DE102015120446A1 - Methods and systems for improving the control of power generator units - Google Patents

Abstract

A control method for optimizing a load reduction operation for a power plant having multiple generator units. The plurality of generator units may each include either an on condition or an off condition during a selected operation period. The method may include: defining competing modes of operation, the competing modes of operation including various possible combinations with respect to which of the generator units have the on-state during the selected operating period and which have the off-state; Defining multiple cases for each of the competing modes of operation, the multiple cases including varying a value of an operating parameter; Receiving performance goals that include a cost function; Receiving an environmental condition prediction; Simulating the load reduction operation of the power plant with a power plant model in view of the value of the operating parameter and the environmental condition prediction; Evaluate a simulation result according to the cost function to select a preferred case.

Description

REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of US Provisional Patent Application No. 61 / 922,555 entitled "TURBINE ENGINE AND PLANT OPERATIONAL FLEXIBILITY AND ECONOMIC OPTIMIZATION SYSTEMS AND PROCESSES RELATED THERETO", filed on Dec. 31, 2013, the provisional application is incorporated herein by reference in its entirety Reference incorporated herein by reference; the present application claims the benefits of the provisional filing date in accordance with 35 U.S.C.119 (e).

BACKGROUND OF THE INVENTION

The invention of the present application relates generally to power generation, and more particularly relates to methods and systems for business and operational optimization and / or improvement of power plants having heat generating units.

In power systems, multiple subscribers or power plants generate electricity, which is then transmitted over shared transmission lines to residential and commercial customers. Note that heat generating units, such as gas turbines, steam turbines, and gas and steam combined cycle power plants, are still responsible for generating a significant portion of the power that such systems require. Each individual power plant within these systems has one or more power generation units, and each of these units typically includes a control system that controls the operation and, in the case of power plants having more than one generator unit, the performance of the power plant as a whole. For example, one of the tasks of a factory operator is to create an exchange curve that represents the cost of electricity production. An exchange curve typically includes a variable incremental cost curve, a variable average cost curve, or other suitable indication of variable costs of power generation, typically expressed in dollars per megawatt hours versus the output in megawatts. Note that a variable average cost curve may represent cumulative costs divided by a cumulative current output for a particular point, and a variable incremental cost curve may represent a cost change divided by a current output change. For example, a curve of the variable incremental cost may be obtained by a first derivation of an input-output curve of the power plant representing cost per hour against the generated power. In a gas and steam combined cycle power plant where waste heat from a fuel burning generator is used to generate steam to drive a supplemental steam turbine, a plot of variable incremental cost may also be obtained by known techniques, but their derivation is more complex.

In most power systems, a business process, commonly referred to as economic dispatch, is used to distribute a system load over a future period to power plants. As part of this process, power plants generate exchange curves at regular intervals and send the exchange curves to a manager or dispatcher of the power system. Such exchange curves represent offers from power plants to generate a portion of the electricity needed by the power system for a future market period. The dispatching / dispatching entity receives the exchange curves from the power plants within their system and evaluates them to determine the degree to which the individual power plants are to be deployed to meet the system's predicted load requirements as efficiently as possible to fulfill. In doing so, the dispatching entity analyzes the exchange curves and generates a deployment plan describing the degree to which the individual power plants will be required during the relevant period, striving to find the lowest generation costs for the system.

Once the deployment plan has been submitted to the power plants, each power plant can choose the most efficient and cost-effective way to meet its load-bearing obligations. Note that the generator units of the power plant include control systems that monitor and control the operation. When the generator units include thermal power generators, such control systems direct the combustion systems and other aspects of the operation. (For purposes of description herein, both a gas turbine and a gas and steam combined cycle power plant will be described, however, it should be understood that certain embodiments of the present invention may be applied to or used in conjunction with other types of power generation units.) , The control system may execute scheduling algorithms that adjust the fuel flow, inlet guide vanes and other control inputs to ensure efficient operation of the turbine. However, the actual output and efficiency of a power plant are influenced by external factors, such as variable environmental conditions, which are not fully predictable. It is obvious that it is through the complexity of such systems and the variability of operating conditions is difficult to predict and control performance, often resulting in inefficient operation.

Machine wear that occurs over time is another difficult fact to quantify, which can have a strong impact on the performance of generator units. It is obvious that the speed of material deterioration, replacement of worn out components, maintenance routine times and other factors will affect the factory's short-term performance and therefore must be taken into account when producing cost curves during the operations planning process and also when the long-term cost-effectiveness of the plant. For example, the life of a gas turbine typically includes limitations expressed in both operating hours and starting numbers. If a gas turbine or one of its components reaches its start limit before its hour limit, it must be repaired or replaced, even if it still has some time remaining based on the hours. The hour-based life of a gas turbine can be extended by lowering the firing temperature, but this reduces the efficiency of the gas turbine, thereby increasing operating costs. Conversely, increasing the firing temperature increases the efficiency, but shortens the life of the gas turbine and increases the maintenance and / or replacement costs. Note that lifetime costs of a heat engine are dependent on many complex factors, but also play a significant role in the economics of the power plant.

Given the complexity of modern power plants, especially those with multiple generating units and the market in which they compete, power plant operators are constantly struggling to increase their economic benefits. For example, a static way of controlling heat generating units, i. the use of static control profiles, such as recorded heat consumption curves derived solely from periodic performance tests, negatively affect compliance with network licensing regulations and operational planning for a power plant. Between these periodic updates, turbine performance may change (e.g., due to material degradation), which may alter startup and load performance. In addition, changes in external factors over the course of a day, which are not taken into account in the turbine control profiles, can result in inefficient operation. To compensate for this type of variability, power plant operators are often overly conservative in planning for future operation, resulting in generator units being under-utilized. At other times, factory operators are forced to operate units inefficiently to meet high commitments.

Unless it is determined whether short-term inefficiencies and / or long-term material degradation materialize, conventional power plant control systems either have to be frequently retuned, which is a costly process, or they must be conservatively operated to prevent material degradation of components To take account of. The alternative would be to risk breaching operational boundary conditions, resulting in excessive material fatigue or failure. Likewise, conventional power plant control systems lack the ability to adapt to changing conditions in the most cost-effective manner. Obviously, this means that power plant use is often far from optimal. Thus, there is a need for improved methods and systems for monitoring, modeling, and controlling power plant operation, particularly those that provide a more complete understanding of the myriad operating modes available to operators of complex, modern power plants, and the associated associated economic disadvantages.

BRIEF DESCRIPTION OF THE INVENTION

The present invention thus relates to a control method for optimizing a load reduction operation for a power plant having a plurality of generator units for a selected operating period. The plurality of generator units may each include either an on condition or an off condition during the selected operation period. The method may include the steps of: defining competing modes of operation, the competing modes of operation including various possible combinations with respect to which of the generator units have the on-state during the selected operating period and which have the off-state; Defining multiple cases for each of the competing modes of operation, the multiple cases including varying a value of an operating parameter over a range; Receiving performance goals including a cost function for evaluating a load reduction operation during the selected period of operation; Receiving an environmental condition prediction for the selected operating period; Simulating the load lowering operation the power plant for the selected operating period with a power plant model including the value of the operating parameters and the environmental condition prediction for each of the multiple cases of the competing operating modes; Evaluating a simulation result of each of the simulations according to the cost function, to thereby select a preferable case from the multiple cases for each of the competing modes of operation.

The above method, wherein the plurality of generator units comprise a plurality of gas turbines; wherein the step of defining the competing modes of operation includes: permuting the plurality of gas turbines to configure an on / off permutation matrix, in the permutations each unique combinations with respect to which of the plurality of gas turbines have the on condition during the selected period of operation; which describe the Aus condition, and wherein each of the permutations of the permutation matrix is defined as one of the competing modes of operation for the selected period of operation.

Each of the above-mentioned control methods may provide that the step of defining multiple cases for each of the competing modes of operation includes varying a value for a first operating parameter over a first range and a value for a second range of operation over a second range.

Each of the above control methods may provide that the step of simulating each of the multiple cases of competing modes of operation with the plant model includes generating suggested parameter sets for each particular multiple case of multiple cases; and wherein the suggested parameter set for each particular multiple case includes among the multiple cases: the value within the first range for the first operating parameter and the value within the second range for the second operating parameter for the particular multiple case; the on-condition and the off-condition for the multiple gas turbines for the competitive mode of operation corresponding to the particular multiple case; and environmental condition prediction data for the selected period of operation.

Any of the above control methods may provide that the step of simulating the multiple cases with the power plant model includes performing a simulation run with the power plant model for each of the proposed parameter sets, the simulation run being configured to simulate a load reduction operation according to input data during the selected operating period that are defined by the proposed parameter set; the performance goals further comprising operational limitations; and wherein the step of evaluating the simulation result from the individual simulation runs includes determining which of the simulation results, if any, violates any of the operability constraints.

Each of the above control methods may provide the step of dividing the selected period of operation into regularly repeating intervals; wherein the step of receiving the ambient condition prediction comprises receiving an ambient condition prediction for each of the intervals; and wherein the step of permuting the plurality of gas turbines comprises permuting the plurality of gas turbines for each of the intervals to configure the permutation matrix for each interval, wherein the permutation matrix for each interval is configured such that for a population of each of the intervals, each of the several gas turbines only one of the on condition and the off condition to the exclusion of each other.

Any of the above control methods may provide that the step of defining competing modes of operation includes defining each of the permutations for each of the permutation matrices for each of the intervals as one of the competing modes of operation.

Any of the above-mentioned control methods may provide that the step of evaluating the simulation result from each of the simulations includes selecting a preferred case from the multiple cases for each of the competing modes of operation for each of the intervals.

Each of the above control methods may provide that the step of simulating each of the multiple cases of competing modes of operation with the plant model includes generating suggested parameter sets for each particular multiple case of multiple cases; wherein the proposed parameter set for each particular multiple case includes: the value within the first range for the first operating parameter and the value within the second range for the second operating parameter for the particular multiple case; the on-condition and the off-condition for the multiple gas turbines for the competitive mode of operation corresponding to the particular multiple case; and environmental condition prediction data for the interval corresponding to the particular multiple case.

Any of the above control methods may provide that step of simulating each of the multiple cases with the power plant model includes performing a simulation run with the power plant model for each of the proposed parameter sets, the simulation run configured to simulate a load-down operation during the selected operating period according to input data defined by the proposed parameter set; and wherein the performance goals further include performance limitations; and wherein the step of evaluating the simulation results from the simulation runs includes determining which of the simulation results, if any, violates any of the operability constraints, and disqualifying any multiple instances that generated the simulation results that violate the operability constraints, as in Question coming preferred cases.

Each of the above-mentioned control methods may provide that the on / off permutation matrix of each of the unique combinations relating to which of the plurality of gas turbines includes the on-condition during one of the intervals of the selected operation period and which includes the off-condition On-condition includes an indication of an operating condition for a particular one of the plurality of gas turbines during one of the intervals, and the off-condition includes a shut-off condition for a particular one of the plurality of gas turbines during one of the intervals.

Each of the above control methods may further comprise the steps of: tuning the power plant model prior to performing the simulation runs, wherein tuning includes: defining a first operating period that occurs before the selected operating period; Defining several operating parameters; Defining a performance indicator, the performance indicator comprising a performance criterion related to the operation of the plant over a defined period of operation, and wherein the performance indicator is defined to depend, at least in part, on a selected operating parameter selected from the plurality of operating parameters; Acquiring readings for the plurality of operating parameters during the first operating period; Calculating a measure of the performance indicator from the measurements; Simulating the operation of the power plant over the first period of operation with the power plant model using the input data, wherein the input data for the simulation comprises a subset of the measurement data for the plurality of operating parameters and wherein the simulation is configured to use a simulated value for the output data predicts the selected operating parameter; Calculating a predicted value for the performance indicator with the simulated value for the selected operating parameter; Comparing the metric with the predicted value for the counter to determine a difference between them; and using the difference to tune the power plant model.

Any of the above control methods may provide that the power plant model includes a plurality of logical statements in which power multipliers correlate process inputs with process outputs from the operation of the power plant; and may further provide that the step of using the difference to match the power plant model comprises an iterative process of making an adjustment to one or more of the power multipliers within the power plant model and then recalculating the predicted value for the performance indicator to effect the adjustment determine the one or more of the performance multipliers to calculate the difference until the predicted value for the performance indicator substantially matches the measure for the performance indicator.

Any of the above control methods may provide that the first operating parameter includes an inlet guide vane setting and the second operating parameter includes a turbine exhaust temperature.

Each of the above control methods may provide that the cost function comprises total fuel consumption by the plurality of gas turbines, and the step of determining the preferred case for the competitive modes includes determining which of the simulation results minimizes total fuel consumption.

Each of the above control methods may provide that the cost function includes a generator output power level for the plurality of gas turbines, and the step of determining the preferred case for the competitive operating modes comprises determining which of the simulation results minimizes the generator output power level during the interval.

Any of the above-mentioned control methods may provide that the performance targets include operating restrictions; wherein the step of evaluating the individual simulation results of the simulation runs includes determining whether any of the simulation runs violates any of the operational constraints to disqualify such simulation runs as eligible optimized simulation runs.

Any of the above control methods may provide that the operating restrictions include at least one of the following: a combustion limit; the maintenance of a vacuum lock on the condenser of the power plant; and a minimum steam temperature in a defined section of a steam turbine.

Any of the above control methods may provide that the cost function includes a total fuel consumption and / or a generator output power level for the multiple gas turbines.

Each of the above-mentioned control methods may further comprise the step of electronically transmitting an output showing the preferred case for each of the competing modes of operation for each of the intervals, wherein the output is the total fuel consumption and / or the generator output power level for the plurality of turbines for each of the includes preferred cases.

These and other features of the present application will become more apparent upon a consideration of the following detailed description of the preferred embodiments taken in conjunction with the drawings and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

1 FIG. 12 is a schematic diagram of a power system according to aspects of the present invention; FIG.

2 Fig. 12 is a schematic illustration of an example of a heat-generating unit as may be used in power plants according to embodiments of the present invention;

3 FIG. 4 is a schematic illustration of an example of a multiple gas turbine power plant according to embodiments of the present invention; FIG.

4 shows an example of the system configuration of a factory control and optimization device according to aspects of the present invention;

5 is a schematic representation of a power plant with a plant control and optimization device with a system configuration according to certain aspects of the present invention;

6 shows a computer system with an example user interface according to certain aspects of the present invention;

7 is an example of an incremental heat demand curve and an effect that can have an impact on the load optimization process;

8th FIG. 10 is a schematic illustration of an example of a plant controller having a power system according to aspects of the present invention; FIG.

9 FIG. 12 is a flowchart of a power plant control method in accordance with aspects of the present invention; FIG.

10 FIG. 11 is a data flow diagram describing an architecture for a plant optimization system for a gas and steam combined cycle power plant according to aspects of the present invention; FIG.

11 shows 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;

12 FIG. 10 is a flowchart of an example of a method for solving parameterized simultaneous equations and constraints according to the present invention; FIG.

13 shows a simplified configuration of a computer system according to a control methodology of embodiments of the present invention;

14 shows an alternative configuration of a computer system according to a control methodology of embodiments of the present invention;

15 FIG. 10 is a flowchart of an example of a control methodology according to examples of aspects of the present invention; FIG.

16 FIG. 10 is a flowchart of an alternative control methodology according to examples of aspects of the present invention; FIG.

17 FIG. 10 is a flowchart of an alternative control methodology according to examples of aspects of the present invention; FIG.

18 Fig. 10 is a flowchart in which an alternative embodiment of the present invention is provided relating to the optimization of a load-lowering operation;

19 FIG. 12 is a flowchart providing an alternate embodiment of the present invention pertaining to the optimization between a load-down and a power down operation; FIG.

20 Figure 5 is a sketch showing available operating modes of a gas turbine during a selected operating period at defined intervals in accordance with aspects of an embodiment of the present invention;

21 Figure 5 is a sketch showing available operating modes of a gas turbine during a selected operating period at defined intervals in accordance with aspects of an embodiment of the present invention;

22 FIG. 10 is a flowchart according to a power plant savings optimization method according to an alternative embodiment of the present invention; FIG.

23 FIG. 3 is a schematic representation of a power plant savings optimization system in accordance with aspects of the present invention; FIG.

24 FIG. 4 is a schematic illustration of a power plant savings optimization system in accordance with alternative aspects of the present invention; FIG.

25 FIG. 4 is a schematic illustration of a power plant savings optimization system in accordance with alternative aspects of the present invention; FIG.

26 Fig. 10 is a schematic diagram of a power plant block optimization system including a block controller;

27 Fig. 10 is a schematic diagram of an alternative power plant block optimization system incorporating a block controller;

28 FIG. 10 is a flowchart illustrating one embodiment of a method for optimizing shutdown of a gas and steam combined cycle power plant; FIG. and

29 FIG. 12 shows an example of a control system in which a model-less adaptive controller according to aspects of the present invention is used.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the invention will now be described in more detail with reference to the accompanying drawings, in which some but not all embodiments are shown. In fact, the invention may be embodied in many different forms and should not be construed as limited to the embodiments disclosed herein; Instead, these embodiments are provided so that this disclosure will satisfy the legal requirements. Like reference numerals designate like 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 generator units. In embodiments, this optimization involves business optimization in which an operator of a power plant decides between alternative modes of operation to improve profitability. Embodiments within a particular power system may be utilized to provide a competitive advantage in obtaining advantageous business hours during the dispatch planning process. An advisory function may allow operators to choose between operating modes based on accurate business comparisons and forecasts. As a further feature, the process of prospecting for fuel for future production periods can be improved so that the fuel stocks are minimized without increasing the risk of deficits. Other configurations of the present invention, as described below, indicate computer-implemented methods and apparatus for modeling power systems and power plants having multiple thermal energy generating units. Technical effects of some configurations of the present invention include generation and resolution of power system models that predict performance under changing physical, operational and / or economic conditions. Examples of embodiments of the present invention combine a power plant model that forecasts performance under changing environmental and operating conditions with a business model that includes business constraints, goals, and market conditions to optimize profitability. In doing so, the optimization system of the present invention can predict optimized targets that can maximize profitability for certain combinations of environmental, operational, contractual, regulatory, regulatory and / or business and market conditions.

1 shows a schematic representation of a power supply system 10 , which shows aspects of the present invention as well as an example of an environment in which embodiments may be practiced. The power supply system 10 may be power generators or power plants 12 have, for example, the illustrated wind and thermal power plants. Note that thermal power plants like generator units Gas turbines, coal-fired steam turbines and / or gas and steam combined cycle power plants may include. In addition, the power supply system 10 other types of power plants (not shown) include, for example, solar, hydropower, geothermal, nuclear and / or other suitable power sources that are already known or to be developed in the future. transmission lines 14 can use different power plants 12 with customers or consumers 16 of the power system 10 connect. It should be understood that the transmission lines 14 represent a power supply or distribution network for the power system and may have multiple sections or subsections as desired or as needed. The electricity coming from the power plants 12 can be generated over the transmission lines 14 to consumers 16 which may include, for example, municipal, private or commercial customers. The power supply system 10 can also storage devices 18 Include those with the transmission lines 14 are connected in order to store energy during periods of overproduction.

The power supply system 10 also has control systems or controls 22 . 23 . 25 which manage or control the operation of several of the components contained therein. For example, a factory controller 22 the operation of the individual power plants 12 Taxes. Load control devices 23 can the operation of different consumers 16 control the part of the power system 10 are. For example, a load control device 23 manage the way or times for a customer's electricity purchase or purchase. An instance responsible for Dispatch 24 may be certain aspects of the operation of the power system 10 manage and may be a power system controller 25 which controls the burden-optimization process by which load-bearing obligations are distributed to participating power plants. The control devices 22 . 23 . 25 that are represented by rectangular blocks can be over communication lines or connections 21 with a communication network 20 connected via which data is exchanged. The connections 21 can be wired or wireless. It is obvious that the communication network 20 may also be associated with a larger communication system or network, or a part thereof, such as the Internet or a private computer network. In addition, the control devices 22 . 23 . 25 over the communication network 20 Information, data and instructions from and to data libraries and resources generally referred to herein as "data resources 26 May be referred to, received and / or transmitted, or alternatively, they may store or house one or more of these data archives. The data resources 26 can include multiple types of data, including market data, operational data, and environmental data. Market data includes information about market conditions, such as energy sales prices, fuel costs, labor costs, regulations, etc. Operational data includes information regarding the operating conditions of the power plant or its generator units, such as temperature or pressure measurements within the power plant, air flow rates, fuel flow rates, etc. Environmental data includes information related to Environmental conditions at the factory, such as outside air temperature, humidity and / or pressure. The market, operational, and environmental data may each include historical data, current condition data, and / or forecast data. For example, the data resources 26 current and predicted meteorological / climatic information, current and future market conditions, past usage history and past performance related to the operation of the power plant, and / or measured parameters related to the operation of other power plants with similar components and / or configurations, as well as other data as desired and / or as needed. In operation, for example, the power supply system or power network control device 25 the dispatching instance 24 Data from the other controllers 22 . 23 within the power system 10 receive and issue instructions to them. Each of the factory and load controllers then controls the system component for which it is responsible and provides information about them to the power grid controller 25 from where she also receives instructions.

2 FIG. 12 is a schematic diagram of an example of a heat generating unit, a gas turbine system. FIG 30 that can be used within a power plant according to the present invention. As shown, the gas turbine system 30 a compressor 32 , a combustion chamber 34 and one drivingly with the compressor 32 connected turbine 36 as well as a component control device 31 on. The component control device 31 can with the factory control device 22 which may be connected to a user input device to receive messages from an operator or technician 39 to recieve. Note that the component controller 31 and the factory controller 22 Alternatively, it may also be combined into a single control device. An inlet channel 40 channels external air to the compressor 32 , As in 3 can be shown, fed water and / or another wetting agent by the inlet channel 40 be channeled to the compressor. The inlet channel 40 may include filters, grids and sound-absorbing devices resulting in a pressure loss through the inlet duct 40 into the inlet guide vanes 41 of the compressor 32 Contribute flowing outside air. An outlet channel 42 Channels combustion gases from an outlet of the turbine 36 for example via emission control and sound-absorbing devices. The sound-absorbing materials and emission control devices can provide back pressure to the turbine 36 invest. The turbine 36 can be a generator 44 drive, which produces electricity, which then passes through the transmission lines 14 through the power system 10 can be distributed.

The operation of the gas turbine system 30 can through multiple sensors 46 monitoring various operating conditions or parameters that exist therein, including, for example, conditions within the compressor 32 , the combustion chamber 34 , the turbine 36 , the generator 44 and the outside environment 33 , For example, temperature sensors 46 Outdoor temperatures, compressor exit temperature, turbine exhaust temperature, and other temperatures within the gas turbine system flow path 30 monitor. Likewise, pressure sensors 46 Monitor external pressure, static and dynamic pressure levels at the compressor inlet, at the compressor outlet, at the turbine exhaust nozzle and other suitable locations in the gas turbine system. humidity sensors 46 For example, wet and dry glass bulb thermometers can measure the outside air humidity in the inlet channel of the compressor. The sensors 46 may also include flow sensors, velocity sensors, flame detector sensors, valve position sensors, vane angle sensors, and other sensors typically used to provide various operating parameters and conditions related to the operation of the gas turbine system 30 to eat. As used herein, the term "parameter" refers to measurable physical properties of the operation that can be used to define the operating conditions in a system, such as in the gas turbine system 30 or another generation system described herein. The operating parameters may include temperature, pressure, humidity, and gas flow characteristics at locations defined along the path of the working fluid, as well as environmental conditions, fuel parameters, and other measures as needed and without limitation. Note that the control system 31 also several actuators 47 through which it controls the operation of the gas turbine system 30 mechanically controls. The actuators 47 may include variable-set electro-mechanical devices or adjustments that allow the manipulation of certain process inputs (ie, manipulated variables) to control process outputs (ie, controlled variables) according to a desired result or operating mode. For example, commands issued by the component controller 31 be generated, cause one or more actuators 47 within the turbine system 30 Valves between the fuel supply and the combustion chamber 34 Adjust the flow level, fuel splits and / or the type of fuel to the combustion chamber. As another example, the tax system 31 generated commands cause one or more actuators to adjust an inlet guide vane adjustment that changes its orientation angle.

The component control device 31 may be a computer system with a processor executing program code to control the operation of the gas turbine system 30 using sensor measurements and instructions from a user or factory operator (hereafter "operator 39 ") To control. As will be explained in more detail below, software provided by the controller 31 include scheduling algorithms for regulating any of the subsystems described herein. The component control device 31 can the gas turbine system 30 partly based on algorithms stored in their digital memory. These algorithms can be, for example, the control device 31 to keep the NOx and CO emissions in the turbine exhaust within certain predefined emission limits or, in another case, to keep the firing temperature of the combustion chamber within predefined limits. Note that the algorithms may include inputs for parameter quantities such as compressor pressure ratio, outside air humidity, inlet pressure loss, turbine exhaust spout, as well as any other suitable parameters. The deployment plans and algorithms used by the component controller 31 environmental changes that affect emissions, combustion chamber dynamics, firing temperature limits under full and part load conditions, etc., can be addressed. As will be described in more detail below, the component controller may 31 Gas turbine deployment scheduling algorithms, such as those that set desired turbine outlet nozzle temperatures and fuel splits in the combustion chamber, are designed to meet performance goals while respecting operational limitations of the gas turbine system. For example, the component control device 31 determine an increase in the combustion chamber temperature and the NOx during a part-load operation to increase the operating margin with respect to the combustion dynamic limit and thereby the Operability, reliability and availability of the generator unit to improve.

