially short period of time after an input change affecting the result, for example, a computer calculation. A period represents the amount of time between each iteration of a task that is repeated periodically. Such repeated tasks may be referred to herein as periodic tasks or cycles. The time period is a design parameter of the real-time system, and the real-time system can be selected based on the importance of the results and / or the ability of the system to implement the processing of inputs to generate the results. Additionally, events that occur in real time occur without substantial intentional delay. In an exemplary embodiment, the calculations can be updated in real time with a periodicity of 1 minute or less. The client system 234 can be a computer that includes a web browser such that the server system 231 is accessible to the client system 234 via the Internet or some other network. Client system 234 may be interconnected to the Internet through a number of interfaces. Client system 234 can be any device that can be interconnected to the Internet. As described in more detail below, the database server 236 is connected to a database 239 that contains information about a plurality of things. In one embodiment, the centralized database 239 includes the aspects of the data resource 26 discussed above, the centralized database 239 being stored on the server system 231 and also through the client system 234 to the server system 231. By logging on, it can be accessed by a potential user at one of the client systems 234. In an alternative embodiment, the database 239 can be stored remotely from the server system 231 and can be decentralized.

  In accordance with aspects of the present invention, some of the control methods discussed above can be developed for use with the system diagrams of FIGS. For example, one method includes simulating power plant performance using a plant performance module of a software code segment that receives power plant monitoring instrument data. Data may be received over the network from a plant controller or database / historian software program running on the server. Any additional plant components, such as an intake conditioning system or an HRSG duct firing system, can be simulated in a manner similar to that used to simulate power plant performance. Determining the performance of each plant component in the same manner allows the entire power plant to be treated as a single plant and determines such setpoints for each component separately. Rather, determine an optimized set point for the power plant. The measurable quantity for each plant component can be parameterized to represent the output per component or power plant efficiency. Parameterizing plant equipment and plant performance includes, but is not limited to, gas turbine compressors, gas turbines, heat recovery steam generators (HRSG), draft fans, cooling towers, condensers, feed water heaters, evaporation Calculation of efficiency for components such as containers, flash tanks, etc. Similarly, heat rate and performance calculations can be parameterized, the resulting simultaneous equations can be solved in real time, and the calculated results can be used without intentional delay from the time each parameter was sampled. It will be recognized that it is possible. Solving parameterized simultaneous equations and constraints also determines current heat balances for power plants and includes but is not limited to operating reserve requirements, electrical system demands, maintenance activities Determining expected performance using current constraints on power plant operation, such as clean water demand, component shutdown, and the like. Solving parameterized equations and constraints also determines parameters to adjust to modify the current heat balance so that the future heat balance is equal to the determined expected performance. Can be included. In an alternative embodiment, solving the parameterized simultaneous equations and constraints is to determine the inlet conditions to the power plant, based on the determined inlet conditions and a predetermined model of the power plant. Predict output, determine power plant's current output, compare predicted output with determined output, and adjust plant parameters until determined output is equal to predicted output Including doing. In an exemplary embodiment, the method also uses a parameterized equation to correlate controllable plant parameters, plant equipment, and plant performance and minimize power plant heat rate. And / or use objective functions that include maximizing power plant profits to define optimization goals and use constraints to physically operate each individual piece of equipment Defining possible limits and / or overall limits, where the overall limits include maximum power production, maximum fuel consumption, and the like.

  FIG. 12 is a flowchart of an exemplary method 250 for solving parameterized simultaneous equations and constraints in accordance with the present invention. The method 250 determines (at 252) a current heat balance for the power plant, (at 254) uses current constraints on the operation to determine expected performance, and (at 256) a future heat balance. Determining parameters for adjusting to modify the current heat balance such that is equal to the determined expected performance. The method 250 also includes determining 258 an inlet condition to the power plant, predicting the power plant output 260 based on the determined inlet condition and a predetermined model of the power plant, Determining an output 262, comparing 264 the predicted output to the determined output, and adjusting 266 plant parameters until the determined output is equal to the predicted output. The described method and the system discussed in connection with FIGS. 10 and 11 provide a cost-effective and reliable means for optimizing a combined cycle power plant. Will be recognized.

  Turning now to FIGS. 13-16, attention will be paid to several flow diagrams and system configurations illustrating a control methodology in accordance with certain aspects of the present invention. In general, according to an exemplary embodiment, a thermal power generation unit, such as a gas turbine system or the like, or a control system for a power plant can include first and second instances of a model, The first and second instances of the model the operation of the turbine, for example, by utilizing a physics based model or by mathematical modeling (eg, transfer function, etc.). The first model (which may also be referred to as the “primary model”) can provide the current operating parameters of the gas turbine system, which is the mode of operation of the turbine and the corresponding operating conditions. Describe. As used herein, “parameters” include, but are not limited to, temperature, pressure, gas flow, and compressor, combustor, and turbine efficiencies at defined locations in the turbine. It represents items that can be used to define the operating conditions of the turbine, such as levels. The performance parameter may also be referred to as a “model correction factor” and represents a factor used to adjust the first or second model to reflect the operation of the turbine. Input to the first model can be sensed or measured and provided by the operator. In addition to current performance parameters, the method of the present invention receives information about external factors or disturbance variables, such as ambient conditions, which may affect the current or future operation of the gas turbine system, or otherwise. It is possible to include obtaining.

  The second model (also referred to as “second order model” or “predictive model”) takes into account current operating parameters such as the manipulated variables and the like, and one or more disturbance variables, Generated to identify or predict one or more operating parameters of the gas turbine system, such as controlled variables. Exemplary operating parameters of the turbine include, but are not limited to, actual turbine operating conditions such as exhaust temperature, turbine power, compressor pressure ratio, heat rate, emissions, fuel consumption, expected income, etc. . Thus, this second model or predictive model can be utilized to indicate or predict turbine behavior at a particular operating set point, performance goals, or operating conditions that are different from current operating conditions. As used herein, the term “model” generally refers to the action of modeling, simulating, predicting or presenting based on the output of the model. Although the term “second model” is utilized herein, in some cases there may not be a difference between the formulation of the first and second models, and “second model” It will be appreciated that “model of” is intended to represent running the first model with adjusted parameters or additional or different inputs.

  Thus, by modeling turbine operating behavior utilizing a second model or predictive model that takes into account external factors and / or different operating conditions, turbine control may be under these different operating conditions or assumed. It can be adjusted to operate more efficiently taking into account external external factors. Thus, this system enables automated turbine control based on modeled behavior and operating characteristics. In addition, the described modeling system generates operator specific scenarios, inputs, operating points, operating targets, and / or operating conditions and predicts turbine behavior and operating characteristics in these operator specific conditions Enable. Predicting such a virtual scenario allows the operator to make more informed control and action decisions such as scheduling, loading, turndown, etc. As used herein, the term “operating point” generally represents an operating point, condition, and / or goal and is not intended to be limiting. Thus, the operating point can represent a target or set point, such as base load, turndown point, and peak fire.

  One exemplary use of the turbine modeling system described includes adjusting turbine operation to meet grid compliance requirements while still operating at the most efficient level. For example, local grid authorities typically specify the requirement that power plants must be able to support the grid during frequency disruptions. Supporting the grid during disruption involves increasing or decreasing the turbine load under certain conditions, depending on the grid conditions. For example, during disruptions, power plants are expected to increase their power output (eg, by as much as 2%) to compensate for other supply shortages. Thus, turbine operation typically constrains the base load point and allows the turbine to be operated at a margined power level (also referred to as a “spare margin”), overrun if necessary. Increased load can be provided without incurring additional maintenance factors associated with firing. As one example, the reserve margin can be 98% of a typical base load, thus allowing the load to be increased to accommodate grid requirements without exceeding 100% base load. (For example, increase by 2%). However, unexpected external factors such as temperature, humidity, or pressure can adversely affect turbine efficiency. As daytime temperatures increase, the heat causes the turbine to operate at lower efficiency and the turbine cannot reach the 100% load as originally planned, so the turbine is 2% of the turbine's need. May not have reserve power. To compensate for it, conventional heat rate curves cause the turbine to operate in a more efficient state throughout the day, taking into account possible mechanical efficiency losses (eg, at 96%, etc.) . However, the turbine modeling system described herein allows the turbine behavior to be modeled in real time according to current external factors (eg, temperature, humidity, pressure, etc.) and thus the current ambient Allows turbine operation to be controlled to operate most efficiently given the conditions. Similarly, future turbine behavior can be predicted to predict turbine behavior in response to daily heat fluctuations, and turbine operation planning will provide the most efficient and economically profitable operation. to enable. As another example, a power plant typically makes a decision as to whether to shut down the gas turbine at night, or simply reduce the power level (eg, turn down). Turbine operating characteristics such as emissions, exhaust temperature, etc. will influence this determination. The turbine modeling system described herein can be utilized to make more intelligent decisions either in advance, in real time, or in near real time. External factors and expected turbine operating parameters can be fed into the second model to determine what the turbine operating characteristics will be. Therefore, considering these characteristics (eg efficiency, emissions, cost, etc.), the modeled characteristics can be used to determine whether the turbine should be shut down or turned down It is.

  As yet another example, a turbine modeling system may be utilized to evaluate the benefits of performing turbine maintenance at a given time. The turbine modeling system of the present invention can be utilized to model turbine operating characteristics at current capabilities based on current performance parameters. An operator specific scenario can then be generated that models the operating characteristics of the turbine when maintenance is performed (eg, improving performance parameter values to show an expected performance increase). For example, as the turbine degrades over time, the performance parameter reflects machine degradation. In some cases, maintenance can be performed to improve their performance parameters and thus the operating characteristics of the turbine. By modeling or predicting improved operating characteristics, a cost-benefit analysis can be performed to compare such costs with the benefits gained from performing maintenance.

  FIG. 13 illustrates an example system 300 that can be used to model turbine operating behavior. According to this embodiment, a power plant 302 is provided that includes a gas turbine having a compressor and a combustor. The intake duct to the compressor feeds ambient air and possibly injected water to the compressor. The configuration of the intake duct contributes to the pressure loss of the ambient air flowing into the compressor. An exhaust duct for the power plant 302 directs combustion gases from the outlet of the power plant 302, for example through emission control and sound absorption devices. The amount of inlet pressure loss and back pressure can change over time due to the addition of components to the intake and exhaust ducts and due to clogging of the intake and exhaust ducts.

  The operation of the power plant 302 may be monitored by one or more sensors that detect one or more observable conditions of the power plant 302, i.e., operating parameters or performance parameters. In addition, external factors such as the ambient environment and the like can be measured by one or more sensors. In many cases, dual or triple sensors can measure the same parameters. For example, a group of redundant temperature sensors can monitor the ambient temperature surrounding the power plant 302, the compressor discharge temperature, the turbine exhaust gas temperature, and other temperatures throughout the power plant 302. Similarly, a group of redundant pressure sensors can monitor ambient pressure and static and dynamic pressure levels at compressor inlets and outlets, turbine exhaust, and elsewhere throughout the engine. A group of redundant humidity sensors can measure the ambient humidity in the intake duct of the compressor. The group of redundant sensors may also include flow sensors, speed sensors, flame detector sensors, valve position sensors, guide vane angle sensors, etc. that sense various parameters related to the operation of the power plant 302. The fuel control system can regulate the fuel flowing from the fuel supply to the combustor. The fuel controller can also select the type of fuel for the combustor.

  As stated, “operating parameters” include temperature, pressure, compressor pressure ratio, gas flow at defined locations in the turbine, load set point, combustion temperature, and level of turbine or compressor degradation. And / or represents items that may be used to define the operating conditions of the turbine system, such as one or more conditions corresponding to the level of efficiency of the turbine or compressor. Some parameters are measured directly. Other parameters are estimated by the turbine model or indirectly known. Still other parameters can represent virtual or future conditions and can be defined by the plant operator. The measured and estimated parameters can be used to represent a given turbine operating condition. As used herein, a “performance indicator” is an operating parameter derived from the value of a particular measured operating parameter and represents a performance standard for power plant operation over a defined period of time. Yes. For example, the performance index includes a heat rate, an output level, and the like.

  As illustrated in FIG. 13, the system 300 includes one or more controllers 303a, 303b, which control the operation of the power plant or power generation unit 302, respectively. It can be a computer system having one or more processors that execute a program for it. Although FIG. 13 illustrates two controllers, it will be appreciated that a single controller 303 may be provided. According to a preferred embodiment, multiple controllers may be included to provide redundant and / or distributed processing. The control action can depend, for example, on sensor inputs or instructions from the plant operator. The program executed by the controller 303 can include scheduling algorithms, such as algorithms for regulating fuel flow to the combustor, grid compliance, algorithms for managing turndown, and the like. The commands generated by the controller 303 can cause an actuator on the turbine to adjust, for example, a valve between the fuel supply and the combustor, fuel flow, split, and fuel type. To be adjusted. The actuator can adjust the inlet guide vanes on the compressor or can activate other control set points on the turbine. It will be appreciated that the controller 303 can be used to generate the first model and / or the second model as described herein in addition to facilitating control of the power plant. Will be. The controller 303 can receive the operator's and / or current modeled output (or any other system output). As previously described, the controller 303 can include a memory that stores programmed logic (eg, software) and also sensed operating parameters, modeled. Data such as operating parameters, operating limits and goals, operating profiles, etc. can be stored. The processor can utilize the operating system to execute the programmed logic and, in doing so, can utilize the data stored thereon. A user can interface with the controller 303 via at least one user interface device. The controller 303 can communicate online with the power plant while operating via the I / O interface, and can communicate offline with the power plant while not operating. Is possible. One or more of the controllers 303 can perform the model-based control system described herein, which includes, but is not limited to, sensing operational and performance parameters. Generating, modeling and / or receiving; generating a first power plant model reflecting current turbine operation; sensing, modeling and / or receiving external factor information; Receiving operator input and other variables such as performance goals; generating a second power plant model that reflects the operation taking into account the additional data supplied; current or future turbine operation Controlling; and / or presenting modeled operating characteristics It will be recognized that that can include that. In addition, it should be appreciated that other external devices, or multiple other power plants or power generation units, can communicate with the controller 303 via the I / O interface. The controller 303 can be remotely located with respect to the power plant it controls. Further, the controller 303 and the programmed logic implemented by the controller 303 can include software, hardware, firmware, or any combination thereof.

  The first controller 303a (which can be the same or different controller as the second controller 303b, as described) models the power plant 302 by the first model or the primary model 305. Which may be operable to model current turbine performance parameters. The second controller 303b may be operable to model turbine operating characteristics under different conditions via a second model or predictive model 306. The first model 305 and the second model 306 can each be a configuration of one or more mathematical representations of turbine behavior. Each of these representations depends on the input value and can generate an estimate of the modeled operating parameter. In some situations, the mathematical representation can generate surrogate operating parameter values that can be used in situations where measured parameter values are not available. The first model 305 is then based on the current performance parameters of the power plant 302 and any other factors such as external factors, operator-supplied commands or conditions, and / or adjusted operating conditions, etc. And may be utilized to provide a basis and / or input to the second model 306 to determine turbine operating characteristics. As explained above, the “second model 306” can simply be an instance of the same model as the first model 305, and the second model 306 is an external factor, different behavior It will be appreciated that additional inputs or different inputs, such as points, are considered, and different performance parameters or turbine behavior are modeled considering the different inputs. The system 301 can further include an interface 307.

  With continued reference to FIG. 13, a brief description of the interrelationships between system components is provided. As described, the first model or primary model 305 models the current performance parameters 308 of the power plant 302. These current performance parameters 308 include, but are not limited to, conditions corresponding to the level of turbine degradation, conditions corresponding to the level of turbine efficiency (eg, heat rate or ratio of fuel to power output), inlet guide vanes. Angle, amount of fuel flow, turbine rotational speed, compressor inlet pressure and temperature, compressor outlet pressure and temperature, turbine exhaust temperature, generator power output, compressor air flow, combustor fuel / air ratio, combustion temperature (turbine Inlet), combustor flame temperature, fuel system pressure ratio, and acoustic properties. Some of these performance parameters 308 can be measured or sensed directly from turbine operation, and some can be modeled based on other measured or sensed parameters. The performance parameter may be provided by the first model 305 and / or generally provided by the controller, such as when sensed and / or measured by the controller, for example. When generating the first model 305, the performance parameter 308 (which is intended to represent any turbine behavior provided by the model) is provided to generate the second model or predictive model 306. Is done. Other variables 309 may be provided to the second model 306 depending on its intended use. For example, other variables can include external factors, such as ambient conditions, that are generally uncontrollable and must be simply addressed. In addition, other variables 309 are generated by a controller specific scenario or operating point (e.g., a controller 303 such as a turbine control based on the first model 305, or otherwise via the controller 303). Turbine operating points, etc.) and measured inputs, which are described as possibly being modeled by the first model 305. It is possible that there are some or all of the same measured input. As described with reference to FIG. 14, an operator specific scenario 313 (eg, one or more operator-supplied commands indicating different turbine operating points or conditions) may also be entered via operator input. Model 306. For example, as one illustrative use case, other variables 309 can include controller specific scenarios, which are additional inputs such as external factors or measured inputs. Based on the second model 306 as one or more inputs when attempting to model current turbine behavior in real time or near real time. In addition to one or more of these additional inputs, by utilizing the first model controller-specific scenario, the expected real-time behavior of the power plant 302 allows these additional inputs to be Taking into account, it can be modeled by the second model 306 and it can be utilized to control the power plant 302 or to adjust the first model 305 by the control profile input 310.

