CN104571068A - Optimized operation control method and system of distributed energy system - Google Patents
Optimized operation control method and system of distributed energy system Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses an optimized operation control method and system of a distributed energy system. The method include: S1, collecting environmental information and actual operation data of a unit so as to acquire a change rule of cold and hot load of a distributed energy station user with season and moment, and establishing a cold, hot and electric load prediction model; S2, optimizing the cold, hot and electric load prediction model on line by introducing real-time calibration factors and the actual operation data of the unit; S3, on the premise that the energy utilization efficiency is met, establishing a dynamic optimized load distribution model according to the dynamic requirements of the predicated cold, hot and electric load by taking a whole-plant economic benefit optimization as an objective, and outputting dynamic optimized load distribution results; S4, based on the whole-plant economic benefit optimization, establishing an optimal combination model according to the dynamic optimized load distribution results, and outputting a unit operation optimization command. High-precision load prediction information can be acquired, a corresponding optimization command is formed, and online optimization control is performed on the load dynamics and unit operation.
Description
Technical field
The present invention relates to a kind of operating and optimization control method and system of distributed energy resource system, particularly a kind of operating and optimization control method and system adding the cooling heating and power generation system of UTILIZATION OF VESIDUAL HEAT IN for Gas-steam Combined Cycle.
Background technology
Distributed energy refers to the energy comprehensive utilization system being distributed in user side, using rock gas or regenerative resource as main drive energy, based on hot and cold, CCHP technology, achieve the energy cascade utilization directly meeting the multiple demand of user, be with efficient, clean, the flexible energy supplying system for feature, become the green emerging energy industry that the world today develops rapidly.
The distinguishing feature that distributed energy resource system runs is that load variations is large, and especially hot and cold load variations is large, and randomness is strong.For meeting cool and thermal power workload demand, system is often in variable parameter operation state, due to lack load prediction link or precision of prediction lower, the deviation of load optimal distribution constantly becomes large, the response characteristic of unit and control system thereof there will be delayed, overshoot, even vibrates, the requirement of comprehensive utilization rate of energy source value when causing system to be difficult to reach design, economic benefit is also difficult to ensure.
Because hot and cold, the electric load of user side is in continuous change, the collocation operational mode of each main equipment of distributed busbar protection is more, throw/move back more unit or main equipment collocation unreasonable, energy consumption increase can be caused, plant consumption increases, economy reduces, therefore, how the operational mode of reasonably optimizing multicomputer is also distributed energy resource system problem demanding prompt solution.And at present, the SIS system of distributed busbar protection is only responsible for real-time production data transmission and form, not responsible decision-making in-situ optimal control and optimizing management, thus can not the operation optimum management of guide field equipment; And the DCS control system of existing distributed busbar protection and SIS production management system independent operating, production management system fails to be associated with control system real-time online, does not have the real-time online running optimizatin function to whole distributed busbar protection.
Summary of the invention
The object of the invention is to, a kind of operating and optimization control method and system of distributed energy resource system is provided, high-precision load prediction information can be obtained, formed and optimize order accordingly, to load dynamically and unit operation carry out vehicle air-conditioning.
For solving the problems of the technologies described above, the present invention adopts following technical scheme: a kind of operating and optimization control method of distributed energy resource system, comprises the following steps:
S1, gathers environmental information and unit actual operating data, with the Changing Pattern of the hot and cold load obtaining distributed busbar protection user with season and moment, then according to described Changing Pattern and the plan of user's steam demand, sets up cool and thermal power load forecasting model;
S2, by introducing in real time verification Summing Factor unit actual operating data, on-line optimization cool and thermal power load forecasting model, by time predict dynamic need that is hot and cold, electric load;
S3, under the prerequisite meeting efficiency of energy utilization, to realize full factory optimal economic benefit for target, the dynamic need that is hot and cold, electric load according to prediction sets up load dynamic optimization apportion model, output load dynamic optimization allocation result;
S4, based on full factory optimal economic benefit, sets up Combinatorial Optimization Model according to load dynamic optimization allocation result, exports unit running optimization order, is optimized and regulates and controls load implementation.