In 3 is a schematic representation of an example of a power plant 12 shown that several generator units or factory components 49 according to aspects of the present invention. The illustrated power plant 12 from 3 is a common configuration and is therefore used to discuss several of the embodiments of the present invention, which are illustrated below. It should be understood, however, that the methods and systems described herein are more generally applicable to power plants having more generator units than in FIG 3 can be applied and sized while also being applicable to power plants having a single generation component, such as those described in U.S. Pat 2 shown. Note that the power plant 12 from 3 a gas and steam combined cycle power plant that has multiple factory components 49 including a gas turbine system 30 and a steam turbine system 50 , The power generation can be done by other factory components 49 be increased, for example by an inlet conditioning system 51 and / or a heat recovery steam generator with a duct combustion system (hereinafter "HRSG duct heating system 52 "). It should be clarified that the gas turbine system 30 , the steam turbine system 50 , which is the HRSG channel firing system 52 and the inlet conditioning system 51 each a control system or the component control device 31 that are electronic with the sensors 46 and the actuators 47 communicates, which are assigned to the individual factory components. As used herein, an inlet conditioning system may be used 51 Unless otherwise specified, designate components used to condition air prior to entry into the compressor, and may include an inlet chiller, evaporator, nebulizer, water feed system, and / or, in some embodiments alternative cases, include a heating element.

During operation, the intake conditioning system cools 51 the air entering the gas turbine system 30 to increase the power generation capacity of the unit. The HRSG channel firing system 52 burns fuel to provide extra heat to increase the supply of steam passing through a turbine 53 expands. This increases the HRSG channel firing system 52 the energy that comes from the hot exhaust gases 55 is supplied from the gas turbine system, thereby increasing the power generation capacity of the steam turbine system.

In an example operation, the power plant steers 12 from 3 a fuel stream for combustion in the combustion chamber 34 of the gas turbine system 30 , The turbine 36 is powered by fuel gases and drives the compressor 32 and the generator 44 on, the electrical power to the transmission lines 14 of the power system 10 supplies. The component control device 31 of the gas turbine system 30 For example, gas turbine system commands may be adjusted in relation to fuel flow rate and receive gas turbine system sensor data, such as air intake temperature, humidity, power output, shaft speed, and exhaust gas temperatures. The component control device 31 may also capture other operating data from pressure and temperature sensors, flow control devices and other devices that monitor the operation of the gas turbine system. The component control device 31 may send data related to the operation of the gas turbine system and instructions from the factory controller 22 with respect to setpoint values for actuators that control control process inputs.

During certain modes of operation, the air that enters the gas turbine system 30 enters through the inlet conditioning system 51 cooled or otherwise conditioned to increase the generating capacity of the gas turbine system. The inlet conditioning system 51 can a cooling system 65 for cooling water and a component control device 31 which controls its operation include. In this case, the component control device 31 Information regarding the temperature of the cooling water as well as instructions regarding the desired feed rate received by the factory controller 22 can come. The component control device 31 the intake conditioning system 51 can also issue commands that cause the cooling system 65 Cooling water produced at a certain temperature and flow rate. The component control device 31 the intake conditioning system 51 may be data related to the operation of the intake conditioning system 51 send.

The steam turbine system 50 can the turbine 53 and the HRSG channel firing system 52 as well as a component controller 31 which, as shown, is associated with the control of its operation. Hot exhaust 55 from the exhaust ducts of the gas turbine system 30 can in the steam turbine system 50 be steered to produce the steam passing through the turbine 53 is extended. It should be understood that HRSG duct firing systems are regularly used to provide additional energy for the production of steam to increase the generation capacity of a steam turbine system. It should be made clear that the rotation caused by the steam in the turbine 53 induces a generator 44 drives to produce electrical energy, which is then within the power system 10 via transmission lines 14 can be sold. The component control device 31 of the steam turbine system 50 For example, the flow rate of the fuel coming from the duct firing device 52 is burned, and thereby increase the production of steam beyond the amount, with exhaust gases 55 can be produced alone. The component control device 31 of the steam turbine system 50 may be data related to the operation of the factory component 49 send and receive from these instructions how they should be operated.

The factory control device 22 from 3 can, as shown, with the individual component control devices 31 be connected and can have these connections with sensors 46 and actuators 47 of several factory components 49 communicate. As part of the control of the power plant 12 can the factory control device 22 simulate their operation. Specifically, the factory control device 22 digital models (or simply "models") that control the operation of each factory component 49 simulate, contain or communicate with such. The model may include algorithms that correlate process inputs with process outputs. The algorithms may include sets of instructions, logic, mathematical formulas, functional relationship descriptions, schedules, data collections, and / or the like. In this case, the factory controller includes 22 : a gas turbine model 60 that the operation of the gas turbine system 30 simulated; an intake conditioning system model 61 that the operation of the inlet conditioning system 51 simulated; and a steam turbine model 62 that the operation of the steam turbine system 50 and the HRSG channel firing system 52 simulated. As a general indication, it should be understood that the systems and their associated models, as well as the individual steps of the methods set forth herein, may be variously subdivided and / or combined without departing from the spirit of the present invention, and that the nature and method of the present invention As each described, is intended as an example, unless otherwise specified or claimed. Using these models, the factory controller can 22 the operation, eg the thermodynamic performance or parameters describing the operation, of the power plant 12 simulate.

The factory control device 22 can then use results from the simulations to determine optimized operating modes. Such optimized operating modes may be described by parameter sets that include multiple operating parameters and / or setpoint values for actuators and / or other operating conditions. As used herein, the optimized operating mode is one that is preferable to at least at least one alternative operating mode according to defined criteria or performance indicators that may be selected by an operator to evaluate a factory operation. Specifically, optimized operating modes, as used herein, are those that are superiorly evaluated as one or more other possible operating modes that were also simulated by the factory model. The optimized operating modes are determined by evaluating how the plant will operate according to the forecast of each model. As discussed below, an optimizer may be used 64 that is, a digital software optimization program, run the digital power plant model according to different parameter sets, and then determine superior or optimized operating modes by evaluating the results. The variations in the setpoints may be generated by disturbances that are applied around the setpoints chosen for analysis. These may be based in part on a past operation. Note that the optimized operating mode of the optimizer 64 can be determined based on one or more defined cost functions. Such cost functions may, for example, consider power production costs, profitability, efficiency or any other criteria imposed by the operator 39 To be defined.

To determine the cost and profitability, the factory control facility can 22 a business model 63 include or communicate with one that tracks the price of electricity and certain other variable costs, such as the cost of the fuel used in the gas turbine system, the intake conditioning system and the HRSG duct fire system. The business model 63 can provide the data provided by the factory controller 22 can be used to judge which of the proposed setpoints (ie, the selected setpoints for which the operation is modeled to determine optimized setpoints) represents minimum production cost or maximum profitability. As with reference to 4 In some embodiments, the optimizer may be discussed in greater detail 64 the factory control device 22 include or operate in conjunction with a filter, such as a Kalman filter, to aid in the tuning, adaptation and calibration of the digital models so that the models operate the power plant 12 exactly simulate. As will be discussed below, the model may be a dynamic one involving a learning mode in which it compares between an actual operation (ie values for measured operating parameters representing the current operation of the power plant 12 and predicted operation (ie, values for the same operating parameters that are predicted by the model). As part of the control system, the filter can also be used to adjust or calibrate the models in real time or near real time, for example every few minutes or every hour or as specified.

The optimized setpoints provided by the factory control unit 22 may be a recommended mode of operation and may include, for example, fuel and air settings for the gas turbine system, temperature and water mass flow for the inlet conditioning system, the degree of channel firing within the steam turbine system 50 include. According to some embodiments, these proposed operating setpoints may be for the operator 39 be provided via an interface device, such as a computer display screen, a printer or speakers. After he knows the optimized setpoints, the operator can then the setpoints in the factory control device 22 and / or the component control device 31 which then generates control information to achieve the recommended operating mode. In such embodiments, where the optimized setpoints do not include predetermined control information to achieve the operating mode, the component controllers may provide the necessary control information and, as discussed in greater detail below, control the closed loop type of factory component in accordance with the recommended mode of operation until the next one Continue the optimization cycle. Depending on the preference of the operator, the factory control device may 22 Optimized setpoints can also be implemented directly or automatically without the involvement of the operator.

In an example operation, the power plant steers 12 from 3 a fuel stream for combustion in the combustion chamber 34 of the gas turbine system 30 , The turbine 36 is powered by fuel gases to the compressor 32 and the generator 44 to power electrical energy to transmission lines 14 of the power system 10 supplies. The component control device 31 For example, gas turbine system commands may be adjusted in relation to the fuel flow rate and receive sensor data from the gas turbine system, such as air intake temperature and humidity, power output, shaft speed, and exhaust gas temperatures. The component control device 31 may also include other operating data from pressure and temperature sensors, flow control devices, and other devices that use the gas turbine system 30 monitor, record. The component control device 31 of the gas turbine system 30 can send data related to the operation of the system and instructions from the factory controller 22 with respect to setpoint values for actuators that control control process inputs.

During certain modes of operation, the air that enters the gas turbine system 30 to be cooled with cold water from the inlet conditioning system 51 to the intake air passage 42 is delivered. Note that the cooling of the air entering a gas turbine may be performed to increase the power generation capacity of the gas turbine. The inlet conditioning system 51 includes a cooling system or a radiator 65 for cooling water and a component control device 31 , In this case, the component controller receives 31 Information about the temperature of the cooling water and commands related to the desired cooling of the intake air. These commands can be obtained from the factory controller 22 come. The component control device 31 the intake conditioning system 51 can also issue commands that cause the cooling system 65 Cooling water produced at a certain temperature and flow rate. The component control device 31 the intake conditioning system 51 may be data related to the operation of the intake conditioning system 51 send and instructions from the controller 22 receive.

The steam turbine system 50 using a HRSG with a duct firing device 52 , a steam turbine 53 and a component controller 31 that may be associated with its operation. Hot exhaust 55 from an exhaust duct 42 of the gas turbine system 30 be in the steam turbine system 50 steered to produce the steam with which it is driven. The HRSG channel firing system 52 can be used to provide additional heat energy to generate steam to the generating capacity of the steam turbine system 50 to increase. The steam turbine 53 drives the generator 44 to produce electrical energy through the transmission lines 14 to the power supply system 10 is delivered. The component control device 31 of the steam turbine system 50 may adjust the flow rate of the fuel passing through the duct firing device 52 is burned. Heat generated by the duct firing device increases steam production beyond the amount produced by exhaust gases 55 from the turbine 36 is produced alone. The component control device 31 of the steam turbine system 50 may be data related to the operation of the system to the factory control device 22 send and receive from these instructions.

The factory control device 22 For example, with the operator 39 and with data resources 26 communicate to receive data about market conditions such as prices and the need for delivered electricity. According to certain embodiments, the factory controller issues 22 Recommendations related to the desired operating setpoints for the gas turbine system 30 , the inlet conditioning system 51 and the steam turbine system 50 to the operator 39 out. The factory control device 22 can provide data on the operation of the components and subsystems of the power plant 12 receive and save. The factory control device 22 For example, a computer system with a processor and memory that stores data can be used with digital models 60 . 61 . 62 . 63 , the optimizer 64 and other computer programs. The computer system may be implemented as a single physical or virtual computing device or may be distributed to local or other computing devices. The digital models 60 . 61 . 62 . 63 may be implemented as a set of algorithms, eg transfer functions, which relate to operating parameters of the individual systems. The models may be a physics-based aero-thermodynamic computer model, a regression fit model, or another suitable computer-implemented model. According to preferred embodiments, the models 60 . 61 . 62 . 63 be tuned, adjusted or calibrated regularly, automatically and in real time or almost in real time, or tuned according to a continuous comparison between a forecasted operation and the measured parameters of actual operation. The models 60 . 61 . 62 . 63 may include filters that receive data inputs related to actual physical and thermodynamic operating conditions of the gas and steam combined cycle power plant. These data entries may be during operation of the power plant 12 be delivered in real time or periodically every 5 minutes, 15 minutes, hourly, daily, etc. The data entries can be compared with data obtained from the digital models 60 . 61 . 62 . 63 can be predicted and based on the comparisons, the models can be continuously refined.

4 shows a schematic system configuration of a factory control device 22 that a filter 70 , an artificial neural network configuration 71 (a "neural network 71 ") And an optimizer 64 according to aspects of the present invention. The filter 70 which may be a Kalman filter, for example, may be the actual data 72 the measured operating parameters of sensors 46 of the power plant 12 with data 73 compare the same operating parameters used by the models 60 . 61 . 62 . 63 and the neural network 71 that the operation of the power plant 12 simulated, forecasted. Differences between actual data and forecasted data can then be filtered 70 used to model the power plant that is powered by the neural network 71 and digital models is simulated to tune.

Note that while certain aspects of the present invention are described herein with reference to models in the form of neural network-based models, it is also contemplated that the present invention may be implemented using other types of models, including physics-based models Models, data driven models, empirically developed models, models based on heuristics, location vector machine models, models developed by linear regression, models developed using "basic principles" knowledge, etc. In order to properly sense the relationship between the manipulated variables and the controlled variables, the power plant model according to some preferred embodiments may also have one or more of the following characteristics. 1 Nonlinearity (a nonlinear model is capable of representing a curve rather than a straight-line relationship between manipulated / disturbance and controlled variables); 2) multiple input / multiple output (the model may be able to capture the relationships between multiple inputs - the manipulated and confounded variables - and multiple outputs - the controlled variables); 3 Dynamics (changes to the inputs do not have to have a direct impact on the outputs, rather there may be a time delay followed by a dynamic response to the changes, for example, it may take several minutes for changes to the inputs throughout the system Since optimization systems run at a given frequency, the model must account for and account for the effects of these changes over time); 4) adaptive (the model can be updated at the beginning of each optimization to reflect current operating conditions); and 5) are derived from empirical data (since each power plant is unique, the model can be derived from empirical data obtained from the current sensing unit). In view of the above requirements, an approach based on a neural network is a preferred technology for implementing the necessary factory models. Neural networks can be developed on the basis of empirical data using sophisticated regression algorithms. Note that neural networks are capable of detecting the nonlinearity that is commonly found in the operation of power plant components. Neural networks can also be used to connect to systems represent several inputs and outputs. In addition, neural networks can be updated using feedback biasing or adaptive online learning. Dynamic models can also be implemented in a neural network-based structure. A number of different types of model architectures have been used to implement dynamic neural networks. Many of the neural network model architectures require a large amount of data to dynamically train the dynamic neural network. Given a robust power plant model, it is possible to calculate the effects of changes in the manipulated variables on the controlled variables. Because the factory model is dynamic, it is also possible to calculate the effects of changes to the manipulated variables over a time horizon in the future.

The filter 70 may generate power multipliers applied to the inputs or outputs of the digital models and the neural network or to modify the weights applied to the logical units and algorithms used by the digital models and the neural network. These actions through the filter reduce the differences between the actual conditions data and the predicted data. The filter continues to perform its operation to further reduce the differences or to respond to fluctuations that may occur. For example, the filter 70 Generating power multiples for the predicted data on the compressor exit pressure and compressor exit temperature in the gas turbine, the efficiency of the gas and steam turbines, the fuel flow to the gas turbine system, the inlet conditioning system and the HRSG duct firing system, and / or other suitable parameters. Note that these categories of operational data reflect operating parameters that are subject to performance degradation over time. By providing performance multipliers for this type of data, the filter can 70 be particularly useful for the adaptation of the models and the neural network to take into account the deterioration of the performance of the power plant.

As in 4 2, according to certain embodiments of the present invention, includes each of the digital models 60 . 61 . 62 . 63 of several factory components 49 of the power plant of 3 Algorithms that are represented by the several graphs and that are used to model the corresponding systems. The models interact and communicate with the neural network 71 , and note that while doing the neural network 71 a model of the entire gas and steam combined cycle power plant 12 forms. In this way, the neural network simulates the thermodynamic and economical operation of the plant. As from the solid arrows in 4 is represented captures the neural network 71 Data coming from the models 60 . 61 . 62 . 63 and provides data to be used as inputs by the digital models.

The factory control device 22 from 4 also has an optimizer 64 For example, a computer program that is connected to the neural network 71 to seek optimal setpoints for the gas turbine system, the intake conditioning system, the steam turbine system, and the HRSG duct firing system to achieve a defined performance goal. For example, the performance goal may be to maximize the profitability of the power plant. The optimizer 64 can cause the neural network 71 the digital models 60 . 61 . 62 . 63 at different operating setpoints. The optimizer 64 may have noise algorithms that help to vary the operating setpoints of the models. The perturbation algorithms cause the simulation of the gas and steam combined cycle power plant provided by the digital models and the neural network to operate at setpoints other than the current factory setpoints for the plant: by simulating the plant's operation different setpoints, the optimizer searches 64 according to operating specifications, which would cause the plant to operate more cheaply or improve performance by any other criteria imposed by the operator 39 can be defined.

According to embodiments, the business model represents 63 Data ready; the optimizer 64 used to determine which setpoints are most profitable. The business model 63 For example, it can store data about fuel costs that are formatted, such as a chart 630 that it correlates fuel costs over time, for example during the seasons of a year. Another diagram 631 can correlate the price achieved for the electric current at different times of the day, weeks or months. The business model 63 can provide data related to the price achieved for the electricity and the cost of the fuel used to produce it (gas turbine fuel, fuel for the ducting and intake conditioning system). The data from the business model 63 can from the optimizer 64 used to assess each of the plant's operating conditions according to operator-defined performance goals. The optimizer 64 can determine which of the operating conditions of the power plant 12 in the face of the operator 39 defined performance goals (which, as used herein, means that it is at least superior to another operating condition). As described, the digital models can be used to control the operation of the factory components 49 of the power plant 12 For example, to model a thermodynamic operation of the gas turbine system, the intake conditioning system or the steam turbine system. The models may include algorithms, such as mathematical equations and look-up tables, that may be stored locally and periodically updated, or remotely via data resources 26 be acquired, which is the answer of the factory components 49 to simulate certain input conditions. Such look-up tables may include measured operating parameters that describe the operation of the same type of components as operate in power plants at other locations.

The thermal model 60 of the gas turbine system 30 includes, for example, an algorithm 600 , which correlates the effect of intake air temperature on power output. Note that this algorithm can show that the power output is of a maximum 601 decreases when the intake air temperature exceeds a threshold temperature 602 increases. The model 60 can also use an algorithm 603 which correlates the heat input of the gas turbine at different power output levels of the turbine. As discussed, the heat input represents the efficiency of a gas turbine or other current sensing unit and is inversely related to efficiency. Lower heat input indicates higher efficiency of thermodynamic power efficiency. The digital model 61 may be a thermodynamic operation of the intake conditioning system 51 simulate. In this case, the digital model includes 61 for example, an algorithm 610 That's the cooling capacity based on energy that is spent on the cooling system 65 the intake conditioning system 51 to operate, so that the calculated cooling capacity indicates the amount of cooling applied to the air entering the gas turbine. It can be a maximum 611 for the cooling capacity of the cooling system 65 can be achieved. In another case, a related algorithm may be used 612 the energy spent on the cooling system 65 to operate, correlate with the temperature of the cooled air flowing into the compressor 32 of the gas turbine system 30 entry. The model 61 For example, it can be shown that the power required to operate the intake conditioning system increases dramatically as the temperature of the air entering the gas turbine drops below the dew point 613 the outside air is lowered. In the case of the steam turbine system 50 can the digital model 62 an algorithm 620 which correlates the power output from the steam turbine system with the energy provided by the HRSG duct firing system 52 is added, for example, with the amount of fuel consumed by channel firing. The model 62 For example, it may indicate that there is an upper threshold 621 for increasing the output of the steam turbine system that can be achieved by the HRSG channel firing system and that in the algorithm 620 is included.

According to certain embodiments of the present invention, the neural network 71 , as in 4 shown with each of the digital models of the multiple factory components 49 of the power plant 12 from 3 interact and enable communication between them. The interaction may include capturing output data from the models and generating input data used by the models to generate further output data. The neural network 71 can be a digital network of interconnected logical elements. The logical elements may each embody an algorithm that accepts data inputs to generate one or more data outputs. A simple logical element can add the value of the inputs to produce output data. Other logical elements can multiply values of the inputs or apply other mathematical relationships to the entered data. The data entries for the individual logical elements of the neural network 71 Weight can be assigned, for example multipliers between one and zero. The weights may be modified during a learning mode that adjusts the neural network to better model the performance of the power plant. The weights may also be adjusted based on commands provided by the filter. The adaptation of the weights of the data inputs to the logical units in the neural network is an example of the way in which the neural network can be dynamically modified during operation of the gas and steam combined cycle power plant. Other examples include modifying weights for data inputs in algorithms (which is an example of a logical unit) in each of the thermodynamic digital models for the steam turbine system, the intake conditioning system, and the gas turbine. The factory control device 22 can be modified in other ways, for example, by adjustments made to the logical units and algorithms based on the data provided by the optimizer and / or the filter.

The factory control device 22 can be an output of recommended or optimized setpoints 74 for the gas and steam combined cycle power plant 12 generate, as shown, an operator 39 may be submitted for approval before being transmitted and by actuators 47 the power plants are implemented. As shown, the optimized setpoints 74 Entries include, by the operator 39 via a computer system like the one below with reference to 6 described or approved. The optimized setpoints 74 For example, a temperature and a mass flow rate for the cooling water generated by the inlet conditioning system and used to cool the air entering the gas turbine system; a fuel flow rate into the gas turbine system; and a channel firing rate. Note that the optimized setpoints 74 then also from the neural network 71 and the models 60 . 61 . 62 . 63 can be used so that the currently running factory simulation can predict operating data that can later be compared to actual operating data so that the factory model can be constantly refined.

5 shows a simplified system configuration of a factory controller 22 with an optimizer 64 and a power plant model 75 , In this embodiment, the factory control device 22 represented as a system that the optimizer 64 and the power plant model 75 has (for example, the neural network 71 and models 60 . 61 . 62 . 63 having the above with reference to 4 have been discussed). The power plant model 75 can the overall operation of a power plant 12 simulate. According to the illustrated embodiment, the power plant includes 12 several generator units or factory components 49 , The factory components 49 For example, they may include heat generating units or other work subsystems as described above, each of which may have corresponding component control devices 31 can have. The factory control device 22 can with the component controllers 31 communicate and can through and with the help of component controllers 31 the operation of the power plant 12 via connections with sensors 46 and actuators 47 Taxes.

Note that power plants can have many variables that affect their operation. Each of these variables can generally be categorized as either inputs or outputs. Inputs 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 can not be manipulated, such as environmental conditions. Output variables are variables, such as power output, which are controlled by manipulating the inputs that can be manipulated. A power plant model is configured to include the algorithmic relationship between inputs, those that can be manipulated, or "manipulated variables" and those that can not be manipulated, or "disturbances", and output or controlled variables that Control variables ", represents. More precisely, control variables are those of the factory operator 22 can be changed to influence controlled variables. Control values include things like valve setpoints that regulate a fuel and air flow. Disturbance variables are variables which have an effect on controlled variables, but which can neither be manipulated nor controlled. Disturbances include environmental conditions, fuel properties, etc. The optimizer 64 determines an optimal set of disturbance setpoints in view of: 1) power plant performance goals (eg, meeting load requirements while maximizing profitability); and (2) restrictions related to the operation of the power plant (eg emission and equipment restrictions).

According to the present invention, an "optimization cycle" may begin at a predetermined frequency (eg every 5 to 60 seconds or 1 to 30 minutes). At the beginning of an optimization cycle, the factory control device 22 current data for disturbances, controlled variables and disturbances from the component control devices 31 and / or directly from sensors 46 the individual factory components 49 recall. The factory operator 22 then can the power plant model 75 to determine optimal setpoints for the manipulated variables based on the current data. This allows the factory operator 22 the factory model 75 with various operating setpoints to determine which set of operating setpoints is most advantageous given the power plant performance goals, which may be referred to as "simulation runs". For example, a performance goal may be to maximize profitability. By simulating the plant's operation with different setpoints, the optimizer searches 64 according to the set of set values, according to the forecast of the factory model 75 an optimal (or at least superior) operation of the plant causes. As already stated, this optimal set of setpoints may be referred to as "optimized setpoints" or "optimized mode of operation". In general, the optimizer has 64 Upon reaching the optimized setpoints, numerous sets of setpoints are compared, and the optimized setpoints have proved superior to those of other sets given the operator-defined performance goals. The operator 39 of the power plant 12 can the Option to approve the optimized setpoints, or the optimized setpoints can be automatically approved. The factory control device 22 can the optimized setpoints to the component control device 31 or alternatively directly to the actuators 47 the factory components 49 so that settings can be adjusted according to the optimized setpoints. The factory control device 22 can be operated as a closed loop to adjust setpoints of disturbances at a given frequency (eg every 10-30 seconds or more) based on the current measured operating conditions.

The optimizer 64 can be used to minimize a "cost function" according to a set of constraints. The cost function is essentially a mathematical representation of a factory performance goal, and the limitations are limits within which the power plant must operate. Such limits may be regulatory, regulatory, environmental, equipment or physical limitations. For example, the cost function for minimizing NOx includes a term that decreases as the NOx level decreases. A common method for minimizing such a cost function is, for example, the so-called "optimization according to the gradient method" or according to the method of the steepest descent. The gradient method is an optimization algorithm that approximates a local minimum of a function by taking steps that are proportional to the negative of the gradient (or proximity gradient) of the function at the current point. It should be understood that a number of different optimization techniques may be used depending on the shape of the model and the cost and constraints. For example, it is contemplated that the present invention may be implemented using a variety of different types of optimization strategies, alone or in combination. These optimization strategies include linear programming, quadratic programming, mixed integer, linear programming, stochastic programming, global nonlinear programming, genetic algorithms, and particle / swarming techniques. In addition, the power plant model 75 be dynamic so that effects of changes over a future time horizon are taken into account. Therefore, the cost function includes terms over a future horizon. Since the model is used to make forecasts over a time horizon, this strategy is referred to as model predictive control, which in 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 and incorporated herein by reference in its entirety.