  Referring to FIG. 14, an operator specific operating mode or scenario 313 is provided as one or more inputs to a second model or prediction model 306 via an interface 307, and then the second model or prediction model. 306 models or predicts future turbine behavior under various conditions. For example, an operator can supply commands to the interface 307 to generate scenarios where the power plant 302 operates at different operating points (eg, different loads, configurations, efficiencies, etc.). As an example for illustrative purposes, a set of operating conditions may be supplied via an operator specific scenario 313, which may be provided the next day (or other future time, such as ambient conditions or demand requirements). This represents the expected condition for (frame). These conditions are then used by the second model 306 to generate the expected or predicted turbine operating characteristics 314 for the power plant 302 during that time frame. When running the second model 306 under an operator specific scenario, the expected operating characteristics 314 include, but are not limited to, base load output capability, peak output capability, minimum turndown point, emission level, Represents turbine behavior, such as heat rate. These modeled or predicted operating characteristics 314 may be useful when planning and promising power production levels, for example, for power spot market planning and bidding.

  FIG. 15 illustrates an exemplary method 320 by which exemplary embodiments of the present invention can operate. A flow chart of the basic operation of a system for modeling a turbine, such as that described with reference to FIGS. 13 and 14, may be performed by one or more controllers. The method 320 may begin at step 325, where the controller determines one or more current performance parameters of the turbine according to the current operation according to the first model or the primary model. It is possible to model. To generate this first model, the controller can receive one or more operating parameters indicative of the current operation of the turbine as input to the model. As explained above, these operating parameters can be sensed or measured and / or they can be modeled so that they can occur if the parameters could not be sensed. Current operating parameters can include any parameter indicative of current turbine operation, as described above. It will be appreciated that the methods and systems disclosed herein do not depend directly on whether the operating parameters are measured or modeled. The controller can include, for example, a model of the gas turbine being generated. The model can be a configuration of one or more mathematical representations of operating parameters. Each of these representations depends on the input value and can generate an estimate of the operating parameter to be modeled. The mathematical representation can generate surrogate operating parameter values that can be used in situations where measured parameter values are not available.

  In step 330, the controller can receive or otherwise determine one or more external factors that can affect current and / or future operations. As explained above, these external factors are typically uncontrollable (though not necessarily so), so incorporating their effects into the second model is Useful for generating the desired turbine control profile and / or operational behavior. External factors can include, but are not limited to, ambient temperature, humidity, or pressure, and fuel composition and / or supply pressure, which can affect turbine operating behavior. These external factors can be measured or sensed (e.g., if the operator requires a predicted scenario based on a virtual scenario or future conditions) or otherwise the operator Manually and / or by a third party information resource (eg, weather service, etc.).

  In step 335, the controller can receive the adjusted operating point and / or other variables and predict turbine behavior at conditions different from the current turbine conditions. Adjusted operating points include, but are not limited to, when modeling a turbine at a spare margin (eg, 98% of base load) or, for example, modeling a turbine at peak load or during turndown In some cases, it may include identifying a desired output level. Operating points include, but are not limited to, hot gas flow path durability (or combustion temperature), exhaust frame durability, NOx emissions, CO emissions, combustor lean blowout, combustion dynamics, compressor surge, Operating limits can be further included, such as compressor icing, compressor aerodynamic limits, compressor clearance, compressor discharge temperature, and the like. Thus, by providing these adjusted operating points or other variables, an operator can provide virtual scenarios for which the turbine model is under those scenarios. , Which may be useful for controlling the future operation of the turbine and / or for planning future power generation and startup and shutdown plans.

  Following step 335 is step 340, in which a second model or predictive model of the turbine is generated by the first model generated in step 325, and optionally external factors, and / or , Based on the adjusted operating point or other variable provided in step 335. Thus, this second model or prediction model can accurately indicate or predict the operating parameters and from there performance indicators related to the turbine during future operating periods.

  In step 345, the modeled performance can be utilized to adjust current or future turbine operation and / or to display the modeled performance to the operator. Thus, when adjusting the current turbine operation, the turbine controller, for example, may modify the current control model (eg, It is possible to receive modeled performance parameters as input for changing the current control profile. This real-time or near real-time control of the turbine will be implemented when the input to the second model generated in step 340 is representative of current turbine conditions or current external factors. It is assumed that For example, when the second model represents a performance characteristic that takes into account temperature, pressure, or humidity and / or a turbine operating or performance parameter that more accurately represents turbine degradation and / or efficiency Real-time or near real-time adjustment at 345 can be performed. FIG. 16 illustrates one exemplary embodiment that can optionally receive operator specific input and generate expected behavior under different operating conditions. Also, the model output generated in step 340 may be displayed to the operator via the interface or otherwise presented. For example, in one embodiment where the operator provides a virtual operating scenario at step 335, the predicted turbine operating characteristics are for analysis and for possible inclusion in future control or planning activities. Can be displayed. Thus, the method 320 may end after step 345, allowing the first model to model the current performance parameters of the turbine, and then add additional external factors, adjusted operating points, or others The same turbine is modeled in consideration of the additional data, and the turbine operation is predicted based on the additional data.

  FIG. 16 illustrates an exemplary method 400 by which alternative embodiments can operate. Provided is an exemplary flowchart of the operation of the system for modeling a turbine, such as that described with reference to FIGS. 13 and 14, such as may be performed by one or more controllers. The The method 400 illustrates the use of the system 301, allowing an operator to optionally supply additional variables, take advantage of modeling capabilities, and predict turbine behavior under virtual scenarios. It is. The method 400 may begin at decision step 405, where the turbine should be modeled according to current turbine operating parameters and performance parameters, or operator supplied parameters. Is to be taken into account when generating the model. For example, if the system is being used to predict a virtual operating scenario, the current performance parameters are transferred to the model (assuming the model already reflects basic turbine behavior and behavior). May not be required as input. Accordingly, if it is determined at decision step 405 that the current parameter should not be utilized, operation proceeds to step 410 where the operator provides different performance parameters and different operations. Allows turbines to be modeled under points and at different operating conditions (eg, at a more degraded state, at different levels of efficiency, etc.). Otherwise, current performance parameters and / or operating parameters are utilized, for example, as described with reference to step 325 of FIG. 15, and operation continues to step 415. In step 415, the controller may model one or more performance parameters of the turbine according to the operator supplied input from step 410 or the current operation of the turbine, depending on the first model or primary model. It is. For example, if, at step 410, a model is generated based at least in part on operator supplied parameters, the model generated at step 415 may have predicted turbine behavior under those performance parameters. Represent.

  Following the step 415 is a decision step 420 in which subsequent modeling (eg, “second model” or “predictive model”) is performed such as current temperature, pressure, or humidity, etc. It is determined whether it should be based on current external factors or on different external factors supplied by the operator. For example, in one scenario, the controller may model turbine operating behavior based on additional data of one or more current external factors, which may be based on current conditions. It will be possible to predict further. However, in another scenario, the controller can be utilized to further model the turbine according to operator-supplied conditions, which makes it possible to predict turbine operating characteristics under various virtual scenarios. Accordingly, if it is determined in step 420 that operator-supplied external factor data should be considered when modeling, operation continues to step 425. Otherwise, operation continues to step 430 utilizing the current external factor. In step 430, the controller identifies external factors that will be considered when generating the second model or predictive model, whether they represent the current state or a virtual factor, receive. Following step 430 is steps 435-445, which consider different operating points in the same or similar manner as described with reference to steps 325-345 of FIG. Optionally, generating a predictive model based on the received data and displaying the predicted behavior, respectively. The method 400 may end after step 445 and optionally model turbine operating behavior based on operator specific scenarios.

  Thus, embodiments described herein utilize turbine models in addition to predicting turbine behavior taking into account current performance parameters and one or more identified external factors, It makes it possible to show the turbine behavior of the actual turbine and the corresponding operating parameters. Accordingly, these embodiments provide the technical effect of showing or predicting turbine behavior at operating points or operating conditions that differ from current turbine operation. In addition, additional technical effects are provided that allow automated turbine control based at least in part on the modeled behavior and operating characteristics, which are optionally turbines in these operator specific conditions. Generating operator specific scenarios, inputs, operating points, and / or operating conditions to predict behavior and operating characteristics may be included. A further technical effect to be realized is the ability to predict various virtual scenarios that allow operators to make more informed control and action decisions such as scheduling, loading, turndown, etc. including. As will be appreciated, reference is made herein to system step diagrams, methods, apparatus, and computer program products according to exemplary embodiments of the invention.

  Referring now to FIG. 17, a flow diagram 500 according to an alternative embodiment of the present invention is illustrated. As will be appreciated, the flow diagram 500 includes aspects that may be used as part of a control method or control system to facilitate optimization of the power plant 501. The power plant 501 can be similar to any of those discussed in connection with FIGS. 2 and 3, but the present invention is not limited to any other, unless otherwise limited in the appended claims. It should be appreciated that it can also be used in connection with types of power plants. In a preferred embodiment, the power plant 501 can include a plurality of thermal power generation units that generate electricity that is sold within the power system market, such as those discussed in connection with FIG. It is. The power plant 501 can include many possible types of operating modes, for example, different ways in which the plant's thermal power unit is involved or operated, the power level of the plant, Includes methods that respond to changing ambient conditions while satisfying load requirements. It will be appreciated that the operating mode can be described and defined by operating parameters that take into account the physical characteristics of a particular aspect of the operation of the power plant 501. As further illustrated in FIG. 17, the present invention may include a power plant model 502. The power plant model 502 can include a computerized representation of the power plant that correlates process inputs and outputs as part of a simulation intended to mimic the operation of the plant. As shown, the present invention further includes a tuning module 503, a plant controller 505, a power plant model 507 to be tuned, a plant operator module 509, and an optimizer 510, each of which: Will be discussed individually.

  The power plant 501 can include a sensor 511 that measures operating parameters. These sensors 511, as well as the operating parameters that these sensors 511 measure, can include any of those already discussed herein. As part of this method, the sensor 511 measures the operating parameter during an initial operating period, a current operating period, or a first operating period (hereinafter “first operating period”). And those measurements can be used to tune the mathematical model of the power plant, and the mathematical model can then be used in subsequent operations as discussed below. Of the optimization process for controlling the power plant 501 in an improved or optimized mode of operation during a period of time or a second period of operation (hereinafter “second period of operation”). Can be used as part. The measured operating parameters can themselves be used to assess plant performance, or can be used in calculations to derive performance indicators related to specific aspects of power plant operation and performance. . As will be appreciated, this type of performance metric can include heat rate, efficiency, power generation capability, and others. Thus, as an initial step, the operating parameters measured by sensor 511 during the first operating period can be used as one or more performance indicators (or calculate values for one or more performance indicators). Can be used to). As used herein, such values for performance indicators (ie, values based on measured values of operating parameters) will be referred to herein as “measured values”. Operational parameter measurements and / or performance indicator measurements may be communicated 512 to both the plant controller 505 and the tuning module 503, as shown. The tuning module 503 tunes the power plant model 502 and uses it from a data matching adjustment or tuning process for use in configuring the tuned power plant model 507, as discussed in more detail below. May be configured to calculate the feedback.

  The power plant model 502 can be a computerized model that is configured to simulate the operation of the power plant 501 as discussed. According to the method, the power plant model 502 can be configured to simulate the power plant operation corresponding to the first operating period of the power plant 501. To accomplish this, the power plant model 502 may be supplied with information and data regarding operating parameters for the first operating period. This information can include any of the operating parameters measured during the first operating period, but the input data for the power plant model 502 is limited to a subset of the operating parameters that are measured. It will be recognized that you get. In this manner, the power plant model 502 can then be used to calculate values for selected operating parameters that are excluded from the input data set. More specifically, the power plant model may be supplied with input data for simulation, which includes many of the values measured for the operating parameters, from which it is selected. Certain measured values related to the operating parameters are omitted. As an output, the simulation may be configured to predict a simulated value for the selected operating parameter. The method can then use the simulated value to predict a value for the performance index. In this case, these values for the performance index will be referred to herein as “predicted values”. In this way, the measured value for the performance index determined directly from the measured power plant operating parameters can have a corresponding predicted value. As shown, the predicted value for the performance indicator may be communicated 514 to the tuning module 503.

  The tuning module 503 can be configured to compare the corresponding measured value and the predicted value for the performance index, and determine the difference therebetween. As will be appreciated, the difference, when calculated accordingly, reflects the error level between the actual performance (or its measurements) and the performance simulated by the power plant model. The power plant model 502 can be tuned based on this difference or feedback 515. Thus, the tuned power plant model 507 is configured. The tuned power plant model 507 may also be referred to as an off-line model or a predictive model, and the tuned power plant model 507 may then perform subsequent operations by simulating the proposed or possible mode of operation. Can be used to determine an optimized mode of operation for a period of time. The simulation can include estimates or predictions about future unknown operating conditions, such as ambient conditions. As will be appreciated, optimization can be based on one or more performance goals 516, in which a cost function is defined. As shown, the performance goal 516 may be communicated to the optimizer 510 through the plant operator module 509.

  The process of tuning the plant model can be configured as an iterative process that includes several steps. As will be appreciated, according to certain embodiments, the power plant model 502 can be configured such that logic statements and / or parameterized equations process input (ie, fuel supply, air supply, etc.) process output. It is possible to include an algorithm that correlates to (electricity generated, plant efficiency, etc.). Tuning the power plant model 502 includes adjusting one of the algorithms in the power plant model 502 and then using the adjusted power plant model 502 to determine the effect of the adjustment. As such, it may include simulating the operation of the power plant 501 for the first operating period. More specifically, the predicted value for the performance indicator may be recalculated to determine the effect that adjustments to the power plant model have had on the calculated difference. If the adjusted power plant model 502 is used to determine that the difference is small, the power plant model 502 may be updated or “tuned” to include that adjustment to advance. It will be further appreciated that the power plant model 502 can be constructed with a plurality of logical statements that include performance multipliers used to reflect changes in the manner in which the power plant operates under specific conditions. . In such a case, tuning the power plant model 502 based on the calculated difference includes a) adjusting for one or more of the performance multipliers, and b) the adjusted performance multiplier. Simulating the operation of the power plant with respect to the first operating period using a power plant model 502 having: c) a performance multiplier so as to determine whether the recalculation results in a difference reduction. Recalculating the predicted values for the performance index using the power plant model 502 as adjusted by: These steps can be repeated until an adjustment made to one of the performance multipliers results in reducing the difference, which reduces the actual performance of the model. This shows that the simulation is more accurately simulated. It will be appreciated that the performance multiplier may be related to expected performance degradation based on, for example, accumulated time of plant operation. In another example where the performance indicator includes power generation capacity, the step of tuning the power plant model 502 recommends an adjustment to the coefficient based on the difference between the measured power generation capacity and the predicted power generation capacity. It is possible to include. Such adjustments can include changes that ultimately result in the predicted power generation capacity being substantially equal to the measured power generation capacity. Thus, the step of tuning the power plant model 502 is such that the predicted or simulated value for the performance index is substantially equal to the measurement value for the performance index (or is within the margin of the measurement value for the performance index). ) Until one or more of the correlations in the power plant model 502 can be modified.

  Once tuned, the method can then use the tuned model 507 to simulate the operation of the proposed power plant. According to a particular embodiment, the next step of the method includes determining which simulated behavior is preferred given a defined performance goal 516. In this way, an optimized mode for operating the power plant can be determined. According to a preferred embodiment, the process of determining an optimized mode of operation can include several steps. First, the multiple proposed modes of operation can be selected or chosen from many possibilities. For each proposed operating mode, a corresponding proposed parameter set 517 can be generated for the second operating period. As used herein, a parameter set defines values for multiple operational parameters, and collectively the parameter set defines or describes aspects of a particular mode of operation. ing. As such, the proposed parameter set may be configured to describe many of the possible operating modes of the power plant 501 or relate to many of the possible operating modes of the power plant 501; It can also be configured as an input data set for a tuned power plant model 507 to simulate operation. Once the operating parameters are generated and organized into a proposed parameter set, the tuned power plant model 507 can simulate the operation of the power plant 501 according to each of the proposed parameter sets. It is. The optimizer 510 can then evaluate the result of the simulated operation 519 for each proposed parameter set 517. An evaluation can be performed according to the performance objectives defined by the plant operator and the cost function defined therein. The optimization process can include any of the methods described herein.

  The cost function defined by the performance goal can be used to evaluate the economic performance of the simulated operation of the power plant 501 over the second operating period. Based on the evaluation, one of the proposed parameter sets can be considered as creating a simulated action that is preferential compared to that produced by the other proposed parameter sets. According to the present invention, the mode of operation that corresponds to or is described by the proposed parameter set that produces the most preferred simulated operation is designated as the optimized mode of operation. Once determined, the optimized mode of operation can be passed to the plant operator for review or communicated to the plant controller for automated implementation, as discussed further below.