In the operating and optimization control method of aforesaid distributed energy resource system, described environmental information comprises local atmospheric temperature, humidity information; Described unit actual operating data comprises rate of load condensate and the work efficiency of unit.
In the operating and optimization control method of aforesaid distributed energy resource system, refrigeration duty in described step S1 is cooling load of air-condition, thermal load comprises industrial steam load and air conditioner heat load, described cool and thermal power load forecasting model is set up by following steps: S11, take current time as starting point, the weather forecast information collected in nearly 3 years (comprises temperature, humidity etc.), utilization index smoothing algorithm obtain by time Changes in weather curve, then contrast with current weather forecast information, revise, set up weather prognosis model, weather condition in following three days is predicted, calculate following one day by time Changes in weather curve,
S12, according to following one day by time Changes in weather curve, statistics energy source station user is no less than use energy (hot and cold) data in a year, the hot and cold load calculating following one day is with the change with time curve in season and moment, again according to the hot and cold load of following a day with the change with time curve in season and moment and the plan of user's steam demand, set up cool and thermal power load forecasting model.
Following one week weather condition is predicted in weather forecast usually, and for distributed energy resource system, main its practicality of consideration, selects following three days as prediction step; Because oversize then without practical value, too short then insufficient to the support of ensuing Exact Forecast " following one day " data.Calculate and forecast that the change with time curve of " following a day " opens and operational plan arrangement to be supplied to the energy source station operations staff unit of second day.
In the operating and optimization control method of aforesaid distributed energy resource system, in described step S2, the described real-time verification factor is the predicted load by instant online data correction gained, and described instant data comprise distributed busbar protection provides temperature from the female pipe of air-conditioning hot water or cold water confession backwater to user and flow.
In the operating and optimization control method of aforesaid distributed energy resource system, in described step S2, described dynamic need that is hot and cold, electric load comprises the following power load distributing situation of energy source station user and the generation moment of peak load and duration.
In the operating and optimization control method of aforesaid distributed energy resource system, in described step S3, load dynamic optimization apportion model, according to predicting hot and cold, the electric load value that obtain, output load dynamic optimization allocation result, respectively overlaps unit to distributed busbar protection and must complete load quantity and allocate; Described load dynamic optimization apportion model is set up according to the following steps:
S31, in a distributed manner energy source station disbursement addition and be objective function, disbursement comprise power consumption expense, consumption gas expense with and operation and maintenance cost;
S32, to meet efficiency of energy utilization for constraint condition, constraint condition comprises user with can the system cool and thermal power product equilibrium condition (energy resource system especially electric system has the advantages that namely produce namely, and the electricity that energy source station provides, heat, cold product must produce and use (dissolving) to balance each other) of demand, unit capacity qualifications and environmental emission restrictive condition;
S33, by calculating, drawing the solution when objective function met under constraint condition is minimum, thus obtaining optimum load dynamic optimization allocation result.
In the operating and optimization control method of aforesaid distributed energy resource system, in described step S4, what obtain according to load dynamic optimization allocation result often overlaps the load completed needed for unit, the inner all possible energy supply of single cover unit (hot and cold, electricity) mode is carried out to the regulable control of Combinatorial Optimization and correlation parameter, meet load supply; Described Combinatorial Optimization Model is set up according to the following steps:
S41, with the addition of unit disbursement with for objective function, unit disbursement comprises power consumption expense and the consumption gas expense use of unit;
S42, constraint condition comprises that cool and thermal power load meets supply values condition, (unit is when variable working condition, variable load operation for the capacity regulating uphill, downhill speed qualifications of unit supply unit, the rate of change of its capacity or Load Regulation and uphill, downhill speed can not be too fast, otherwise cause system unstable, damage unit, thus must have speed to limit) and operation interval qualifications;
S43, by calculating, drawing the solution when objective function met under constraint condition is minimum, thus obtaining best array mode.