Restrictions can be imposed on both process inputs (containing manipulated variables) and process outputs (including controlled variables) of the power plant above the future time horizon. Typically, constraints are imposed on the manipulated variables that are consistent with limits consistent with the factory controller. Limitations of the outputs may 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 may 64 Calculate the full curve over which the manipulated variable moves beyond the time horizon in the future, for example, over one hour. Thus, for an optimization system that runs every 30 seconds, 120 values can be calculated over a future time horizon of one hour for each manipulated variable. Since factory model or performance targets or constraints may change before the next optimization cycle, the factory controller could 22 / the optimizer 64 only the first value in the time horizon for each manipulated variable as optimized setpoint for the individual manipulated variables to the component control devices 31 output. In the next optimization cycle, the factory model 75 updated based on the current conditions. The cost function and restrictions can also be updated if they have changed. The optimizer 64 can then be used to recalculate the set of values for the manipulated variables over the time horizon, and the first value in the time horizon for the individual disturbances is used as a setpoint for each individual manipulated variable to the component controller 31 output. The optimizer 64 This process can be repeated for each optimization cycle, thus maintaining optimal performance throughout the power plant 12 is affected by unforeseen changes to such things as load, environmental conditions, fuel properties, etc.

It will be up now 6 Reference is made to the illustration where an environment and a user input device for a plant control device and a control program according to an embodiment are shown. Although other configurations are possible, the embodiment includes a computer system 80 with an ad 81 , a processor 82 , a user input device 83 and a memory 84 , Aspects of the computer system 80 can at the power plant 12 be arranged while other aspects of it and be arranged over the communication network 20 can be connected. As discussed, the computer system 80 with the individual Generator units or other factory components 49 of the power plant 12 be connected. The power plant components 49 can a gas turbine system 30 , a steam turbine system 50 , an inlet conditioning system 51 , a HRSG channel firing system 52 and / or any related subsystems or subcomponents, or any combination thereof. The computer system 80 can also be as needed or as desired with one or more sensors 46 and actuators 47 be connected. As already said, the sensors can 46 be configured to sense operating conditions and parameters of the components and signals related to these conditions to the computer system 80 pass. The computer system 80 may be configured to receive these signals and to use them in the ways described herein, transmitting signals to one or more actuators 47 include. However, unless otherwise required, the present invention may include embodiments that are not configured to be the powerhouse 12 directly control and / or detect operating conditions directly by sensor. In configurations of the present invention, the power plant 12 control and / or sensing areas of operation, such input or control may be by receiving and / or transmitting signals from at least one or at least one software or hardware system that interacts more directly with physical components of the plant and its sensors and actuators , to be provided. The computer system 80 may include a power plant control program ("control program") that controls the computer system 80 being able to manage data on a factory controller by performing the processes described herein.

In general, the processor performs 82 Program code that defines the control program, at least partially in memory 84 fixed. During execution of the program code, the processor 82 Processing data, resulting in reading and / or writing of transformed data from / to the memory 84 leads. The ad 81 and the input device 83 can empower a person with the computer system 80 and / or one or more communication devices to enable a user of the system to interact with the computer system 80 communicate using a communication connection. In embodiments, a communication network, such as networked hardware / software, may be the computer system 80 enable it to communicate with other devices inside and outside a node in which it is installed. As such, the control program of the present invention may manage a set of interfaces that enable individuals and / or systems as users to interact with the control program. Further, as discussed below, the control program may manage (eg, store, retrieve, generate, manipulate, organize, present, etc.) data, such as control data, using any solution.

The computer system 80 may include one or more non-dedicated computing products capable of executing program code, such as the control programs defined herein, installed therein. Note that "program code," as used herein, means any collection of instructions in any language, code, or notation that causes a computing device having information processing capability to perform a particular action, either directly or in any combination of the following: (a ) Conversion to another language, code or spelling; (b) reproduction in another material form; and / or (c) decompression. Additionally, computer code may include object code, source code, and / or executable code and may form part of a computer program product when residing on at least one computer-readable medium. It should be understood that the term "computer-readable medium" means one or more types of material-expression medium, whether known or still to be developed, from which a copy of the program code may be recognized, reproduced or otherwise communicated by a computing device. When the computer executes the computer program code, it becomes an apparatus for practicing the invention, and specific logic circuits are created on a non-dedicated microprocessor by configuring the microprocessor with computer code segments. A technical effect of the executable instructions is the implementation of a power plant control method and / or system and / or computer program product that uses models to improve or make more useful or optimize operating characteristics of power plants in order to maximize the economic yield of a power plant in the face of expected environmental conditions. and / or market conditions, performance parameters and / or lifecycle costs associated with making more efficient use. In addition to using up-to-date information, past and / or predicted information may be used, and a feedback loop may be established to more efficiently operate the plant during fluctuating conditions. The computer code of the control program may be written in computer instructions issued by the factory controller 22 are executable. In that sense, that of the computer system 80 executed control program can be realized as any combination of system software and / or application software. Further, the control program may be implemented using a set of models. In this case, a module can be the computer system 80 enable a set of tasks to be used by the control program and may be separately developed and / or implemented separately from other portions of a control program. As used herein, the term "component" means any configuration of hardware, with or without software, implementing the functions described in connection therewith using any solution, while the term "module" means program code incorporating a computer system into the computer Being able to implement the actions described thereon using any solution. If it is in memory 84 of the computer system 80 that is the processor 82 A module is an essential part of a component that implements the actions. It should be understood, however, that two or more components, modules, and / or systems may share some or all of their hardware and / or software. Further, it should be understood that some of the functions discussed herein do not need to be implemented or that additional functions are part of the computer system 80 may be included. If the computer system 80 comprises several computing devices, it may be that in each computing device only a part of the control program (eg one or more modules) is located. Regardless, the computing devices, if the computer system 80 includes multiple computing devices communicating over each type of communication link. While performing a process described herein, the computer system may 80 and communicate with one or more other computer systems using any type of communication link.

As discussed herein, the control program enables the computer system 80 the implementation of a power plant control product and / or process. The computer system 80 can retrieve power plant control data using any solution. For example, the computer system 80 Generating and / or generating power plant control data, retrieving power plant control data from one or more data stores, archives or sources, receiving power plant control data from another system or device within or outside a power plant, a component controller, and / or the like. In another embodiment, the invention provides a method of providing a copy of program code, such as a power plant control program, that may partially or fully implement a process described herein. Note that aspects of the invention may be implemented as part of a business process that performs a subscription, advertising, and / or fee-based process described herein. A service provider might offer to implement a power plant control program and / or method as described herein. In this case, the service provider may manage (eg, create, maintain, support) a computer system, such as the computer system 80 that performs a process described herein for one or more customers.

Power plant computer models can be constructed and then used to control and optimize power plant operation. Such plant models can be dynamically and iteratively updated by constantly comparing actual (ie measured) operating parameters with the same parameters as predicted by the factory model. When creating and maintaining such models, instructions may be written or otherwise provided that contain the processor 82 of the computer system 80 to generate a library of power system generator units and components (a "component library") in response to a user input. In some configurations, a user input and the generated library include properties of the component with the library as well as rules to generate scripts according to operational and feature values. These feature values can be compiled from data stored locally in memory 84 are stored, and / or are retrieved from a central data archive, which is located at a remote location. The component library can include intangible components, such as business or legal components. Examples of business components are fuel purchases and sales, and examples of legal components are emission limits and rights. These immaterial components can be modeled using mathematical rules just as components that represent material equipment can be modeled using mathematical rules. The instructions may be configured to assemble a configuration of components of a power supply system from the library that may be configured by an operator. A library of the components of a power system may be provided so that a user may select components therefrom to replicate an actual power plant or to create a hypothetical one. Note that each component can have several properties that are user-specific can be used to input certain values that correspond to operating conditions of a modeled actual or hypothetical power plant. Scripts can be generated for the composite components of the power system and their configuration. The generated scripts may include mathematical relationships within and / or between the components of the power system, including business and / or legal components, that may be used to configure the components of the power system. The computer system 80 can then solve mathematical relationships and results of the solution on the display 81 Show. In configurations where signals from the computer 80 can be sent, the signals can be used to control an energy system according to the results of the solution. Otherwise, results may be displayed or printed and used to set and / or determine parameters of the physical equipment and / or to use certain intangible parameters, such as fuel purchases and / or sales, to achieve a preferred or optimized operating mode. The library of factory components may include a centralized data archive that represents a continuous collection of data related to how the individual components work under different parameters and conditions. The central data repository can be used to provide "plug data", for example, when it is determined that sensor data is unreliable.

It will now be with reference to the 7 to 9 provide a more detailed discussion of the load optimization process, including ways in which the control systems discussed above may be used to optimize such dispatching procedures, both from the perspective of a central entity responsible for a power system and individual power plants that are part of such a system, depending on how the case is stored. Note that from the perspective of a central dispatcher instance, the goal of the load optimization process is to dynamically respond to changing variables, including changing load requirements or environmental conditions, while minimizing production costs within the system. Note that for the participating power plants, the overall goal is to use available capacity while minimizing generation costs to maximize business value. Given the complexity of power systems, the process of load optimization typically involves the dispatcher frequently adjusting the loads of the power plants involved. If the process is successful, it will result in available power plants being run at full load, where their incremental cost is about the same - resulting in minimized generation costs - while respecting system constraints such as maximum and minimum allowable loads, system stability and so on. Note that accurate incremental cost data is necessary for optimizing load optimization. Such incremental data has primary components that include fuel costs and additional fuel consumption. The additional fuel consumption data is usually reported as a graph of additional heat expenditure versus power output. Specifically, the incremental heat rate, IHR (incremental heat rate), of a thermal energy generating unit is defined as the slope of the heat input curve, the heat input of the unit being the ratio of heat input to current output at each load. Errors in these data cause units to be burdened with loads that do not minimize the total cost of production.

Several things can introduce errors into the curves for extra heat. These can be divided into two groups. A first category includes things that produce errors that exist when the data is passed to the dispatcher. For example, if the data is collected through testing, errors due to inaccurate instruments will be included in all calculations performed on them. As will be discussed in more detail below, certain aspects of the present invention include ways to validate the accuracy of sensors during data collection and timely detection of instances where collected data may be unreliable due to sensor malfunctions. A second category of errors includes things that cause data to become less accurate over time. For example, if the performance of a generator unit changes due to material degradation or repair or changes in environmental conditions, the additional heat usage data used for the dispatch is incorrect until that data is updated. One aspect of the present invention is the detection of such parameters of thermal energy generating units that significantly affect calculations of the additional heat input. The knowledge of such parameters and their relative importance can then be used to determine how often Dispatch data should be updated to reflect the true performance of the work.

Errors in the extra heat input data lead to situations where power plants are not loaded correctly, which typically results in increased power system generation costs. For example, a situation is created as in 7 where the true additional heat input is different than the extra heat used in the dispatch process. When loading the device, the Dispatch instance uses the extra heat data that contains an "E" error as indicated. (Note that in 7 It is believed that the additional heat input of a power system is not affected by the load imposed on the particular unit, which may be substantially true if the power system is one that is large compared to the respective generator unit. As shown, the generator unit is loaded at L ₁ , which is the load where the additional heat input of the unit and that of the system are equal based on the information available. If the correct information on the additional heat input were used, the unit would be loaded at L ₂ , the load where the true additional heat input of the plant is equal to the additional heat input of the power system. Note that the fault leads to underutilization of the power plant. In cases where the alternative is true, ie where the positioning of the incorrect curve for the additional heat input is reversed relative to the true additional heat input, the error will result in over-loading the unit, which may make it necessary to operate them inefficiently to meet the load obligation imposed on them. Note that from the perspective of the central dispatch instance of the power plant, reducing errors in data used in the dispatch process reduces the fuel cost of the entire system, increases the efficiency of the system, and / or the risk of load demand can not be met, lowers. For the operators of power plants within the system, the reduction of such errors should promote the full utilization of the plant and increase the business benefits.

The 8th and 9 show a schematic representation of a factory control device 22 or a flowchart 169 a control method according to aspects of the present invention. In these examples, procedures are presented that represent business optimization within a power system that uses load optimization to distribute loads among potential suppliers. The basic process for stress optimization is one that can be applied in different ways and between any two levels that are common within the layer hierarchy that is common to many power systems. For example, in one case, the load optimization process may be used as part of a competitive process by which a central government agency or industry association allocates load portions to several competing companies. Alternatively, the same load optimization principle can be used to split a load on power plants owned by the same owner to minimize generation costs for the owner of the power plant. It can also be applied at the plant level as a way for an operator or plant control facility to split its load requirements among the various available local generator units. Note that unless otherwise stated, the systems and methods of the present invention are generally applicable to any of these possible manifestations of the stress optimization process.

In general, the load optimization process seeks to minimize generation costs within a power system by creating a dispatch plan in which the incremental cost is about the same for each participating power plant or generator unit. Note that several terms are often used to describe the stress optimization process defined as follows. A "forecast horizon" is a predefined period of time over which an optimization is to be carried out. For example, a typical forecast horizon can range from a few hours to a few days. An "interval" within the forecast horizon is a predefined time resolution for the optimization, ie the above-mentioned "optimization cycle", which describes how often an optimization is performed during the forecast horizon. For example, a typical time interval for an optimization cycle may be a few seconds to a few minutes. Finally, a "forecast length" is the number of time intervals for which the optimization is to be performed, and can be obtained by dividing the forecast horizon by the time interval. Thus, a forecast length is for a 12-hour forecast horizon and a 5-minute time interval 144 Time intervals.

Aspects of the present invention provide methods of controlling and / or controlling power plants, as well as methods and systems for optimizing performance, cost-effectiveness, and efficiency. For example, according to the present invention, lowest variable operating costs can be found for a heat generating unit or power plant having variable performance characteristics and cost parameters (ie, fuel costs, environmental conditions, market conditions, etc.) and life cycle costs (ie variable operation and cost) its impact on maintenance plans, component replacement, etc.). By varying one or more parameters of a thermal energy production unit that takes into account such factors, a greater business benefit may be derived over its useful life. For example, in power plants incorporating a gas turbine, firing temperatures may be varied based on operating profile, environmental conditions, market conditions, predictions, power plant performance, and / or other factors to achieve a desired load level in a more cost effective manner. As a result, the disposal of parts having a remaining hourly service life can be reduced to start-limited units. Further, a power plant control system having feedback loops that is substantially updated with real-time data from sensors that are periodically tested and confirmed to be in proper operation allows further factory optimization. That is, in accordance with certain embodiments of the present invention, by introducing a real time feedback loop between the power plant control system and a competent dispatch instance, a target load and resource scheduling of units may be established on highly accurate swap curves that are constructed based on real time turbine performance parameters.

8th shows the schematic design of an example of the factory control device 22 in accordance with aspects of the present invention; Note that the factory control device 22 may be particularly well suited for the implementation of the method 169 from 9 , That is why the 8th and 9 However, it should be made clear that any aspect can be used for more general purposes. This in 8th illustrated power supply system 10 includes "a power plant 12a ), the factory control device 22 is dedicated, as well as "other power plants 12b "That represent power plants within the power system, with the power plant 12a compete. As shown, the power supply system includes 10 also a responsible dispatch instance 24 that have a dedicated system controller 25 the dispatch process between all participating power plants 12a . 12b managed within the system.

The power plant 12a can have numerous sensors 46 and actuators 47 involve, through which the factory control device 22 Operating conditions monitored and controls the operation of the plant. The factory control device 22 can with numerous data resources 26 communicate, which may be located in a location other than themselves and which are accessible via a communications network and / or which are locally contained and accessible via a local area network. As shown, the schematic representation of the factory controller includes 22 several subsystems that are separated by the multiple fields. These subsystems or "fields" have been shared mainly for their function to facilitate the description. Note, however, that discrete fields may or may not represent individual chips or processors or other discrete hardware elements, and may not, but need not, separate sections of computer program code executing within the factory controller unless otherwise specified is specified. The same is true of the procedure 169 divided into two main sections or fields, but this is for convenience and to simplify the description. Note that each of the separate fields, the in 8th are shown to one or more sections in the factory control device 22 can be combined, as well as any or all of the separate blocks or steps in 9 are shown.

The procedure 169 from 9 For example, with a control section 170 which can receive or collect current information and data for use (in step 171 ), which may include market data, operational data and / or environmental data. Within the factory control facility 22 can be a corresponding control module 110 be arranged to get this kind of data from data resources 26 or any other suitable source. The control module 110 can also be configured to have a target load 128 from the responsible Dispatch instance 24 receives (however, such a target load at startup may not be available and a predefined initial target load is used). Environment data may be received from remote or local data archives and / or forecasting services and may be a component of the data resources 26 be included. Environmental data may also be collected via environmental sensors surrounding the power plant 12a are placed around, or can communicate with the Dispatch instance through a communication link 24 be received. In accordance with aspects of the present invention, environmental data includes past data, current and / or forecast data, environmental conditions for the power plant 12a which may include, for example, temperature, relative humidity, air pressure, etc. Market data may be received from remote or local data archives and / or forecasting services and may be a component of the data resources 26 be included. Market data may also be communicated via a communication link with the responsible dispatch instance 24 be received. According to Aspects of the present invention include market data data from the past, current and / or forecast data, the market conditions for the power plant 12a which include, for example, energy sales prices, fuel costs, labor costs, etc. Operating data may also be received from data archives and / or forecast data and may be used as a component of the data resources 26 be included. Operating data may include data from multiple sensors 46 be collected within the power plant 12 and its components 49 stationed and measure the physical parameters related to factory operation. Operating data may include past data, current and / or forecast data, as well as a variety of process inputs and outputs.

As in 9 can be an initial setpoint for the power plant 12 be determined, for example with a control model 111 in the factory control facility 22 from 8th , For example, the control model 111 be configured to have thermodynamic and / or physical details of the power plant 12 and use additional information, such as environmental data or market data or process data, to obtain a value of an operating parameter for the power plant 12 to determine (in step 172 from 9 ). For example, in one case, the value of an operating parameter may be a value that would be necessary to achieve a power output sufficient to meet a target load. The specific value can be used as the initial setpoint for the respective operating parameter of the power plant 12 be used (also step 172 from 9 ). Note that examples of such operating parameters may include a fuel flow rate, a firing temperature, a position of inlet guide vanes (if vanes are present), a vapor pressure, a vapor temperature, and a vapor flow rate. A performance counter can then be modeled using a performance model 112 the factory control device 22 be determined (in step 173 from 9 ). The performance indicator can provide an operating characteristic, such as the efficiency of the power plant 12 deliver. The performance model 112 can be configured to have thermodynamic and physical details of the power plant 12 as well as the setpoints used by the control model 111 be determined to an operating characteristic of the power plant 12 to determine. The performance model 112 may be configured to accommodate additional information such as environmental conditions, market conditions, process conditions, and / or other relevant information.

Additionally, in accordance with certain aspects of the present invention, an estimate of life cycle costs (LCC) of the power plant may be provided 12 be made (in step 174 from 9 ), for example with an LCC model 113 that in the factory control facility 22 from 8th is included. The LCC model 113 , which may be a computer program or the like, may be configured to provide physical and / or cost information about the power plant 12 as well as setpoints from the control model 111 uses estimated life cycle costs of the power plant 12 to determine. The life cycle costs may include, for example, total costs, maintenance costs and / or operating costs of the power plant 12 over its lifetime. The LCC model 113 In addition, it can be configured to reflect the results of the performance model 112 for greater accuracy. The LCC model 113 therefore, can determine the specific setpoints of the control model 111 and the operating characteristics from the performance model 112 as well as according to desire use further information to the life span of the power plant 12 as well as the costs of operation and / or maintenance of the power plant 12 to estimate during its lifetime. As stated earlier, the life of a power plant can be expressed in terms of hours of operation and / or number of starts, and a given power plant has an anticipated life that can be specified by a manufacturer of the power plant. Thus, predefined values for the anticipated lifetime may be at least at a starting point of the LCC model 113 and / or an enhancement module 114 be used.

By using information from other embodiments of the invention, such as results of determining an initial setpoint, a performance indicator, and a prospective lifetime, may be an optimization problem for the power plant 12 be solved (in step 175 ) as described below. Such an optimization problem may involve multiple equations and variables, depending on the depth of the desired analysis, and may include an objective function, which in some embodiments may be an LCC-based objective function. The solution may be to provide an improved or more convenient operating parameter for the power plant 12 by, for example, minimizing an LCC-based objective function (also step 175 ). In embodiments, the solution to the optimization problem may be through an enhancement module 114 the factory control device 22 from 8th be performed.

As is known from optimization theory, an objective function represents a characteristic or parameter to be optimized, and may take into account many variables and / or parameters, depending after how the optimization problem is defined. In an optimization problem, an objective function may be maximized or minimized depending on the particular problem and / or parameter presented by the objective function. For example, as stated above, according to some embodiments, an objective function expressing LCC would be minimized to produce at least one operating parameter that may be used to power the power plant 12 operate so that LCC remains as low as possible. An optimization problem for the power plant 12 or at least one objective function may include such factors as power plant characteristics, location parameters, customer specifications, results from the control model 111 , the performance model 112 and / or the LCC model 113 Environmental conditions, market conditions and / or process conditions as well as additional information that may be appropriate and / or desired. Such factors may be included in terms of an objective function such that, for example, an LCC-based objective function includes maintenance costs and operating costs that are presented over time, where time is a forecast horizon based on estimated life of components. Note that complex objective functions and / or optimization problems may be used in the implementation of the present invention, as any may involve many or all of the various functions and / or factors described herein.

Maintenance costs can be, for example, by modeling parts of the power plant 12 determined to assess material degradation based on various parameters, such as those already discussed. Note that every part of the power plant 12 can be modeled for this purpose. In a practical application, however, the parts could be those with fewer, larger sections or less, selected sections of the power plant 12 may be modeled, and / or constants or plug-values may be used for some parts instead of modeling. However much detail is used, minimizing such LCC-based objective function is part of an optimization problem that can vary for a particular power plant as a result of many factors, such as those listed above, and these can provide at least one improved or more convenient operating parameter of the power plant 12 , for example, according to a minimization of the LCC include. In addition, those skilled in the art will recognize that at least one condition may be applied to the optimization problem, such as a predefined operating time and downtime, a predefined highest and / or lowest temperature at various locations in the power plant 12 , a predetermined torque, a predetermined power output, and / or other conditions that may be desired and / or appropriate. Unless otherwise stated, it is within the purview of one skilled in the art to determine which conditions should be applied in which way to a particular optimization problem. Further, those skilled in the art will recognize situations in which additional optimization theory may be applied, for example, by adding a slip variable to provide a workable solution to the optimization problem.

Known methods may be used, for example, by the enhancement module 114 ( 8th ) to an optimization problem for the operation of the power plant 12 to solve. For example, integer programming, linear, mixed-integer linear, mixed-integer non-linear, and / or other technique may be used, as appropriate and / or as desired. In addition, as can be seen from the objective function example, the optimization problem can be solved over a forecast horizon, which is a value array for at least one operating parameter of the power plant 12 supplies. Although the improvement or enhancement of convenience may be performed over a relatively short forecast horizon, for example, 24 hours or even minutes, the enhancement module may 114 ( 8th ) use a longer forecast horizon, for example, up to an anticipated end of the life of the power plant 12 , depending on the desired depth of analysis. In embodiments, initial setpoints may be determined, for example, by the control model 111 be determined ( 8th ) in response to the solution of the optimization problem and / or as part thereof to obtain an improved or more appropriate or optimized setpoint. In addition, an iteration may be determined by determining an initial target value, determining a value of a performance indicator, determining estimated LCC costs, and improving and increasing the convenience (in steps 172 - 175 from 9 ) can be used to specify results and / or control setpoints of the power plant 12 continue to improve and / or make more useful.

As will be described, an exchange curve section 180 generate an exchange curve or a set of exchange curves, for which an example above with reference to 7 was shown. In the factory control facility 22 can control information 115 from the control module 110 and / or data resources 26 can from a exchange curve module 120 be received (in step 181 from 9 ). In certain embodiments, control information includes 115 : Regulation setpoints, performance, environmental conditions and / or market conditions. This information may also be referred to as "ongoing" information. In addition, an environmental condition prediction can 121 and / or a market condition forecast 122 be received (in step 182 ). According to certain embodiments, a database 123 and may store up-to-date information, "current" information and / or historical information, including any or all environmental conditions, market conditions, power plant performance information, swap curves, regulation setpoints, and / or any other information that may be appropriate. Database 123 Can be used to provide information to the operation of the power plant 12 to simulate (in step 183 ), for example with an offline model 124 of the power plant 12 ,

The offline model 124 may include a model that is the control model 111 is similar, but may also include additional modeling information. For example, the offline model 124 the control model 111 , the performance model 112 , the LCC model 113 and / or contain additional modeling information in part or in whole. By running the offline model 124 with setpoints and / or information from the LCC improvement or enhancement of its usefulness may be the output of the offline model 124 used to estimate the cost of electricity production for each time interval in a forecast horizon and for different values of the power output of the power plant 1 to one or more exchange curves 125 to generate (in step 184 ) to the responsible Dispatch instance 24 be sent or otherwise issued (in step 185 ). The offline model 124 may include any suitable information, such as information from the past, current and prediction information, for determining the estimated operating costs and / or conditions of the power plant 12 use. In addition, the offline model 124 in embodiments (in step 186 ), for example through a model matching module 126 , Tuning may include, for example, parameters for the offline model 124 be periodically adjusted based on information provided by other parts of the factory control facility 22 received and / or spent to the actual operation of the power plant 12 better reproduce the operation of the power plant 12 to simulate better. If the factory control device 12 For a given set of operating parameters, an actual operating condition is observed, different from what the offline model does 124 has predicted, can differ, the factory control device 12 hence the offline model 124 change accordingly.

In addition to the exchange curves 125 from the power plant 12a , the responsible Dispatch instance can 24 , as shown, exchange curves 125 received by other power plants, which are controlled by it. The responsible dispatch instance 24 can the exchange curves 125 evaluate and can generate a dispatch plan to a load of a power system 10 to manage something. The responsible dispatch instance 24 In addition, it may take into account predicted environmental conditions, a load forecast, and / or as needed and as desired, other information from different local or remote data resources 26 can receive to which she has access. As shown, the dispatch plan included by the responsible dispatch instance 24 produced, a control signal for the power plant 12 that has a target load 128 includes and on the factory control device 22 can answer as described above.