  According to a preferred embodiment, the method of the present invention can be used to evaluate a particular mode of operation and to determine and recommend a preferred alternative. As will be appreciated, the power generation unit of the power plant 501 is controlled by an actuator having a variable set point, which is controllably linked to a control system such as a plant controller 505. The operating parameters of the power plant 501 can be classified into three categories: manipulated variables, disturbance variables, and controlled variables. The manipulated variable evaluates the controllable process input that can be manipulated via the actuator to control the controlled variable, while the disturbance variable is an uncontrollable process input that affects the controlled variable. To evaluate. A controlled variable is a process output that is controlled against a defined target level. According to a preferred embodiment, the control method includes receiving an expected value for the disturbance variable for the second period of operation (ie, the period of operation for which the optimized mode of operation is being calculated). It is possible. Disturbance variables can include ambient conditions such as ambient temperature, pressure, and humidity. In such a case, the proposed parameter set generated for the second operating period can include a value for the disturbance variable that is related to an expected value for the disturbance variable. More specifically, the generated value for each ambient condition parameter can include a range of values for each of the ambient condition parameters. This range can include, for example, a low case, a medium case, and a high case. It will be appreciated that having multiple cases may allow plant operators to plan for the best / worst case scenario. Expected values can include likelihood rating corresponding to different cases, which allows plant operators to plan for various operational contingencies and / or hedge losses. It is possible to further support to do.

  Generating the proposed parameter set can include generating a target level for the controlled variable. The target level can be generated to correspond to a competing or alternative mode of operation of the power plant 501 and can include operator input. Such operator input can be facilitated by the plant operator module 509. According to a preferred embodiment, such a target level can include a desired power level for the power plant 501, which can occur assuming a past usage pattern for the plant. It can be based on the output level. As used herein, “power level” reflects the load level or level of electricity generated by the power plant 501 for commercial power distribution during the second operating period. The step of generating the proposed parameter set can include generating multiple cases where the power level remains the same or constant. Such constant power levels can reflect the base load for a set of plants or power generation units. Multiple target levels can be generated when each target level corresponds to a different level of engagement from each of the power generation units, and these are derived to obtain possible modes of operation assuming historical usage. obtain. The method can then determine the most efficient mode of operation given the known constraints. Additionally, the proposed parameter set can be generated such that disturbance variables maintain a constant level for multiple cases generated for each target level. The constant level for the disturbance variable can be based on the expected value received. In such cases, according to one aspect of the present invention, the step of generating the proposed parameter set is optimal for achieving a base load level given the expected or expected ambient conditions. Generating a plurality of cases in which the manipulated variable is varied over each range to determine a generalized mode of operation. According to exemplary embodiments, the cost function may be defined as plant efficiency or heat rate, or may include more direct economic indicators such as operating costs, income, or profits. . Thus, the most efficient way to control the power plant 501 can be determined in situations where the base load is known and disturbance variables can be predicted with a relatively high level of accuracy. In such cases, the optimized mode of operation determined by the present invention can be a specific control solution that can be used by the plant controller 505 to achieve a more optimal function (ie, a specific set point, and / or , And thus a range relating to actuators that control the manipulated variables of the power plant). When calculated in this way, the control solution represents an optimized mode of operation for satisfying a defined or contracted target load given the expected values for various disturbance variables. This type of functionality can serve as an optimization advisor or check for daily or inter-market periods, which is more efficient operation that still satisfies previously fixed load levels Analyze actions in progress in the background for the purpose of finding modes. For example, as the market period covered by previous power bids progresses, ambient conditions become known, or at least a level of confidence that accurately predicts ambient conditions is estimated during the bidding process. Increases beyond that. Given this, the method can be used to optimize the control solution to meet the powered load given a more reliable knowledge of ambient conditions. This particular functionality is illustrated in FIG. 17 as a second parameter set 517 and as a simulated operation 519 associated with the second parameter set 517. Thus, the optimization process of the present invention can also include a “fine tuning” aspect, whereby a simulation run on the tuned power plant model 507 is more efficient for a control solution. Advises and then it can be communicated to the plant controller and implemented by the plant controller.

  Another aspect of the invention includes its usage to optimize fuel purchase for the power plant 501. It will be appreciated that power plants typically make regular fuel purchases from a fuel market that operates in a particular manner. Specifically, such fuel markets are typically operated on a prospective basis, where the power plant 501 predicts the amount of fuel required for future operating periods and then predicts it. Make purchases based on. In such a system, the power plant 501 seeks to maximize profits by maintaining a low fuel inventory. However, the power plant 501 has a supply of purchased fuel that is insufficient to periodically purchase excess fuel and generate an amount of power that the plant contracts to provide during the power supply process. It avoids costly situations. This type of situation can occur, for example, when changing ambient conditions result in power generation that is less efficient than expected or when the true power generation capacity of the power plant is overestimated. There is. Some aspects of the present application that have already been discussed are to determine an optimized mode of operation and use it to calculate a very accurate prediction for the required fuel supply. It will be appreciated that it can be used. That is, the present optimization process can provide a more accurate prediction regarding plant efficiency and load capacity, which can be used to estimate the amount of fuel required in future operating periods. This allows plant operators to maintain a smaller margin for fuel purchase, which benefits the economic performance of the plant.

  According to an alternative embodiment, the present invention includes a method for optimizing plant performance, in which a forecast planning period is defined and used in an optimization process. As will be recognized, the forecast planning period is the future period of operation, which is for the purpose of determining the mode of operation optimized with respect to the initial time interval of the forecast planning period. Divided into regularly repeating intervals. Specifically, the operation of the power plant is optimized by optimizing performance throughout the forecast planning period, which then determines the optimized mode of operation for the initial time interval. used. As will be recognized, the process is then repeated to determine how the power plant should operate during the next time interval, which As such, it is the initial time interval for the next iteration of the optimization cycle. For this subsequent optimization, the forecast planning period can remain the same, but is redefined for what is currently defined as the initial time interval. This means that the forecast planning target period can be effectively advanced into the future with additional time intervals for each iteration. As already mentioned, the “proposed parameter set” includes values for a plurality of operating parameters, thereby defining or describing one of the possible operating modes for the power plant 501. Represents a data set. According to a preferred embodiment, the process of determining an optimized mode of operation when involved in a forecast planning period can include one or more of the following steps. First, a plurality of proposed planning target period parameter sets are generated for the forecast planning target period. As used herein, a “suggested planning period parameter set” includes a proposed parameter set for each of the time intervals of the forecast planning period. For example, a 24-hour forecast planning period may be defined as including 24 one-hour time intervals, where a proposed planning period parameter set is proposed for each of the 24 time intervals. This means that it includes a parameter set. As a next step, the proposed planning period parameter set is used to simulate behavior over the forecast planning period. Then, for each of the simulation runs, a cost function is used to evaluate the economic performance and which of the proposed planned time period parameter sets is most suitable, i.e. used herein. As shown in the figure, it is determined whether the "optimized planning period simulation run" is represented. According to an exemplary embodiment, the mode of operation described in the optimized planning period simulation run for the initial time interval of the forecast planning period is then the period of operation corresponding to the initial time interval. Can be specified as an optimized mode of operation. The optimization process can then be repeated for subsequent time intervals. The present invention may include receiving expected values for disturbance variables for each of the time intervals defined in the forecast planning period. The proposed planning period parameter set is then generated such that the proposed parameter set corresponding to each of the time intervals includes a value for the disturbance variable that is related to the expected value received for the disturbance variable. Can be.

  As will be recognized, the proposed planning period parameter set can be generated to cover a range of values for disturbance variables. As described above, the range can include multiple cases for each of the disturbance variables, and can include high and low values that represent the upper and lower of the expected value, respectively. According to any of the described embodiments, the steps of simulating the mode of operation and determining the optimized mode of operation from the simulation can be repeated and configured into an iterative process. Will be recognized. As used herein, each iteration is referred to as an “optimization cycle”. It will be appreciated that each iteration can include defining a period of subsequent or next operation for optimization. This subsequent period can occur immediately after the period of operation optimized by the previous cycle, or for the purpose of preparing a case, eg, a power supply bid, or an alternative maintenance schedule For the purpose of advising on the economic impact of the method, it is possible to include periods of operation corresponding to future periods, as is the case when the method is used.

  The step of tuning the power plant model 502 may be repeated to update the tuned power plant model 507. In this way, a tuned power plant model 507 that reflects recent tuning can be used with an optimization cycle to produce more effective results. According to an alternative embodiment, the optimization cycle and the cycle of tuning the power plant model 502 can be separated from each other such that each cycle repeats according to its own schedule. In other embodiments, the power plant model 502 may be updated or tuned after a predetermined number of iterations of iteration. The updated and tuned power plant model 507 is then used in subsequent optimization cycles until a predetermined number of iterations have been performed to initiate another tuning cycle. In certain embodiments, the tuning cycle occurs after each optimization cycle. According to an alternative embodiment, the number of optimization cycles that initiate tuning of the power plant model 502 is related to the number of time intervals in the forecast planning period.

  The present invention, as stated, can optimize the operation of the power plant 501 according to performance goals, which can be defined by the plant operator. According to a preferred embodiment, the method is used to economically optimize the operation of the power plant. In such cases, the performance goal includes and defines a cost function that provides a basis for economic optimization. According to an exemplary embodiment, the simulated behavior for each proposed parameter set includes, as output, a predicted value for the selected performance metric. The cost function may include an algorithm that correlates the predicted value for the performance index to operating costs or some other indication of economic performance. Other performance indicators that may be used in this way include, for example, power plant heat rate and / or fuel consumption. According to an alternative embodiment, the simulation output includes predicted values for hot gas flow path temperatures for one or more of the thermal power generation units of power plant 501, which are consumed components Can be used to calculate lifetime costs. This cost reflects the predicted degradation cost associated with the hot gas flow path component resulting from the simulated operation. The cost function may further include an algorithm that correlates the predicted value for the performance metric with motion revenue. In such a case, the operating revenue can then be compared to the operating cost to reflect the net revenue or net profit for the power plant 501. The method can further include receiving an expected price for electricity sold in the market over an optimized period, wherein the selected performance metric is the power output level. The power level of electricity can then be used to calculate the expected operating revenue for the next period of operation. Thus, the method can be used to maximize economic benefits by comparing operating costs and revenues.

  As will be recognized, the performance goal may be further defined to include selected operability constraints. According to certain alternative embodiments, the method qualifies any of the proposed parameter sets that produce simulated behavior that violates any one of the defined usability constraints. Including the steps of: Usability constraints include, for example, discharge threshold, maximum operating temperature, maximum mechanical stress level, and legal or environmental regulations, contract conditions, safety regulations, and / or machine or component usability thresholds and limits Can be included.

  The method includes generating a proposed parameter set 517 that describes an alternative or possible mode of operation of the power plant 501 as previously described. As shown, the proposed parameter set 517 can be generated in the plant operator module 509 and can include input from a plant manager or a human operator. Broadly speaking, the possible modes of operation can be considered as competing modes, and for the competing modes, a simulation is performed to determine the mode of operation that best meets the performance objectives and assumed conditions. It has become. According to exemplary embodiments, these alternative modes of operation may be selected or defined in several ways. According to a preferred embodiment, alternative modes of operation include various power levels for the power plant 501. The power level, as used herein, relates to the level of electricity generated by the power plant 501 for commercial distribution in the market during a defined market period. The proposed parameter set can be configured to define multiple cases at each of the different power levels. Several power levels can be covered by the proposed parameter set, and the selected power level can be configured to match the range of possible power for the power plant 501. It will be appreciated that the range of possible output levels may not be linear. Specifically, due to the multiple power generation units of the power plant and the scalability limitations associated with them, the proposed parameter set is more feasible given the specific configuration of the power plant 501 or At a preferred level, it can be grouped or concentrated.

  As stated, each of the competing modes of operation can include multiple cases. For example, if competing modes of operation are defined differently, multiple cases may be chosen so that the output level reflects the different manners achieved thereby. If the power plant has multiple power generation units, multiple cases at each power level can be differentiated by how each of the thermal power generation units is operated and / or involved. . According to one embodiment, several generated cases are differentiated by changing the percentage of the power level provided by each of the power generation units. For example, the power plant 501 can include a combined cycle power plant 501, in which the thermal power generation unit includes a gas turbine and a steam turbine. Additionally, the gas turbine and steam turbine may be augmented by an intake conditioning system such as a chiller and an HRSG duct firing system, respectively. As will be appreciated, the intake conditioning system can be configured, for example, to cool the inlet air of a gas turbine to enhance its power generation capability, and the HRSG duct firing system can be It can be configured as a secondary heat source for the boiler to enhance the power generation capacity of the steam turbine. According to this example, the thermal power generation unit comprises a gas turbine or alternatively a gas turbine augmented by an intake conditioning system and a steam turbine or alternatively a steam augmented by an HRSG duct firing system. Including turbines. The multiple cases covered by the proposed parameter set then include the case where these particular thermal power units are involved in different ways while still satisfying different power levels chosen as competing operating modes. It is possible. The simulated behavior can then be analyzed to determine which reflects the optimized mode of operation according to defined criteria.

  According to an alternative embodiment, the proposed parameter set can be derived to obtain different modes of operation and the economic benefits of maintenance operations can be calculated. To accomplish this, one of the competing modes of operation can be defined as being assumed that the maintenance operation is completed prior to the period of operation selected for optimization. . This mode of operation may be defined to reflect the expected performance increase with the completion of this maintenance operation. An alternative mode of operation may be defined as no maintenance operation is performed, which means that multiple case simulations for this mode of operation will not include an expected performance increase. The results from the simulation can then be analyzed so that the economic effect is better understood, and multiple scenarios are used, and the scenarios are different (eg, fuel price fluctuations or unexpected ambient conditions) Etc.) can affect the results. As will be appreciated, using the same principle, competing modes of operation can include a turndown mode and a shutdown mode.

  The present invention further includes different schemes, in which optimization processes can be used by power plant operators to automate the process and improve efficiency and performance. According to one embodiment, as illustrated in FIG. 17, the method is calculated to obtain approval by a human operator before the power plant 501 is controlled according to an optimized mode of operation. Communicating the optimized mode of operation 521 to the plant operator module 509. In the advisor mode, the method can be configured to present alternative modes of operation and the economic ripple effects associated with each mode, such that such alternatives become aware of the plant operator. Yes. Alternatively, the control system of the method can function to automatically implement an optimized solution. In such a case, the optimized mode of operation can be communicated electronically to the plant controller 505 to facilitate control of the power plant 501 in a manner consistent with it. In a power system that includes an economical power supply system for distributing power generation between groups of power plants 501, the optimization method of the present invention is more accurate and competitive for submission to a central authority or power supply command center. Can be used to generate bids. As those skilled in the art will appreciate, the optimization mechanism described above generates bids that reflect true power generation capacity, efficiency, and heat rate, while the power plant chooses a different mode of operation. It can be used to provide plant operators with useful information regarding economic trade-offs that will occur in future market periods. This type of increased accuracy and additional analysis minimizes the risk that the power plant will remain competitive in the bidding process, while at the same time obtaining unprofitable power results due to unexpected contingencies. Help to ensure.

  FIGS. 18-21 illustrate exemplary embodiments of the present invention related to power plant turndown and / or shutdown operations. As illustrated in the flow diagram 600 of FIG. 18, which may be referred to as a “turndown advisor”, the first embodiment provides a defined or selected period of operation (“selected operation period”). Teaches a method and system for simulating and optimizing turndown levels for power plants in between. In a preferred embodiment, the method is used with a power plant having a plurality of gas turbines, which can include a combined cycle plant having a plurality of gas turbines and one or more steam turbines. The tuned power plant model can be used to determine an optimized minimum load for operating the power plant at a turndown level during a selected operating period. As described above, an “optimized” mode of operation may be defined as an operating mode that is considered or preferred over one or more other possible modes of operation. The mode of operation for the purposes of these embodiments includes the assignment of specific power generation units to meet a load start / stop plan or other performance goal, as well as the physical configuration of the power generation units in the power plant. Is possible. Such functionality allows the present invention to take into account multiple plant combinations when reaching an optimized or enhanced mode of operation, where multiple plant combinations are associated with each power generation unit. This means taking into account different turndown configurations as well as configurations that shut down one or more of the units while other units remain operating at full or turndown levels. The method further adds other constraints such as operability constraints, performance objectives, cost functions, operator inputs, and ambient conditions in the calculation of enhanced turndown operating modes for power plants that enhance performance and / or efficiency. It is possible to take into account. The method may be used to optimize the turndown mode of operation as described herein and / or as accurately described in the appended claims. And predicted ambient conditions can be taken into account, and one of the power generation units can change when the unit configuration and / or control is changed and the actual conditions deviate from those predicted conditions. One or more movements are dynamically adjusted. According to a preferred embodiment, such performance is defined at least in part as minimizing the level of fuel usage or consumption over the proposed turndown operation period.