Realize a running optimizatin control system for the distributed energy resource system of preceding method, comprising:
Information acquisition module, for gathering environmental information and unit actual operating data;
Cool and thermal power load forecasting model, for the hot and cold load of data acquisition distributed busbar protection user that gathers according to the information acquisition module Changing Pattern with season and moment, again according to described Changing Pattern and the plan of user's steam demand, set up cool and thermal power load forecasting model;
Module is optimized in load prediction, for by introducing in real time verification Summing Factor unit actual operating data, on-line optimization cool and thermal power load forecasting model, by time predict dynamic need that is hot and cold, electric load;
Load dynamic optimization distribution module, for under the prerequisite meeting efficiency of energy utilization, to realize full factory optimal economic benefit for target, the dynamic need that is hot and cold, electric load according to prediction sets up load dynamic optimization apportion model, output load dynamic optimization allocation result;
Load implementation Optimum Regulation module, for based on full factory optimal economic benefit, sets up Combinatorial Optimization Model according to load dynamic optimization allocation result, exports unit running optimization order, is optimized and regulates and controls load implementation.
In the running optimizatin control system of aforesaid distributed energy resource system, described cool and thermal power load forecasting model comprises:
By time Changes in weather curve computing module, for taking current time as starting point, collect the weather forecast information (comprising temperature, humidity etc.) in nearly 3 years, utilization index smoothing algorithm obtain by time Changes in weather curve, then carry out with current weather forecast information contrasting, revising, set up weather prognosis model, the weather condition in following three days predicted, calculate following one day by time Changes in weather curve;
Cooling and heating load change with time curve computing module, according to following one day by time Changes in weather curve, statistics energy source station user be no less than in 1 year with can (hot and cold) data, the hot and cold load calculating following a day is with the change with time curve in season and moment.
Compared with prior art, optimal control method of the present invention can overview be " certain, two optimizations "; " necessarily " refers to that hot and cold needed for user every day, electric load is certain, and energy source station must be accomplished to supply according to quantity, in time, that is load be constrained to fixed; " necessarily " described herein means " determination " but not " fixing ", and namely workload demand is a determined value all the time, and this determined value is provided by the cool and thermal power load prediction module of this method; " two optimize " refers to the purposes optimization of high pressure steam and low-pressure steam two type steam, and namely under the condition meeting workload demand, whereabouts and sendout how to optimize two kinds of steam make the economic benefit of full factory best; Its control strategy is: first carry out load distribution in power plants, then carries out optimization and the regulation and control thereof of load implementation.
The present invention is optimum for target with the overall target of the economic benefit and comprehensive utilization rate of energy source that realize distributed energy resource system operation, use load prediction, on-line identification, mathematical modeling, intelligent computation, the technology such as safety communication, the real time data that coupling system runs and historical data, by to generating, refrigeration, the assessment of the efficiency data of heat supply three kinds of energy resource systems, set up and solve optimized load dynamic optimization apportion model, provide optimized control and traffic order (comprising mode of operation and corresponding numerical value), and order is assigned to distributed monitoring control system, by DCS system to generating, refrigeration, heating unit controls, thus realize the optimized operation of distributed energy resource system, reach efficient low-consume, economic coupling, reliable and secure operation target.
The present invention has the following advantages:
(1) optimum load dispatch of intelligence
Full factory cool and thermal power load in real time, automatically realizes the optimum allocation between each monoblock.
(2) higher load-response-speed and degree of regulation
Provide rational unit plus-minus and start-stop time, optimize load overshoot, make system have higher cool and thermal power load-response-speed and improve about 8% and degree of regulation raising about 5%.
(3) best combination of economic benefit and comprehensive utilization rate of energy source
System can be run according to the optimum matching mode of economic benefit and efficiency of energy utilization, and wherein economic benefit improves about 5%, and efficiency of energy utilization improves about 3%.
(4) unit operation more steadily, safe reliability significantly improves
Combinationally use mode, the supply of level and smooth regulation and control load by what optimize unit, make the operation of unit more steady, security, reliability be improved significantly, the serviceable life of unit extends.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the embodiment of the present invention;
Fig. 2 is the system architecture schematic diagram of the embodiment of the present invention;
Fig. 3 is the system architecture diagram of the embodiment of the present invention.
Below in conjunction with the drawings and specific embodiments, the present invention is further illustrated.