Note that the inclusion of lifecycle costs in the analysis as described herein may serve to increase the scope and accuracy of the plant models used in the optimization process, thereby enabling improvements to the process. Swap curves 125 As described above, variable costs can be represented (measured in dollars per megawatt hour versus power plant output in megawatts). The exchange curves 125 may include an exchange rate of variable incremental costs and an exchange rate of variable average costs. It will be appreciated that embodiments of the present invention have their generated exchange curves 125 allow for precise determinations of variable costs. Using embodiments of the present invention, it has been found that variable increment cost swapping curves accurately predict actual swap cost variable cost curves, while a variable average cost swap curve has very accurately predicted actual variable average cost swap curves. The accuracy of the swap curves generated by embodiments of the present invention shows that the various models used in the factory controller 22 from 8th be used to provide a sufficiently meaningful model for the purpose described.

Now, referring to the 10 to 12 Other aspects of the present invention are described with reference to and including certain systems and methods mentioned above. 10 is a data flow diagram that depicts an architecture for a Network Optimization System 200 shows that can be used in a gas and steam combined cycle power plant with gas and steam turbine systems. In the specified embodiment, a system includes 200 Monitoring and control instruments 202 . 204 For example, the sensors and actuators discussed above, each associated with the gas turbine engine. 202 ) and the steam turbine system ( 204 ) are associated. Each of the monitoring and control instruments 202 . 204 may display signals indicative of measured operating parameters to a factory controller 208 send. The factory control device 208 receives the signals, processes the signals according to the given algorithms and sends control signals to monitoring and control instruments 202 . 204 to influence changes to the factory operation.

The factory control device 208 is via an interface with a data acquisition module 210 connected. The data acquisition module 210 can communicate with a database / archive (Historian) 212 which retains archived data for future reference and analysis. A heat balance module 214 can request data from the data collection model 210 and the database / archive 212 received to process algorithms that tune a power plant's mass and energy balance model to be as close as possible to measured data. Discrepancies between the model and the measured data may indicate errors in the data. Note that a power module 216 Use factory equipment models to predict the likely performance of key factory components and equipment. The difference between the expected and actual performance may indicate a deterioration in the condition of the equipment, parts and components such as fouling, calcification, corrosion and breakage. In accordance with aspects of the present invention, the power module 216 track age-related material degradation so that performance issues with the most significant impact on plant performance can be identified.

As shown, an optimization module 218 be included. The optimization module 218 may include a methodology for optimizing plant encumbrance. For example, according to embodiments, the power plant may be loaded on the basis of heat input or incremental heat input based on the assumption that the heat input is equivalent to monetary resources. In an alternative scenario in which the power plant includes an additional manufacturing process (not shown) for which steam is used directly (ie, when the produced steam is diverted from power generation in the steam turbine for another manufacturing purpose), it should be noted that the optimization module 218 can solve an optimization problem, whereby a component can be loaded with a higher heat input. For example, in certain situations, a need for steam may exceed a need for electricity, or the power output may be limited by electrical system requirements. In such cases, the loading of a lower efficiency gas turbine may allow better heat utilization without increasing the current output above a threshold. In such scenarios, loading the component with a higher heat input is the economically optimized alternative.

The optimization module 218 can be selected between an online (automatic) and an offline (manual) mode. In online mode, the optimizer calculates 218 automatically current business parameters, such as the cost of electricity generated, the incremental cost at each level, the cost of process steam and the operating profit of the plant at regular intervals, for example in real time or every five minutes. An off-line mode can be used to simulate continuous-duty performance, simulate "what-if" scenarios, analyze budgeting and refurbishment options, and current power generation capacity, the desired heat input, a correction of a current factory operation Conditions to predict the impact on operating restrictions and maintenance actions and fuel consumption. The optimizer 218 calculates a profit-optimized output of the plant based on real-time data on business costs, producer prices, load level and material degradation rather than output based on efficiency by combining plant heat balances with a plant's financial model. The optimizer 218 can be tuned to fit the individual material deterioration of each component and can be a recommendation issue 220 produce and / or can issue a regulation issue 222 to produce. The recommendation issue 220 recommends operators where to set controllable parameters of the power plant to optimize the individual components of the plant in order to maximize profitability. In the embodiment, the recommendation output is 220 a computer display screen communicably connected to a computer containing the optimization module 218 performs. In an alternative embodiment, the referral output is a display screen at a workstation located elsewhere, this workstation has a network on the optimization module 218 accesses. The regulation issue 222 can get data from the optimization module 218 receive and calculate optimized setpoints and / or bias settings for the modules of the system 200 to implement a real-time control.

11 is a simplified block diagram of a real-time thermal power plant optimization system 230 according to aspects of the present invention, a server system 231 and several client subsystems, also called client systems 234 be designated and the communicable with the server system 231 are connected. As used herein, real-time results that exist within a very short period of time after a change in the inputs affect the outputs, such as computer calculations. The period represents the length of time between individual iterations of a regularly repeated task. Such repeated tasks may be referred to herein as periodic tasks or cycles. The period is a design parameter of the real-time system based on the importance of the result and / or ability of the system processing the processing of the results to produce the output. In addition, events that take place in real time take place without significant deliberate delay. In the embodiment, calculations can be updated in real time with a periodicity of one minute or less. The client systems 234 may be computers that include a web browser, so the server system 231 for client systems 234 accessible via the Internet or any other network. The client systems 234 can be connected to the Internet via many interfaces. The client systems 234 could be any device that is able to connect to the internet. A database server 236 is with a database 239 which contains information relating to many things, as described in more detail below. In one embodiment, a centralized database 239 , the aspects of the data resources discussed above 26 includes, on the server system 231 stored and is for potential users on one of the client systems 234 by logging in to the server system 231 about the client systems 234 accessible. In an alternative embodiment, the database is 239 at a location other than the server system 231 stored and does not have to be centralized.

In accordance with aspects of the present invention, certain of the control methods discussed above for use in conjunction with system diagrams may 10 and 11 be developed. For example, one method includes simulating power plant performance using a plant power module of a software code segment that receives instrument data from a power plant monitoring facility. The data may be received via a network from a factory controller or a database / archival software program running on a server. Any additional factory components, such as an intake conditioning system or an HRSG duct combustion system, may be simulated in a manner similar to that used to simulate power plant performance. Determining the performance of each factory component in the same manner allows the entire power plant to be treated as a single plant to determine optimized setpoint values for the power plant rather than separately determining such setpoints for each component. Measurable quantities for each factory component can be parameterized to express the output or efficiency of the power plant based on each individual component. Parameterization of factory equipment and performance includes calculating the efficiency for components such as a gas turbine compressor, a gas turbine, a heat recovery steam generator (HRSG), a draft train, a cooling tower, a radiator, a feedwater heater, an evaporator, a flash tank, and so forth note that heat and performance calculations can be parameterized and the resulting simultaneous equations solved in real time so that computational results are available without intentional delay from the time the individual parameters are sampled. Solving parametric simultaneous equations and constraints may also include determining a current heat balance for the power plant, determining an anticipated performance using current limitations of the plant's operation, including a necessary rotating reserve, electrical system requirements, maintenance activities, fresh water requirements, and so on Standstill of components. Solving parameterized equations and constraints may also include determining adaptation parameters to modify the current heat balance so that a future heat balance will match the particular expected performance. In an alternative embodiment, solving parameterized simultaneous equations includes determining inlet conditions for the power plant, predicting an output of the power plant based on the determined inlet conditions and a given model of the power plant, determining a current output of the power plant, comparing the predicted output with the specific output and adjusting factory parameters until the specific output of the predicted Output is the same. In embodiments, the method also includes correlating controllable factory parameters, factory equipment, and factory performance using parameterized equations, defining the objective of the optimization using an objective function that includes minimizing the heat input of the power plant and / or maximizing the benefit of the power plant, and defining the physically possible operating range for each individual part of the equipment and / or total limits using constraints, the total limits including maximum power production, maximum fuel consumption, and so on.

12 is a flowchart of an example of a method 250 for solving parametrized simultaneous equations and constraints according to the present invention; The procedure 250 involves determining a current heat balance for the power plant (at 252 ), determining an expected performance using current operating limitations (at 254 ) and determining adaptation parameters for modifying the current heat balance (at 256 ), so that a future heat balance corresponds to the specific expected performance. The procedure 250 also includes determining inlet conditions for the power plant, 258 predicting an output of the power plant based on the determined inlet conditions and a given model of the power plant, 260 determining a current output of the power plant, 262 comparing the predicted output with the particular output 264 , and adjusting factory parameters, 266 until the specific output of the predicted output is the same. It should be noted that the method described and the systems described with reference to the 10 and 11 to provide a cost-effective and reliable way to optimize gas and steam combined cycle power plants.

Now, referring to the 13 to 16 "Attention is directed to the several flowcharts and system configurations that illustrate a control methodology in accordance with certain aspects of the present invention. In general, in one embodiment, a control system for a thermal energy production unit, such as the gas turbine system, or a power plant may include first and second instances of a model modeling the operation of the turbine, for example, using physics-based models or mathematical modeling (eg, transfer functions, etc.). ). The first model (which may also be referred to as a "primary model") may provide current operating parameters of the gas turbine system describing the turbine operating mode and operating conditions corresponding thereto. As used herein, "parameters" refers to things that can be used to define the operating conditions of the turbine, including temperatures, pressures, gas flows at defined locations in the turbine, and efficiencies of the compressor, combustor, and turbine, etc. Performance parameters may also be referred to as "model correction factors," which refers to factors used to adjust the first or second models to reflect the operation of the turbine. Inputs to the first model may be sensed or measured and provided by an operator. In addition to current performance parameters, the method of the present invention may include receiving or otherwise obtaining information about external factors or disturbances, such as environmental conditions, that may affect current or future operation of the gas turbine system.

The second model (also referred to as a "secondary model" or "predictive model") is generated to receive one or more operating parameters, such as controlled variables, of the gas turbine system, taking into account the current operating parameters, such as manipulated variables, and the one or more disturbances determine or forecast. Examples of operating parameters of the turbine include actual turbine operating conditions, such as outlet temperature, turbine output, compressor pressure ratios, heat input, emissions, fuel consumption, expected yields, and the like. Therefore, this second or predictive model may be used to indicate turbine behavior at particular operating setpoints, performance goals, or operating conditions that differ from current operating conditions. As used herein, the term "model" generally refers to the action of modeling, simulating, forecasting, or displaying based on the output of the model. Note that while the term "second model" is used herein, in some cases there may be no difference between the formulation of the first and second models, so the "second model" is the execution of the first model with adjusted parameters or additional or other input.

Thus, by modeling the turbine performance using the second or predictive model taking into account external factors and / or other operating conditions, the turbine control may be adjusted to make operation more efficient under these other operating conditions or in view of the unanticipated external factors. This The system may therefore enable automatic turbine control based on the modeled behavior and the modeled operating characteristics. In addition, the modeling system described enables the creation of operator-specified scenarios, inputs, operating points, operating objectives, and / or operating conditions to predict turbine behavior and operating characteristics at these operator dictated conditions. Predicting such hypothetical scenarios allows operators to make more informed control and operational decisions, such as mission planning, load, load reduction, etc. As used herein, the term "operating points" generally means operating points, conditions, and / or objectives, and is not intended to be limiting , Thus, operating point may designate a target or set point, for example, a base load, a load lowering point, a maximum firing temperature, and the like.

An example used for the described turbine modeling system involves adjusting the turbine operation to comply with grid licensing regulations while operating at the highest efficiencies. For example, regional power authorities typically dictate that the power plants must be able to support a grid during frequency disturbances. Supporting the grid during disturbances involves increasing or decreasing the turbine load under certain conditions, depending on the grid condition. For example, a power plant is expected to increase its output of power generated during a fault (e.g., by as much as 2%) to compensate for other supply bottlenecks. Therefore, turbine operation typically restricts the base load point for the turbine to operate at an output power level that leaves a margin (also referred to as "reserved capacity") so that the increased load can be provided, if necessary, without the additional maintenance factor associated with Overburning is associated with evoke. For example, the reserved margin may be 98% of what the base load would typically be, allowing for an increase in load network requirements (e.g., by 2%) without exceeding the base load of 100%. However, unanticipated external factors, such as temperature, humidity, or pressure, can negatively impact turbine efficiency. When it gets warmer during the day, a turbine may not have the 2% reserve it needs because the heat causes the turbine to operate less efficiently, and the turbine can not reach 100% of the load originally used for the turbine she was planned. To compensate, conventional heat input curves, given the possible loss of engine efficiency, cause the turbine to operate in a more efficient state throughout the day (e.g., 96%, etc.). The turbine modeling system described herein, however, allows the modeling of turbine behavior in real time according to current external factors (e.g., temperature, humidity, pressure, etc.) and thus control of turbine operation for the most efficient operation in the current environmental conditions. Likewise, future turbine behavior may be predicted, for example, to predict turbine behavior in response to thermal variability over the course of a day, thereby allowing turbine operation planning to achieve the most efficient and economically sustainable operation. As another example, power plants typically decide whether gas turbines should be shut down at night or simply their output power levels should be reduced (e.g., by load reduction). Turbine operating characteristics, such as emissions, exhaust gas temperature and the like, affect this decision. Using the turbine modeling system described herein, decisions may be made on a smarter basis, either in advance or in real time or almost in real time. External factors and expected turbine operating parameters may be fed to the second model to determine what the turbine operating characteristics would be. Thus, the modeled characteristics may be used to determine whether a turbine should be shut down or its load lowered, taking into account these characteristics (e.g., efficiency, emissions, cost, etc.).

As another example, a turbine modeling system may be used to evaluate the benefit of performing turbine maintenance at a particular time. The turbine modeling system of the present invention may be used to model the operating characteristics of the turbine at its current capacities based on current performance parameters. Then, an operator predetermined scenario may be generated that models the turbine operating characteristics if maintenance is performed (e.g., an improvement in the performance parameter values to show an expected performance increase). For example, the performance parameters reflect engine wear as the turbine degrades over time. In some cases, maintenance can be carried out to improve these performance parameters and thus also the operating characteristics of the turbine. By modeling or forecasting the improved operating characteristics, a cost-benefit analysis can be performed to compare the benefits achieved by performing maintenance with the costs incurred.

13 shows an example of a system 300 , which can be used to model a turbine operating behavior. According to this embodiment, a power plant 302 specified, which includes a gas turbine with a compressor and a combustion chamber. An inlet channel to the compressor directs outside air and possibly injected water into the compressor. The configuration of the inlet channel contributes to a pressure loss of outside air flowing into the compressor. An outlet channel 302 directs combustion gases from the outlet of the power plant 302 For example, about emission control and sound-absorbing devices. The amount of inlet pressure loss and backpressure may vary over time due to the addition of components to the inlet and outlet ports and due to plugging of the inlet and exhaust ports.

The operation of the power plant 302 may be monitored by one or more sensors that provide one or more observable conditions or operating or performance parameters of the power plant 302 detected. In addition, external factors, such as the outside environment, may be measured by one or more sensors. In many cases, two or three multiple sensors can measure the same parameter. For example, groups of multiple sensors may affect the outside temperature in the vicinity of the power plant 302 , the compressor inlet temperature, the turbine exhaust temperature, as well as other temperatures throughout the power plant 302 monitor. Similarly, groups of multiple pressure sensors can monitor the external pressure and static and dynamic pressure levels at the compressor inlet and outlet, at the turbine outlet, and at other locations throughout the turbine. Groups of multiple moisture sensors can measure the outside air humidity in the inlet channel of the compressor. Groups of multiple sensors may also include flow sensors, velocity sensors, flame detector sensors, valve position sensors, vane angle sensors, or the like that sense various parameters related to the operation of the power plant 302 Respectively. A fuel control system may regulate the fuel flowing from a fuel supply to the combustion chamber. The fuel control device may also select the type of fuel for the combustion chamber.

As stated, "operating parameters" refers to things that can be used to define the operating conditions of the turbine system, such as temperatures, pressures, combustor pressure ratios, gas flows at defined locations in the turbine, load setpoints, firing temperature, as well as one or more conditions associated with that Extent of material deterioration of the turbine or the compressor and / or the efficiency of the turbine or the compressor correspond. Some parameters are measured directly. Other parameters are estimated from the turbine models or are indirectly known. Still other parameters may represent hypothetical or future conditions and may be defined by the factory operator. The measured and estimated parameters may be used to represent certain turbine operating conditions. As used herein, "performance indicators" are operational parameters derived from the values of certain measured operating parameters and are a performance criterion for operation of the power plant over a defined period of time. For example, performance indicators include heat input, output power level, etc.

As in 13 is shown, the system includes 300 one or more control devices 303a . 303b , each of which may be a computer system having one or more processors executing programs for the operation of a power plant or generator unit 302 to control. Even though 13 illustrates two control devices, it should be made clear that even a single control device 303 can be provided. According to a preferred embodiment, multiple controllers may be included to facilitate redundant and / or distributed processing. For example, the control actions may depend on sensor inputs or instructions from factory operators. The programs used by the controller 303 may include scheduling algorithms, such as those for regulating the fuel flow to the combustor, to address network licensing requirements, load reduction, and so on. The commands issued by the controller 303 can cause actuators on the turbine to adjust valves between the fuel supply and the combustion chambers to regulate the fuel flow, the distribution, and the type of fuel. The actuators can adjust inlet guide vanes on the compressor or activate or control other control setpoints on the turbine. Note that the controller 303 may be used to generate the first and / or second model in addition to facilitating control of the power plant, as described herein. The control device 303 may receive an output from the operator and / or a currently modeled output (or any other system output). As already described, the control device 303 include memory that stores programmed logic (eg, software) and can store data, such as sensory acquired operating parameters, modeled operating parameters, operating limits and goals, Operating profiles and the like. A processor may use the operating system to execute the programmed logic and may use data stored therein. Users can connect to the controller via at least one user interface device 303 connect. The control device 303 can communicate online with the power plant via an I / O interface while it is operating, as well as communicating offline with the power plant when it is not in service. Note that one or more of the control devices 303 can accomplish the execution of the model-based control system described herein which may include, but is not limited to, sensing, modeling, and / or receiving operating parameters and performance parameters; Generating a first power plant model that reflects a current turbine operation; sensory capture, modeling and / or receiving information about external factors; Receiving input from the operator, such as performance goals and other variables; Generating a second power plant model that reflects the operation in light of the additional data provided; Controlling the current or future turbine operation; and / or presenting modeled operating characteristics. In addition, it should be understood that other external devices or several other power plants or generator units are connected to the controller 303 can communicate via I / O interfaces. The control device 303 may be located in a location other than the power plant controlled by it. Furthermore, the control device 303 and the logic implemented thereby includes software, hardware, firmware, or any combination thereof.

The first control device 303a (Which, as I said, the same control device may be as the second control device 303b , or which may be different), may be suitable for the power plant 302 through a first or primary model 305 modeling, which includes modeling the turbine's current performance parameters. The second control device 303b may be suitable for providing turbine operating metrics under different conditions via a second or predictive model 306 to model. The first model 305 and the second model 306 may each be an arrangement of one or more mathematical representations of the turbine behavior. Each of these representations may rely on input values to generate an estimate of a modeled operating parameter. In some circumstances, the mathematical representations may generate a replacement operating parameter value that may be used under circumstances where a measured parameter value is not available. The first model 305 can then be used to provide a foundation and / or input for the second model 306 to provide turbine operating parameters based on the current performance parameters of the power plant 302 and any other factors, such as external factors, operator-provided commands or conditions, and / or adjusted operating conditions. Note that, as described above, "the second model 306 "Can simply be an instance of the same model as the first model 305 that takes into account additional or other inputs, such as external factors, other operating points, to model other performance parameters or other turbine behavior in light of other inputs. The system 301 may also have an interface 307 include.

Still on 13 A brief description of the mutual relationship between the system components is provided. As described, the first or the primary model models 305 current performance parameters 308 of the power plant 302 , The current performance parameters 308 may include conditions that correspond to the degree of degradation of the turbine, conditions that correspond to turbine efficiency (eg, heat input or output ratio), inlet guide vane angle, amount of fuel flow, turbine speed, pressure and temperature at the compressor inlet , the pressure and temperature at the compressor outlet, the turbine exhaust temperature, the generator power output, the compressor airflow, the combustor air ratio, the firing temperature (turbine inlet), the combustor flame temperature, the fuel system pressure ratios, and acoustic characteristics. Some of these performance parameters 308 can be measured directly from turbine operation, and some can be modeled based on other measured or sensory parameters. The performance parameters can be from the first model 305 can be provided and / or can generally be provided by the control device, for example if they are sensory detected and / or measured by the control device. After the generation of the first model 305 become the performance parameters 308 (to denote any turbine behavior provided by the model) to the second or predictive model 306 to generate. Other variables 309 may depend on the intended use for the second model 306 to be provided. For example, the other variables may include external factors, such as environmental conditions, which are generally uncontrollable and must be easily addressed. In addition, the other variables 309 a predetermined by the controller scenario or such an operating point (eg, a turbine operating point, by the control device 303 is generated or otherwise provided, for example, a turbine control based on the first model 305 etc.), measured inputs, which may be some or all of the measured inputs, possibly as described in the first model 305 be modeled include. As explained below with reference to 14 can be described by the operator given scenario 313 (eg, one or more operator supplied commands indicating different turbine operating points or conditions) about inputs from the operator to the second model as well 306 to be delivered. For example, in one usage example, the other variables 309 include a scenario dictated by the operator as one or more inputs to the second model 306 is provided when trying to model a current turbine behavior based on additional inputs, such as external factors or measured inputs, in real time or near real time. Using an operator-specified scenario of the first model in addition to one or more of these additional inputs, the prospective real-time behavior of the power plant 302 through the second model 306 be modeled taking into account these additional inputs, which in turn can be used to power the plant 302 to steer or to the first model 305 through control profile inputs 310 adapt.

It will open 14 Reference where an operator-specified mode of operation or scenario 313 as one or more inputs via the interface 307 for the second or predictive model 306 which then models a future turbine behavior under different conditions. For example, an operator may issue commands to the interface 307 deliver to generate a scenario in which the power plant 302 at a different operating point (eg with different loads, a different configuration, different efficiency, etc.). As an illustrative example, a set of operating conditions may be beyond the scenario given by the operator 313 providing conditions that are predicted for the following day (or any other future time frame), such as environmental conditions or demand requirements. These conditions may differ from the second model 306 used to provide projected or projected operating characteristics 314 for the power plant 302 to generate during this timeframe. In the execution of the second model 306 in the scenario specified by the operator, the expected operating characteristics 314 a turbine behavior such as a base load output capacity, a peak output capacity, lowest load lowering points, emission levels, heat input, and the like. These modeled or projected operating characteristics 313 can be useful when planning and allocating performance production levels, for example, for market planning and for tenders for days in advance.

15 shows an example of a method 320 with which an embodiment of the invention can operate. Provided is a flow chart of the basic operation of a system for modeling a turbine that may be performed by one or more controllers, such as those described with reference to FIGS 13 and 14 are described. The procedure 320 can at step 325 begin, in which the controller can model based on a first or primary model one or more current performance parameters of a turbine according to the current operation. To generate this first model, the controller may receive as input to the model one or more operating parameters indicative of current operation of the turbine. As described above, these operating parameters may be sensed or measured and / or may be modeled, which may be the case if the parameters can not be sensed. The current operating parameters may include any parameter indicating a current turbine operation as described above. Note that the methods and systems disclosed herein do not directly depend on whether the operating parameters are measured or modeled. The control device may include, for example, a generated model of the gas turbine. The model may be an arrangement of one or more mathematical representations of the operating parameters. Each of these representations may rely on input values to generate an estimate of a modeled operating parameter. The mathematical representations may generate a replacement operating parameter value that may be used under circumstances where a measured parameter value is not available.

In step 330 For example, the controller may receive or otherwise determine one or more external factors that may affect current and / or future operation. As described above, these external factors are typically (but not necessarily) uncontrollable, and therefore the inclusion of their impact in the second model is beneficial to the generation of the desired turbine control profile and / or operational behavior. External factors may include, but are not limited to, outside temperature, outside air humidity, or barometric pressure, as well as fuel composition and / or delivery pressure, which may affect the Turbine operating behavior can affect. These external factors may be measured or sensed, may be estimated by an operator or otherwise provided manually (for example, if the operator requires probable behavior due to hypothetical scenarios or future conditions) and / or may be from third informed sources (eg, weather services, etc.). to be provided.

In step 335 For example, the controller may receive adjusted operating points and / or other variables to predict turbine behavior under a condition other than the current turbine condition. Adapted operating points may include, among other things, determining the desired output level, for example, when modeling the turbine with a reserved capacity (eg, 98% of the base load), or modeling the turbine with, for example, a peak load or during a load reduction. Operating points may also include operating limits, including but not limited to hot gas path resistance (or firing temperature), exhaust frame resistance, NOx emissions, CO emissions, combustor lean burn, combustion dynamics, compressor stall, compressor icing, compressor aero-mechanical limits, Compressor clearance and compressor outlet temperature. Thus, by providing these adjusted operating points or other variables, the operator may provide hypothetical scenarios for which the turbine model predicts the operating characteristics within those scenarios, which may be useful to control future operation of the turbine and / or future power generation and allocation to plan.

On step 335 follow step 340 in which a second or predictive model of the turbine based on the in step 325 generated first model and optionally the external factors and / or the adjusted operating points or other variables, which in step 335 be provided is generated. This second or predictive model may thus more accurately indicate or forecast operating parameters and therefrom performance indicators for the turbine during a future period of operation.