  The turndown advisor of the present invention takes into account several factors, criteria, and / or operating parameters in reaching an optimized or enhanced turndown solution and / or recommended turndown action. It is possible. According to a preferred embodiment, these include, but are not limited to, gas turbine engine operating limits (ie, temperature operating limits, aerodynamic operating limits, fuel split operating limits, lean blow operating limits, mechanical Operating limits and emission limit values), gas turbine and steam turbine control systems, minimum steam turbine throttle temperature, vacuum seal maintenance on condensers, and system or system control configurations or lineups, etc. Including other factors such as: One of the optimization outputs can include a recommended operating mode and configuration of one or more power plants, such as wind, solar, reciprocating engines, nuclear And / or different types of power plants, including other types. It will be appreciated that the recommended mode of operation can be initiated automatically or electronically communicated to the plant operator for approval. Such control may be implemented via an off-premises or on-premises control system that is configured to control the operation of the power generation unit. Additionally, in situations where the power plant includes multiple gas turbine engines, the output of the method should be such that any of the gas turbines should continue to operate and which should be shut down during the turndown period. Can be identified, which is the process discussed in more detail in connection with FIG. For each gas turbine for which the advisor recommends continued operation during the turndown period, the method can further calculate the load level. Another output can include calculating the total load on the power plant during the turndown period, and is predicted to be adjusted when conditions change, as stated. Calculating an hourly target load profile based on ambient conditions. The present invention can also calculate the predicted fuel consumption and emissions of the power plant during the turndown period. The output of the disclosed method can include an operational lineup / configuration assuming control setpoints available to the power generation unit and plant, and more efficiently achieve the target power generation level. ing.

  As discussed above, traders and / or plant managers (hereinafter “plant operators” unless otherwise distinguished) are not bound by existing contract terms, typically such as the power spot market. Bid on those power plants in a promising market. As an additional consideration, the plant operator is tasked with ensuring that sufficient fuel supply is maintained so that the power plant can meet a target or contracted power generation level. However, in many cases, the fuel market will operate in a prospective manner, and will have a favorable price condition available to power plants that are or willing to promise future fuel purchases in advance. It has become. More specifically, the sooner the fuel is purchased, the more advantageous the price. Given these market dynamics, in order for a power plant to achieve an optimized or high level of economic benefits, plant operators can use other power generation units to take advantage of its power generation capacity. Have to bid the plant competitively, at the same time 1) so that fuel can be purchased in advance to ensure a lower price, and 2) without the need for a large fuel buffer, Accurately estimate the fuel required for future power generation periods so that a low fuel inventory can be maintained. If successful, the plant operator will ensure a better price by quickly committing to future fuel purchases, while not over-purchasing or supplying fuel that requires unnecessary and costly reserve fuel. There is no shortage of purchases that risks shortage.

  The method of the present invention, in particular, relates to the preparation of a power supply bid to secure a share of the power generation market, so by identifying an IHR profile for a specific configuration of a power generation unit or plant, Efficiency and profitability can be optimized or enhanced. The method may include identifying an optimal power allocation across multiple power generation units in the power plant or across several plants. The method can take into account the operational and control configurations available to those power generation units, and replace possible arrangements, thereby reducing or minimizing, if selected. It is possible to implement a bid that allows generation of power over the bidding period at a reduced cost. In doing so, the method can take into account all applicable physical, regulatory and / or contractual constraints. As part of this overall process, the method may be used to optimize or enhance turndown and shutdown operations for power plants having multiple power generation units. This procedure includes, for example, taking into account possible extrinsic conditions such as weather conditions or ambient conditions, gas quality, power unit reliability, and attendant obligations such as steam generation, etc. It is possible. The method is used to enumerate the IHR profiles for multiple power generation units with multiple configurations, as well as the control settings for the selected turndown configuration, and then the exogenous conditions envisaged in preparation for the plant feed tender Can be controlled for.

  One common decision for an operator relates to whether the power plant is turned down or shut down during off-peak periods such as at night when demand or load requirements are minimal. As will be appreciated, the outcome of this determination is highly dependent on the plant operator's understanding of the economic ripple effects associated with each of these possible modes of operation. In certain cases, the decision to turn down the power plant can be readily apparent, but the optimal minimum load to maintain the power plant during the turn-down period remains uncertain. That is, the plant operator has decided to turn down the power plant over a specific period of time, but the operator is confident about the turndown operating point that will run several power units in the power plant in the most cost-effective manner. Can not have.

  The turndown advisor of FIG. 18 can be used as part of a process to recommend an optimal minimum load for operating a power plant. This advisor function can further recommend best practices for power plants, given specific conditions of ambient conditions, economic inputs, and operating parameters and constraints. From these inputs, the process can calculate the best operating level, as will be discussed in more detail in connection with FIG. 19, and then the operating parameters required for power plant control. Can be recommended. As will be appreciated, this functionality can result in some ancillary benefits, such as extended component life, more efficient turndown operation, and economical Includes improved performance and improved accuracy when making fuel purchases.

  As illustrated in flow diagram 600, specific information and associated criteria may be gathered during the initial steps. In step 602, data, variables, and other factors associated with the power plant system and the power generation unit may be determined. These can include any of the factors or information listed above. According to a preferred embodiment, an ambient profile can be received, which can include an estimate of ambient conditions during a selected operating period. Also, relevant emission data can be collected as part of this step, which can include emission limits for power plants as well as previous emissions. Another factor includes data related to potential sales of power and / or steam during a selected period of operation. Other variables that can be determined as part of this step are the number of gas turbines in the plant, the number of combustion and control systems for each of the gas turbines, and any other that may be relevant to the calculations discussed below. Including plant-specific restrictions.

  In step 604, the duration of the proposed turndown operation (or “selected operation period”) may be defined in detail. As will be appreciated, this is defined by the user or plant operation and can include selected operating periods, during which selected turndown operating modes are available. Analysis is desired. The definition of the selected operation period is its expected length, as well as a user specified start time (ie, the time at which the selected operation period will start) and / or stop time (ie, selected). It is possible to include the time during which the operation period ends. This step may further include defining an interval within the selected operating period. The interval may be configured to subdivide the selected operating period into a plurality of sequential fixed time periods. For the example provided herein, the interval will be defined as one hour, and the selected operating period will be defined as including a plurality of one hour intervals.

  In step 606, the number of gas turbines involved in the optimization process for the selected operating period may be selected. This can include all or some of the gas turbines in the power plant. As described in more detail below, the method further includes consideration of other power generation units in the power plant, such as a steam turbine system, and considers their operating conditions during a selected operating period. It is possible to put in. The determination of the gas turbine involved in the turndown operation can include prompting or receiving input from the plant operator.

  In step 608, the method may construct a permutation matrix given the number of gas turbines determined as part of the proposed turndown operation during the selected operating period. As will be appreciated, the permutation matrix is a matrix that includes various ways in which multiple gas turbine engines may be involved or operated during a selected period of operation. For example, as illustrated in the exemplary permutation matrix 609 of FIG. 18, the permutation matrix for the two gas turbine cases includes four different combinations that cover each of the possible configurations. Specifically, if the power plant includes a first gas turbine and a second gas turbine, the permutation matrix includes the following rows or cases: a) the first gas turbine and the second gas Both turbines are "on", i.e. operated in turndown operation; 2) both the first gas turbine and the second gas turbine are "off", i.e. shutdown operation 3) the first gas turbine is “on” and the second gas turbine is “off”; and 4) the first gas turbine is “off” The second gas turbine is “on”. As will be appreciated, in a single gas turbine case, only two substitutions are possible, while for three gas turbines, seven different rows or cases are possible, Each represents a different configuration of how the three gas turbine engines may be involved during a particular time frame in terms of “on” and “off” operating conditions. In connection with FIG. 17 and in connection with the optimization process discussed in the text relating to FIG. 17, each case or row of the permutation matrix is considered to represent a different or competing mode of operation. obtain.

  As part of the steps represented by steps 610, 613, 614, 616, and 618, the method can construct a proposed parameter set for the proposed turndown operation. As stated, the selected period of operation can be divided into a number of time intervals spanning one hour. The process for constructing the proposed parameter set can begin at step 610, where a determination is made as to whether each of the intervals has been addressed. If the answer to this question is yes, the process can continue to the output step (ie, step 611), as shown, where the output of the turndown analysis is output. Is provided to the operator 612. If all of the intervals are not covered, the process can continue to step 613, where one of the intervals not yet covered is selected. Then, in step 614, ambient conditions can be set for the selected interval based on the received expectation. Following step 616, the process may select a row from the permutation matrix, and in step 618, the gas turbine on / off state may be set according to the particular row.

  From there, the method can continue along two different paths. In particular, the method can continue to the optimization step represented by step 620, while also continuing to the decision step in step 621, where the process includes all permutations of the permutation matrix. Or determine whether a row has been covered for the selected interval. If the answer to this is “no”, the process may loop back to step 616 where a different replacement row for the interval is selected. If the answer to this is “yes”, the process can continue to step 610, as shown, to determine if all of the intervals have been covered. Yes. As will be appreciated, once all of the permutation matrix rows for each interval have been addressed, the process can proceed to the output step of step 611.

In step 620, the method can optimize performance using a tuned power plant model, as previously discussed in FIG. Consistent with this approach, multiple cases may be generated for each of the competing operating modes, i.e., for each row of the permutation matrix for each selected operating period interval. According to a preferred embodiment, the method generates a proposed parameter set in which several operating parameters are varied to determine the influence on the selected operating parameter or performance index. For example, according to this embodiment, the proposed parameter set may include manipulating setpoints for inlet guide vanes (“IGV”) and / or turbine exhaust temperature (“T _exh ”). Yes, to determine which combination will produce a minimized total fuel consumption rate for a power plant, given the expected ambient conditions for a particular row on / off state and a particular interval . As will be appreciated, the operation of minimizing fuel consumption while satisfying other constraints associated with the turndown operation represents one mode, which provides one turndown performance. Or it can be economically optimized or at least economically enhanced for several alternative modes of operation.

As shown, according to certain embodiments, cost functions, performance goals, and / or usability constraints may be used by the present invention during this optimization process. These can be provided via the plant operator and are represented by step 622. These constraints can include limits on IGV setpoints, T _exh limits, combustion limits, etc., as well as limits associated with other thermal systems that may be part of the power plant. For example, in a power plant with a combined cycle system, the operation or maintenance of the steam turbine during turndown operation can present certain constraints, for example, maintaining a minimum steam temperature or condenser vacuum seal. is there. Another usability constraint is that certain accessory systems can be affected in certain modes of operation and / or certain subsystems such as evaporative coolers and chillers are mutually exclusive. It is possible to include the necessary logic.

  Given the spacing of the permutation matrix and the different rows, the optimization results can be communicated to the plant operator in step 611 once the method has made a round through the iterations. These results can include optimized cases for each row of the permutation matrix for each of the time intervals. According to one example, the output describes an optimized operation, which is defined by a cost function of fuel consumption for the power plant for each of the permutations for each of the intervals. Specifically, the output is a possible plant configuration (as represented by the row of the permutation matrix) for each interval, while also satisfying operability constraints, performance goals, and assumed ambient conditions. Each of which may include the minimum required fuel (optimized using a tuned power plant model according to the methods already described). According to another embodiment, the output includes optimization in the same manner that minimizes the power output level (ie, megawatts) for potential plant configurations for each of the intervals. As will be recognized, some of the possible plant configurations (as represented by the permutation of the permutation matrix) may impose operational constraints regardless of the fuel supply to generate power levels. There is a possibility that it cannot be satisfied. Such results may be discarded and not further considered or reported as part of the output of step 611.

  19 and 20 illustrate that the power plant gas turbine may be operated over a selected period of operation including a defined interval ("I" in the figure), subject to typical constraints associated with transient operation. The scheme is shown schematically. As will be appreciated, transient operation includes switching the power generation unit between different modes of operation, and includes those that go into or out of shutdown mode of operation. As shown, a plurality of operating paths or sequences 639 may be implemented depending on: 1) the initial state 640 of the gas turbine; and 2) at intervals that can change subject to transient operating constraints. A decision made as to whether to change the operating mode. As will be appreciated, a number of different sequences 639 represent a number of ways in which the power generation unit may be operated over the indicated intervals.

  As will be appreciated, the output of the method of FIG. 18 may be used in conjunction with the diagrams of FIGS. 19 and 20 to constitute a proposed turndown operation sequence for the power generation unit of the power plant. Is possible. That is, FIGS. 19 and 20 show examples of how power generation units of a power plant can be involved and how their operating modes can be modified over time. It is shown that if the operating mode of the power generation unit remains unchanged, the operating mode of the unit is modified from the shutdown operating mode to the turndown operating mode, and the operating mode of the unit is changed from the shutdown operating mode. It is possible to include a case where it is modified to a turndown mode of operation. As shown, the transient operating constraints used in this example require that modifying the operating mode remain in the modified operating mode for at least two of the intervals at a minimum. That's what it means. Many sequences (or paths) for a power generation unit to reach the last interval represent turn-down operation sequences that the unit may be able to use subject to transient operation constraints.

  As will be appreciated, the analysis results from FIG. 18, ie, optimized turndown behavior for each of the matrix permutations, select multiple preferred cases from the possible turndown behavior sequences. Which can be referred to as the proposed turndown operation sequence. Specifically, given the results of the method described in connection with FIG. 18, the proposed turndown operation sequence performs according to a selected cost function (eg, MW power or fuel consumption, etc.). Can be selected from the case of turndown operation that satisfies plant performance objectives and constraints while also optimizing. The considerations illustrated in FIGS. 19 and 20 represent a scheme for determining whether a turndown operation sequence is achievable subject to transient operation constraints. That is, the proposed turndown operation sequence reached by the combined analysis of FIGS. 18-20 is an operation sequence that meets the time limitations associated with moving units from one operation mode to another. is there.

  Turning now to FIG. 21, a method is provided for further modeling and analyzing the power plant turndown behavior. As will be appreciated, this method can be used to analyze turndown costs versus shutdown costs for the specific case with a single power generation unit over a defined time interval. However, it can also be used to analyze plant-level costs, where recommendations are sought on how the operation of several power generation units can be controlled over selected operating periods with multiple intervals. In this way, the outputs of FIGS. 18 and 20 can be assembled to constitute a possible mode of operation or sequence over multiple spans of span, which will then be demonstrated as will be demonstrated. 21 methods can be analyzed to provide a better understanding of turndown behavior over a wider duration of operation.

  As already discussed, the plant operator must periodically determine between the turndown mode and the shutdown mode during off-peak hours. Certain conditions can make the decision simple and clear, but in many cases, among other things, the increased complexity of modern power plants and the multiple thermal power plants normally contained within each Given a unit, it is difficult. As will be appreciated, deciding whether to turn down or shut down a power plant is highly dependent on full recognition of the economic benefits associated with each mode of operation. The present invention, in accordance with an alternative embodiment illustrated in FIG. 21, provides a plant to obtain an improved understanding of tradeoffs associated with each of these different modes of operation to enhance decision making. Can be used by the operator. According to a particular embodiment, the method of FIG. 21 can be used in conjunction with the turndown advisor of FIG. 18 to enable a combination advisor function, which is 1) known Given the conditions and economic factors, we recommend the best strategy between the power-down unit and the power-down unit in the power-down plant, and 2) turn-down operation is the best policy for some of those units. If so, an optimal minimum turndown load level is recommended. In this way, plant operators can determine the power plant units based on which represents the best economics for the power plant, given specific scenarios of ambient conditions, economic inputs, and operating parameters. It is possible to more easily identify situations that should be turned down rather than shut down or vice versa. Additional benefits such as extending component part life are also possible. It should also be appreciated that the methods and systems described in connection with FIGS. 18 and 21 can be used separately.

  In general, the method of the flow diagram 700 can be part of a “turndown advisor” or can be referred to herein as a “turndown advisor” and the method of the flow diagram 700 is Applying data from user input and analytical operations to perform calculations that evaluate the costs associated with turning down the power plant and the costs associated with shutting down the power plant. As will be appreciated, the flow diagram 700 of FIG. 21, in accordance with certain preferred embodiments, makes use of this advisor mechanism by leveraging the tuned power plant model discussed in detail above. provide. As part of this functionality, the present invention advises various results, economic results, and other results between turning down and shutting down the power plant during off-peak demand periods. It is possible. The present invention can provide relevant data that reveals whether it is preferable to turn a power plant down rather than shut it down over a specific market period. According to certain embodiments, operations with lower costs can then be recommended to the plant operator as appropriate measures, but incidental issues that can affect the decision as presented herein. Or other considerations may be communicated to the plant operator. The method can generate potential costs, as well as the probability of incurring such costs, and these considerations can affect the final decision on which mode of operation is preferred There is sex. Such considerations can include, for example, a complete analysis of both short-term operating costs as well as long-term operating costs associated with plant maintenance, operating efficiency, emission levels, equipment upgrades, and the like.