Embodiment
Embodiments of the invention 1: a kind of operating and optimization control method of distributed energy resource system, as shown in Figure 1, comprise the following steps:
One, data acquisition
Gather environmental information and unit actual operating data; Described environmental information comprises local atmospheric temperature, humidity information; Described unit actual operating data comprises rate of load condensate and the work efficiency of unit.
Two, cool and thermal power load forecasting model is set up
General power plant is with generated energy how much for target, and other is all subsidiary product; Distributed busbar protection based on supply of cooling, heating and electrical powers technology is then adopt to run in " cold (heat) fixed electricity " mode.That is, energy source station fundamental purpose produces refrigeration duty or thermal load, generating then becomes subsidiary product, in the energy source station design phase, determine that unit capacity configures according to demand that is cold or thermal load, generated energy be then root thus unit capacity determined, be referred to as cold (heat) fixed electricity ".Thus cool and thermal power load optimal distribution is actually the Optimizing Allocation of cooling and heating load.Refrigeration duty is defined as cooling load of air-condition, and thermal load comprises industrial steam load and air conditioner heat load.Wherein air conditioner cold-heat load is by low-pressure steam as drive source, is realized respectively by absorption refrigeration unit and plate type heat exchanger; Industrial steam load high pressure steam as drive source, and is realized by the combination of one or more of extracted steam from turbine, spike boiler and temperature-decreased pressure reducer three kinds of modes.
The hot and cold load obtaining distributed busbar protection user according to the environmental information gathered is with the Changing Pattern in season and moment, again according to described Changing Pattern and user's steam demand plan (plan of user's steam demand submits to using of energy source station can plan to obtain mainly through 9 former users in evening the previous day), set up cool and thermal power load forecasting model, specific as follows:
1, take current time as starting point, collect the weather forecast information (comprising temperature, humidity etc.) in nearly 3 years, utilization index smoothing algorithm obtain by time Changes in weather curve, then carry out with current weather forecast information contrasting, revising, set up weather prognosis model, weather condition in following three days is predicted, calculate following one day by time Changes in weather curve;
2, according to following one day by time Changes in weather curve, statistics energy source station user is no less than use energy (hot and cold) data in a year, the hot and cold load calculating following one day is with the change with time curve in season and moment, again according to the hot and cold load of following a day with the change with time curve in season and moment and the plan of user's steam demand, set up cool and thermal power load forecasting model.
In the application " by time " be meant to enumerate out by per hour.
Three, optimal prediction model
For improving the precision of cool and thermal power load forecasting model, introduce verification Summing Factor unit actual operating data in real time, online, real-time optimization cool and thermal power load forecasting model, by time predict dynamic need that is hot and cold, electric load.
The described real-time verification factor is the predicted load by instant online data correction gained, and described instant data comprise distributed busbar protection provides temperature from the female pipe of air-conditioning hot water or cold water confession backwater to user and flow.
Described dynamic need that is hot and cold, electric load comprises the following power load distributing situation of energy source station user and the generation moment of peak load and duration.
Four, load dynamic optimization distributes
Under the prerequisite meeting efficiency of energy utilization, to realize full factory optimal economic benefit for target, dynamic need that is hot and cold, electric load according to prediction sets up load dynamic optimization apportion model, output load dynamic optimization allocation result, respectively overlap unit to distributed busbar protection must complete load quantity and allocate, mainly complete industrial steam load, the optimization of low-pressure steam load between many cover Combined Cycle Unit distributes, and the load cooperate optimization of meeting an urgent need during peak-load condition between boiler and Combined Cycle Unit distributes.The optimization of described industrial steam load is assigned as the load distribution between supplying heat source, and described supplying heat source comprises Combined Cycle Unit extracted steam from turbine, spike boiler and temperature-decreased pressure reducer; The optimization that the optimization of described low-pressure steam load (or claiming HVAC steam load) is assigned as between steam turbine filling, plate type heat exchanger and absorption refrigeration unit distributes.
