WO2022048127A1 - 一种电热泵-热电联合系统的优化调控方法及系统 - Google Patents

一种电热泵-热电联合系统的优化调控方法及系统 Download PDF

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WO2022048127A1
WO2022048127A1 PCT/CN2021/080499 CN2021080499W WO2022048127A1 WO 2022048127 A1 WO2022048127 A1 WO 2022048127A1 CN 2021080499 W CN2021080499 W CN 2021080499W WO 2022048127 A1 WO2022048127 A1 WO 2022048127A1
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unit
heat pump
power
combined system
electric heat
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PCT/CN2021/080499
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French (fr)
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房方
金顺平
仲心萌
刘吉臻
胡阳
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华北电力大学
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Priority to JP2021522043A priority Critical patent/JP7261507B2/ja
Priority to US17/472,866 priority patent/US20220074620A1/en
Publication of WO2022048127A1 publication Critical patent/WO2022048127A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Definitions

  • the invention relates to the technical field of energy operation, in particular to an optimal control method and system for an electric heat pump-heat and electricity combined system.
  • the constraints of determining electricity by heat are mainly decoupled by adding heat storage tanks, electric heat conversion devices (such as electric heat pumps) in the combined heat and power system, and optimizing the structure and performance of the system. Further promote wind power and photovoltaic consumption. Realize the coordinated operation of power generation and heating units, rationally dispatch the two types of energy, heat and electricity, and optimize the output of cogeneration units, wind turbines, and photovoltaics.
  • the environmental pollution of energy is of great significance.
  • the current scheduling optimization method of combined heat and power system aggregates each unit in the system into a whole, and establishes a combined heat and power scheduling model with the objectives of optimizing the overall economic benefit of the system and the lowest operating cost of the system.
  • these methods do not take into account that each unit may belong to different subjects, and the decision-making behavior of different subjects affects each other, that is, there may be a conflict between the overall optimization of the system and the optimal self-benefit pursued by each subject. It may not be the optimal solution for each subject, which will cause each subject to deviate from the optimal solution and seek other solutions to obtain higher returns.
  • game theory mainly studies the complex behavior between multiple independent agents that interact with each other, and is suitable for solving multi-agent and multi-objective optimization problems.
  • the present invention provides an optimal control method and system for an electric heat pump-heat and power combined system, which can treat each unit as a different subject, and control with the goal of maximizing the self-interest of each subject, which can not only meet the needs of thermoelectricity
  • the load demand of the user can also make each unit subject to the greatest degree of satisfaction with its income.
  • the present invention provides an optimal control method for an electric heat pump-heat-electricity combined system, characterized in that the steps of the method include:
  • thermoelectric scheduling optimization scheme using the particle swarm algorithm to solve the game model, and obtain the thermoelectric scheduling optimization scheme of each unit.
  • the above-mentioned aspects and any possible implementation manners further provide an implementation manner.
  • the process of using the particle swarm algorithm to solve the game model includes:
  • step S3 The establishment of the game model in step S3 is constrained by the balance of electric heating supply and demand and the operating conditions of the unit, and the interests of each subject in the electric heat pump-heat and power combined system are maximized. into optimization goals.
  • an implementation manner is further provided, wherein the electric heat pump-heat and power combined system includes a wind turbine, a photovoltaic unit, an electricity storage system, a thermoelectric unit, and an electric heat pump.
  • an implementation manner is further provided, and the output model of the wind turbine is:
  • v t is the real-time wind speed at time t
  • vi is the cut -in wind speed of the wind turbine
  • v o is the cut-out wind speed of the wind turbine
  • v r is the rated wind speed of the wind turbine
  • P WZ is the installed capacity value of the wind turbine
  • the revenue function model of the wind turbine is:
  • Iw Iwsell + Iwa - Iwm ;
  • I w wind turbine revenue I wsell represents the wind turbine electricity sales revenue
  • I wa represents the wind turbine subsidy income
  • I wm represents the wind turbine maintenance cost.
  • an implementation manner is further provided, and the output model of the photovoltaic unit is:
  • ⁇ pv is the power derating coefficient of the photovoltaic unit
  • P PVZ is the installed capacity of the photovoltaic unit
  • At is the actual irradiance of the photovoltaic unit at time t
  • a s is the irradiance under standard conditions
  • ⁇ T is the power temperature coefficient
  • T stp is the temperature under standard conditions
  • T is the real-time temperature
  • the revenue function model of the photovoltaic unit is:
  • I pv I pvsell +I pva -I pvm ;
  • I pv is the revenue of photovoltaic units