  As will be appreciated, the turndown advisor is implemented using the systems and methods described above, and in particular, many of the systems and methods discussed in connection with FIGS. obtain. The turndown advisor of FIG. 21 can collect and use one or more of the following types of data: user-specified start and stop times for the proposed turndown operation period (ie, Period during which turndown mode of operation is analyzed or considered); fuel cost; ambient conditions; time-off breaker; use of AC power source; sales / price of power or steam during the relevant period; operation and maintenance over that period Cost; User input; Calculated turndown load; Expected emissions for operation; Current emissions level consumed by the power plant and limits on defined regulatory period; Specification for turning gear operation; Purge process Regulations and equipment related to power plant operation Fixed costs related to mode; costs related to start-up operation; plant start-up reliability; imbalance fees or surcharges related to delayed start-up; emissions related to start-up; fuel used for auxiliary boiler when steam turbine is present Ratio; and historical data on how the power plant gas turbine was operating in turndown and shutdown modes of operation. In certain embodiments, as discussed below, the output from the present invention can include: a recommended mode of operation for the power plant over the relevant time period (ie, turndown operation). Mode and shutdown operating mode); costs associated with each mode of operation; recommended plant operating load and load profile over time; recommended time to initiate unit startup; and emissions consumed year-round And emissions remaining for the rest of the year. According to certain embodiments, the present invention can calculate or predict power plant fuel consumption and emissions over an associated period of time, which is then for one or more particular gas turbine engines. Can be used to calculate the cost of turndown versus shutdown. The method can use the cost of each gas turbine in shutdown mode and turndown mode to determine the combination with the lowest operating cost. Such optimization can be based on different criteria, which can be defined by the plant operator. For example, the criteria can be based on revenue, net revenue, emissions, efficiency, fuel consumption, and the like. In addition, according to an alternative embodiment, the method determines whether a purge credit should be taken; the gas turbine unit to be shut down and / or the gas turbine unit to be turned down (for example, Certain actions can be recommended, such as based on historical startup reliability and potential imbalance fees that can be imposed due to delayed start. The present invention can be further used to enhance forecasts related to fuel consumption to make more accurate purchases of potential fuels, or alternatively, purchases of fuel for future market periods That would have a positive impact on maintaining fuel prices and / or less fuel inventory or margins.

  FIG. 21 illustrates an exemplary embodiment of a turndown advisor according to an exemplary embodiment of the present invention, which is in the form of a flow diagram 700. The turndown advisor may be used to advise on the relative cost over a period of future operation that shuts down the power plant or a portion thereof while operating the power generation unit in turndown mode. According to this exemplary embodiment, possible costs associated with the shutdown and turndown modes of operation can be analyzed and then communicated to the plant operator for appropriate action.

  As an initial step, specific data or operating parameters can be collected that affect the operating cost during the selected turndown operating period or that can be used to determine the operating cost. These are grouped accordingly among turndown data 701; shutdown data 702; and common data 703, as shown. Common data 703 includes those cost items related to both the shutdown and turndown modes of operation. The common data 703 includes, for example, a selected operation period, and an analysis of the turndown operation mode is performed over the selected operation period. Two or more selected operating periods can be defined with respect to competing modes of turndown operation and analyzed separately, so that broader optimization is to be realized over an extended time frame Will be recognized. As will be appreciated, defining the selected operating period can include defining the length of the period and its start or end point. Other common data 703 can include data regarding fuel prices, various emission limits for power plants, and ambient conditions, as shown. With respect to emission limits, the data collected will already have limits that can be imposed during a defined regulatory period, such as one year, the amount already generated by the power plant, and the applicable regulatory period. It is possible to include the degree applied. In addition, emissions data can include charges or other costs associated with exceeding any of the limits. In this way, the method may be informed about the current situation of the power plant against annual or periodic regulatory restrictions, as well as possible violations and the fines associated with such non-compliance. Is possible. This information may relate to a decision on whether to shut down or turn down the power generation unit when each type of operation has a different impact on plant emissions. With respect to ambient condition data, such data can be obtained and used in accordance with those processes that have already been described herein.

  The turndown mode of operation has data inherently associated with determining the operating cost associated with the turndown mode of operation so that it will be recognized. Such turndown data 701 includes revenue that may be earned via power generated while the power plant is operating at a turned down level, as shown. More specifically, the turndown mode of operation is a mode of operation where power generation continues at a lower level, but there is a possibility that the power will generate revenue for the power plant. As long as this is done, the revenue can be used to offset some of the other operating costs associated with the turndown mode of operation. Thus, the method includes receiving a price or other economic indication associated with the sale or commercial use of power generated by the plant while operating in turndown mode. This can be based on historical data, and the revenue earned can depend on the turndown level at which the power plant operates.

  The turndown data 701 can further include operations and maintenance associated with operating the plant at a turndown level during a selected operating period. This can also be based on historical data, and such costs may depend on the turndown level for the power plant and how the power plant is configured. In some cases, this fee may be reflected as an hourly cost that depends on the load level and similar behavior history records. The turndown data 701 may further include data related to plant emissions while operating in the turndown mode.

  Shutdown data 702 also includes a number of items specific to the shutdown mode of operation, and this type of data can be collected at this stage of the method. According to a particular embodiment, one of these is data relating to the operation of the turning gear during the shutdown period. In addition, data regarding various aspects of the shutdown operation will be defined. This can include, for example, data related to the following: relating to the length of time required to bring the generator unit from the normal load level to the state in which the turning gear is engaged. The shutdown operation itself, which can include historical data; the length of time that the power plant remains shut down according to the selected duration of operation; the length of time that the power generation unit typically stays on the turning gear And data on the process that is restarted or brought back online after the power generation unit shuts down, as well as the time, startup fuel requirements, and startup emissions data needed to do this. In determining the startup time, information such as possible startup types for the power generation unit, and associated specifications, etc. may be determined. As those skilled in the art will recognize, the startup process may depend on the time that the power plant remains shut down. Another consideration that affects start-up time is whether the power plant includes certain mechanisms that can affect or shorten start-up time, and / or the power plant operator Whether to choose to get involved in either. For example, the purge process can increase the startup time if necessary. However, purge credits may be available if the power plant is shut down in a particular manner. The fixed cost associated with the shutdown operation includes the fixed cost associated with the startup and can be verified during this step, along with the costs specific to any of the associated power generation units. Emission data associated with power plant startups and / or shutdowns may also be identified. These may or may not be based on an operational history record. Finally, data related to start-up reliability for each of the thermal power generation units can be identified. As will be recognized, if the process of bringing the unit back online includes a delay that results in a power plant that is unable to meet its load obligation, the power plant may be charged a fee, surcharge, and / or Damages can be imposed. These costs can be determined and considered in view of historical data related to startup reliability, as discussed in more detail below. Thus, such charges are discounted to reflect the potential burden and / or spending to hedge or insure against such risks. May be included.

  From the initial data collection steps 701 to 703, the exemplary embodiment illustrated in FIG. 21 can proceed through a turndown analyzer 710 and a shutdown analyzer 719, each of which corresponds to It may be configured to calculate an operating cost for the operating mode to be performed. As shown, each of these analyzers 710, 719 can proceed towards providing cost, emissions, and / or other data to step 730, where the possibility is Data for certain turndown and unit shutdown scenarios are compiled and compared, and finally, in step 731, output can be made to the power plant operator. As will be discussed, this output 731 may include costs and other considerations for one or more of the possible scenarios, and eventually the specific action and It is possible to recommend the reason.

  With respect to the turndown analyzer 710, the method can first determine the load level for the proposed turndown operation during the selected operation period. As discussed further below, most of the costs associated with turndown operations can be highly dependent on the load level at which the power plant operates, and how the plant is responsible for generating that load. Depending on how the various thermal power units are involved (ie which thermal power units are turned down and which are shut down), for example. Can be included. The turndown load level for the proposed turndown operation can be determined in several different ways according to alternative embodiments of the present invention. First, the plant operator can select a turndown load level. Second, the load level can be selected through analysis of historical records regarding past turndown levels at which the plant has operated efficiently. From these records, the proposed load level can be used, for example, for efficiency, emissions, achievement of one or more site-specific goals, and alternative commercial use for power generated during turndown conditions. Analysis and selection can be based on criteria supplied by the operator, such as sex, ambient conditions, and other factors.

  As a third method of selecting a turndown level for the proposed turndown operation, a computer-implemented optimization program, such as that described in connection with FIG. Can be used to calculate In FIG. 21, this process is represented by steps 711 and 712. In step 711, an optimized turndown level can be calculated by proposing a turndown mode of operation, and then in step 712 by analyzing whether the operating limits for the power plant are satisfied. . As will be appreciated, a more detailed description of how this is achieved is provided above in connection with FIG. By using such a process to optimize the turndown level, the turndown operating mode selected for comparison to the shutdown alternate mode for the selected operating period represents an optimized case And, assuming this, it will be recognized that the comparison between the turndown alternative mode and the shutdown alternative mode will be meaningful. As described in connection with FIG. 18, the minimum turndown level may be calculated via an optimization process that optimizes the turndown level according to operator-selected criteria and / or cost functions. One of the functions can be the level of fuel consumption during the proposed turndown period. That is, an optimized turndown level can be determined by optimizing fuel consumption towards a minimum level while also meeting all other operational limits or site-specific performance goals.

  From there, the method of FIG. 21 determines the cost associated with the proposed turndown mode of operation for the selected period of operation according to the characteristics of the turndown mode of operation determined via steps 711 and 712. It is possible. As shown, step 713 can calculate the fuel consumption, and then the fuel cost for the proposed turndown operation. In accordance with the just-discussed exemplary embodiment that describes optimization based on minimizing fuel consumption, the fuel cost simply obtains the fuel level calculated as part of the optimization step. And then multiply by the price for the assumed or known fuel. In the next step (Step 714), the revenue derived from the power generated during the selected operating period is used to determine the proposed turndown level and commercial demand availability during the selected operating period. It can be calculated as a premise. Then, in step 716, operating costs and maintenance costs can be determined. The operating and maintenance costs associated with the proposed turndown operation can be calculated via any conventional method and can depend on the turndown level. Operating and maintenance costs can be reflected as hourly charges derived from historical records of turndown operations and are part of the expected lifetime of various component systems used during the proposed turndown operation It is possible to include a component usage fee that reflects In the next step indicated by step 717, the net cost for the proposed turndown mode of operation for the selected operating period is calculated by adding the cost (fuel, operation and maintenance) and revenue. It can be calculated by subtracting.

  The method also includes step 718, which determines the plant emissions over the selected operating period given the proposed turndown mode of operation, which is referred to as “emission impact”. obtain. The net cost and emissions impact can then be provided to the compile and compare step, which is represented as step 730 so that the cost and emissions impact of different turndown scenarios can be analyzed. Finally, a recommendation can be provided in the output step 731 as further discussed below.

  Looking at the shutdown analyzer 719, the shutdown analyzer 719 calculates the aspects related to operating one or more of the power generation units of the power plant in the shutdown mode of operation during the selected operating period. Can be used. As part of this aspect of the invention, operations including the procedure of shutting down the power plant and then restarting it at the end of the selected period can be analyzed for cost and emissions. According to a preferred embodiment, the shutdown analyzer 719 can determine the proposed shutdown mode of operation as part of the initial steps 720 and 721, where the proposed shutdown mode of operation is optimized. It is possible to represent the shutdown operation mode. The proposed shutdown mode of operation includes the process of shutting down one or more of the power generation units and then restarting the unit back to the online state at the end of the selected period of operation. As will be appreciated, the length of the time period during which the power generation unit is not in operation can determine the type of startup process that the power generation unit may be available to. For example, whether hot startup or cold startup is available depends on whether the shutdown period is short or long, respectively. In determining the proposed shutdown mode of operation, the method can calculate the time required for the startup process to return the power generation unit to the operational load level. In step 721, the method of the present invention can be checked to confirm that the proposed shutdown operation procedure satisfies all operating limits of the power plant. If one of the operating limits is not satisfied, the method can return to step 720 to calculate an alternative startup procedure. This can be repeated until an optimized startup procedure is calculated that satisfies the operating limits of the power plant. As will be appreciated, in accordance with the methods and systems discussed above, a tuned power plant model can be used to simulate an alternative shutdown mode of operation, with an associated operating period and The optimized case is determined based on the project ambient conditions.

  Given the proposed shutdown mode of operation of steps 720 and 721, the process can continue by determining the costs associated with it. The initial steps include analyzing the characteristics of the startup process that the shutdown mode of operation includes. In step 722, the process may determine certain operating parameters of startup, which may include a determination as to whether a purge is needed or required by the plant operator. Given the determined startup, the fuel cost may be determined at step 723. According to an exemplary embodiment, shutdown analyzer 719 then calculates a cost associated with a delay that may be incurred during the startup process. Specifically, as shown in step 724, the process can calculate the probability of such a delay. This calculation can include as input the type of startup, as well as a historical record of past startups of the associated power generation unit in the power plant, and data on the startup of such power generation units in other power plants. . As part of this, the process can calculate the costs associated with the proposed shutdown mode of operation, which reflects the fines such as the probability of a start delay occurring and the amount of damages that will be imposed. It is. This cost can include any cost associated with the hedging tactic, which causes the power plant to pass a portion of the risk of incurring such a premium to a service provider or other insurance company.

  In step 726, the method may determine a cost associated with operating the turning gear during the shutdown process. Given the shutdown period, the method is able to calculate the speed profile for the turning gear, and this is used to determine the cost for the auxiliary power required to operate the turning gear. The As will be appreciated, this represents the power required to maintain turning of the gas turbine rotor blade as it cools, which prevents warping or deformation Therefore, warping or deformation will occur if the blade is otherwise allowed to cool in a stationary position. In step 727, the operating cost and maintenance cost for the shutdown operation may be determined as shown. The operating and maintenance costs associated with the proposed shutdown can be calculated via any conventional method. Operating and maintenance costs can include component usage fees that reflect a portion of the expected lifetime of the various component systems used during the proposed shutdown operation. In the next step indicated by step 728, the net cost for the proposed shutdown mode of operation for the selected operating period is calculated by adding the determined cost of fuel, turning gear, and operation and maintenance. Can be calculated. The method can also include step 729, where the plant emissions over the selected operating period are determined given the proposed shutdown mode of operation, as described above, It can be referred to as “effect of emission” in the operation mode. The net cost and emissions impact can then be provided to the compilation and comparison step of step 730.

  In step 730, the method may compile and compare various plant turndown operating modes for the selected operating period. According to one embodiment, the method can analyze competing turndown modes of operation identified as part of the methods and processes described in connection with FIGS. In step 730, the compiled cost data for each of the competing turndown modes of operation and the impact of emissions can be compared and provided as an output as part of step 731. In this way, recommendations are provided on how the power plant should be operated during the selected turndown operation period according to how competing modes of operation are compared. In particular, the recommendations include which of the turbines should be shut down, which of the turbines should be turned down, and the turndown level at which the turbine should be operated.

  Emission data may also be provided as part of the output of step 731, particularly when the analyzed mode of competing operation has similar economic results. As will be recognized, how each alternative mode affects plant emissions and, given that effect, the potential for non-compliance during the current regulatory period. Notifications can be provided, as well as the associated economic results. Specifically, the accumulated emissions of one or more power plant contaminants during the regulatory period can be compared to an overall limit value that is acceptable during that time frame. According to certain preferred embodiments, the step of communicating the result of the comparison is based on the current regulated emission period relative to the emission rate derived by averaging cumulative emission limits over the current regulated emission period. It may include indicating a power plant emission rate derived by averaging the cumulative emission levels for the power plant over a portion. This can be done to determine how much the power plant is compared to an acceptable average emission rate without incurring violations. The method is capable of determining emissions that are still available to the power plant during the current regulatory period, and there are sufficient levels available to accommodate any of the proposed modes of operation. It is possible to determine whether or not the impact of emissions will unacceptably increase the probability of future regulatory violations.

  As an output, the method can provide a recommended measure, which is an economic point between the proposed turndown mode and the shutdown mode of operation and otherwise. Advise on advantages / disadvantages for both points. Recommendations can include reporting costs, as well as detailed breakdowns between costed categories, and reporting assumptions made in calculating costs. Additionally, recommended actions can include a summary of any other considerations that can influence the decision to select the most preferred mode of operation. These can include information related to applicable emission limits and regulatory periods, as well as what is the current cumulative emissions of the power plant. This can include the power plant operator being notified about the risk of breaching emission thresholds, as well as any mode of operation that unreasonably increases the costs associated with such violations.