In step 345 the modeled power may be used to adjust the current or future turbine operation and / or to display the modeled power to an operator. Thus, when the current turbine operation is adjusted, the turbine controller may receive the modeled performance parameters as inputs for changing a current control model (eg, a first model) or a current control profile, for example, by modifying various setpoints and / or reference values used for the current turbine control , It is expected that this real-time or near-real-time control of the turbine will be performed when the inputs to the second model, in step 340 generated are representative of the current turbine conditions or the current external factors. For example, a real-time or fast real-time adjustment in step 345 when the second model represents performance characteristics taking into account the current temperature, pressure or humidity, and / or considering operating parameters or performance parameters of the turbine that more accurately represent turbine wear and / or efficiency. 16 describes an embodiment that optionally may receive operator-specific inputs and generate a behavioral prediction under another operating condition. The output of in step 340 generated model can also be presented to an operator via an interface or otherwise. For example, in an embodiment where the operator in step 335 Provides hypothetical operating scenarios that will display prospective turbine operating characteristics for analysis and possible inclusion in future control or planning activities. Thus, the process can 320 after step 345 after modeling the current performance parameters of the turbine through a first model and then modeling the same turbine considering additional external factors, adjusted operating points or other additional data to predict turbine operation based on this additional data.

16 shows an example of a method 400 with which an alternative embodiment can work. An example of a flowchart of operating a system for modeling a turbine that may be performed by one or more controllers, such as those described with reference to FIGS 13 and 14 are described. The procedure 400 represents the use of the system 301 in which an operator may optionally provide additional variables to use the modeling capabilities for predicting turbine behavior in hypothetical scenarios. The procedure 400 can with the decision step 405 begin by determining whether the turbine should be modeled according to current turbine operating parameters and performance parameters or whether parameters supplied by the operator should be taken into account when the model is generated. For example, if the system is used to predict hypothetical operating scenarios, then it may be that no current performance parameters are needed as inputs to the model (under Assumption that the model already reflects the basic operation and basic behavior of the turbine. If in the decision step 405 it is determined that no current parameters need to be used, then the operations go to step 410 in which the operator provides other performance parameters, thereby allowing modeling of the turbine at a different operating point and under a different operating condition (eg, in a more worn condition, at a different efficiency, etc.). Otherwise, the current performance parameters and / or operating parameters are used, such as with reference to step 325 from 15 described, and the operations go to step 415 further. In step 415 For example, the controller may determine, by a first or primary model, one or more parameters of a turbine according to either the operator supplied input of step 410 or model the current operation of the turbine. For example, if the model is at least partially based on in step 410 generated by the operator parameters, then that is in step 415 generated model representative of the predicted turbine behavior under these performance parameters.

On step 415 follows the decision step 420 determining whether subsequent modeling (eg, the "second model" or the "predicted model") is based on current external factors, such as the current temperature, current pressure or humidity, or other external factors, such as those determined by Operators should be based. For example, in one scenario, the controller may model the performance of the turbine based on the additional data of one or more current external factors, which would allow further prediction of turbine behavior in the face of current conditions. However, in another scenario, the controller may be used to further model the turbine according to operator supplied conditions, which would allow for the prediction of turbine operating characteristics within various hypothetical scenarios. When in step 320 it is determined that the data provided by the operator on external factors should be taken into account in the modeling, then the operations go to step 425 further. Otherwise, the operations go to step 430 continue and use current external factors. In step 430 For example, the controller receives external factors to be considered when generating the second or predictive model, whether they are representative of the current state or hypothetical factors. On the step 430 follow the steps 435 - 445 which optionally allow for consideration of different operating points and in which the predictive model is generated based on the received data and the expected behavior in the same or a similar manner as with reference to the steps 325 - 345 from 15 described is displayed. The procedure 400 can after the step 445 after modeling the turbine operating behavior optionally based on operator supplied scenarios.

These embodiments therefore provide a technical effect of indicating or predicting turbine behavior at operating points or under operating conditions that are different than current turbine operation. A further technical effect is provided that enables automatic turbine control based at least in part on modeled behavior and operating characteristics, optionally including the creation of operator-specified scenarios, inputs, operating points, and / or operating conditions, turbine behavior, and operating characteristics to predict under these conditions imposed by the operator. Another implemented technical effect includes the ability to predict various hypothetical scenarios that allow operators to make more informed control and operational decisions, such as scheduling, loading, load lowering, etc. Note that herein are step-by-step diagrams of systems, devices, and computer program products according to embodiments of the invention reference is made.

It will be up now 17 Reference where a flowchart 500 according to an alternative embodiment of the present invention is shown. Note that the flowchart 500 Aspects included as a control method or as part of a control system to facilitate the optimization of a power plant 501 can be used. The power plant 501 may be similar to those with reference to the 2 and 3 It should be understood that, unless otherwise specified in the appended claims, it should be understood that the present invention may be practiced with reference to other types of power plants. In a preferred embodiment, the power plant 501 have a plurality of heat generating units that generate electricity that are sold within a power system market, such as that with respect to 1 has been discussed. The power plant 501 For example, there may be many possible types of operating modes, including, but not limited to, the various capabilities such as plant thermal power generation units, plant output levels, how the plant responds to changing environmental conditions while meeting a load request, and so on. you Note that the operating modes can be described and defined by operating parameters, the physical characteristics of certain aspects of the plant's operation 501 consider. As in further 17 As shown, the present invention may be a power plant model 502 include. The power plant model 502 may include a computerized representation of the power plant that correlates process inputs and outputs as part of a simulation used to mimic the operation of the plant. As illustrated, the present invention further includes a voting module 503 , a factory control device 505 , a coordinated power plant module 507 ; a factory-operating module 509 ; and an optimizer 510 , which will be discussed individually below.

The power plant 501 can sensors 511 include measuring operating parameters. These sensors 511 As well as the operating parameters they measure, they may include any of those already discussed herein. As part of the present method, the sensors 511 Measurements of operating parameters during an initial, current, or first period of operation (hereinafter, "first period of operation") may be used to tune a power plant mathematical model, which will then be used as part of an optimization process, as discussed below can be to the power plant 501 during a subsequent or second period (hereafter "second operating period") in an improved or optimized mode of operation. The measured operating parameters may themselves be used to evaluate factory performance, or they may be used in calculations to derive performance indicators relating to certain aspects of the operation and performance of the power plant. Note that performance indicators of this type may include the amount of heat, the efficiency, the generation capacity and others. Thus, as an initial step, operating parameters derived from the sensors 511 during the first period of operation, when one or more performance counters are used (or used to calculate values for). As used herein, such values for performance indicators (ie, those based on measurements of operating parameters) are referred to as "measurements." The measurements of the operating parameters and / or the measurements for the performance indicators may, as shown, be sent both to the factory controller 505 as well as the voting module 503 be transmitted. The voting module 503 As will be discussed in more detail below, it may be configured to provide feedback from a data reconciliation process for use in tuning the power plant model 502 used the tuned power plant model 507 to configure.

The power plant model 502 can, as discussed, be a computerized model that is configured to control the operation of the power plant 501 simulated. According to the present method, the power plant model 502 be configured to simulate a power plant operation that is the first period of operation of the power plant 501 equivalent. To achieve this, the power plant model can 502 with information and data concerning the operating parameters of the first operating period. While this information may include any of the operating parameters that are measured during the first period of operation, it should be noted that the input data for the power plant model 502 may be limited to a subset of the measured operating parameters. In this way, the power plant model 502 then used to calculate values for the selected operating parameters that were excluded from the inputted data set. Specifically, the power plant model may be supplied with input data for the simulation that includes many of the values measured for the operating parameters, but of which certain measured values for selected operating parameters have been omitted. As output, the simulation can be configured to predict a simulated value for the selected operating parameter. The present invention may use the simulated values to predict values for the performance indicators. In this case, these values for the performance indicators are referred to herein as "forecast values." In this way, the measurements for the performance indicators, which have been determined directly from measured operating parameters of the power plant, may have corresponding forecast values. As shown, the forecast values for the performance counters can be sent to the reconciliation module 503 be transmitted 514 ,

The voting module 503 can be configured to compare the corresponding metering and forecasting values for the performance counters to determine a difference between them. Note that the difference thus calculated reflects a measure of deviation between the actual performance (or its measurements) and the performance simulated by the power plant model. The power plant model 502 can based on this difference or feedback 515 be matched. In this way, the coordinated power plant model 507 configured. The coordinated power plant model 507 , which may also be referred to as an off-line or predictive model, may then be used to optimize operating modes by simulating proposed or possible operating modes to determine a subsequent operating period. The simulations may include estimates or predictions of future, unknown operating conditions, such as environmental conditions. Note that optimization is based on one or more performance goals 516 based on a cost function. As shown, the performance goals 516 the optimizer 510 via the factory-operating module 509 be communicated.

The process of tuning the factory model may be configured as a repetitive process involving several steps. Note that according to certain embodiments, the power plant model 502 May include algorithms in which logical statements and / or parameterized equations correlate process inputs (ie, fuel supply, air supply, etc.) with process outputs (generated electricity, factory efficiency, etc.). The step of tuning the power plant model 502 may be adjusting one of the algorithms in the power plant model 502 and then simulating the operation of the force 501 over the first period of operation using the adapted power plant model 502 to determine the effect of the adjustment made. Specifically, the forecast value for the performance indicator can be recalculated to determine the effect that the power plant model has on the calculated difference. If it turns out the difference using the adapted power plant model 502 smaller is the power plant model 502 be updated or "co-ordinated" to accommodate this adaptation in the future. Note also that the power plant model 502 can be constructed with multiple logical statements that include performance multipliers that are used to reflect changes in the way the power plant operates under certain conditions. In such cases, tuning the power plant model 502 on the basis of the calculated difference, the steps include: a) making adjustments to one or more of the performance multipliers; b) simulating the operation of the power plant over the first period of operation with the power plant model 502 having the adjusted performance multiplier; and c) recalculating the forecast value for the performance indicator using the power plant model 502 that has been adjusted by the power module to determine if the recalculations will result in a diminished difference. These steps may be repeated until an adjustment made to one of the performance multipliers results in a reduction in the difference, indicating that the model more accurately simulates the current performance. Note, for example, the performance multiplier may affect expected performance degradation based on accumulated operating hours of the plant. In another example, where the performance indicator includes generating capacity, the step of tuning the power plant model may be 502 recommending adjustments to factors based on a difference between a measured generation capacity and a predicted generation capacity. Such adjustments may include changes that ultimately result in the predicted generation capacity being substantially equal to the measured generation capacity. Thus, the step of tuning the power plant model 502 modifying one or more correlations within the power plant model 502 until the predicted or simulated value for a performance indicator is substantially equal (or within a margin) to the measured value for the performance indicator.

After the vote, the procedure then the matched model 507 use to simulate proposed operation of the power plant. In accordance with certain embodiments, a next step of the present method includes determining which simulated operation in view of defined performance goals 516 is preferable. In this way, optimized operating modes of the power plant can be determined. According to a preferred embodiment, the process of determining an optimized mode of operation may include several steps. First, several proposed modes of operation can be selected or selected from among the many possibilities. For each of the proposed modes of operation, appropriate suggested parameter sets may be provided 517 generated for the second period of operation. As used herein, a parameter set defines values for a plurality of operating parameters such that the parameter set as a whole defines or describes aspects of a particular operating mode. Thus, the proposed parameter sets can be configured to accommodate many of the potential operating modes of the power plant 501 describe or affect, and they can be used as input data sets for the co-ordinated power plant model 507 to simulate an operation. Once the operating parameters have been generated and organized into the proposed parameter sets, the tuned power plant model may be used 507 the operation of the power plant 501 simulate according to each one. The optimizer 510 can then see the results of the simulated operation 519 for each of the proposed parameter sets 517 evaluate. The evaluation may be consistent with the performance objectives defined by the plant operator and the cost functions defined therein. The optimization process may include any of the methods described herein.

Cost functions defined by the performance goals may be used to provide a business performance of the simulated operation of the power plant 501 to evaluate over the second operating period. Based on the evaluations, one of the proposed sets of parameters may be considered one that produces a simulated operation that is preferable to that produced by the other proposed sets of parameters. In accordance with the present invention, the mode of operation corresponding to or described by the proposed parameter set producing the most advantageous simulated operation is referred to as the optimized mode of operation. Once optimized, as discussed in more detail below, the optimized operating mode may be forwarded to the factory operator for consideration, or may be communicated to the factory controller for automatic implementation.

According to a preferred embodiment, methods of the present invention may be used to evaluate certain modes of operation to determine and recommend advantageous alternatives. Note that the generator units of the power plant 501 be controlled by actuators having variable setpoints controllable with a control system, such as the factory control device 505 are connected. The operating parameters of the power plant 501 can be divided into three categories: manipulated variables, disturbance variables and controlled variables. The manipulated variables relate to controllable process inputs which can be manipulated via actuators in order to control the controlled variables, while the disturbances relate to non-controllable process inputs which have an effect on the controlled variables. The controlled variables are the proposed outputs that are controlled with respect to defined target levels. According to preferred embodiments, the control method may include receiving prediction values for the disturbances for the second operating period (ie, the operating voltage for which an optimized operating mode has been calculated). The disturbances may include environmental conditions, such as outside air temperature, pressure and humidity. In such cases, the proposed sets of parameters generated for the second period of operation may include values for the disturbances pertaining to the predicted values for the disturbances. More specifically, the generated values for each environmental condition parameter may include a range of values for each of the environmental condition parameters. The range may include, for example, a lower case, a middle case, and an upper case. Note that a factory operator can plan best / worst case scenarios by having multiple cases. The predictive values may include probability ratings corresponding to the various cases, which may further assist the operator of the plant to plan for different operating contingents and / or to hedge against losses.

The step of generating the proposed parameter sets may include generating target levels for the controlled variables. The target levels can be generated to match competing or alternative modes of operation of the power plant 501 and may include an operator input. Such input from the operator may be from the factory-operating module 509 be required. According to a preferred embodiment, such target levels may be a desired output power level for the power plant 501 based on output levels that are likely in view of past usage patterns for the plant. As used herein, "output power level" reflects a load level or level of the power plant 501 during the second period of operation generated electricity for commercial distribution. The step of generating the suggested parameter sets may include generating a plurality of cases where the output power level remains the same or constant. Such a constant output power level may reflect a base load for the factory or set of generator units. Multiple target levels can be generated, each corresponding to a different commitment level of the generator units, resulting in operating modes that are likely in view of past usage. The method may then determine the most efficient mode of operation in view of known limitations. In addition, the proposed parameter sets can be generated so that the disturbances maintain a constant level for the multiple cases generated for each target level. The constant level for the disturbances can be based on received predictive values. In such cases, in accordance with one aspect of the present invention, the step of generating the proposed parameter sets involves creating multiple instances wherein the manipulated variables are varied over ranges to determine an optimized mode of operation for achieving a baseline level in view of the anticipated or expected environmental conditions. According to embodiments, the cost function is defined as the plant's efficiency or heat input, or may include a more direct business indicator, such as operating costs, revenue, or profit. In this way, the most cost effective method of controlling the power plant 501 in situations where a base load is known and disturbances can be predicted with a relatively high degree of accuracy. The optimized operating mode that may be determined by the present invention in such cases may be configured to include a particular control solution (ie, certain setpoints and / or ranges therefor for the actuators that control the manipulated variables of the power plant) the factory control device 505 could be used to achieve a more optimal function. Calculated in this way, the control solution represents the optimized mode of operation to achieve a defined or contracted target load, given the values anticipated for the various disturbances. This type of functionality can serve as a day-to-day or within-market optimization advisor or check that silently analyzes ongoing operations for the purpose of finding more efficient operating modes that still achieve fixed load levels. For example, in the course of the market period covered by the previous dispatch call, environmental conditions will be known or at least the confidence level with respect to their exact forecasting will be higher than what was estimated during the tendering process. In view of this, the present method can be used for optimized control solutions to achieve the allocated load given the safer knowledge of environmental conditions. This special functionality is in 17 as second parameter sets 517 and simulated operation 519 in relation to the second parameter sets 517 shown. In this way, the optimization process of the present invention may also include a "fine-tuning" aspect through the simulation runs on the tuned power plant model 507 recommend one or more effective control solutions, which can then be sent to and implemented by the factory control facility.

Another aspect of the present invention includes use for optimized fuel purchases for the power plant 501 , Note that power plants typically make regular fuel purchases on fuel markets that operate in a certain way. More specifically, these fuel markets typically operate on a forecast basis, with the power plants 501 Predict the amount of fuel needed for a future operating period and then make purchases based on these forecasts. In such systems, the power plants strive 501 after that, to maximize their profits by keeping their fuel stocks low. The power plants 501 however, regularly buy extra fuel to avoid the expensive situation of not having enough stock of purchased fuel to produce the amount of power that the plant has committed to deliver during the dispatch process. Such a situation can occur if, for example, changed environmental conditions lead to less efficient power generation than programmed or the true generating capacity of the power plant is overestimated. Note that several aspects of the present invention already discussed may be used to determine an optimized mode of operation and, based thereon, to calculate a high accuracy forecast for the required fuel stock. That is, the present optimization techniques can provide a more accurate forecast of plant efficiency and load capacities that can be used to estimate the amount of fuel needed for a future period of operation. This allows factory operators to make fuel purchases scarcer, which benefits the plant's operational performance.

The present invention, according to an alternative embodiment, includes a method of optimizing factory performance by defining a forecast horizon and using it in the optimization problem. Note that a forecast horizon is a future operating period that is divided into regularly recurring intervals to determine an optimized operating mode for an initial time interval of the forecast horizon. Specifically, power plant operation is optimized by optimizing performance over the entire forecast horizon, which is then used to determine an optimized operating mode for the initial period. Note that the process may then be repeated to determine how the power plant should operate during the next time interval, which, as should be noted, becomes the initial period with respect to the next repetition of the optimization cycle. For this subsequent optimization, the forecast horizon may remain the same, but it will be redefined relative to what is now defined as the initial time interval. This means that the forecast horizon is effectively pushed further into the future for each repetition over an additional time interval. As said, a "suggested parameter set" refers to a data set containing values for multiple operating parameters and thereby defines or describes one of the possible operating modes for the power plant 501 , In a preferred embodiment, the process of determining the optimized operating mode in cases including a forecast horizon may include one or more of the following steps: First, multiple proposed horizon parameter sets are generated for the forecast horizon. As used herein, a "proposed horizon parameter set" includes a suggested parameter set for each of the time intervals of the forecast horizon. For example, a 24-hour forecast horizon may be defined to have 24 1-hour time intervals which means that the proposed horizon parameter set includes suggested parameter sets for each of the 24 time intervals. In a next step, the proposed horizon parameter sets are used to simulate operation over the forecast horizon. Then, for each of the simulation runs, the cost function is used to evaluate a business performance to determine which of the proposed horizon parameter sets is most advantageous or, as used herein, an "optimized horizon simulation run". According to embodiments, the operating mode described within the optimized horizon simulation run for the initial period of the forecast horizon may then be referred to as the optimized operating mode for the operating period corresponding to the initial period. The optimization process can then be repeated for the following time intervals. The present invention may include receiving prediction values for the disturbance variables for each of the time intervals defined within the forecast horizon. The proposed horizon parameter sets are then generated so that the proposed parameter sets corresponding to the individual time intervals include values for the disturbances related to the predicted values received for the disturbances.

Note that the proposed horizon parameter sets can be generated to cover a range of values for the disturbances. As before, this range may include multiple cases for each of the disturbances and may include large and small values, each representing cases above and below the predictive values. Note that according to each of the embodiments described above, the steps of simulating operating modes and determining optimized operating modes therefrom may be repeated and configured as a repetitive process. As used herein, each repetition is referred to as an "optimization cycle." Note that each iteration may involve defining a next or next operating period for optimization. This subsequent period may come immediately after the operating period optimized from the previous cycle or may include an operating period corresponding to a future period, which may be the case, for example, when the present method is used for the purposes of creating dispatch bids or advice on the impact of alternative maintenance plans.

The steps of tuning the power plant model 502 can be repeated to the tuned power plant model 507 to update. In this way, a coordinated power plant model 507 , which reflects a recent poll, can be used with optimization cycles to produce more effective results. According to alternative embodiments, the optimization cycle and the cycle of tuning the power plant model 502 be independent with respect to each other so that each cycle follows its own plan. In other embodiments, the power plant model 502 updated or reconciled after a predefined number of repetitions of the optimization cycle. The updated coordinated power plant model 507 is then used in the following optimization cycles until the predefined number of repetitions has occurred to initiate a different tuning cycle. In certain embodiments, the tuning cycle follows each optimization cycle. According to alternative embodiments, the number of optimization cycles is a vote of the power plant model 502 initiate, related to the number of time intervals of the forecast horizon.

As said, the present invention can be the operation of power plants 501 according to performance goals that can be defined by the plant operator. According to preferred embodiments, the present method is used to optimize the operation of the power plant business. In such cases, the performance goals include and define a cost function that provides the criteria of business optimization. According to embodiments, the simulated operation includes output for the individual proposed parameter sets as output for selected performance counters. The cost function may include an algorithm that correlates correlating the forecast values for the performance indicators with operating costs or any other indication of business performance. Other performance indicators that may be used in this manner include, for example, heat input and / or fuel consumption of the power plant. According to alternative embodiments, simulation results include forecast values for hot gas path temperatures for one or more thermal power generation units of the power plant 501 which can be used to calculate life cycle costs of a used component. These costs reflect costs for predicted material degradation associated with the hot gas path components resulting from the simulated operation. The cost function may further include an algorithm that correlates forecast values for the performance indicators with operating revenue. In such cases, the operating income can then be compared with the operating costs to obtain a net profit or profit for the power plant 501 reflect. The present method can further comprising the step of receiving an anticipated price of electricity sold within the market for the period just optimized, and the selected performance indicators may include an expenditure level of the electricity that may then be used to provide an expected operating revenue for the upcoming period of operation to calculate. In this way, the present method can be used to maximize business revenue by comparing operating costs and revenue.

Note that performance objectives may be further defined to include selected operability limitations. In accordance with certain alternative embodiments, the present method includes the step of disqualifying any of the suggested parameter sets that produce a simulated operation that violates any of the defined operability constraints. Operability limitations may include, for example, emission thresholds, maximum operating temperatures, maximum mechanical stress levels, etc., as well as legal or environmental regulations, safety regulations, and / or thresholds and limits for machinery and / or components.

As said, the present method involves generating suggested parameter sets 517 , the alternative or possible operating modes of the power plant 501 describe. As shown, the proposed parameter sets 517 in the factory driver module 509 can be generated and may include inputs from a plant manager or from persons as operators. Generally speaking, the possible modes of operation may be considered as competing modes for which simulations are performed to determine the mode of operation that best meets the performance goals and expected conditions. According to embodiments, these alternative modes of operation may be selected or defined in various ways. According to a preferred embodiment, the alternative operating modes include different output power levels for the power plant 501 , Output power level, as used herein, means the amount of electricity that comes from the power plant 501 generated during a defined market period for commercial distribution. These suggested parameter sets may be configured to define multiple cases of the different output power levels. Multiple output power levels may be covered by the proposed parameter sets, and those that are selected may be configured to operate with a range of possible output power levels for the power plant 501 to match. Note that the range of possible output power levels need not be linear. More specifically, because of the multiple generator units of the power plant and the associated scalability constraints, the proposed parameter sets may be grouped or concentrated to levels, given the particular configuration of the power plant 501 more accessible or preferred.

As I said, each of the competing modes of operation can involve multiple cases. In cases where the competing modes of operation are defined differently, the multiple cases may be chosen to reflect a different way in which the output power level is achieved. Where the power plant has multiple generator units, the multiple cases at each output power level can be distinguished according to how the individual thermal power generation units are operated and / or deployed. According to an embodiment, the multiple generated cases are distinguished by varying the percentage of the output power level provided by the generator units. For example, the power plant 501 a gas and steam combined cycle power plant 501 in which heat generating units include gas and steam turbines. Additionally, the gas and steam turbines may each be assisted by an intake conditioning system, such as a chiller, and an HRSG duct firing system. Note that, for example, the intake conditioning system may be configured to cool the intake air of the gas turbine to increase its generating capacity, and the HRSG duct combustion system may be configured as a secondary heat source for the boiler to increase the generation capacity of the steam turbine. According to this example, the heat generating units include the gas turbine or, alternatively, the gas turbine reinforced by the intake conditioning system, or alternatively, the steam turbine reinforced by the HRSG duct combustion system. The multiple cases covered by the proposed parameter sets may then include cases where these particular thermal energy generating units are used in a variety of ways while still achieving the various output power levels selected as competing operating modes. The simulated operation may then be analyzed to determine which reflects an optimized mode of operation according to a defined criterion.

According to an alternative embodiment, the proposed parameter sets may be pulled to different operating modes to calculate business benefits of maintenance operations. To achieve this, one of the competing modes of operation can be considered one in which it is assumed that the maintenance operation will be completed before the period of operation chosen for the optimization. This mode of operation may be defined to reflect a power gain that is expected to accompany completion of this maintenance operation. An alternate mode of operation may be defined as one in which the maintenance operation is not performed, meaning that the simulation of the multiple cases for that mode of operation would not include the expected performance gain. The results from the simulations can then be analyzed to better understand the business impact, and the multiple cases can be used to show how different scenarios (eg, fuel price fluctuations or unexpected environmental conditions) affect the outcome. Note that the competing modes of operation may include a load reduction mode and a shutdown mode based on the same principles.

The present invention also includes various ways in which the optimization process of power plant operators can be used to automate processes and improve efficiency and performance. According to one embodiment, the method includes, as in 17 is shown the step of notifying a calculated optimized operating mode 521 for the factory-operating module 509 for approval by a person as operator before the power plant 501 is controlled in accordance with the optimized operating mode. In an advisory mode, the present invention may be configured to present alternative operating modes and the associated business effects associated therewith to alert the factory operator to such alternatives. Alternatively, the control system of the present invention may function to automatically implement optimized solutions. In such cases, the optimized operating mode of the factory controller 505 be automatically communicated to a controller of the power plant 501 in a consistent manner. In power systems, which is a load optimization system for sharing electricity generation among a group of power plants 501 The optimization method of the present invention may be used to produce more accurate and competitive bids submitted to the central entity or dispatcher. As one of ordinary skill in the art will appreciate, the optimization features already described may be used to generate bids that reflect true generation capacity, true efficiency, and heat input, while also providing useful information to plant operators regarding the business dependencies that the power plant is exposed to future market periods by choosing between different operating modes. Increased accuracy of this kind and additional analysis help to ensure that the power plant remains competitive in the tendering process, while also minimizing the risk of highly unprofitable dispatch results due to unpredictable randomness.