Wherein, described load dynamic optimization apportion model is set up according to the following steps:
1, in a distributed manner energy source station disbursement addition and be objective function, disbursement comprise power consumption expense, consumption gas expense with and operation and maintenance cost;
2, to meet efficiency of energy utilization for constraint condition, constraint condition comprises user with can the system cool and thermal power product equilibrium condition of demand, unit capacity qualifications and environmental emission restrictive condition;
3, by calculating, drawing the solution when objective function met under constraint condition is minimum, thus obtaining optimum load dynamic optimization allocation result.
Five, the optimization of load implementation and regulation and control
Based on full factory optimal economic benefit, Combinatorial Optimization Model is set up according to load dynamic optimization allocation result, what obtain often overlaps the load completed needed for unit, export unit running optimization order, to the inner all possible energy supply of single cover unit (hot and cold, electricity), mode carries out Combinatorial Optimization and correlation parameter (as extraction valve aperture, pressure and temperature reducing valve opening etc.) carries out regulable control, meet load supply, meet the flatness requirement of Parameters variation.Mainly complete the mode adjusting between the inner each regulation and control unit of single fighter, the operational mode optimization of each train unit met under heat supply prerequisite that draws gas mainly for steam turbine, the object of optimization realizes single fighter maximizing the benefits, determines the mode of operation of single fighter.Such as, under a certain industrial steam demand operating mode, combination is optimized to extracted steam from turbine, temperature-decreased pressure reducer, emergent boiler etc., and calculates optimum extraction valve aperture, pressure and temperature reducing valve opening and emergent boiler load amount etc., realize power unit in economic operation; Under a certain cooling load demand operating mode, the optimal combination of HVAC steam between heat exchange, filling etc. and bestly to regulate.
Described Combinatorial Optimization Model is set up according to the following steps:
1, with the addition of unit disbursement with for objective function, unit disbursement comprises power consumption expense and the consumption gas expense use of unit;
2, constraint condition comprises cool and thermal power load and meets supply values condition, the capacity regulating uphill, downhill speed qualifications of unit supply unit and operation interval qualifications;
3, by calculating, drawing the solution when objective function met under constraint condition is minimum, thus obtaining best array mode.
Embodiments of the invention 2: a kind of running optimizatin control system realizing the distributed energy resource system of method described in embodiment 1, as shown in Figure 2, comprising:
Information acquisition module, for gathering environmental information and unit actual operating data;
Cool and thermal power load forecasting model, for the hot and cold load of data acquisition distributed busbar protection user that gathers according to the information acquisition module Changing Pattern with season and moment, again according to described Changing Pattern and the plan of user's steam demand, set up cool and thermal power load forecasting model;
Module is optimized in load prediction, for by introducing in real time verification Summing Factor unit actual operating data, on-line optimization cool and thermal power load forecasting model, by time predict dynamic need that is hot and cold, electric load;
Load dynamic optimization distribution module, for under the prerequisite meeting efficiency of energy utilization, to realize full factory optimal economic benefit for target, the dynamic need that is hot and cold, electric load according to prediction sets up load dynamic optimization apportion model, output load dynamic optimization allocation result;
Load implementation Optimum Regulation module, for based on full factory optimal economic benefit, sets up Combinatorial Optimization Model according to load dynamic optimization allocation result, exports unit running optimization order, is optimized and regulates and controls load implementation.
Wherein, described cool and thermal power load forecasting model comprises: by time Changes in weather curve computing module, for taking current time as starting point, collect the weather forecast information (comprising temperature, humidity etc.) in nearly 3 years, utilization index smoothing algorithm obtain by time Changes in weather curve, then carry out with current weather forecast information contrasting, revising, set up weather prognosis model, weather condition in following three days is predicted, calculate following one day by time Changes in weather curve; Cooling and heating load change with time curve computing module, according to following one day by time Changes in weather curve, statistics energy source station user be no less than in 1 year with can (hot and cold) data, the hot and cold load calculating following a day is with the change with time curve in season and moment.
By load prediction optimize module provide by time the determined value of cool and thermal power workload demand, then the load optimal distribution of level of factory is carried out by load dynamic optimization distribution module, distribute to based on unit real-time performance and often overlap unit rational cool and thermal power load setting value, finally complete the mode adjusting between the inner each regulation and control unit of single fighter by load implementation Optimum Regulation module, thus realize the on-line operation decision optimization of distributed busbar protection.