  The present invention can further include a unified system architecture or an integrated computing control system, which provides many capabilities of the functional aspects described above. Enable and improve efficiently. Power plants often operate over different markets, different government jurisdictions, and different time zones, even if they are co-owned, and many types of stakeholders and decision makers who participate in their management Exist under various types of services and other contractual commitments. Among such various settings, a single owner can control and operate multiple power plants, each of which has multiple power generations across overlapping markets. Has a unit and type. Owners can also have different criteria for assessing effective power plant operation, including, for example, unique cost models, response times, availability, flexibility, cybersecurity, functionality And differences inherent in the manner in which separate markets operate. However, as will be recognized, most current power trading markets rely on various off-line generated files supplied by multiple parties and decision makers, which are traders, plant managers, and regulators. Including those sent between. Given such complexity, the capacity of power plants and / or power units within a market segment extends, for example, from individual power units to power plants or from power plants to such plants. It may not be fully understood across a multi-layered hierarchy that extends to the fleet. As such, each successive level of the electricity trading market typically hedges the performance reported by the following levels. This translates into inefficiencies for the owner and a decrease in revenue, as successive hedges increase the degree of underutilization of the system. Another aspect of the invention functions to alleviate the fragmentation underlying these problems, as discussed below. According to one embodiment, a system or platform is developed that performs analysis on a unified system architecture, collects and evaluates historical data, and performs analysis of what-if scenarios or alternative scenarios. Unified architecture enables more efficient functions and components, such as power plant modeling, operation decision support tools, power plant operation and performance prediction, and optimization according to performance goals It is possible to According to certain aspects, a unified architecture is for power plants, such as components that are local to the power plant and components that are hosted on a centrally hosted or cloud-based infrastructure, for example. This can be achieved through integration with remote components. As will be appreciated, such aspects of integration can allow enhanced and more accurate power plant models without affecting the consistency, effectiveness, or timeline of results. . This can include utilizing a tuned power plant model that has already been discussed on locally hosted and externally hosted computing systems. Given its deployment on an externally hosted infrastructure, the system architecture can be conveniently expanded to handle additional sites and units.

  Turning now to FIGS. 22-25, an extensible architecture and control system is presented that controls a power plant fleet where multiple power generation units are distributed across several locations. Can be used to support many requirements associated with managing, optimizing and optimizing. The local / remote hybrid architecture may be used based on specific criteria or parameters that are situation specific or case specific, as provided herein. For example, certain aspects of system functionality are hosted locally, while other aspects are centrally hosted environments, such as cloud-based infrastructure, and pool data from all of the power generation units And serve as a common data repository, which also scrubs data through cross-reference values from common equipment, configurations, and conditions, while also supporting analytic functions An owner or operator with a series of power plants may wish to be able to use it to do so. How to choose the right architecture for each of the various types of owners / operators can focus on the critical interests that drive the operation of the power plant, as well as the unique characteristics of the power market in which the plant operates. Is possible. According to certain embodiments, performance calculations can be performed locally, as provided below, to support closed-loop control of specific power plants, improve cybersecurity, or near real-time It provides the response speed required to accommodate the process. On the other hand, the system can be configured such that the data flow between the local system and the remote system includes local data and model tuning parameters, where the local data and model tuning parameters are For generation, the tuned power plant model is then used for analysis such as alternative scenario analysis and so on. A remote or centrally hosted infrastructure can be used to adapt the interaction with the common plant model according to the unique needs of different user types that need access to the common plant model. Additionally, strategies for expansion can be determined based on response times and service contracts that depend on specific aspects of a particular market. If faster response times for final result availability are required, the analysis process can be extended in terms of both software and hardware resources. The system architecture further supports redundancy. If any system running the analysis becomes inoperable, processing can continue on redundant nodes that contain the same power plant model and historical data. A unified architecture can bring applications and processes together, enhances performance, and increases the range of functionality, providing both technical and commercial benefits. It has come to be realized. As will be recognized, such benefits include the easy integration of new power plant models; separation of procedures and models; different operators while presenting data in a unique manner according to each operator's needs. Enabling users to share the same data in real time; easy upgrades; and compliance with NERC-CIP restrictions to send supervisory controls.

  FIG. 22 illustrates a high level logic flow diagram or method for fleet level optimization in accordance with certain aspects of the present invention. As shown, a fleet can include multiple power generation units or assets 802, which can represent separate power generation units across multiple power plants, or the power plant itself. is there. Fleet assets 802 may be owned by a single owner or entity, seeking contractual rights to generate the share of load required by the customer grid, across one or more markets, It is possible to compete against other such assets. Asset 802 may include multiple power generation units having the same type of configuration. In step 803, performance data collected by sensors at various assets of the plant may be electronically communicated to a central data repository. Then, in step 804, the measured data is reconciled, as described below, so that a more accurate or more practical indication of the performance level for each asset is determined, or Can be filtered.

  As explained in detail above, one way in which this reconciliation can be done is to compare the measured data with the corresponding data predicted by the power plant model, As discussed, it may be configured to simulate the behavior of one of the assets. Such models can be referred to as offline models or predictive models, such models can include physics-based models, and the reconciliation process tunes the model periodically To maintain and / or improve the accuracy with which the model represents actual behavior through simulation. That is, as discussed in detail earlier, the method can use the most recently collected data in step 805 to tune the power plant model. This process creates a model for each of the assets, ie, a model for each of the power generation units and / or power plants, and a more generalized model that covers aspects of the operation or fleet operation of multiple power plants Tuning can be included. The reconciliation process also allows data collected to resolve discrepancies and / or to identify anomalies, particularly data collected from the same type of assets with similar configurations, between similar assets 802. It can be accompanied by being compared between. During this process, severe errors can be eliminated, given the collectiveness and redundancy of the compiled data. For example, it is possible to follow a sensor with a higher accuracy capability, or a sensor that has just been checked and has proven to work correctly. In this way, the collected data can be compared, cross-checked, verified, and reconciled to create a single consistent set of data that can be used to calculate more accurate actual fleet performance. It is supposed to build. This set of data can then be used to tune the offline asset model, which is then determined by simulating an optimized control solution for the fleet during future market periods. It can be used, for example, to enhance the competitiveness of a power plant during a power bidding procedure.

  At step 806, as shown, the true performance capability of the power plant is determined from the reconciled performance data and the tuned model of step 805. Then, in step 807, fleet assets 802 may be collectively optimized given the selected optimization criteria. As will be appreciated, this can involve the same process already discussed in detail above. In step 808, an optimized supply curve or asset schedule may be created. This can explain the manner in which assets are scheduled or operated, and each asset can be involved, for example, to satisfy a proposed or virtual load level for a power plant fleet. Can be explained. The criteria for optimization may be chosen by the asset operator or owner. For example, the optimization criteria can include efficiency, revenue, profitability, or some other measurement.

  As shown, subsequent steps may include communicating an optimized asset schedule as part of a bid for a load generation contract for a future market period. This can include communicating an optimized asset schedule to an energy trader at step 809, which then submits a bid according to the optimized asset schedule. As will be appreciated, in step 810, a bid may be used to participate in the overall power system power supply process, where a plurality of load loads are positioned in the system. It is distributed between the power plant and the power generation unit, many of which can be owned by competing owners. Bids or offers for the power supply process may be configured according to defined criteria, such as variable power generation costs or efficiency, as determined by a specific power supply command center of the power system. In step 811, the result of the power system optimization is to generate an asset schedule that reflects how the various assets in the power system should be involved to meet the predicted demand. Can be used. The asset schedule in step 811 reflects the result of the overall system optimization or powering process, which can then be communicated back to the owner of asset 802, for example, in step 812, for each of the assets. An operation set point (or, among other things, an operation mode) that may include a load to be operated can be communicated to a controller that controls the operation of the asset 802. In step 813, the controller can calculate the control solution, then communicate the control solution and / or directly control the asset 802, meeting the contracted load requirements during the power supply process. It is supposed to let you. Fleet owners can adjust the manner in which one or more power plants operate according to changing conditions, so as to optimize profitability.

  FIG. 23 illustrates data flow between a local system and a remote system, according to an alternative embodiment. As stated, certain functionality may be hosted locally, while other functionality may be hosted off-site in a centrally hosted environment. The method of choosing an appropriate architecture according to the present invention involves determining considerations that are important drivers of the operation of assets in the fleet. Thus, considerations such as cyber security issues may require that certain systems remain local. Also, time-consuming performance calculations remain locally hosted and the necessary timeline is maintained. As shown in FIG. 23, the local plant control system 816 can capture sensor measurements and communicate data to the tuning module 817, which is particularly relevant to FIG. Thus, as previously discussed, the tuning or data matching adjustment process may be completed using performance calculations that compare the values predicted by the plant or asset model with actual or measured values. Via the data router 818, as shown, the model tuning parameters and reconciled data can then be communicated to a centrally hosted infrastructure, such as a remote central database 819. From there, the model tuning parameters are used to tune the offline power plant model 820, and then the offline power plant model 820 is used to optimize and replace future fleet operations as described above. Specific scenarios or “what-if” analyzes are provided to advise between possible or competing modes of operation of an asset fleet.

  The results of the analysis performed using the offline power plant model 820 can be communicated to the fleet operator via the web portal 821, as shown. Web portal 821 can provide users with customized access 822 for fleet management. Such users can include plant operators, energy traders, owners, fleet operators, engineers, and other interested parties. In accordance with user interaction with web portal access, decisions regarding recommendations offered by analysis performed using the offline power plant model 820 may be made.

  24 and 25 illustrate a schematic system configuration of a unified architecture according to certain alternative aspects of the present invention. As illustrated in FIG. 25, the remote central repository and analysis component 825 can receive performance and measured operational parameters from several assets 802 to perform fleet level optimization. Fleet level optimization can be based on additional input data, which can be based on, for example, the current amount of fuel stored and available at each power plant, the fuel for each power plant. Region-specific prices, region-specific prices for electricity generated at each power plant, current weather forecasts, and differences between remotely located assets and / or outage and maintenance schedules Can be included. For example, a component overhaul schedule for a gas turbine may mean that short-term operation at higher temperatures is more economical. The process can then calculate a supply curve that includes an optimized variable power generation cost for the fleet of the power plant. Additionally, the present invention can allow for a more automated bid preparation, as shown, where the bid is forwarded directly to the system-wide power supply command authority 826, at least in certain circumstances. Can thereby be configured to bypass the energy trader 809. As illustrated in FIG. 25, the results of power system optimization (via the system-wide power supply command authority) are used to determine which various assets in the power system meet the expected demand. It is possible to create an asset schedule that reflects how it should be involved. This asset schedule can reflect the optimization of the entire system and can be communicated back to the fleet owner of asset 802, as shown, to provide an operational set point for the asset and The mode of operation can be communicated to a controller that controls each asset in the system.

  Thus, the method and system can be developed according to FIGS. 22-25, whereby the fleet of power plants operating within a competitive power system has been associated with enhanced performance and future market periods. Optimized for bidding. Current data regarding operating conditions and parameters may be received in real time from each of the power plants in the fleet. The power plant and / or fleet model can then be tuned according to current data, so that the model accuracy and the range of prediction continue to improve. As will be appreciated, this can be achieved through a comparison between the measured performance index and the corresponding value predicted by the power plant or fleet model. In the next step, a tuned power plant model and / or a fleet level model is used, and for each of the power plants in the fleet based on competing modes of operation that are simulated using the tuned model. It is possible to calculate the true power generation capacity. Optimization is then performed using the true plant capacity and optimization criteria defined by the plant or fleet operator. Once the optimized mode of operation is determined, an asset schedule can be created that calculates the optimal operating point for each of the power plants in the fleet. As will be appreciated, the operating points can then be transferred to different power plants to control each according to those operating points, or alternatively, the operating points can be Can serve as a basis for making bids to submit to.

  Also in connection with centralizing the control and optimization of multiple power units, FIGS. 26 and 27 illustrate a power system 850 in which the block controller 855 includes a plurality of power blocks. Used to control the 860. The power block 860 can define a fleet 861 of power generation assets (“assets”), as shown. As will be appreciated, these embodiments provide another exemplary application of the optimization and control methods described above in more detail, but optimization to fleet levels. Including expanding the perspective of In doing so, the present invention can further offer a scheme to reduce certain inefficiencies that still affect modern power generation systems, especially power generation systems having a large number of remote and various thermal power generation units. It is. Each of the assets is any of the thermal power units discussed herein, such as, for example, gas turbines and steam turbines, and related subcomponents such as HRSG, intake air conditioners, duct burners, etc. Can be expressed. An asset may be operable according to multiple power generation configurations, depending on how subcomponents are involved. Power generation from the plurality of power blocks 860 can be centrally controlled by a block controller 855. With respect to the system of FIG. 27, which will be discussed in more detail below, the block controller 855 can control the system according to an optimization process, which includes asset and power block health, and power generation. Take into account other factors that may be specific to one of the assets or power block 860, including schedules, maintenance schedules, and location dependent variables. In addition, learning from operational data collected from similarly configured assets and power blocks that are not part of the fleet can be utilized to further refine the control strategy.

  Typically, a conventional asset controller (which is shown as “DCS” in FIG. 26) is local to the power generation asset and operates substantially in isolation. Due to this, such a controller cannot take into account the current health of the power block 860 and / or other assets comprising the fleet 861. As will be appreciated, this missing perspective, when considered from that perspective, leads to sub-optimal power generation for the fleet 861. With continued reference to the methods and systems already described, particularly those associated with FIGS. 3, 4, and 17-25, the exemplary embodiment spans grouped assets or power blocks. He teaches a fleet level control system that enables several system-wide benefits, including enhanced power sharing strategies, cost efficiency, and improved efficiency.

  As shown, the control system can interact with the asset controller, as represented by block controller 855. The block controller 855 can also communicate with the grid 862 and the central power supply command authority or other operational authority associated with its management. Thus, for example, supply and demand information can be exchanged between the fleet 861 and the central authority. According to an exemplary embodiment, supply information, such as power supply bids, may be based on block controller optimization of the fleet 861. The present invention can further include an optimization process that occurs between bid periods, which periodically optimizes the scheme in which the fleet 861 is configured to satisfy an already established load level. Can be used to Specifically, such inter-bid optimization can be used to address dynamic and unexpected operating variables. Appropriate control measures for the assets of the power block 860 may be communicated by the block controller 855 to the control system within each of the power blocks 860, or more directly to the assets. According to a preferred embodiment, the control solution implementation of the block controller 855 can include allowing it to override the asset controller when certain predetermined conditions are met. . Factors affecting such overrides include variable power generation costs for each power block / asset, remaining useful part life for hot gas flow path components, changing levels of demand, changing ambient conditions, and others. It is possible to include.

  The block controller 855 can be communicatively linked to several power blocks 860 of the fleet 861, as shown, and can be communicatively linked directly to an asset, thereby allowing many data inputs. The control solution that can be received and described herein is based on that data input. The optimization procedure can take into account one or more of the following inputs: health and performance degradation; power generation schedule; grid frequency; maintenance and inspection schedule; fuel availability; fuel cost; Situation patterns and predictions; past problems and equipment failures; true performance capabilities; life-life models; startup and shutdown features; past and present measured operating parameter data; weather data; As discussed in more detail in connection with other embodiments, the inputs may include detailed current data and historical data regarding operating parameters measured for each of the fleet 861's power generation assets. . All such past and present inputs can be stored according to conventional methods, for example, in a central database, thereby requiring any of the procedural steps described herein. Thus, it is made available according to the question from the block controller 855.

  The cost function can be developed according to the selection of the fleet operator. According to a preferred embodiment, a weighted average sum of free robustness indexes may be used to determine a preferred or optimized power sharing configuration. The free robustness index can include optimization, for example, according to several factors applicable to a given demand or fleet power level. These factors can include thermal stress and mechanical stress; degradation or loss, including the rate of degradation; power generation costs; and / or fuel consumption. As such, this embodiment can be used to address several ongoing issues associated with fleet control and, among other things, optimizes performance across several power blocks with multiple different power generation assets To do.

  Data input can include those of the types already discussed herein, including those related to computer modeling, maintenance, optimization, and model-free adaptive learning processes. For example, according to this embodiment, the computer model, transfer function, or algorithm may be an asset, and / or collective, power block or fleet operation (or a specific aspect of operation) for various scenarios. It can be developed and maintained as can be simulated below. The results from the simulation can include values for a specific performance index, which represents an estimate of the fleet performance over an asset, power block operation and performance aspects, or a selected period of operation. Performance indicators can be selected due to known or developed correlations with one or more cost results and can therefore be used to compare the economic aspects of the respective simulations. A “cost result”, as used herein, can include any positive or negative economic effect associated with the operation of the fleet 861 over a selected period of operation. Thus, the cost result can include any revenue earned from the generation of power over that period, as well as any operating and maintenance costs incurred by the fleet. These operating and maintenance costs can include the resulting degradation to the assets of the fleet given the scenario and the resulting simulated operation from each. As will be appreciated, the data extracted from the simulation results can be used to calculate which alternative modes of operation for the fleet are more desirable or cost effective.

  Models for assets, blocks, or fleets include algorithms or transfer functions developed through physics-based models, adaptive or learned “model-free” process input / output correlations, or combinations thereof Is possible. Baseline degradation or loss models can be developed that correlate process inputs / outputs with degradation or loss data for each asset type. Thus, degradation or loss data, and associated cost results, may be calculable based on predicted values for the operating parameters of the fleet's proposed, alternative, or competing operating modes, which are , According to certain embodiments, may be differentiated by the manner in which assets and power blocks are involved, the manner in which power generation is shared across fleet assets, and other factors described herein . As stated, learning from similarly configured assets can be used to inform or further refine the models used as part of this process. For example, a degradation model may be developed to calculate equipment degradation and loss that is assumed given values for a selected performance metric. Such degradation can then be used to calculate an economic effect or cost result for each of the competing modes of operation. These economic effects relate to degradation to asset performance, wear to components, useful part life consumed (ie, part of the useful life of components consumed during the period of operation), and emissions, for example Measures of value such as cost, regulatory fees, fuel consumption, and other variable costs depending on power level can be included. As will be appreciated, the overall fleet, among other things, can be detrimental to the degradation and consumption of useful component life for a particular asset, which can occur non-linearly and depend on dynamic and / or location-specific variables. Significant cost savings by distributing fleet power levels to minimize overall fleet degradation where minimization is shared across assets to minimize power generation capacity and efficiency Can be realized over time.