The 18 to 21 illustrate embodiments of the present invention relating to a load lowering and / or shutdown operation of a power plant. The first embodiment, in the flowchart 600 from 18 - which may be referred to as "load reduction advisor" - teaches methods and systems for simulating and optimizing a load reduction level for the power plant during a defined or selected operating period ("selected operating period"). In preferred embodiment, the present method is used with multiple gas turbine power plants which may include gas and steam combined cycle power plants having multiple gas turbines and one or more steam turbines. The tuned power plant model may be used to determine an optimized minimum load to operate the power plant at a lowered load level during the selected operating period. As previously stated, an "optimized" mode of operation may be defined as one that is considered superior or evaluated as one or more other possible modes of operation. An operating mode for the purpose of these embodiments may include transmitting the task of meeting a load obligation or other performance goals to particular power generation units as well as the physical configuration of the generator units within a power plant. Such functionality means that upon attaining an optimized or improved mode of operation, the present invention may contemplate a variety of factory combinations taking into account the various load reduction configurations of each generator unit as well as configurations that disable one or more of the units while others continue to operate at a full load or lowered load level. The method may further include other limitations, such as operability limitations, performance objectives, cost functions, operator inputs, and environmental conditions, in its calculation of an improved power plant down load operating mode that enhances performance and / or efficiency. The present method, as described herein and / or set forth in the appended claims, may include current and predicted environmental conditions for optimization of the load reduction mode as well as a change in unit configuration and / or control to dynamically adjust the operation of one or more of the generator units when actual conditions deviate from those predicted. In accordance with a preferred embodiment, such performance is defined, at least in part, as one that minimizes the amount of fuel utilization or fuel consumption over the proposed load reduction operating period.

The load reduction advisor of the present invention may consider several factors, criteria, and / or operating parameters when achieving an optimized or improved load reduction solution and / or recommended load reduction action. In preferred embodiments, these include, but are not limited to, the following: gas turbine operating limits (ie, temperature, aerodynamics, fuel distribution, lean extinction, mechanical, and emission limits); Gas turbine and steam turbine control systems; the minimum steam turbine throttle temperature; the maintenance of the vacuum seal on the radiator as well as other factors, such as the configuration or installation of systems or their control. One of the results of the optimization may include a recommended operating mode and the configuration of the power plant or a plurality of power plants, the plurality including various types of power plants, including wind, solar piston, nuclear, and / or other types. Note that the recommended operating mode can be initiated automatically or transmitted electronically to a factory operator for approval. Such control may be implemented via off-premise or on-premise control systems configured to control operation of the generator units. In addition, in situations where the power plant includes multiple gas turbines, the output of the present method may include determining which of the gas turbines should continue to operate and which should be shut down during the load reduction operation, which is a process. the more detail with reference to 19 is discussed. For each of the gas turbines that the advisor recommends for continued operation during the load-reduction period, the present method may further calculate a load level. A further result may include calculating the total load for the power plant during the load reduction period as well as the hourly target load profile based on the predicted environmental conditions which, as stated, may be adjusted as conditions change. The present invention may also calculate the programmed fuel consumption and emissions of the power plant during the load reduction period. The result of the disclosed method may include the operational set-up / configuration in view of the control setpoints available to the generator units and the plant to more efficiently achieve the desired generation levels.

As described above, traders and / or plant managers (hereafter referred to as "factory operators" unless they distinguish between them) not bound by existing terms and conditions typically present their power plants in a prospective market, such as a one-day market lies in the future. As an additional consideration, plant operators have the task of ensuring that sufficient fuel stocks are maintained to enable the plant to achieve targeted or contractually assured production levels. However, fuel markets in many cases function prospectively so that advantageous pricing is possible for power plants that are willing or able to commit themselves in advance to future fuel purchases. More specifically, the earlier the fuel is purchased, the cheaper the price. In order for a power plant to achieve an optimized or high level of economic return, the plant operator must, given this market momentum, give the plant a competitive bid compared to other generator units to use its generation capacity and accurately estimate the fuel that will be used for future production periods is needed so that: 1) the fuel can be bought in advance to secure the lower price; and 2) no large fuel cache is needed to enable lean stockkeeping. If successful, the plant operator will secure better prices by earning early fuel purchases, but not buying too much, resulting in unnecessary and expensive fuel reserves, or under-buying, thereby risking a fuel shortage.

Methods of the present invention may optimize or improve the efficiency and profitability of power generation activities by providing an IHR profile for a generator unit or a particular factory configuration, particularly because this is related to the creation of a dispatch bid to secure a market share , The present method may include prescribing an optimal generation allocation between multiple generator units within a power plant or between multiple plants. The present method may take into account the operating and control configurations available to such generator units which permute and thereby enable possible arrangements Bid, if selected, allows for generation of performance over the bidding period at reduced or minimized cost. As a result, the present method may take into account all applicable physical, regulatory and / or contractual constraints. As part of the overall process, the present method may be used to optimize or improve a load reduction and shutdown operation for a power plant having multiple generator units. This approach may include consideration of expected exogenous conditions, such as weather or environmental conditions, gas quality, generator unit reliability, as well as additional obligations, such as steam generation. The present method may be used to specify IHR profiles for a plurality of multi-configuration generator units as well as control settings for the selected load-reduction configuration and then to perform control over the expected exogenous conditions for the dispatch dispatch of the plant.

A common decision that operators must make is whether to perform a load reduction or shutdown of the power plant outside of full load operating times, for example, at night when demand or load requirements are minimal. Note that the outcome of this decision depends heavily on whether the plant operator understands the business impact on each of these possible operating modes. In certain cases, the decision to lower the load on the power plant may be obvious, while the optimal minimum load at which the power plant is to be maintained during the load reduction period is not clear. That is, although the factory operator has decided to reduce the load of the power plant over a period of time, the operator is uncertain about the operating points during load lowering, where the multiple generator units of the power plant are to be operated in the most cost effective manner.

The Load Reduction Advisor of 18 can be used as part of a process that recommends an optimal minimum load at which the power plant should operate. This advisor function may also recommend the best course of action for the power plant given a particular scenario of environmental conditions, business inputs, and operational parameters and constraints. From these inputs, the process can calculate the best operating levels and then determine the operating parameters needed to control the power plant, which will be discussed in more detail below 19 will be discussed. Note that this functionality may result in several additional benefits, including extended component life, more efficient load reduction operation, improved performance, and greater fuel purchase accuracy.

As in the flowchart 600 can display certain information and relevant criteria during the initial steps. In step 602 Data, variables and other factors related to power plant systems and generator units can be determined. This may include any of the factors or information listed above. In accordance with a preferred embodiment, an environmental profile may be received including a prediction of environmental conditions during the selected period of operation. Relevant emissions data may also be collected as part of this step, which may include emission limits as well as emissions previously incurred by the power plant. Another factor includes data related to the potential sale of electricity and / or steam during the selected operating period. Other variables that may be determined as part of this step include the number of factory gas turbines, the combustion and control systems for each of the gas turbines, as well as any other plant-specific constraints that may be relevant to the calculations discussed below.

In step 604 the period for the proposed load reduction operation (or the "selected period of operation") can be accurately determined. Note that this may be defined by a user or plant operation and includes a selected period of operation in which an analysis of available load reduction modes is desired. The definition of the selected operating period may include its expected length as well as a user specified start time (ie, the time at which the selected operating period begins) and / or stop time (ie, the time at which the selected operating period ends). This step may further include defining an interval within the selected operating period. The interval may be configured to divide the selected period of operation into a plurality of consecutive and periodically spaced periods. For the example given herein, the interval should be defined as one hour and the selected operating period should be defined to include several of the one-hour intervals.

In step 606 the number of gas turbines involved in the optimization process for the selected operating period can be selected. This may include all gas turbines of the power plant or any part thereof. The method may further include viewing other generator units in the power plant, such as steam turbine systems, and taking into account their operating conditions during the selected operating period, as described in more detail below. The determination of the gas turbines involved in the load reduction operation may involve the interrogation or receipt of input from the factory operator.

In step 608 For example, the present method may configure a permutation matrix based on the number of gas turbines that have been determined as part of the proposed load reduction operation during the selected operating period. Note that the permutation matrix is a matrix that includes the various ways in which the multiple gas turbines can be deployed or operated during the selected operating period. As in the exemplary permutation matrix 609 from 18 For example, in the case of two gas turbines, the permutation matrix includes four different combinations covering each of the possible configurations. More specifically, if the power plant includes first and second gas turbines, the permutation matrix includes the following lines or cases: a) the first and second gas turbines are both "on", ie, operating in a load dump mode 2) the first and second Gas turbine are both "off", ie they are operated in a shutdown mode; 3) the first gas turbine is "on" and the second gas turbine is "off" and 4) the first gas turbine is "off" and the second gas turbine is "on". Note that in the case of a single gas turbine, only two permutations are possible, while for three gas turbines seven different lines or cases would be possible, each of which represents a different configuration for how the three gas turbines will operate during a given time frame "And" off "operating states can be used. In relation to 17 and the optimization process discussed in the accompanying text, any case or series of permutation matrix can be thought of as representing another or concurrent mode of operation.

As part of the steps taken by the steps 610 . 613 . 614 . 616 and 618 can be configured, the present method can configure proposed parameter sets for the proposed load reduction operation. As mentioned, the selected period of operation can be divided into the several hourly time intervals. The process of configuring the suggested parameter sets can be done in step 610 begin where it is determined whether each of the intervals has been addressed. If the answer to this question is yes, the process may proceed to an output step (ie, step 611 ), in which the result of the load reduction analysis is given to an operator 612 is submitted. If not all intervals have been covered, the process may go to step 613 continue by selecting one of the uncovered intervals. In step 614 Environmental conditions may then be set for the selected interval based on received predictions. The process is going to move 616 where it can select a line from the permutation matrix, and in step 618 Set an on / off state of the gas turbine according to the respective row.

From there, the present process can proceed along two different paths. More specifically, the method may proceed to an optimization step, which may be from step 620 but it may also become a decision step in step 621 continue where the process determines whether all permutations or rows of the permutation matrix have been covered for the selected interval. If the answer to this is "no", the process may go to step 616 Jump back where another permutation line is selected for the interval. If the answer to this is yes, the process may proceed as shown 610 go on to determine if all intervals have been covered. Note that once all the rows of the permutation matrix have been addressed for each interval, the process goes to the output step of step 611 can go on.

In step 620 For example, the present method can optimize performance using the tuned power plant model, as previously discussed in US Pat 17 has been discussed. In accordance with this approach, multiple cases may be generated for each of the concurrent modes of operation, ie, each of the rows of the permutation matrix for each of the intervals of the selected period of operation. In accordance with a preferred embodiment, the present method generates suggested parameter sets in which multiple operating parameters are varied to determine the effect on a selected operating parameter or performance indicator. For example, in accordance with this embodiment, the proposed parameter _{sets may} include manipulating inlet _{guide vane} ("IGV") and / or turbine outlet temperature ("T _exh ") _settings to determine which combination is in view of the on / off state of the particular one Series and the environmental conditions predicted for the respective interval, a minimized total To give fuel consumption rate for the power plant. Note that an operation that minimizes fuel consumption while meeting the other limitations associated with the load dump operation is one way in which the performance of a load reduction can be optimized or at least operationally improved with respect to one or more other operating modes ,

As illustrated, cost functions, performance goals, and / or operability limitations of the present invention may be used during this optimization process, in accordance with certain embodiments. These can be provided via a factory operator, what step by step 622 is pictured. These limitations may include limits on the settings of the IGV, T _exh limits, combustion limits, etc., as well as those associated with other thermal power systems that are part of the power plant. For example, in gas and steam combined cycle power plants, the operation or maintenance of the steam turbine during load-dumping operation may present certain limitations, such as the maintenance of a minimum steam temperature or a vacuum seal of the radiator. Another operability limitation may include the necessary logic that certain auxiliary systems may be affected in certain modes of operation and / or certain subsystems may be mutually exclusive, such as evaporative coolers and chillers.

After the present process has iterated through the intervals and the various lines of the permutation matrix, the results of the optimization can be passed to the plant operator in step 611 be communicated. These results may include an optimized case for each of the rows of the permutation matrix for each of the time intervals. In one example, the output describes optimized operation defined by a cost function of the fuel consumption for the power plant for each of the permutations for each of the intervals. More specifically, the output may include the minimum fuel demand (optimized using the tuned power plant model according to already described methods) for each of the possible factory configurations (represented by the rows of the permutation matrix) for each interval while meeting performance constraints, performance goals, and expected environmental conditions. In another embodiment, the output includes an optimization that minimizes a generator output power level (ie, megawatts) for the possible factory configurations for each of the intervals in the same manner. Note that some of the possible factory configurations (represented by the permutations of the permutation matrix) may not be able to meet operational limitations, regardless of the fueling to produce an output power level. Such results may be discarded and not considered further or as part of the outcome of step 611 be communicated.

The 19 and 20 Graphically illustrate ways in which a gas turbine of a power plant over a selected period of operation, including defined intervals ("I" in the figures), can be operated in the face of typical transient operation constraints. Note that a transient operation involves switching a generator unit between different modes of operation, including those involving a transition to or from a shutdown mode of operation. As shown, several operating paths or sequences 639 which depends on the following: 1) an initial state 640 the gas turbine; and 2) the decisions made with respect to how to change the modes of operation at the intervals where changes are possible in view of the transient operating limitations. Note that several different sequences 639 represent the several ways that the generator unit can be operated over the intervals shown.

Note that the output of the method of 18 in conjunction with the diagrams of 19 and 20 can be used to configure proposed load reduction operating sequences for the generator units of a power plant. This means, 19 and 20 show examples of how a generator unit of a power plant can be used and how its modes of operation are modified over time, which may include cases where the operating mode of the generator unit remains unchanged, cases where the mode of operation of the unit changed from a shutdown mode to a load mode low mode as well as cases where the operating mode of the unit is modified from a shutdown mode of operation to a load lowering mode of operation. As illustrated, the transient operating restriction used in this example is that modifying an operating mode requires the unit to remain in the modified operating mode for a minimum of at least two of the intervals. The many sequences (or paths) over which the generator unit arrives at the last interval represent the possible load reduction operating sequences available to the unit in view of the transient operating limitations.

Note that the analysis results of 18 - ie, the optimized load reduction operation for each of the matrix permutations - may be used to select from among the possible load reduction operation sequences a plurality of preferred cases, which may be referred to as proposed load reduction operation sequences. More specific can be given in conjunction with 18 described method, the proposed load reduction operation sequences are selected from cases of a load reduction operation that meet factory performance goals and constraints while optimizing performance according to a selected cost function (eg, MW output or fuel consumption). The in the 19 and 20 Considerations presented represent one way in which load dumping operation sequences can be achieved in light of transient operating limitations. That is, the proposed load reduction sequences in which the combined analysis of the 18 to 20 are operational sequences that are consistent with time constraints associated with the transition of a unit from one mode of operation to another.

Well, you should 21 Considering where a method is provided to further model and analyze the load reduction operation of a power plant. Note that this method can be used to analyze load reduction costs versus shutdown costs for certain cases involving a single generator unit over a defined time interval. However, it may also be used to analyze plant-level costs where a recommendation is sought in terms of ways to control the operation of multiple generator units over a selected operating period at multiple intervals. In this way, the output of the 18 and 20 in order to configure possible operating modes and limitations over the span of multiple intervals, which, as will be shown, then according to 21 can be analyzed to provide a more in-depth understanding of load reduction operation over a wider operating range.

As already discussed, factory operators must periodically choose between load reduction and shutdown modes of operation during periods when no full load operation is taking place. Although certain conditions may make the decision easy, it is often difficult, especially in view of the increased complexity of the modern power plant and the multiple heat generating units that are commonly included in each. Note that the decision for a load-down mode or shut-down of a power plant is significantly dependent on a full appreciation of the business benefits associated with each operating mode. The present invention may according to an alternative embodiment, which in 21 can be used by factory operators to gain a better understanding of the trade-offs associated with each of these different operating modes to facilitate decision-making. According to some embodiments, the method of 21 together with the load lowering operating guide 18 be used to provide a combined advisory function that: 1) recommends the best course of action between load-lowering and shut-down operating modes for the generating unit generator units in the face of known conditions and business factors; and 2) the lowest optimum sink load level recommends when a load dump operation is the best practice for some of these units. In this way, plant operators can provide situations in which the lower load units of the power plants should be run or shut down based on what is the best business practice for the power plant given a particular scenario of environmental conditions, business inputs, and business parameters. easier to decide. Additional benefits, such as extending the life of components, are also possible. Also note that in terms of the 18 and 21 described methods and systems can be used separately.

General specifies the procedure of the flowchart 700 - which may also be part of, or may be referred to herein as a "load reduction advisor" - user inputs and data from analytical operations to perform calculations that evaluate costs associated with a load reduction of a power plant from those of the shutdown. Note that the flowchart 700 from 21 provides this advisory feature according to some embodiments using the tuned power plant model discussed in detail above. As part of this functionality, the present invention may make recommendations as to the various outputs, be they commercial or otherwise, between load lowering and shutting down a power plant during times when no full load is being driven. The present invention can provide relevant data that clarifies whether power plant load reduction is preferable to shutdown over a given market period. In certain embodiments, then, the lower cost operation may be considered by the factory operator appropriate action may be recommended, but as noted herein, the factory operator may also be notified of additional issues or other considerations that may affect the decision. The present method may indicate potential costs as well as the likelihood that such costs will be incurred, and these considerations may influence the final decision as to which operating mode is preferable. Such considerations may include, for example, a complete analysis of both short-term operating costs and long-term operating costs associated with plant maintenance, plant efficiency, emissions levels, plant upgrades, etc.

It should be noted that the load reduction guide may be implemented using many of the systems and methods described above, particularly those discussed in connection with FIGS 16 to 20 have been discussed. The Load Reduction Advisor of 21 may capture and use one or more of the following types of data: user specified start and end times for the proposed load reduction period (ie, the period of time for which the load reduction mode of operation is analyzed or considered); Fuel costs; Environmental conditions; Timeout trigger; Use of alternative power; Sale / price of electricity or steam during the relevant period; Operating and maintenance costs over the period; User input; calculated lowering load; forecast emissions for operation; current emission levels used by the power plant and the limits for defined regulatory periods; Specifications relating to the operation of the rotary device; Rules and equipment relating to rinsing processes; Fixed costs for power plant operating modes; Costs in relation to the starting operation; Start-up safety of the factory; Quantity deviation charges or penalties for start delays; Emissions in connection with starting up; Fuel consumption rate for auxiliary boiler, if steam turbine is present; and historical data related to how the power plant's gas turbines worked in the load reduction and shutdown modes of operation. In some embodiments, the performance of the present invention, as discussed below, may include: a recommended operating mode (ie, load-lowering and shut-down mode of operation) for the power plant over the relevant period of time; Costs associated with the individual operating modes; a recommended factory load and a load profile over time; a recommended time to initiate the startup of a unit; as well as emissions that have been used in one year and emissions that remain for the year. According to certain embodiments, the present invention may calculate the fuel consumption and emissions of the power plant over the relevant time period, which may then be used to calculate the cost of a load reduction versus shutdown for one or more concrete gas turbines. The present method can use the cost of the individual gas turbines in the load reduction and shutdown modes to determine the combination that has the lowest cost of ownership. Such optimization may be based on various criteria that can be defined by the factory operator. For example, the criteria may be based on yield, net yield, emissions, efficiency, fuel consumption, etc. In addition, according to alternative embodiments, the present method may recommend concrete actions, such as whether to purgish or not (Purge Credit); which gas turbine units should be shut down and / or at which the load should be lowered (which may be based, for example, on start-up safety and potential quantity deviation fees caused due to a start-up delay). The present invention may also be used to improve fuel consumption forecasts to make future fuel purchases more accurate or, alternatively, to allow for fuel purchases for market periods that are further in the future, which has a positive impact on fuel prices and / or. or should have the provision of a leaner fuel supply or travel.

19 shows an embodiment of a load reduction guide according to an embodiment of the present invention in the form of a flowchart 700 , The load reduction advisor may be used to indicate the relative cost of shutting down one or a portion of a power plant over a future period of operation while simultaneously operating others of the generator units in the load reduction mode. According to this embodiment, the potential costs associated with the shutdown and load reduction modes may be analyzed and then communicated to a factory operator to take appropriate action.

As an initial step, certain data or operating parameters may be detected that affect operating costs during the selected load-down period or that may be used to determine them. These are divided, as shown, into: load lowering data 701 , Shutdown data 702 and shared data 703 , The common data 703 include such cost items that relate to both shutdown and load reduction operations. The common data 703 contain for example, the selected period of operation for which the analysis of the load reduction mode is performed. Note that more than just a selected operating period may be separately defined and analyzed for competing load-lowering operating modes, so that a wider optimization over an extended time frame is achieved. Note that the definition of the selected operating period may include defining the length of the period as well as its start or end point. As shown, other common data 703 include: the price of the fuel; the various emission limits for the power plant and data related to environmental conditions. With regard to emission limit values, the data collected may include limit values that may be used during a defined regulatory period, for example during one year, and how far the applicable regulatory period has already progressed. Emission data may also include penalties or other costs associated with exceeding any of these limits. In this way, the present method may be informed of the current state of the power plant in terms of annual or periodic regulatory limits as well as the likelihood of possible injury and penalties associated with such non-compliance. This information may be important in deciding whether generator units should be shut down or their load lowered as each type of operation has a different impact on factory emissions. As for environmental condition data, such data may be obtained and used in accordance with those processes already described herein.

Note that the load reduction mode of operation has data that is uniquely relevant to determining the associated operating costs. Such load reduction data 701 As shown, the yield that can be achieved from the generated power while the power plant is operating at the lowered load level. More specifically, the load lowering operation mode is one in which the power generation continues, albeit at a low level, and therefore, there is the possibility that this power brings a yield to the power plant. Insofar as this happens, the revenue may be used to carry some of the other operating costs associated with the load dump mode of operation. Thus, the present invention involves receiving a price or other business indication in connection with the sale or commercial use of the power produced by the factory while operating in the load-down mode. This may be based on historical data, and the yield achieved may depend on the lowered load level at which the power plant is operating.

The load reduction data 701 may further include operation and maintenance associated with operating the factory at the lowered load level during the selected period of operation. This may also be based on historical data, and these costs may depend on the lowered load level for the power plant and how the power plant is configured. In some cases, this change may be reflected as costs per hour, which are dependent on the load level and records of similar operation in the past. The load reduction data 701 may further include data related to factory emissions during load lowering mode operation.

The load reduction data 702 Also, there are several points that are unique to the load reduction mode of operation, and this type of data can be captured at this stage of the current process. According to certain embodiments, this includes data related to the operation of the rotary device during the shutdown period. In addition, data is defined in relation to the various phases of the shutdown operation. These may include, for example, data relating to: the shutdown operation per se, which may include history data over the length of time necessary to bring the generator units from a regular load level to a state where the rotary apparatus is used; the length of time that the power plant remains switched off according to the selected operating period; the length of time that the generator unit typically remains on the rotating device; and data related to the process by which the generator units are restarted or brought back online after they have been shut down, as well as with respect to the time required to do so, starting fuel demand and launch emission data. In determining the startup time, information regarding the possible types of start-up for the generator unit and related specifications may be determined. As one skilled in the art will appreciate, start-up processes may depend on the time over which the power plant remains shut down. Another consideration that affects the start-up time is whether the power plant includes certain features that affect or shorten the start-up time and / or whether the operator of the power plant chooses to use one of these features. For example, a purge process, if necessary, can extend the startup time. However, purging may be postponed if the power plant has been shut off in a certain way. Fixed costs in The shutdown operation, including those associated with startup, may be determined during this step, as well as costs specific to one of the generator units concerned. Emissions data related to the startup and / or shutdown of the power plant can also be determined. These can be based, among other things, on records of past operations. Finally, data relating to the starting safety of each of the heat generating units can be determined. Note that fees, penalties and / or penalties may be imposed on power plants if the unit's re-grid delivery process involves delays that result in the power plant being unable to meet load obligations. These costs can be determined and, as will be discussed in more detail below, given the history of start-up safety. In this way, such fees may be discounted to reflect and / or include the likelihood of incurring costs in order to protect or hedge against the risk of such charges.

From the initial data collection steps of 701 to 703 can the in 19 illustrated embodiment of a load reduction analyzer 710 and a shutdown analyzer 719 each of which may be configured to charge operating costs for the operating mode to which it corresponds. As shown, each of these analyzers 710 . 719 Proceed so that it costs, emissions and / or other data for step 730 where data is collected and compared with respect to possible load reduction and unit shutdown scenarios so that finally in step 731 an issue can be made to a power plant operator. As will be discussed, this issue may 731 Costs and other considerations for one or more of the possible scenarios may include and may eventually suggest a particular action or the reasons for it.

What the load lowering analyzer 710 1, the method may first determine the load level for the proposed load reduction operation during the selected operating period. As will be discussed in more detail below, much of the cost associated with the load dump operation may depend heavily on the load level on which the power plant is operating, as well as how the plant is configured to generate that load, which may include, for example the different heat generating units are used (ie in which the load is lowered and which of them are turned off). The lowered load level for the proposed load reduction operation may be determined in several different ways according to alternative embodiments of the present invention. First, the factory operator can select the lowered load level. Then the load level can be selected by analyzing historical data relating to earlier lowered load levels at which the factory has been operating efficiently. From these records, a proposed load level may be determined based on criteria provided by the operator, such as efficiency, emissions, meeting one or more site-specific targets, availability of alternative commercial uses for the power generated during load dump operation, environmental conditions as well other factors are analyzed and selected.