Native system can be divided into decision optimization station and energy efficiency management station two large divisions, primarily of a set of real-time configurable decision optimization hardware system, a set of trilogy supply energy efficiency management and load prediction computing platform and related software bag composition.For making system cloud gray model at efficiency data optimum state, native system is arranged on DCS public system part.
Decision optimization station is responsible for calculating real-time running data and optimizing, and the order of final formation decision optimization; Classification to system operation data and management are responsible in energy efficiency management station.
Hardware device mainly comprises the equipment such as decision optimization station, energy efficiency management station, optimal controller, power transfer device.Decision optimization station and energy efficiency management station are arranged in engineer station's (comprising often stand a main frame and a display); Optimal controller is placed on electric room, placed side by side with other racks of DCS public system; Operator's console and two power transfer devices are arranged in engineer station, for decision optimization station and energy efficiency management station provide uninterrupted power source.
Native system is summed up as " division of labor clear and definite, cooperatively interact " with the relation of DCS system, and native system stresses system real-time optimization, and DCS stresses system and controls in real time.As shown in Figure 3, native system obtains on-the-spot actual operating data by DCS, then carries out calculating and optimizing, and is formed to optimize to order and be sent to DCS system to perform, and completes the optimization to distributed busbar protection and control.
In addition, native system is verified by following steps:
1, simulation access.Utilize the distributed energy resource system of national energy distributed energy Technology R & D Center integrated with control research/development platform, simulate the system configuration of existing true distributed busbar protection, realize the dry run of system, repeatedly verify, guarantee the stability of system, possess the condition of reaching the standard grade.
2, on line open loop debugging.Native system is connected with DCS system, tests data communication rates between the two and message capacity, and the correctness of checking data communication.
3, line closed loop debugging.Native system is carried out on-line operation debugging, after each load section testing results, Collection and analysis field data and unit operation parameter, update the system associated control parameters.
Claims (9)
1. an operating and optimization control method for distributed energy resource system, is characterized in that, comprises the following steps:
S1, gathers environmental information and unit actual operating data, with the Changing Pattern of the hot and cold load obtaining distributed busbar protection user with season and moment, then according to described Changing Pattern and the plan of user's steam demand, sets up cool and thermal power load forecasting model;
S2, by introducing in real time verification Summing Factor unit actual operating data, on-line optimization cool and thermal power load forecasting model, by time predict dynamic need that is hot and cold, electric load;
S3, under the prerequisite meeting efficiency of energy utilization, to realize full factory optimal economic benefit for target, the dynamic need that is hot and cold, electric load according to prediction sets up load dynamic optimization apportion model, output load dynamic optimization allocation result;
S4, based on full factory optimal economic benefit, sets up Combinatorial Optimization Model according to load dynamic optimization allocation result, exports unit running optimization order, is optimized and regulates and controls load implementation.
2. the operating and optimization control method of distributed energy resource system according to claim 1, is characterized in that: described environmental information comprises local atmospheric temperature, humidity information; Described unit actual operating data comprises rate of load condensate and the work efficiency of unit.
3. the operating and optimization control method of distributed energy resource system according to claim 1 and 2, it is characterized in that: the refrigeration duty in described step S1 is cooling load of air-condition, thermal load comprises industrial steam load and air conditioner heat load, and described cool and thermal power load forecasting model is set up by following steps:
S11, take current time as starting point, collect the weather forecast information in nearly 3 years, utilization index smoothing algorithm obtain by time Changes in weather curve, then carry out with current weather forecast information contrasting, revising, set up weather prognosis model, the weather condition in following three days predicted, calculate following one day by time Changes in weather curve;
S12, according to following one day by time Changes in weather curve, statistics energy source station user is no less than the use energy data in a year, the hot and cold load calculating following one day is with the change with time curve in season and moment, again according to the hot and cold load of following a day with the change with time curve in season and moment and the plan of user's steam demand, set up cool and thermal power load forecasting model.