  Thus, taking into account the anticipated conditions for future market periods, which may include expected demand expectations and ambient condition expectations, multiple competing modes of operation for the fleet have been optimized or at least May be selected for analysis and / or simulation to determine a suitable fleet mode of operation. Each of the competing fleet operating modes can describe a unique power generation configuration for the fleet 861. Competing fleet operating modes can be developed to include parameter sets and / or control settings that define the specific power generation configuration with which a particular fleet output level is reached. As stated, the fleet output level can be selected in a number of ways. First, it can be selected to reflect an already known fleet output level, for example, an output level established through a recently concluded supply process, where the optimization process is optimized Can be used to determine an optimized fleet configuration, and an optimized fleet configuration satisfies a particular power level. Also, the fleet power level may be selected according to the expected load level, expected customer demand, and / or other expected conditions given historical power generation records. Alternatively, the fleet output level can vary over a selected range. In this way, variable power generation costs for the fleet 861 can be calculated and then used, for example, as part of a bidding procedure, to inform competitive bidding preparations. Thus, the form in which the fleet output level is defined by the fleet operator may be used, and at some times activities around preparing competitive bids will be supported, while at other times, The output level can be selected to support the advisor function, and the advisor function operates to optimize the fleet performance because the actual conditions can deviate from the assumed conditions.

  According to exemplary operations, a parameter set describing each of the competing fleet operating modes can be developed, as shown by the more detailed system of FIG. 27, and for each of the competing fleet operating modes. , Different scenarios or cases can be developed, in which the operable variables are variable over the selected range, so as to determine the effect of the change on the overall behavior of the fleet Yes. Different cases for competing fleet operating modes may be configured to cover alternative schemes, where the fleet output level is shared across power block 860 and / or assets. According to another example, different cases may be selected based on alternative configurations available for a particular one of the assets, which includes various schemes, depending on the various schemes, Is involved. For example, some cases may involve the involvement of certain subcomponents of assets, such as duct burners or intake conditioners, and it is recommended that other assets operate at shutdown or turndown levels However, the power generation capacity has been increased. Other scenarios can explore situations where their asset composition is somewhat changed or reversed.

  As illustrated in FIG. 27, the block controller 855 can communicate with a data and analysis component 865, which can include a number of modules, the modules The relevant data is collected, normalized, stored, and made available in response to a query from the block controller 855. The data recording module can receive real-time and historical data input from a monitoring system associated with the power generation asset. Also, modules associated with performance monitoring can be included and one or more offline models associated therewith can be maintained. Each of these modules can function substantially consistent with the other embodiments discussed herein. A learning module may also be included for collection of operational data from similarly configured assets or power blocks that are not operating in the fleet 861. As will be recognized, this data can support the learning function, which provides a deeper and more complete understanding of the behavior of the asset. Such data can also be used to normalize the measured data collected from the fleet 861, such that the performance degradation of the power generation asset can be accurately calculated, which includes fuel characteristics, It can include taking into account the effects of other variables that can affect output capability and efficiency, such as ambient conditions.

  As described in connection with FIGS. 24 and 25, fleet level optimization can be based on locality dependent variables. These variables are conditions that are unique and may reflect conditions that apply to a particular asset or power block, for example, the current amount of fuel that is stored and available at each asset Location-specific prices for fuel for each asset; location-specific market prices for electricity generated in each asset; differences between current weather forecasts and remotely located assets in the fleet Points; and can include outage schedules and maintenance schedules for each asset. For example, a component overhaul scheduled for gas turbine assets may mean that short-term operation at higher temperatures is more economically beneficial. As shown, the data and analysis component 865 can include a module to account for these differences.

  Block controller 855, as shown, is a module for power generation models (which can include asset models, block models, fleet models, and degradation or loss models), optimizers, and cost functions. Can further be included. Assets, power blocks, and / or fleet models can be generated, tuned, and / or reconciled and maintained according to methods already described herein. These models can be used to simulate or otherwise predict fleet behavior, or selected portions thereof, over a selected duration of operation, and an optimizer module is defined. A suitable scenario can be determined according to a cost function. More specifically, the results from the simulation can be used to calculate a cost result for each, which can include revenue, operating costs, degradation, consumed useful component life across power blocks and / or fleet assets. , And other sums of costs described herein may be included. As will be recognized, revenue can be determined via multiplying the planned power level by the market unit price. As stated, the cost calculation can include a degradation model or algorithm that correlates the economic outcome to the manner in which the asset operates in the simulation. Performance data from the simulation results can be used to determine the operating cost of the entire fleet, degradation, and other losses as already described. As will be appreciated, certain cost considerations, such as fixed mode operating costs, etc., will not differ significantly between competing fleet operating modes, and as such Can be excluded from the calculation. Additionally, the simulations described herein can be configured to include the entire fleet of assets or a portion thereof, and in predicting cost results as provided herein. It is possible to focus on limited aspects of asset behavior that have been found to be particularly relevant.

  According to certain embodiments, the cost function module can include a free robustness index to efficiently differentiate between alternative modes of operation. The free robustness index can represent the average sum of losses that occur in the power block. The robustness index can include factors that indicate the cost associated with the useful component life consumed, which is consumed across assets such as hot gas flow path parts and compressor blades in a gas turbine. It is possible to be the sum of the component lifetimes to be made. For example, in accordance with one of the competing fleet modes of operation, the power generation assets scheduled to be shut down during the selected period of operation can contribute to the useful component life consumed for each shutdown / startup procedure. A corresponding economic loss will occur. On the other hand, power generation assets that are scheduled to operate at full load during the same period of operation can cause losses corresponding to the time of their operation. As will be appreciated, such losses are specific thermal and mechanical loads that are expected given the expected load levels and operating parameters to satisfy a specific load level. Can be further calibrated to reflect, for example, it can depend on factors such as expected ambient conditions, fuel characteristics, and the like. Other economic losses can be included in the sum of the fleet losses to derive a cost result for each of the competing fleet operating modes. These can include the total fuel consumption for the fleet asset, as well as the economic impact of emission levels predicted, for example, assuming simulation results.

  When the sum of revenue and / or loss for the entire fleet is completed for each simulated scenario, the method includes calculating one or more preferred or optimized cases Is possible. The method may then include one or more outputs related to the preferred or optimized case. For example, a suitable or optimized case can be communicated electronically to the fleet operator, such as through a user interface 866. In such cases, the output of the method can be power block / asset health advisor, power sharing recommendation, outage planner, optimal setpoint control solution for power block, DCS override, and / or expected power generation schedule. Can be included. The output can also include an automated control response, and the automated control response can include automatically overriding one of the asset controllers. According to another alternative, the output can include generating a power bid according to one or more of a preferred or optimized case. As will be appreciated, the output of the method can allow fleet savings in a number of ways, as shown above the user interface 866. First, for example, a suitable power sharing configuration can minimize, reduce, or advantageously share fleet degradation, which can generate power over future operating periods. And can have a significant impact on efficiency. Second, the advisor function can be configured to optimize the maintenance interval, or at least enhance the maintenance interval, using the components described, so that the degradation loss is recoverable. Both unrecoverable and unrecoverable. Monitoring and predicting the rate of degradation and effectively scheduling / executing maintenance procedures such as compressor cleaning or filter cleaning ensure that the gas turbine operates most efficiently It becomes.

  Turning now to FIG. 28, another related aspect of the present invention is discussed, which describes a more specific example of controlling the operation of multiple gas turbine engines operating as power blocks. doing. As will be appreciated, a gas turbine engine may be located at a particular power plant or across several remote power plants. As already discussed, controlling the gas turbine block to optimize or enhance power sharing is a challenge. Current control systems do not effectively synchronize across multiple engine blocks; instead, each engine is individually based on the simple sharing of power levels that the power block is collectively responsible for. Be substantially involved. As will be appreciated, this often leads to unbalanced and inefficient rates of degradation. Thus, there is a need for a more optimal control strategy, especially across multiple gas turbines that promote more cost-effective loss or degradation rates when units are collectively considered as power blocks. There is a need for a system controller that provides an efficient power sharing strategy. For example, if a gas turbine block has several engines with the same rating, the present invention should determine which of the units should operate at a higher power level based on the current degradation state of the engine. It is possible to make recommendations on which and which should operate at a reduced level. The present invention can accomplish this in accordance with the aspects already discussed herein, and in particular according to those discussed with respect to FIGS. As those skilled in the art will appreciate, the benefits of such functionality include increased gas turbine life and performance; improved life prediction (which allows for a more competitive and / or risk sharing service agreement) Greater operational flexibility for the power block as a whole; and robust multi-purpose optimization that efficiently takes into account operational trade-offs, which may include, for example, hot gas flow paths It is possible to relate to the consumption of useful parts, the current degradation level, the rate of degradation, and the current power generation performance such as demand, efficiency, fuel consumption, etc.

  One manner in which this can be achieved is according to system 900, where system 900 will be described with respect to FIG. As shown, a plurality of gas turbines 901 may be operated as part of a power block or “block 902”. As discussed as part of the system above, operational parameters 903 for each of the assets 901 can be collected and communicated electronically to the block controller 904. According to a preferred embodiment, the operating parameters can include rotor speed, compressor surge margin, and blade tip clearance. As will be appreciated, the compressor surge margin can be calculated with respect to the measured rotor speed, and the blade tip clearance can be measured according to any conventional method including, for example, a microwave sensor. As a further input, the block controller can receive a record 905 from a database component such as any of those already discussed, such as rotor speed, surge margin, blade tip clearance, control settings, ambient It is possible to record current and past operating parameter measurements, including condition data, etc., to adaptively correlate process inputs and outputs.

  According to a preferred embodiment, the block controller 904 can be configured to operate as a model-free adaptive controller. The model free adaptive controller may include a neural network based setup with inputs (eg, via record 905) from each of the gas turbines corresponding to demand, heat rate, etc. As will be appreciated, model-free adaptive control is a particularly effective control method for unknown discrete-time nonlinear systems with time-varying parameters and time-varying structures. Model-free adaptive control design and analysis is designed to “learn” predictive correlations or algorithms that focus on process inputs and outputs and explain the relationship between them. The correlation between the measured input and output of the system is controlled. Working in this manner, the block controller 904 can derive control commands or recommendations that can be communicated to the master control system 907 as an output 906 for implementation. According to a preferred embodiment, the output 906 from the block controller 904 includes a suitable or optimized power sharing command or recommendation. According to other embodiments, the output 906 is related to a modulated coolant flow for the hot gas path component of the gas turbine 901 and / or a modulated IGV setting for the compressor unit of the gas turbine 901. Command or recommendation.

  The master control system 907 can be communicatively linked to the gas turbine 901 of the power block 902 and is adapted to implement a control solution given the output 906. Also, as shown, the master control system 907 can communicate such information to the block controller 904. As so configured, the control system of FIG. 28 is operable to control several gas turbines of power block 902, enhanced according to a defined cost function, or To generate combined loads or power levels in an optimized manner (eg, contract power levels that can be determined by a power bidding process, etc., where the gas turbine is collectively responsible for it) ing. This control solution can include recommending a percentage of the combined power level that each of the gas turbines should contribute. In addition, the master control system 907 can include a physics-based model for controlling the gas turbine according to an optimized mode of operation, as previously discussed.

  According to an exemplary embodiment, for example, clearance and surge margin data may be tracked for each of the gas turbines. If the clearance or surge margin data for any of the gas turbines is determined to exceed a predetermined threshold, that particular turbine can be operated at a reduced load. If it is not possible to operate the gas turbine at reduced levels, other recommendations may be made, such as modulating the coolant flow to the IGV settings or hot gas flow path components. On the other hand, if one of the gas turbines is selected to operate at a reduced level, the optimized power generation configuration can be compared to other gas turbines to make up for any shortfall. It may include recommending that one or more operate at a higher / peak load. The method can select higher / peak load turbines based on surge margin and clearance data to collectively extend operating life while maintaining higher block power levels and efficiency. In addition, it has the desired effect of balancing the current level of degradation and the rate of degradation between the gas turbines in the power block. As stated, the rate of performance degradation and useful part life consumption can occur non-linearly and can depend on parameters that are variable across geographically dispersed units, so that the block level described herein can be Savings can be realized by using a viewpoint and sharing the load in a manner that optimizes the cost result for the block. Thus, real-time data determined to be extremely effective and efficient in evaluating performance degradation levels, degradation rates, remaining part life, and true performance capabilities for block gas turbines (especially surge margin and By taking into account (clearance data), power generation can be shared to optimize the cost across block 902.

  Referring now to FIG. 29, another exemplary embodiment of the present invention includes a system and method that provides a more efficient and / or optimized shutdown of a combined cycle power plant. As will be appreciated, during a combined cycle plant shutdown, the controller will typically gradually reduce the fuel flow to the gas turbine and reduce the rotor speed towards the minimum speed. ing. This minimum speed can be referred to as the “turning gear speed”. The reason is that it represents the speed at which the rotor is engaged by the turning gear and thereby rotated to prevent thermal bending of the rotor during the shutdown period. Depending on the nature of the gas turbine engine, the fuel flow can be stopped at about 20 percent of typical full speed, and the turning gear is engaged at about 1 percent of full speed. However, gradually reducing the fuel flow in this manner does not provide a direct relationship with reducing rotor speed. Rather, large unyielding changes in rotor speed over the shutdown period are typical. The change in rotor speed can then cause a significant difference in the fuel to air ratio, due to the fact that air intake is a function of rotor speed, while fuel flow is not. Yes. Such changes can then lead to combustion temperatures, transient temperature gradients, emissions, coolant flow, and other fairly rapid changes. Changes in shutdown behavior can have an impact on turbine clearance and, therefore, overall turbine performance and component lifetime.

  Accordingly, there is a need for a combined cycle shutdown controller that improves plant shutdown by correcting one or more of these problems. Preferably, such a controller will control the rate of deceleration of the turbine rotor and related components over time, minimizing non-uniform shutdown changes, thereby minimizing negative effects on engine systems and components. It has come to become. According to certain embodiments, a more effective controller functions to optimize rotor stress and rotor speed and torque slew rate. The shutdown controller can also modify the variability of the subsystem so that, for example, coolant flow and wheel space temperature are maintained at suitable levels. According to a preferred embodiment, the method is capable of controlling the rate of rotor and associated component deceleration over time, in a manner that reduces costs, plant losses, and other negative effects. It is designed to minimize shutdown changes. In accordance with the systems and methods already described, the control methodology can function so that factors that affect shutdown costs are optimized according to operator-defined criteria or cost functions. One manner in which this can be achieved is according to process 920, where process 920 will be described with respect to FIG. As will become apparent to those skilled in the art, aspects of process 920 are based on the subject matter already discussed herein, with particular reference to the discussion associated with FIGS. It will be summarized for brevity and will not repeat completely.

  According to one embodiment, the shutdown procedure and / or combined cycle shutdown controller of the present invention is configured as a conventional loop forming controller. The controller of the present invention can include model-free adaptive control as well as model-based control aspects, as precisely described in the appended claims. The combined cycle shutdown controller can include a target shutdown time controller and an actual shutdown time controller, and can control most if not all aspects of plant shutdown. Controllers include exhaust spread, wheel space temperature, clearance, surge margin, steam and gas turbine rotor stress, gas turbine rotor deceleration rate, demand, fuel flow, current power production, grid frequency, secondary firing , And input such as drum level can be received. Based on these inputs, the shutdown controller, as described in more detail below, provides a time range for shutdown (eg, shutdown rate), rotor deceleration slew rate, corrected coolant flow, and It is possible to compute a corrected inlet guide vane profile and / or the desired generator reverse torque during shutdown. According to certain embodiments, each of these outputs can be used to offset potentially harmful shutdown changes detected by one of the power plant sensors. The combined cycle shutdown controller can provide an RPM / slew rate trajectory for the time profile and a rate of deceleration for the appropriate current power production profile due to the shutdown operation, both of which are HRSG, steam turbine, and It is possible to take into account a combined cycle system, such as a boiler. The shutdown controller is capable of controlling the components described herein until turning gear speed is achieved, and thus more optimal steam turbine and HRSG operability conditions while reducing component stress. I will provide a.

  FIG. 29 is a flowchart illustrating an embodiment of a process 920 suitable for shutting down a combined cycle power plant, such as the power plant 12 described with reference to FIG. Process 920 may be implemented as computer code executable by a combined cycle shutdown controller and may be initiated after receiving a shutdown command (step 921). The shutdown command may be received based on, for example, a maintenance event, a fuel change event, etc. The process 920 can then retrieve the current state of the plant component (step 922), which is sensed for any of the sensors, systems, and / or methods already described herein. Can be collected, stored, and read. The current state of plant components can include, for example, turbine rotor speed, component temperature, exhaust temperature, pressure, flow rate, clearance (ie, distance between rotating and stationary components), vibration measurements, etc. is there. The state of the plant adds current power production and costing data such as, for example, the cost of not producing power, the cost of electricity at market rates, green credits (eg, emission credits), etc. Can be included.