A third method for selecting the lowered load level for the proposed load reduction operation may be a computer-implemented optimization program, such as that described in relation to FIG 18 can be used to calculate an optimized lowered load level. In 19 This process is through the step 711 and 712 shown. An optimized lowered load level can be achieved by suggesting load-lowering modes in step 711 and then analyzing whether the operational boundary conditions for the power plant are met. Note that a more detailed description of how this can be achieved with reference to above 18 has been provided. Note that using a process such as this to optimize the lowered load level, the load reduction operating modes selected for comparison with the shutdown alternatives for the selected operating period represent an optimized case, and in light of this, the comparison between the load reduction and the shutdown alternatives is meaningful. As with respect to 18 is specified, the minimum lowered load level may be calculated using an optimization process that optimizes the lowered load level according to operator selected criteria and / or cost functions. One of the functions may be the measure of fuel consumption during the proposed load reduction period. That is, the optimized lowered load level may be determined by optimizing fuel consumption to a lowest level while meeting all other operational boundary conditions or site specific performance goals.

From there, the present method of 19 determine the costs with the proposed Load lowering operating mode for the selected operating period according to the characteristics of the load lowering operating mode, via the steps 711 and 712 be determined, are associated. As shown, step 713 Calculate the fuel consumption and from it the fuel costs for the proposed load reduction operation. According to the embodiment just discussed, which describes optimization based on minimizing fuel consumption, fuel costs may be derived simply by taking the fuel level calculated as part of the optimization step and then multiplying by the expected or known fuel price. In a next step (step 715 ), the yield derived from the power generated during the selected operating period may be calculated in view of the proposed lowered load level and the availability of a commercial demand during the selected operating period. Then in step 716 Operating and maintenance costs are determined. The operating and maintenance costs associated with the proposed load reduction operation may be calculated by any conventional method and may depend on the lowered load level. The operating and maintenance costs may be presented as an hourly fee derived from previous load-downtime records and may include a component usage charge reflecting a portion of the expected life of various system components used during the proposed load-down operation. In a next step, by step 717 can be calculated net cost for the proposed load reduction mode of operation for the selected period of operation by adding the costs (fuel, operation and maintenance) and subtracting the yield.

The present method may also include a step 718 which determines factory emissions over the selected period of operation in view of the proposed load reduction mode of operation, which may be termed the "emission effect". The net cost and the emission effect may then be fed to a compilation and comparison step, referred to as step 730 so that the cost and the emission effect of different load reduction scenarios can be analyzed so that finally in an output step 731 a recommendation can be made, as discussed in more detail below.

Now consider the shutdown analyzer 719 it may be used to calculate aspects related to the operation of one or more of the generator units of the power plant in a shutdown mode of operation during the selected period of operation. As part of this aspect of the invention, operations including the operations by which the power plant is shut down and then restarted at the end of the selected time period may be analyzed for costs and emissions. According to a preferred embodiment, the shutdown analyzer 719 as part of initial steps 720 and 721 determine a proposed shutdown mode of operation that may represent an optimized shutdown mode of operation. The preferred shutdown mode of operation includes processes whereby one or more of the generator units are shut down and then restarted to reconnect the units to the network at the end of the selected period of operation. It should be noted that the length of the period during which a generator unit is not in operation determines the type of possible start-up processes that are available for it. For example, the availability of a warm or cold start depends on whether the shutdown period is short or long. In determining the proposed shutdown mode of operation, the present method may calculate the time required for the startup process to bring the generator unit back to an operating load level. In step 721 For example, the method of the present invention may perform a check to ensure that the proposed shutdown operation meets all operational boundary conditions of the power plant. If any of the operational boundary conditions is not met, the procedure may proceed to step 720 return to calculate an alternative startup run. This can be repeated until an optimized startup run has been calculated that meets the operational boundary conditions of the power plant. Note that in accordance with the methods and systems discussed above, the tuned power plant model may be used to simulate alternative shutdown modes of operation to determine optimized cases in view of the particular operating period and project environment conditions.

Given the proposed shutdown mode of operation of the steps 720 and 721 The process can be continued by determining the associated costs. Initial steps involve analyzing the nature of the start-up process involved in the shutdown mode of operation. In step 722 the process may determine the actual start-up operating parameters that may include a determination as to whether or not a purge is required or required by a factory operator. Given the specific startup, fuel costs may be in step 723 be determined. In one embodiment, the shutdown analyzer calculates 719 then costs associated with the delays that sometimes incurred during the startup process. More precisely, the process as in step 724 is calculated to calculate the probability of such a delay. This calculation may include as inputs the type of start-up as well as historical data relating to previous start-up events of the respective generator units at the power plant as well as data relating to the start-up of such generator units at other power plants. As part of this, the process may calculate costs related to the proposed shutdown mode of operation, reflecting the likelihood of a startup delay occurring and the penalties that will result, such as penalties. These costs may include any costs associated with a hedging strategy whereby the power plant transfers some of the risk of such penalties to a service provider or other insurer.

In step 726 For example, the current method may determine costs associated with the operation of the rotary device during the shutdown process. The method may determine a speed profile for the rotating device in light of the shutdown period and, using it, the cost of the additional power needed to operate the rotating device. Note that this is the power required to keep the rotor blades of the gas turbine spinning while cooling, which is done to avoid the distortion or deformation that would otherwise occur when placing the blades in a stationary position would prevent it from cooling down. In step 727 can, as shown, operating and maintenance costs for the shutdown operation are determined. The operating and maintenance costs associated with the proposed shutdown may be calculated by any conventional method. The operating maintenance costs may include a component usage charge that reflects a portion of the expected life of various system components used during the proposed shutdown operation. In a next step, by step 728 For example, net cost for the proposed shutdown mode of operation for the selected period of operation may be calculated by adding the determined cost of the fuel, the rotary device, and operation and maintenance. The present method may also include a step 729 in which factory emissions are determined over the selected period of operation in view of the proposed shutdown mode of operation, which in turn may be referred to as the "emissions effect" of the operating mode. The net cost and the emission effect may then be used for the compilation and comparison step of step 730 to be provided.

In step 730 For example, the current process may compile and compare various workload reduction modes for the selected operating period. According to one embodiment, the current method may analyze competing load reduction modes of operation as part of the reference to FIGS 18 to 20 described methods and processes have been determined. In step 730 For example, the compiled cost data and emissions effects may be compared for each of the competing load reduction modes of operation and output as part of the step 731 to be provided. In this way, according to the result of the comparison of operating modes, a recommendation can be made as to how the power plant should operate during the selected load reduction operating period, including which of the turbines should be shut down and which of the turbines should be run at lower load and lowered Load levels at which they should be operated.

Emission data can also be part of the output of step 731 especially in cases where the analyzed competing modes of operation bring similar business results. It should be noted that also a communication as to how the respective alternatives affect the factory emissions and, given the impact, the likelihood of non-compliance during the current regulatory period are provided, as well as a business related result. More specifically, the accumulated emissions of one or more pollutants during the regulatory period may be compared to the total limits permitted during that timeframe. According to certain preferred embodiments, the step of communicating the result of the comparison may include displaying an emission rate of the power plant by forming the average of a cumulative emission level for the power plant over a portion of a current emissions adjustment period relative to an emission rate determined by forming an average a cumulative emission limit over the current emissions regulatory period. This can be done to determine how the power plant is compared to the average emission rate that is allowed without causing an infringement. The method may determine the emissions still available to the power plant during the current regulatory period, and whether or not there is sufficient latitude to handle each of the proposed modes of operation, or rather whether the emission effect is likely to violate Regulations inadmissible increased in the future.

As an output, the present method may provide a recommended action that makes a recommendation as to the advantages / disadvantages, both economic and otherwise, between the proposed load reduction and shutdown modes of operation. The recommendation may include the indication of costs and a detailed breakdown of the categories in which those costs were incurred and the assumptions under which they were calculated. In addition, the recommended action may include a summary of any other considerations that might influence the decision, thereby selecting the most advantageous mode of operation. This may include information regarding applicable emission limits and regulatory periods, as well as how the current cumulative emissions of the power plant relate to it. This may include informing power plant operators of any operating modes that may increase the risk of non-compliance with emission thresholds in an unacceptable manner, as well as the costs associated with such violations.

The present invention may further include a unified system architecture or integrated computer control system that efficiently enables and improves the performance of many of the functional aspects described above. Power plants - even those with the same owners - often operate in different markets, state legal systems and time zones, are managed by many stakeholders and decision makers, and exist under different types of service and other contractual arrangements. In view of such various circumstances, one and the same owner may control and operate a number of power plants, each having multiple generator units and types in overlapping markets. Owners may also have different criteria for evaluating an effective power plant operation, including, for example, different cost models, response times, availability, flexibility, cyber security, functionality, and differences in the way separate markets operate. However, it should be made clear that most electricity trading markets are based on various off-line generated files presented to multiple parties and decision makers, including those shared between dealers, plant managers and regulatory authorities. In view of this complexity, it may be that the capacities of power plants and / or generator units within a market segment are not fully understood, in particular over the layered hierarchy, for example, ranging from individual generator units to power plants or power plants to parks from such power plants. Thus, each subsequent level of the electricity trading market typically secures the power communicated from the underlying level. This leads to inefficiencies and yield losses for owners, since the successive protection leads to underutilization of the system. Another aspect of the present invention, as discussed below, has the function of mitigating the disconnectedness underlying the problems. In one embodiment, a system or platform is developed that can perform analyzes, gather and evaluate historical data, and perform what-if or alternative scenario analyzes on a unified system architecture. The unified system architecture may more efficiently enable various functions, various components, such as power plant modeling, business decision support tools, power plant operation and performance prediction, and optimization according to performance objectives. In certain aspects, the unified architecture may achieve this by integrating local power plant components with ones located elsewhere, such as those located on a centrally hosted or cloud-based infrastructure. Note that aspects of such integration can enable improved and more accurate power plant models without impacting the consistency, efficiency, or timelessness of the results. This may include the use of tuned power plant models already discussed on local and externally provisioned or hosted computer systems. Given their use on an externally deployed infrastructure, the system architecture can be conveniently scaled to handle additional plants and units.

It will now be on the 22 to 25 Reference is made to show that scalable architecture and control systems can be presented that can be used to support the numerous requirements associated with controlling, managing, and optimizing a power plant fleet in which multiple generator units are distributed across multiple locations are. A local / remote hybrid architecture, as referred to herein, may be used based on certain criteria or parameters that are site or case specific. For example, an owner or operator having a number of power plants may wish to have certain aspects of the system functionality provided locally, while others may be a centrally provided environment, for example in a cloud-based infrastructure, to collect data from all generator units and to serve as a shared data archive that can be used to transfer the data over Scrubbing common device values, configurations, and conditions while also supporting analytical functions. The process of selecting the appropriate architecture for each of the various owner / operator categories may target the major concerns underlying the operation of the power plants, as well as the specific characteristics of the electricity market in which the plants operate. According to certain embodiments, as indicated below, power calculations may be performed locally to assist the control of a particular power plant, to improve cyber security, or to provide the response speed needed to handle fast real-time processing. On the other hand, the present system may be configured such that the data flow between local and remote systems includes local data and model tuning parameters that are transmitted to the centrally provided infrastructure to create a tuned power plant model that is then used for the analysis, for example the analysis of an alternative scenario. Remote or centrally deployed infrastructure can be used to tailor interactions with a common factory model to the unique needs of the various types of users who need to access it. In addition, a scaling strategy can be determined based on response time and performance agreements that depend on the unique aspects of a particular market. If shorter response times are required for the availability of the final results, the analytical processes can be scaled in terms of both software and hardware resources. The system architecture also supports redundancy. If any system performing an analysis can no longer operate, processing may continue on a redundant node having the same power plant models and historical data. The unified architecture can bring together applications and processes to enhance performance and extend the range of functions to achieve both technical and commercial benefits. Note that such benefits include: easy integration of new power plant models; the separation of processes and models; the ability for different operators to share the same data in real time while also presenting the data in a unique way according to the needs of each operator; comfortable upgrades; and compliance with NERC-CIP restrictions on sending regulatory oversight.

22 FIG. 3 shows a high level logic flowchart or power plant savings level optimization process in accordance with certain aspects of the present invention. As illustrated, the power plant fleet may include multiple generator units or plants 802 which may be separate generator units across multiple power plants or in the power plants per se. The facilities 802 The power plant fleet may be owned by a single owner or a single entity and may compete with other such facilities over one or more markets for contractual rights to generate shares of the load required by a customer network. The facilities 802 may include multiple generator units having the same type of configuration. In step 803 For example, performance data collected by the sensors at the plant's various plants can be electronically transmitted to a central data archive. Then the measurement data in step 804 be adjusted or filtered, so that, as will be described below, an indication of the power level for each system is determined with a higher degree of accuracy or truth.

As described in detail above, one way to perform this alignment is to compare the measurement data with corresponding data predicted by power plant models that, as discussed, may be configured to simulate the operation of one of the plants. Such models, which may also be referred to as offline or predictive models, may include physics-based models, and the matching process may be used to periodically tune the models to the accuracy with which the models represent actual operation through simulation. maintain and / or improve. That is, as discussed in detail, the method at step 805 Use the latest recorded data to tune the power plant models. This process may involve tuning the models for each of the plants, ie each of the generating units and / or power plants, as well as general models covering the operation of multiple power plants or aspects of a power plant operation. The voting process can also involve the data collected between similar assets 802 be compared to eliminate deviations and / or detect anomalies, in particular data that has been detected by the same type of system with similar configurations. During this process, gross errors can be eliminated given the collective and redundant nature of the compiled data. For example, sensors that are capable of higher accuracy or those that are known to have been recently tested and that have proven to work accurately can be given high esteem. In this way, the collected data can be cross-checked by comparison, be verified and reconciled to construct a single consistent dataset that can be used to more accurately calculate an actual performance of a fleet of power plants. This data set can then be used to tune models of equipment that can then be used to simulate and determine optimized control solutions for the power plant fleet during a future market period, which can be used, for example, to improve competitiveness during dispatch calls ,

In step 806 As shown, the true performance capabilities of the power plant will be derived from the adjusted performance data and the coordinated models from step 805 certainly. Then in step 807 the facilities 802 of the power plant park can be optimized together in view of a selected optimization criterion. Note that this may involve the same process as discussed in detail above. In step 808 An optimized exchange curve or an optimized system plan can be created or generated. This may describe the way in which the plants are planned or operated and the degree to which they are deployed to cover, for example, a proposed or hypothetical load level for the power plant fleet. The optimization criteria can be selected by the operator or owner of the equipment. For example, the optimization criteria may include efficiency, yield, profitability, or any other measure.

As illustrated, the following steps may include communicating the optimized asset plan as part of a bid for Lease Generation Contracts for future market periods. This can be done in step 809 communicate the optimized plan to energy traders, who then submit a bid according to the optimized plan. Note that in step 810 The bids can be used to participate in a dispatching process at the level of a power system that distributes a load to multiple power plants and generator units that are located within the system and many of which may belong to competing owners. The bids or quotes for the dispatch process may be configured according to a defined criterion, such as variable generation costs or efficiency, as determined by the respective dispatcher of the power system. In step 811 For example, the results of optimizing the power system may be used to generate a plant plan that reflects how the various plants in the power system should be deployed to meet the predicted needs. The plant plan of step 811 , which reflects the result of the system-wide optimization or dispatch process, can then turn to the owners of the assets 802 be notified so in step 812 Operating setpoints (or particular modes of operation), which may include, for example, the load at which the individual plants are operated, may be communicated to a controller that controls the operation of the plants 802 controls. In step 813 For example, the controller may calculate and then transmit a control solution and / or the equipment 802 directly to meet the load requirements that it has committed to take over during the Dispatch process. Owners of power plant parks can adjust the way one or more power plants work when conditions change to optimize profitability.

23 illustrates the data flow between local and remote systems according to an alternative embodiment. As stated, certain functions may be provided locally while other off-site functions are provided in a centrally-deployed environment. The method of choosing the appropriate architecture according to the present invention involves determining the considerations that are important drivers for the operation of the plants within the power plant fleet. Thus, considerations such as cyber security concerns may require that certain systems remain local. Time-consuming performance calculations will continue to be provided locally so that the necessary timeliness can be maintained. As in 23 can be a local factory tax system 816 Retrieve sensor measurements and send the data to a voting module 817 convey, where, as in particular with respect to 17 As already discussed, a reconciliation or reconciliation process may be completed using performance calculations that compare actual or measured values to those predicted by the plant or asset model. Via a data router 818 then, as shown, the model tuning parameters and adjusted data may be communicated to a centrally provided infrastructure, such as a remote central database 819 , From there, the model tuning parameters are used to represent the offline power plant model 820 which can then be used, as described above, to optimize future fleet operation, provide an alternate scenario or "what if" analysis, and provide recommendations for possible or competing operating modes of the plant parks.

The results of the analyzes using the offline power plant model 820 can be done, as shown, through a web portal 821 to be transmitted to fleet operators. The web portal 821 allows users to manage their fleet with tailored access 822 provide. Such users may include plant operators, energy traders, owners, power plant operators, engineers, as well as other stakeholders. According to the user's interaction via the web portal access, decisions regarding the recommendations made by the analytics may be made using the offline power plant model 820 to be hit.

The 24 and 25 FIG. 12 illustrates a schematic system configuration of a unified architecture according to certain alternative aspects of the present invention 25 can be a remote, central archive and analysis component 825 Performance and measured operating parameters of several plants 802 received to perform an optimization at power plant savings level. Power plant savings level optimization may be based on additional input data, which may include, for example: the actual fuel quantities stored and available at each power plant, the location-specific price of fuel for each power plant, the site-specific price of electricity generated at each power plant current weather forecasts and the dissimilarities between remote installations and / or shutdown and maintenance plans. For example, a planned component overhaul for a gas turbine may mean that short-term operation at higher temperatures is more economical. The process can then calculate an exchange curve that includes optimized variable generation costs for the power plant fleet. In addition, as shown, the present invention may allow for more automated bidding so that, at least in some circumstances, the bid will go directly to the Dispatch instance responsible for the entire system 826 can be transferred and thereby energy traders 809 can be bypassed. As in 25 For example, the power system optimization results (via the system-wide dispatch instance) may be used to generate a plant plan that reflects how the various plants in the power system should be deployed to meet the predicted needs fulfill. This plant plan can reflect a system-wide optimization and can, as shown, again the owners of the plant 802 can be communicated so that operating setpoints and operating modes for the plants of the control device can be communicated, which controls the individual systems in the system.

Thus, methods and systems according to the 22 to 25 which will optimize a park from power plants operating within a competing power system towards better performance and bidding for future market periods. Actual data related to operating conditions and parameters can be received in real time from the individual power plants belonging to the park. The power plant and / or power plant parking models can then be tuned according to the current data, so that the accuracy and forecasting range of the models are continually improved. Note that this can be achieved by comparing measured performance counters with corresponding values predicted by power plant or power plant park models. As a next step, the power plant savings plan co-ordinated power plant models and / or models may be used to calculate true generation capacities for each of the power plants within the power plant fleet based on competing operating modes simulated with the tuned models. Then, an optimization is performed using the true factory capacities and optimization criteria defined by the plant or power plant park operator. After determining an optimized operating mode, a plant plan can be produced that calculates optimal operating points for each of the power plants within the power plant fleet. Note that the operating points may then be transferred to the various power plants to control each one accordingly or, alternatively, the operating points may be used as a basis on which bids are submitted for submission to the Central Dispatch Instance.

Likewise with regard to the centralization of the control and optimization of several power generation units, the 26 and 27 a power supply system 850 in which a block control device 855 is used to power multiple power blocks 860 to control. The performance blocks 860 as stated, can create a power plant park 861 from the generator plants ("plants"). Note that these embodiments provide another example of application for the optimization and control methods described in more detail above, but also include extending the optimization perspective to a power plant savings layer. The present invention may also provide opportunities to reduce certain inefficiencies that still impact modern power generation systems, particularly those having a large number of remote and varying heat generation units. Each of the plants may be any of the thermal energy generating units discussed herein, such as gas and steam turbines, as well as associated subcomponents such as HRSGs, inlet conditioners, sewer combustion chambers, etc. The plants may be constructed in accordance with several Generator configurations with respect to the way the sub-components are used to be operable. The power generation from the several power blocks 860 can be centrally controlled by a block controller 855 to be controlled. What the system is in 27 which will be described in more detail below, the block controller may 855 Control the system according to optimization processes that take into account the functionality of plants and power generation blocks, as well as generation plans, maintenance plans, and other factors such as one of the plants or power generation blocks 860 own, including site-dependent variables. In addition, learning operational data collected from similarly configured plants and power generation blocks but not part of the power plant fleet can be used to further refine control strategies.

Typically, conventional plant control devices (which are known in 26 specified as "DCS"), local to the generator equipment and operate substantially isolated. Therefore, such control devices fail to account for the current operability of the other plants making up the power generation block 860 and / or the power plant park 861 consists. It should be noted that this lack of perspective leads to less optimal power generation for the power plant fleet 861 leads, if you look at it from this perspective. It is still referred to the already described methods and systems, in particular to those which use the 3 . 4 and 17 to 25 where the current embodiment is shown to teach a power plant savings layer control system that provides multiple system-wide benefits including improved power sharing strategies, cost efficiency, and improved efficiencies across clustered plants or power generation blocks.

As indicated, the control system represented by the block controller 855 to interact with the plant controllers. The block controller 855 can also connect to the net 862 as well as communicate with a central dispatch or other government entity associated with its administration. In this way, supply and demand information can be shared between the power plant fleet 861 and a central instance. According to one embodiment, supply information, for example dispatch bids, may be on the power plant fleet optimization 861 are based on the block controller. The present invention may further include optimization processes that occur between bidding periods that may be used periodically to optimize the way the power plant fleet operates 861 is configured to reach an already established load level. More specifically, such bidding optimizations can be used to respond to dynamic and unanticipated operational variables. Suitable control actions for the power generating units 860 can from the block controller 855 to the control systems within each power generation block 860 or communicated directly to the facilities. According to preferred embodiments, the implementation of control solutions may be the block controller 855 include enabling them to override plant controllers if certain predefined conditions are met. Factors that affect such override may include variable generation costs for each of the power plant blocks / plants, the remaining useful life of hot gas path components, the change in demand levels, the change in environmental conditions, and others.

The block controller 855 As shown, it may be communicable with the multiple power generation blocks 860 of the power plant park 861 as well as being directly connected to the equipment and thereby can receive many data inputs on which the control solutions described herein are based. The optimization procedures may take into account one or more of the following inputs: health and conduction degradation; Power generation plans; Power frequency; Maintenance and inspection plans; Fuel availability; Fuel costs; Fuel consumption patterns and forecasts; Problems and failure of devices in the past; true power generation capacities; Life models; Startup and shutdown features; past and present measurement operational parameter data; Weather information; Cost data, etc. As will be described in more detail with respect to other embodiments, inputs may include detailed data related to measured operating parameters for each of the power plant's generator plants 861 from the present and the past. All of these prior and current inputs may be stored, for example, in a central database, and thus upon request from the block controller, in accordance with conventional techniques 855 which may be necessary according to one of the method steps described herein.

A cost function may be developed according to the preferences of a power plant operator. According to a preferred embodiment, a weighted average sum of a robustness index for the power plant park may be used to provide advantageous or optimized To determine configurations for power distribution. For example, the robustness index for the power plant park may include optimization according to several factors applicable to a particular demand or output power level of the power plant fleet. These factors may include: thermal and mechanical loads; Material degradation or loss, including the rate of material degradation, production cost and / or fuel consumption. In this way, the present embodiment can be used to address a number of existing issues related to power plant power control, particularly power optimization across multiple power generation blocks with multiple and varying power plants.

Data inputs may include the types discussed herein, including those involving computer modeling, maintenance, optimization, and model-free adaptive learning. For example, in accordance with the present embodiment, computer models, transfer functions, or algorithms may be developed and maintained so that the operation (or specific aspects of operation) of the plants and / or, in general, the power generation blocks or power plant fleet can be simulated under various scenarios. Results from the simulations may include values for certain performance indicators that represent predictions regarding aspects of the operation and performance of the plants, the performance of power generation blocks, or the power plant fleet over the selected operating period. The performance counters can be selected for a known or developed correlation with one or more cost outcomes, and thus can be used to compare the business aspects of each simulation. A "cost result", as used herein, may include any business impact, positive or negative, associated with the operation of the power plant fleet 861 over the selected operating period. Cost results may thus include any revenue derived from the generation of electricity over the period, as well as any operating and maintenance costs caused by the power plant fleet. These operating and maintenance costs may include resulting material deterioration of the plants of the power plant fleet in light of the scenarios and the simulated operating results that result therefrom. Note that data that may be extracted from the simulation results may be used to calculate which of the alternative operating modes for the power plant park is more desirable or more cost effective.

The models for the plants, blocks, or power plant park may include algorithms or transfer functions developed by physics-based models, adaptive or learned "model-free" process input / output correlations, or combinations thereof. Baseline degradation or loss models can be developed that correlate process inputs / outputs with degradation or loss data for each asset type. The degradation or loss data and related cost results may thus be computable based on the operating parameter prediction values of proposed alternative or competing modes of operation of the power plant park, which, according to certain embodiments, by the manner in which the plants and power generation blocks The way in which the generation is shared among the plants of the power plant fleet can be differentiated as well as according to other factors described herein. As mentioned, learning from similarly configured assets can be used to further refine the models used as part of this process. For example, a degradation model may be developed that calculates a material degradation and loss over time in light of the values for the selected performance indicators. Such material degradation can then be used to calculate the business consequences or the cost outcome for each of the competing modes of operation. These business consequences may include degradation of plant performance, wear of components, a consumed life (ie, the portion of the useful life of a component consumed during an operating period), as well as other measurements of values, for example, emissions costs , government imposed charges, fuel consumption as well as other variable costs that depend on an output level. It should be noted that material degradation and service life for a particular asset may accumulate in a non-linear fashion, depending on dynamic and / or other site-specific variables, and therefore significant cost savings over time by distributing the power plant's output power level can be achieved in order to minimize material degradation of the power plant parks altogether, especially when this minimization is distributed to the plants in order to minimize the impact on the generation capacity and efficiency of the power plant fleet as a whole.