4. the operating and optimization control method of distributed energy resource system according to claim 1, it is characterized in that: in described step S2, the described real-time verification factor is the predicted load by instant online data correction gained, and described instant data comprise distributed busbar protection provides temperature from the female pipe of air-conditioning hot water or cold water confession backwater to user and data on flows.
5. the operating and optimization control method of the distributed energy resource system according to claim 1 or 4, it is characterized in that: in described step S2, described dynamic need that is hot and cold, electric load comprises the following power load distributing situation of energy source station user and the generation moment of peak load and duration.
6. the operating and optimization control method of distributed energy resource system according to claim 1 and 2, it is characterized in that: in described step S3, load dynamic optimization apportion model is according to predicting hot and cold, the electric load value that obtain, output load dynamic optimization allocation result, respectively overlaps unit to distributed busbar protection and must complete load quantity and allocate; Described load dynamic optimization apportion model is set up according to the following steps:
S31, in a distributed manner energy source station disbursement addition and be objective function, disbursement comprise power consumption expense, consumption gas expense with and operation and maintenance cost;
S32, to meet efficiency of energy utilization for constraint condition, constraint condition comprises user with can the system cool and thermal power product equilibrium condition of demand, unit capacity qualifications and environmental emission restrictive condition;
S33, by calculating, drawing the solution when objective function met under constraint condition is minimum, thus obtaining optimum load dynamic optimization allocation result.
7. the operating and optimization control method of the distributed energy resource system according to claim 1 or 6, it is characterized in that: in described step S4, what obtain according to load dynamic optimization allocation result often overlaps the load completed needed for unit, the inner all energy-provision way of single cover unit are carried out to the regulable control of Combinatorial Optimization and correlation parameter, meet load supply; Described Combinatorial Optimization Model is set up according to the following steps:
S41, with the addition of unit disbursement with for objective function, unit disbursement comprises power consumption expense and the consumption gas expense use of unit;
S42, constraint condition comprises cool and thermal power load and meets supply values condition, the capacity regulating uphill, downhill speed qualifications of unit supply unit and operation interval qualifications;
S43, by calculating, drawing the solution when objective function met under constraint condition is minimum, thus obtaining best array mode.
8. realize a running optimizatin control system for the distributed energy resource system of method described in claim 1 ~ 7 any one, it is characterized in that, comprising:
Information acquisition module, for gathering environmental information and unit actual operating data;
Cool and thermal power load forecasting model, for the hot and cold load of data acquisition distributed busbar protection user that gathers according to the information acquisition module Changing Pattern with season and moment, again according to described Changing Pattern and the plan of user's steam demand, set up cool and thermal power load forecasting model;
Module is optimized in load prediction, for by introducing in real time verification Summing Factor unit actual operating data, on-line optimization cool and thermal power load forecasting model, by time predict dynamic need that is hot and cold, electric load;
Load dynamic optimization distribution module, for under the prerequisite meeting efficiency of energy utilization, to realize full factory optimal economic benefit for target, the dynamic need that is hot and cold, electric load according to prediction sets up load dynamic optimization apportion model, output load dynamic optimization allocation result;
Load implementation Optimum Regulation module, for based on full factory optimal economic benefit, sets up Combinatorial Optimization Model according to load dynamic optimization allocation result, exports unit running optimization order, is optimized and regulates and controls load implementation.
9. the running optimizatin control system of distributed energy resource system according to claim 8, is characterized in that: described cool and thermal power load forecasting model comprises:
By time Changes in weather curve computing module, for taking current time as starting point, collect the weather forecast information in nearly 3 years, utilization index smoothing algorithm obtain by time Changes in weather curve, then carry out with current weather forecast information contrasting, revising, set up weather prognosis model, the weather condition in following three days predicted, calculate following one day by time Changes in weather curve;
Cooling and heating load change with time curve computing module, according to following one day by time Changes in weather curve, statistics energy source station user be no less than in 1 year with can data, the hot and cold load calculating following a day is with the change with time curve in season and moment.
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| CN201510052320.6A Active CN104571068B (en) | 2015-01-30 | 2015-01-30 | The operating and optimization control method and system of a kind of distributed energy resource system |
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