  In the next step, costing or loss data may be retrieved by interrogating various systems including, for example, accounting systems, future trading systems, energy market systems, or combinations thereof (step 923). . Historical data may additionally be read (step 924). Historical data includes log data about system performance, maintenance data, historical data for the entire fleet (eg, logs from other components in the plant located at various geographical locations), inspection reports, And / or historical cost data.

  The process can then derive an algorithm for plant shutdown degradation or loss associated with the shutdown operation (step 925). Such derivation includes several inputs including, for example, historical operational data related to gas turbine systems, steam turbine systems, HRSG units, as well as historical operational data for any other subcomponents that may be present. Can be determined using type. According to certain embodiments, various models or algorithms may be developed, thereby deriving combined cycle shutdown losses. As discussed more fully in the discussion related to FIG. 4, such an algorithm can be used to select selected operating parameters or performance indicators such as temperature, pressure, flow rate, clearance, stress, vibration, shutdown time, etc. Can function to provide a total power plant shutdown loss. As will be recognized, consistent with other embodiments described herein, an alternative proposed or competing shutdown mode for a combined cycle power plant is a combined cycle power plant model. Can be simulated in That is, a combined cycle power plant model can be developed, tuned and maintained, and then used to simulate alternative or competing shutdown modes of operation, predicting for a particular predetermined performance index Derived values are derived. The predicted value for the performance parameter can then be used to calculate the shutdown cost according to the derived loss algorithm.

  For example, an algorithm can be developed that correlates with shutdown loss and predicted thermal stress profile, which predicts the predicted value for a particular performance parameter given the operating parameters associated with one of the competing shutdown operating modes. Can be determined from According to certain embodiments, such losses can reflect the overall economic impact of competing shutdown operating modes, such as degradation to hot gas flow path components and / or shutdown modes. Can be taken into account as a percentage of useful part life consumed as well as any resulting performance degradation for the plant and can be calculated from the onset of shutdown to the realized time of shutdown, The realized time of shutdown can be, for example, when a turning gear speed is realized. Similarly, loss algorithms include compressor and turbine mechanical stress; clearance between stationary and rotating parts; shutdown emissions; shutdown fuel consumption; steam turbine rotor / stator thermal stress; boiler drum pressure gradient, etc. It can be developed to determine the associated loss.

  Process 920 may then derive an enhanced or optimized shutdown mode of operation (step 926), which may include RPM / slew rate over time profile and / or combined cycle power plant shutdown. It is possible to include a rate of deceleration relative to the current power production profile that is particularly well suited to. For example, the optimized shutdown mode of operation may be determined as best accommodating and taking into account the operation of the steam turbine, HRSG unit, boiler, and / or other components of the combined cycle plant. According to one embodiment, the controller takes the above inputs and derives the expected conditions therefrom at various RPM / throughtime parameters to minimize stress and / or optimize shutdown costs. An RPM / through curve plotted along the time axis is derived. Similarly, the rate of deceleration for the current power production profile may more desirably include a fuel flow that improves current power production based on shaft deceleration (when compared to other proposed shutdown modes). Is possible. According to other embodiments, a cost function may be defined, which derives and selects other preferred or optimized other shutdown operating modes. As will be appreciated, for example, scenarios for minimizing stresses and / or losses for gas turbines, scenarios for minimizing stresses and / or losses for steam turbines, stresses and / or losses for HRSG, Minimizing scenarios or combinations thereof can be derived. Depending on how the cost function is defined, a more optimized shutdown mode of operation can be used for power production during the shutdown period, plant emissions, fuel consumption, and / or no limitation Or a combination thereof or the like.

  The combined cycle shutdown controller may further include a control system for shutting down the power plant according to the optimized shutdown mode. According to a preferred embodiment, the control system can include a physics-based modeler or model-based controller, which then derives a control solution given an optimized shutdown mode . The model-based controller can derive control inputs and settings for controlling the actuators and control devices so that the combined cycle power plant can control the shutdown period according to a preferred or optimized shutdown mode of operation. It can be operated in between. For example, a shutdown controller can operate a fuel valve to match a desired fuel flow rate, while additionally controlling the steam valve of the steam turbine to control the steam turbine shutdown, The exhaust inlet guide vanes are controlled to control the exhaust flow into the HRSG. By combining the control of the various components approximately simultaneously with each other, the shutdown for the plant can be improved and adapted to the desired scenario.

  Although the present invention has been described with respect to what is presently considered to be the most practical and preferred embodiments, the present invention should not be limited to the disclosed embodiments, in contrast thereto. It is to be understood that this invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

DESCRIPTION OF SYMBOLS 10 Power system 12 Power generation plant 12a Power generation plant 12b Power generation plant 14 Transmission line 16 Load 18 Storage device 20 Communication network 21 Connection 22 Plant controller 23 Load controller 24 Power supply command authority 25 Power system controller 26 Data resource 30 Gas turbine system 31 Component controller 32 Compressor 33 Ambient environment 34 Combustor 36 Turbine 39 Operator 40 Intake duct 41 Inlet guide vane 42 Exhaust duct 44 Generator 46 Sensor 47 Power plant actuator 49 Power plant component 50 Steam turbine system 51 Inlet conditioning system 52 HRSG duct firing system 53 Steam turbine 55 Hot exhaust gas 6 Gas turbine model 61 Intake conditioning system model 62 Steam turbine model 63 Economic model 64 Optimizer 65 Refrigeration system 70 Filter 71 Neural network 72 Actual data 73 Predicted data 74 Optimized set point 75 Power plant model 80 Computer system 81 Display 82 Processor 83 User input device 84 Memory 110 Control module 111 Control model 112 Performance model 113 LCC model 114 Enhancement module 115 Control information 120 Offer curve module 121 Ambient condition forecast 122 Market condition forecast 123 Database 124 Offline model 125 Offer curve 126 Model Tuning module 128 Target load 16 9 Method 170 Control Section 171 Step 172 Step 173 Step 174 Step 175 Step 180 Offer Curve Section 181 Step 182 Step 183 Step 184 Step 185 Step 186 Step 200 Plant Optimization System 202 Monitoring and Control Instrument 204 Monitoring and Control Instrument 208 Plant Controller 210 Data collection module, data collection model 212 Database / historic 214 Heat balance module 216 Performance module 218 Optimizer module 220 Advisory output 222 Closed feedback loop control output 230 Real-time thermal power plant optimization system 231 Server system 234 Client system System 236 database server 239 database 250 method 300 system 301 system 302 power plant 303 controller 303a controller 303b controller 305 first model, primary model 306 second model, prediction model 307 interface 308 current performance parameter 309 other variables 310 Control Profile Input 313 Operator Specific Scenario 314 Predicted Turbine Operating Characteristics 320 Method 325 Step 330 Step 335 Step 340 Step 345 Step 400 Method 405 Decision Step 410 Step 415 Step 420 Decision Step 425 Step 430 Step 435 Step 440 Step 445 Step 500 Floda Agram 501 Power plant 502 Power plant model 503 Tuning module 505 Plant controller 507 Tuned power plant model 509 Plant operator module 510 Optimizer 511 Sensor 515 Feedback 516 Performance goal 517 Second parameter set 519 Simulated operation 521 Optimization Mode of operation 600 algorithm 601 maximum value 602 threshold, step 603 algorithm 604 step 606 step 608 step 609 permutation matrix 610 algorithm, step 611 maximum chilling capability value, step 612 related algorithm, operator 613 dew point, step 614 step 616 step 618 steps 620 Algorithm, step 621 Upper threshold level, step 622 Step 630 Chart 631 Chart 639 Sequence 640 Initial state 700 Flow diagram 701 Turndown data 702 Shutdown data 703 Common data 710 Turndown analyzer 711 Step 712 Step 713 Step 714 Step 716 Step 717 Step 718 Step 719 Shutdown Analyzer 720 Step 721 Step 722 Step 723 Step 724 Step 726 Step 727 Step 728 Step 729 Step 730 Step 731 Step, Output 802 Asset 803 Step 804 Step 805 Step 806 Step 807 Step 808 Step 809 Step, Energy Trader 810 Step 811 Step 812 Step 813 Step 816 Local Plant Control System 817 Tuning Module 818 Data Router 819 Remote Central Database 820 Offline Power Plant Model 821 Web Portal 822 Customized Access 825 Remote Central Repository And analysis component 826 system-wide power supply command authority 850 power system 855 block controller 860 power block 861 fleet 862 grid 865 analysis component 866 user interface 900 system 901 gas turbine, asset 902 power block 903 operating parameters 9 4 block controller 905 records 906 the output 907 master control system 920 processes 921 step 922 step 923 step 924 step 925 step 926 step 927 step

Claims (20)

A control method for optimizing turndown operation for a power plant (12, 12a, 12b, 302, 501) having a plurality of power generation units over a selected operation period, the plurality of power generation units comprising: Each including either an on condition or an off condition during the selected operating period, the method comprising:
Defining competing operating modes, wherein the competing operating modes include, during the selected operating period, which of the plurality of power generation units includes the on condition and which includes the off condition. Including steps that may differ in terms of
Defining a plurality of cases for each of the competing operating modes, the plurality of cases comprising changing values of operating parameters over a range;
Receiving a performance goal including a cost function for evaluating the turndown behavior during the selected duration of operation;
Receiving an ambient condition estimate for the selected operating period;
Each of the plurality of cases of the competing operation mode is selected using a power plant model (75, 502), assuming values of the operation parameter (903) and the ambient condition prediction (121). Simulating the turndown operation of the power plant (12, 12a, 12b, 302, 501) for an operating period;
Evaluating a simulation result from each of the simulations according to the cost function, and then selecting a preferred case from the plurality of cases for each of the competing modes of operation. The control method for optimizing the turndown operation | movement about the power plant (12,12a, 12b, 302,501) which has. The plurality of power generation units includes a plurality of gas turbines,
Defining the conflicting modes of operation;
Replacing the plurality of gas turbines to form an on / off permutation matrix (609), wherein permutation is performed during the selected operating period; Describing each unique combination in terms of which of the plurality of gas turbines (901) includes the on-condition and which includes the off-condition;
The method of claim 1, wherein each of the permutations of the permutation matrix (609) is defined as one of the competing operational modes for the selected operational period. The step of defining a plurality of cases for each of the competing modes of operation varies a value for the first operating parameter (903) over a first range and a second operating parameter over a second range. The control method of claim 2, comprising changing a value for (903). The step of simulating each of the plurality of cases of the competing operating mode using the power plant model (75, 502) is proposed parameters for each particular case of the plurality of cases. Including generating a set,
For a particular plurality of cases in each of the plurality of cases, the proposed parameter set is:
With respect to the specific plurality of cases, the value in the first range for the first operating parameter (903) and the second range for the second operating parameter (903). The value of
With respect to the competing modes of operation to which the particular plurality of cases corresponds, the on condition and the off condition for the plurality of gas turbines (901);
4. The control method according to claim 3, comprising data relating to the ambient condition prediction (121) for the selected operating period. The step of simulating the plurality of cases using the power plant model (75, 502) uses a simulation run using the power plant model (75, 502) for each of the proposed parameter sets. The simulation run is configured to simulate a turndown operation during the selected operation period according to input data defined by the proposed parameter set;
The performance goal further includes operability constraints;
The step of evaluating the simulation results from each of the simulation runs comprises determining, if any, which of the simulation results violates any of the operability constraints. 4. The control method according to 4. Subdividing the selected operating period into regularly repeating intervals;
Receiving the ambient condition prediction (121) comprises receiving an ambient condition prediction (121) for each of the intervals;
The step of replacing the plurality of gas turbines (901) includes replacing the plurality of gas turbines (901) for each of the intervals to form the permutation matrix (609) for each interval. , With respect to the entirety of any of the intervals, each of the plurality of gas turbines (901) configures only one of the on condition and the off condition and excludes the other. The control method according to claim 3, wherein the permutation matrix (609) for is constructed. The step of defining competing modes of operation comprises defining each of the permutations for each of the permutation matrix (609) for each of the intervals as one of the competing modes of operation; The control method according to claim 6. 8. The step of evaluating the simulation results from each of the simulations includes selecting a preferred case from the plurality of cases for each of the competing modes of operation for each of the intervals. Control method. The step of simulating each of the plurality of cases of the competing operating mode using the power plant model (75, 502) is proposed parameters for each particular case of the plurality of cases. Including generating a set,
For each particular plurality of cases, the proposed parameter set is
With respect to the specific plurality of cases, the value in the first range for the first operating parameter (903) and the second range for the second operating parameter (903). The value of
With respect to the competing modes of operation to which the particular plurality of cases corresponds, the on condition and the off condition for the plurality of gas turbines (901);
The control method according to claim 8, comprising data related to the ambient condition prediction (121) for the interval to which the specific plurality of cases correspond. The step of simulating each of the plurality of cases using the power plant model (75, 502) uses the power plant model (75, 502) for each of the proposed parameter sets, Performing a simulation run, wherein the simulation run is configured to simulate a turndown operation during the selected operation period according to input data defined by the proposed parameter set. ,
The performance goal further includes operability constraints;
The step of evaluating the simulation result from the simulation run, determining, if any, which of the simulation results violates any of the operability constraints; 10. The control method according to claim 9, further comprising disqualifying any of the plurality of cases that created the simulation result that violated the operability constraint for consideration as one of the plurality of cases. The on / off permutation matrix (609) includes one of the plurality of gas turbines (901) including the on-condition and one of the off-conditions during one of the intervals of the selected operating period. Including all of the unique combinations, wherein the on-condition is an indication of an operating condition for a particular one of the plurality of gas turbines (901) during one of the intervals. The control method of claim 9, wherein the off-condition comprises a shutdown condition for a particular one of the plurality of gas turbines (901) during one of the intervals. Tuning the power plant model (75, 502) prior to performing the simulation run, the tuning step comprising:
Defining a first operating period prior to the selected operating period;
Defining a plurality of said operating parameters (903);
Defining a performance metric, the performance metric comprising a performance metric relating to the operation of the power plant (12, 12a, 12b, 302, 501) over a defined period of operation, the performance metric comprising: Defined at least in part as a function of a selected operating parameter (903) selected from the plurality of operating parameters (903);
Sensing measurements for the plurality of operating parameters (903) during the first operating period;
Calculating a measurement value for the performance index from the measurement value;
Using the power plant model (75, 502) to simulate the operation of the power plant (12, 12a, 12b, 302, 501) over the first operating period using input data. The input data for the simulation includes a subset of the measured values for the plurality of operating parameters (903), and as output data, the simulation simulates for the selected operating parameters (903). A step configured to predict a measured value;
Using the simulated value for the selected operating parameter (903) to calculate a predicted value for the performance metric;
Comparing the measured value to the predicted value of the performance index and determining a difference therebetween;
10. The control method according to claim 9, comprising tuning the power plant model (75, 502) using the difference. The power plant model (75, 502) includes a plurality of logic statements, where the performance multiplier is a process input from the operation of the power plant (12, 12a, 12b, 302, 501). Correlate to the process output of
The step of using the difference and tuning the power plant model (75, 502) until the predicted value for a performance index is substantially equal to the measured value for the performance index. Make adjustments to one or more of the performance multipliers in the plant model (75, 502), then recalculate the predicted value for the performance index and the 1 for the calculation of the difference The control method of claim 12, comprising an iterative process for determining the effect of the adjustment of one or more performance multipliers. The control method of claim 9, wherein the first operating parameter (903) comprises inlet guide vane settings and the second operating parameter (903) comprises turbine exhaust temperature. The cost function includes total fuel consumption by the plurality of gas turbines (901), and the step of determining the preferred case for the competing mode of operation, wherein any of the simulation results is the total fuel consumption. The control method according to claim 14, further comprising: determining whether to minimize. The cost function includes power generation levels for the plurality of gas turbines (901), and the step of determining the preferred case for the competing modes of operation determines which of the simulation results are between the intervals. 15. The control method according to claim 14, comprising determining whether to minimize the power generation output level. The performance target includes operability constraints,
The step of evaluating each of the simulation results of the simulation run determines whether any of the simulation runs violates any of the operability constraints and considers it as the optimized simulation run 15. The control method of claim 14, comprising disqualifying any such simulation run. The operability constraint includes at least a combustion limit, maintenance of a condenser vacuum seal of the power plant (12, 12a, 12b, 302, 501), and a minimum steam temperature in a defined portion of the steam turbine. The control method according to claim 17, comprising one. The method of claim 2, wherein the cost function includes at least one of total fuel consumption and power output level for the plurality of gas turbines (901). Electronically transmitting an output reporting the preferred case for each of the competing modes of operation for each of the intervals, wherein the output is the plurality of gas turbines for each of the preferred cases. The control method of claim 19, comprising at least one of the total fuel consumption and the power output level for (901).