Thus, taking into account conditions that may apply for a future market period predicting environmental demand and prediction, several competing modes of operation for this power plant park may be selected for analysis and / or simulation to determine an optimized or at least preferred operating mode of the power plant fleet. Each of the competing operating modes can provide a unique generation configuration for the power plant park 861 describe. The competing operating modes of the power plant fleet may be developed to include parameter sets and / or control sets that define the unique generation configurations that achieve a particular power plant power output level. As mentioned, the output power level of the power plant fleet can be selected in several ways. First, it may be selected to reflect an already known power plant power output level, such as an output power level established via a recently completed dispatch process, so that the optimization process may be used to determine an optimized power plant park configuration. through which this concrete output level is reached. The output power level of the power plant fleet may also be selected according to an expected load level in the light of past history records, expected customer demand, and / or other anticipated conditions. Alternatively, the output power level of the power plant fleet may be varied over a selected range. In this way, the variable generation costs for the power plant park 861 be used, for example, as part of a tendering procedure to inform about the creation of a bid for a tender. Thus, the way in which the output power level of the power plant fleet is defined by the operator of the power plant fleet can be used to assist in one case activities to bid for a tender, while at other times the output power level can be selected such that a guide function is provided, which serves to optimize the performance of the power plant fleet when actual conditions do not reach the expected ones.

In accordance with an example operation, as described by the more detailed system of FIG 27 developing parameter sets describing each of the competing operating modes of the power plant fleet, and for each of the competing modes of operation of the power plant fleet, different scenarios or cases can be developed within which manipulable variables are varied over a selected range to determine the effect of the variation on the power plant To determine the overall operation of the power plant park. The different cases for the competing modes of operation of the power plant fleet may be configured to cover alternative possibilities, such as the power plant power output level to the power generation blocks 860 and / or equipment is divided. As another example, the various cases may be selected based on alternative configurations available to some of the plants, including the different ways in which each of the plants is used. For example, some cases may involve the use of certain sub-components of the equipment, such as duct burners or intake conditioners, to increase power generation capacity while recommending that other equipment operate at shut-down or load-down levels. Other scenarios may take advantage of situations in which such equipment configurations are slightly varied or the other way round.

As in 27 is shown, the block control device 855 with a data and analysis component 865 which may include a plurality of modules through which relevant data is acquired, normalized, stored and, upon request, the block controllers 855 be made available. A data logging module may receive real-time and historical data inputs from a monitoring system associated with gensets. A module related to performance monitoring may also be included, and in relation to this, one or more offline models may be maintained. Each of these modules may function substantially consistently with other embodiments discussed herein. A learning module for collecting operational data from similarly configured plants or power generation blocks not within the power plant fleet 861 work can also be included. Note that this data may support a learning function that will provide a deeper and more complete understanding of the operation of the equipment. Such data may also be used to collect measurement data from the power plant park 861 to normalize so that power degradation of the generator sets can be accurately calculated, which may include taking into account the effects of other variables such as fuel characteristics, environmental conditions, etc., which may also affect output capacity and efficiency.

As in connection with the 24 and 25 a power plant-level optimization can be based on location-dependent variables. These variables can be conditions reflect, which are unique and which for some apply to specific installations or power generation blocks and which may include, for example: the actual quantities of fuel stored and available in each installation; the location-specific fuel price for each installation; the location-specific market price for electricity generated at each installation; current weather forecasts and deviations between plants within the power plant park located elsewhere; and shutdown and maintenance plans for each plant. For example, a planned component overhaul for a gas turbine plant may mean that short-term operation at higher temperatures is more economically advantageous. As shown, the data and analysis components 865 include a module that takes these differences into account.

The block controller 855 For example, as shown, modules directed to power generation models (which may include plant models, block models, power plant savings models as well as degradation or loss models) may include an optimizer and a cost function. The plant, power generation, and / or power plant storage models may be created, tuned, and / or balanced and maintained in accordance with the methods already described herein. These models may be used to simulate or otherwise predict the operation of the plant fleet or a selected portion thereof over the selected period of operation, such that the optimizer module is able to determine a preferred scenario according to a defined cost function. Specifically, the results from the simulations may be used to calculate a cost outcome for each individual, which may include summation over the power plant generating units and / or plants, yield, operating costs, material degradation, usage time, and other costs referred to herein. Note that the output may be determined by a projected output power level multiplied by a market unit price. Calculation of the cost, as stated, may include degradation models or algorithms that correlate a business outcome to the way the equipment operates within the simulations. Performance data from the simulation results can be used to determine operating costs, material degradation, and other losses over a power plant fleet, as previously described. Note that certain cost considerations, such as fixed aspects of operating costs, may be barely distinguishable between the competing operating modes of the power plant fleet, and thus may be excluded from such calculations. In addition, the simulations described herein may be configured to include all or part of the plant fleet and to focus on limited aspects of plant operation that, as indicated herein, have proven to be particularly relevant to forecasting cost outcomes.

In certain embodiments, the cost function module may include a robustness index for the power plant park to efficiently distinguish between alternative modes of operation. The robustness index for the power plant park may represent an average of the accumulated losses accumulated within the power generation blocks. The robustness index may include a factor indicative of costs related to a consumed life, which may be a summation of the useful life of components over the equipment, such as hot gas path parts and compressor blades in gas turbines. For example, a generator plant that is scheduled to be shut down during the selected period of operation according to one of the competing modes of operation of the power plant park may cause a business loss according to the period of use consumed per shutdown / startup operation. Whereas a generator system that is scheduled to work under full load during the same period of operation, can cause a loss corresponding to these hours of operation. Note that such losses may be further calibrated to specifically reflect the thermal and mechanical loads expected in view of the load level and operating parameters that are predicted to meet a given load level, such as those factors such as predicted environmental conditions, Fuel parameters etc. may depend. Other business losses may be included in the summation of power plant fleet losses to derive a cost outcome for each of the competing operating modes of the power plant fleet. These may include a summation of the fuel consumption for the plants of the power plant park as well as, for example, the economic effects of predicted emission levels in view of the simulation results.

Once the summation of revenue and / or loss over an entire power plant fleet for each of the simulated scenarios is completed, the present method may include the step of calculating one or more preferred or optimized cases. The present method may then include one or more outputs related to the preferred or optimized cases. For example, the preferred or optimized cases are transmitted electronically to an operator of the power plant park, for example via a user interface 866 , In such cases, outputs of the present method may include: a power block advisor for the functionality of blocks / plants; a recommendation for the power distribution, a standstill planner; an optimal setpoint control solution for the power generation blocks, a DCS override; and / or an expected generation plan. The output may also include an automatic control response, which may include automatic override of one of the plant controllers. According to another alternative, an output may include the generation of a dispatch bid according to one or more of the preferred or optimized cases. Note that the outputs of the procedure, as of the user interface 866 savings on the power plant park can be made in several ways. First, for example, preferred power sharing configurations may minimize, reduce, or advantageously distribute material degradation of the power plant fleet that may significantly affect generation capacity and efficiency over future operating periods. Second, an advisory function using the described components may be configured to optimize or at least improve maintenance intervals by which losses due to material degradation can be mitigated, those to be rebalanced, as well as those that are not compensated. Monitoring and predicting the rate of material degradation and effectively scheduling / performing maintenance operations, such as compressor washes or filter cleaning, will ensure that the gas turbine operates as efficiently as possible.

Now it will 28 Considering another related aspect of the present invention that describes the more concrete example of controlling multiple gas turbines operating in a current sense block. Note that the gas turbines may be located at a particular power plant or across multiple remote power plants. As discussed earlier, control of gas turbines to optimize or improve power sharing is a challenge. Today's control systems do not effectively synchronize over a block of multiple turbines, and instead employ substantially each of the turbines individually based on a less sophisticated distribution of the output power level for which the powerhouse block is overall responsible. Note that this often leads to imbalances and inefficient material degradation rates. Thus, there is a need for further optimized control strategies, and more particularly to a system controller that provides efficient power sharing strategies across multiple gas turbines that promote a more cost effective loss or material degradation rate when the units are collectively considered as a power generation block. For example, if a gas turbine block has multiple turbines with the same ratings, the present invention may provide recommendations based on the current material degradation condition of the turbine, which should work from the higher output power level units and which should operate at lowered levels. The present invention may achieve this in accordance with aspects that have already been discussed herein, particularly those discussed with reference to FIGS 24 to 27 have been discussed. As one of ordinary skill in the art will appreciate, the benefits of such functions include: extended life and improved gas turbine performance; better lifetime predictions, allowing for more competitive and / or risk-sharing service contracts; greater operational flexibility for the power generation block as a whole; and robust optimization with more than one goal that efficiently accounts for operational considerations, which may include, for example, an already consumed hot gas path lifetime, current material degradation and degradation rates, and current power generation performance such as demand, efficiency, fuel consumption, and so on.

One way in which this can be achieved is according to a system 900 which is now referring to 28 is described. As stated, several gas turbines 901 as part of a power generation block or "block 902 " operate. As discussed as part of the above system, operating parameters 903 for each of the plants 901 detected and electronically to a block control device 904 be transmitted. In a preferred embodiment, the operating parameters may include a rotor speed, a compressor pumping limit, and a blade tip pitch. Note that the compressor pumping limit may be calculated in relation to a measured rotor speed and that the blade tip pitch may be measured according to any conventional method, for example, with microwave sensors. As further input, the block controller may record 905 received from a database component, such as one of those already discussed, that can record current and past operational parameter measurements, including rotor speed, surge line, blade tip distance, control settings, Environmental condition data, etc., to adaptively correlate process inputs and outputs.

According to preferred embodiments, the block control device 904 be configured to operate as a model-free adaptive controller. The model-free adaptive controller may include a neural network-based device that inputs (eg, via the records 905 ) of each of the gas turbines according to a need, a heat input, etc. Note that model-free adaptive control is a particularly effective control method for unknown, time-discrete non-linear systems with time varying parameters and time varying structures. The design and analysis of model-free adaptive control places a focus on process inputs and outputs to "learn" predicted correlations or algorithms that explain the relationships between them. Correlations between measured inputs and outputs of the system are controlled. By functioning in this manner, the block controller may 904 Derive control commands or recommendations, and these can be implemented as output 906 to a master control system 907 be transmitted. According to a preferred embodiment, the output includes 906 from the block controller 904 a preferred or optimized power split command or recommendation. According to other embodiments, the output 906 Commands or recommendations related to a modulated coolant flow for gas turbine hot gas path components 901 and / or modulated IGV settings for the compressor units of the gas turbine 901 include.

The master control system 907 can communicate with the gas turbines 901 of the power generation block 902 be connected to control solutions in the face of the issue 906 u implement. As shown, the master control system 907 this information also to the block controller 904 transmit The control system of FIG 28 can work that way it's the multiple gas turbines of the power generation block 902 in an improved and optimized manner according to a defined cost function controls to produce a combined load or output level - for example, a contracted expenditure level, which can be determined by a dispatch tendering process and for which the gas turbines are jointly responsible. This control solution may include recommending a percentage of the combined output power level that each of the gas turbines should contribute. In addition, the master control system 907 a physics-based model for controlling the gas turbines according to the optimized mode of operation, as already discussed.

For example, in one embodiment, distance and surge limit data may be tracked for each of the gas turbines. If it is determined that distance or surge limit data for one of the gas turbines exceeds a predefined threshold, the corresponding turbine may operate at a reduced load. If operation of this gas turbine at a reduced level is not possible, other recommendations may be made, such as modulation of IGV settings or coolant flow to hot gas path components. In contrast, if one of the gas turbines is selected to operate at a reduced level, the optimized power generation configuration may include recommendations that one or more of the other gas turbines should operate at a higher / full load to compensate for any deficit. The method may select the higher / full load turbines based on surge margin and clearance data with the desired effect of offsetting current material degradation levels and deterioration rates between the gas turbines of the power generation block to increase overall operating time, while providing a higher output power level and higher efficiency of the block are kept higher. As stated, performance degradation rates and occupancy utilization of components may arise nonlinearly and depend on parameters that vary over geographically dispersed units, and therefore savings may be achieved through the use of the power plant box level perspective described herein to distribute a load in a manner that optimizes a cost score for the block. Thus, power generation can be distributed to costs over the block 902 taking into account real-time data (in particular surge margin and distance data) that has been determined to be very critical and efficient in evaluating performance degradation levels, degradation rates, remaining component life, and true performance capacity for the block's gas turbines.

Now it will open 29 Referring now to another embodiment of the present invention, which includes systems and methods that enable more efficient and optimized shutdown of gas and steam combined cycle power plants. Note that during shutdown of a gas and steam combined cycle power plant, a controller typically reduces fuel flow to the gas turbine gradually to a rotor speed toward a minimum Reduce speed. This minimum speed may be referred to as the "rotating device speed" because it represents the speed at which the rotor is carried along by a rotating device and thereby rotated to prevent heat-induced bending of the rotor during the shut-down period. Depending on the nature of the gas turbine, the fuel flow may be stopped at about twenty percent of a typical full load with the rotary device engaged at about one percent of full speed. However, by reducing the fuel flow in this gradual manner, there is no direct relationship with the reduction in speed. Rather, large and uncontrollable variations in the speed of the rotor over the shutdown period are typical. The variations in rotor speed can then cause significant differences in the fuel-air ratio, due to the fact that the air intake is a function of the rotor speed, while this is not the case with the fuel flow. Such variations may then result in significant and abrupt changes in, among other things, burning temperatures, transition temperature gradients, emissions, fuel flow. The variations in turn-off behavior can affect turbine spacings and thus the performance of the turbine as a whole and the lifetime of the components.

Therefore, there is a need for a gas and steam combined cycle power plant control device that improves factory shutdown by solving one or more of these problems. Preferably, such a controller would control the rate of deceleration of the turbine rotor and associated components over time to minimize non-uniform shutdown changes and thereby minimize the negative impact on the turbine systems and components. In certain embodiments, a more effective controller is to optimize rotor loads and rotor speed and torque slew rates. The shutdown controller may also correct for variability in subsystems such that, for example, a coolant flow and a wheelspace temperature remain at preferred levels. In accordance with preferred embodiments, the present method may control the rate of deceleration of the rotor and associated components over time to minimize shutdown variations in a manner that reduces cost, factory losses and other adverse effects. In accordance with the systems and methods already described, the control methodology may operate to optimize factors that affect shutdown costs according to operator defined criteria or cost functions. One way this can be achieved is according to a process 920 who now referring to 29 is described. The skilled person will recognize that aspects of the process 920 focus on an item that has already been discussed herein - in particular, with reference to the discussion according to the 3 and 4 - which, for the sake of brevity, is summarized but not completely repeated.

According to an embodiment, the shutdown procedures and / or the shutdown controller for a gas and steam combined cycle power plant according to the present invention are configured as conventional looping control. The controller of the present invention may include aspects of model-free adaptive control as well as model-based control as set forth in the appended claims. The gas and steam combined cycle power plant controller may include a target cutoff time control means and a current cutoff time control means, and may control the cost of most, if not all, aspects of the factory shutdown. The controller may include inputs such as exhaust distribution, wheel room temperature, clearance, surge limit, gas turbine rotor loads, demand, fuel flow, current power production, grid frequency, secondary firing, drum levels, and so forth. Based on these inputs, the shutdown controller may calculate a shutdown time range (eg, a shutdown rate), a rotor retardation slew rate, a corrected coolant flow, and a corrected inlet guide vane profile and / or a generator retarding torque desired during shutdown, as described in more detail below , According to certain embodiments, each of these outputs may be used to compensate for potentially harmful shutdown variations detected by one of the power plant sensors. The shutdown controller of the combined cycle power plant may provide a profile of a trajectory of RPM versus time and a profile of a slowdown rate against current power production that is more appropriate for shutdown operations, and both may account for combined cycle power plant systems such as HRSG, steam turbines, boiler, and the like. The shutdown controller may control the components described herein until a rotator speed is reached, thereby providing optimized steam turbine and HRSG operating capability conditions while reducing component loading.

29 is a flowchart that is one embodiment of a process 920 shows that to shut down a gas and steam combined cycle power plant is suitable, for example, the power plant 12 , with respect to 3 has been described. The process 920 may be implemented as computer code executable by the shutdown controller of the combined cycle power plant, and may be initiated after a shutdown command has been received (step 921 ). The shutdown command may be received, for example, based on a maintenance event, a fuel change event, and so on. The process 920 can then retrieve a current state of factory components (step 922 ) which may be sensed, captured, stored and retrieved via any of the sensors, any of the systems and / or methods already described herein. The current state of factory components may include, for example, turbine rotor speed, component temperature, exhaust gas temperature, pressures, flow rates, distances (eg, distances between rotating and stationary components), vibration measurements, and the like. The plant's condition may also include current power production and costing data, such as the cost of unused power, the cost of electricity at current prices, green credits (eg, emission rights), and the like.

As a next step, costing or loss data can be retrieved (step 923 ), for example by consulting various systems, including billing systems, futures trading systems, energy market systems or a combination thereof. History data can also be retrieved (step 924 ). The historical data may include system performance records, maintenance data, power plant savings level historical data (eg, logs from other components in plants located at different geographical locations), inspection reports, and / or past cost data.

The process may then derive algorithms relating to factory shutdown degradation or factory shutdown faults related to a shutdown operation. Such derivatives may be determined using multiple types of input, including, but not limited to, previous operating data related to gas turbine systems, steam turbine systems, HRSG units, as well as any other subcomponents that may be present. According to certain embodiments, various models or algorithms may be developed by which shutdown losses of gas and steam combined cycle power plants are derived. As discussed in the discussion 4 Such algorithms may have the function of providing summation of power plant shutdown losses based on values for selected operating parameters or performance indicators, such as temperatures, pressures, flow rates, distances, loads, vibration, shutdown time, and the like. Note that consistent with the other embodiments described herein, alternative, proposed, or competitive shutdown modes for the gas and steam combined cycle power plant may be simulated in a gas and steam combined cycle power plant model. That is, a gas and steam combined cycle power plant model can be developed, tuned and stored and then used to simulate alternative or competitive shutdown modes to derive forecast values for certain predefined performance counters. The performance parameter predictions can then be used to calculate shutdown costs according to the derived loss algorithms.

For example, algorithms may be developed that correlate shutdown losses and a predicted heat load profile that may be determined from the predicted value for particular performance parameters in view of the operating parameters related to one of the competing shutdown modes of operation. According to certain embodiments, such losses may reflect an overall business consequence of the competitive shutdown mode of operation, and may, for example, account for material degradation of hot gas path components and / or consumed percentage of useful life in view of the shutdown mode, as well as any performance degradation of the factory, and may be from initiation of shutdown until to be calculated, for example, when the turn-off speed is reached. Likewise, loss algorithms can be developed to determine losses related to: mechanical loads on the compressor and turbine; Distances between stationary and rotating components; Shutdown emissions; Shutdown fuel consumption; thermal load of the steam turbine rotor / stator; Boiler drum pressure gradient, etc.

The process 920 may then derive an improved or optimized shutdown mode of operation (step 926 ), which may include a profile of RPM versus time and / or a profile of deceleration rate versus current power production that is particularly well suited for shutting down the gas and steam combined cycle power plant. For example, the optimized shutdown mode of operation may be determined as the one the operation of the steam turbine, the HRSG unit, the boiler and / or other components of the gas and steam combined cycle power plant is best suited and takes it into account. In one embodiment, the controller retrieves the above inputs and derives therefrom expected conditions at various RPM / time slope-time parameters to derive an RPM slope curve plotted along a time axis and minimizes the loads and / or optimizes shutdown costs , Also, profiles of deceleration rate versus current power production may include a fuel flow that more advantageously enhances current power production based on deceleration of the shaft (as compared to other proposed shutdown modes of operation). According to another embodiment, cost functions may be defined by which to derive and select other, more preferred or optimized shutdown modes of operation. Note that scenarios may be derived that minimize, for example, gas turbine load and / or loss, minimize stress and / or loss for the steam turbine, minimize stress and / or loss for the HRSG, or a combination thereof. Depending on how the cost function is defined, further optimized shutdown modes may be determined based on criteria such as the cost of power production during the shutdown period, factory emissions, fuel consumption, and / or combinations thereof, without limitation.

The controller of the gas and steam combined cycle power plant may further include a control system for shutting down the power plant according to the optimized shutdown mode. According to a preferred embodiment, this control system may include a physics-based modeler or a model-based controller, which then derives a control solution in the light of the optimized shutdown mode. The model-based controller may derive inputs and settings for control actuators and controllers so that the gas and steam combined cycle power plant operates during the shutdown period according to the preferred or optimized shutdown mode of operation. For example, the shutdown controller may actuate fuel valves to match a desired fuel flow rate while simultaneously controlling gas turbine exhaust inlet guide vanes to control the exhaust gas flow into the HRSG, while also controlling steam valves of the steam turbine to control shutdown of the steam turbine. By combining different components at approximately the same time, the shutdown of the plant can be improved and can match desired scenarios.

Although the invention has been described in conjunction with what is presently considered to be the most practical and most preferred embodiment, it should be understood that the invention is not limited to the disclosed embodiment, but rather is intended to cover various modifications and equivalent arrangements are included within the spirit and scope of the appended claims.

A control method for optimizing a load reduction operation for a power plant having multiple generator units. The plurality of generator units may each include either an on condition or an off condition during a selected operation period. The method may include: defining competing modes of operation, the competing modes of operation including various possible combinations with respect to which of the generator units have the on-state during the selected operating period and which have the off-state; Defining multiple cases for each of the competing modes of operation, the multiple cases including varying a value of an operating parameter; Receiving performance goals that include a cost function; Receiving an environmental condition prediction; Simulating the load reduction operation of the power plant with a power plant model in view of the value of the operating parameter and the environmental condition prediction; Evaluate a simulation result according to the cost function to select a preferred case.

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited non-patent literature

• 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 [0091 ]

Claims (10)

A control method for optimizing a load reduction operation for a power plant having a plurality of generator units for a selected operating period, the plurality of generator units each having either an on condition or an off condition during the selected operation period, the method comprising the steps of: Defining competing modes of operation, wherein the competing modes of operation include various possible combinations with respect to which of the plurality of generator units during the selected period of operation includes the on-condition and which includes the off-condition; Defining multiple cases for each of the competing modes of operation, the multiple cases including varying a value of an operating parameter over a range; Receiving performance goals including a cost function for evaluating the shutdown operation during the selected period of operation; Receiving an environmental condition prediction for the selected operating period; Simulating the load reduction operation of the power plant for the selected operating period with a power plant model in view of the value of the operating parameter and the environmental condition prediction for each of the multiple cases of the contending operating modes; and Evaluate a simulation result for each of the simulations according to the cost function to select a preferred case from the multiple cases for each of the competing modes of operation.  The method of claim 1, wherein the plurality of generator units comprises a plurality of gas turbines; wherein the step of defining the competing modes of operation includes: Permuting the plurality of gas turbines to configure an on / off permutation matrix, in the permutations each unique combinations with respect to which of the plurality of gas turbines during the selected operating period includes the on-condition and which includes the off-condition; and wherein each of the permutations of the permutation matrix is defined as one of the competing modes of operation for the selected period of operation.  The control method of claim 1 or 2, wherein the step of defining multiple cases for each of the contending modes of operation includes varying a value for a first operating parameter over a first range and a value for a second range of operation over a second range.  The control method of any one of the preceding claims, wherein the step of simulating each of the multiple cases of the contending modes of operation includes generating suggested parameter sets for each multiple case of the multiple cases; and where for each single multiple case of multiple cases the proposed parameter set includes: the value within the first range for the first operating time period and the value within the second range for the second operating time period for the respective multiple case; the on-condition and the off-condition for the multiple gas turbines for the concurrent operation mode corresponding to the respective multiple case; and Data relating to the environmental condition prediction for the selected period of operation;  The control method of claim 1, wherein the step of simulating the multiple cases with the power plant model includes performing a simulation run with the power plant model for each of the proposed parameter sets, wherein the simulation run is configured to simulate a load-down operation during the selected operating period according to input data that are defined by the proposed parameter sets; the performance goals further comprising operational limitations; and wherein the step of evaluating the simulation result from each of the simulation runs determines which one or even one of the simulation results violates any of the operability constraints. A control method according to any one of the preceding claims, comprising the step of dividing the selected period of operation into regularly repeating intervals; wherein the step of receiving the ambient condition prediction comprises receiving an ambient condition prediction for each of the intervals; and wherein the step of permuting the plurality of gas turbines comprises permuting the plurality of gas turbines for each of the intervals to configure the permutation matrix for each interval, wherein the permutation matrix for each interval is configured such that for a population of any one of the plurality of intervals Gas turbine only one of the on condition and the off condition excluding each other. A control method according to any one of the preceding claims, wherein the step of defining competing modes of operation includes defining each of the permutations for each of Permutation matrices for each of the intervals as one of the competing modes of operation.  A control method according to any one of the preceding claims, wherein the step of evaluating the simulation result from each of the simulations comprises selecting a preferred one of the multiple cases for each of the competing modes of operation for each of the intervals.  A control method according to any one of the preceding claims, wherein the step of simulating each of the multiple cases of competing modes of operation includes generating suggested parameter sets for each multiple case of the multiple cases, where for each single multiple case the proposed parameter set includes: the value within the first range for the first operating time period and the value within the second range for the second operating time period for the respective multiple case; the on-condition and the off-condition for the multiple gas turbines for the concurrent operation mode corresponding to the respective multiple case; and Environmental condition prediction data for the interval corresponding to each multiple case. The control method of claim 1, wherein the step of simulating each of the multiple cases with the power plant model includes performing a simulation run with the power plant model for each of the proposed parameter sets, the simulation run being configured to perform a load-down operation during the selected operating period Simulates input data defined by the proposed parameter sets; and the performance goals further comprising operational limitations; and wherein the step of evaluating the simulation results from the simulation runs determines whether or not any of the simulation results violate any of the operability constraints, and disqualifying each of the multiple instances that produced the simulation result that violated the operability constraints for consideration as one of the preferred cases.

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