  • I pvsell is the electricity sales revenue of photovoltaic units
  • I pva is the subsidy income of photovoltaic units
  • I pvm is the maintenance cost of photovoltaic units.
  • an implementation manner is further provided, and the output model of the power storage system is:
  • C e, t+1 is the remaining power of the battery at time t+1
  • C e, t is the remaining power of the battery at time t
  • ⁇ is the self-discharge efficiency of the battery
  • ⁇ c and ⁇ d are the charging and discharging efficiencies of the battery, respectively
  • Pe t is the charging and discharging power of the battery
  • ⁇ t is the charging and discharging time.
  • an implementation manner is further provided, and the output model of the thermal power unit is:
  • P pc, t is the electric power of the pure condensing condition at time t
  • P chp, t is the electric power of the thermoelectric unit at time t
  • Q chp, t is the thermal power of the thermoelectric unit at time t
  • ⁇ chp is the electrothermal conversion coefficient
  • the revenue function model of the thermal power unit is:
  • I chp I ssell -I sf -I sm -I sa ;
  • I chp is the income of the thermal power unit
  • I ssell is the income of the thermal power unit from selling electricity and heat
  • I sf is the fuel cost of the thermal power unit
  • Ism is the maintenance cost of the thermal power unit
  • I sa represents the abandoned wind that the thermal power unit needs to pay. Abandonment cost.
  • ⁇ is the heating efficiency of the electric heat pump
  • Q U is the heat energy converted by the electric heat pump
  • W is the electric energy consumed by the electric heat pump
  • Q pu is the heating capacity of the electric heat pump
  • P pu is the input power of the electric heat pump.
  • the present invention provides an optimal control device for an electric heat pump-heat-electricity combined system, characterized in that the device is used to realize any one of the control methods described above;
  • the device includes a control module and a communication module; the communication module is respectively connected with each unit of the electric heat pump-heat-electricity combined system for collecting data of each unit and sending regulation instructions to each unit; the control module is used for storing each unit.
  • the output model, revenue function model, constraint conditions and game model of the unit are solved, and the optimal control scheme is obtained by solving each model.
  • the present invention can obtain the following technical effects:
  • the invention no longer regards all the units in the electric heat pump-heat and power combined system as a whole for optimization, but treats each unit as a different subject, each subject aims at maximizing its own interests, so the game theory is used.
  • a non-cooperative game model of thermoelectric dispatching is established, which is beneficial to deal with the diversity of subjects in the electric heat pump-heat and power combined system;
  • the invention combines the particle swarm algorithm and iterative algorithm to solve the game model, wherein the particle swarm algorithm simulates the process of each subject searching for the optimal solution under the given conditions, and the iterative algorithm simulates each subject's decision to change the decision of other subjects. Response; by solving the obtained Nash equilibrium solution, the optimal scheme of dispatching output of each unit can be determined. This output scheme can not only meet the load demand of thermal power users, but also satisfy each subject.
  • Fig. 1 is the flow chart of the optimization control method of the electric heat pump-heat-electricity combined system provided by an embodiment of the present invention
  • FIG. 2 is a block diagram of the structure of an electric heat pump-heat and power combined system provided by an embodiment of the present invention
  • FIG. 3 is a characteristic curve diagram of a cogeneration unit provided by an embodiment of the present invention.
  • FIG. 5 is a flowchart of a particle swarm algorithm for solving a game model provided by an embodiment of the present invention
  • FIG. 9 is a typical daily photovoltaic power generation power curve provided by an embodiment of the present invention.
  • FIG. 10 is a typical daily electricity load demand curve provided by an embodiment of the present invention.
  • FIG. 11 is a typical daily heat load demand curve provided by an embodiment of the present invention.
  • FIG. 16 is a heating curve of an electric heat pump provided by an embodiment of the present invention.
  • thermoelectric dispatching problem of the system is a decision-making optimization problem with multi-agent participation. Based on the operating output characteristics and revenue function of each unit in the electric heat pump-heat and power combined system, how to optimize the scheduling decision of each unit and obtain the corresponding output plan is the technical problem to be solved by this patent.
  • the present invention is aimed at an electric heat pump-heat and power combined system involving multiple subjects such as cogeneration units, wind turbines, photovoltaics, etc., based on the established unit output model and its revenue function model, and fully considers each The behavior of the main body and the corresponding interests, while considering the mutual influence of each behavior, with the balance of electricity and heat supply and demand, the operating conditions of the unit as constraints, and the optimization goal of maximizing the interests of each main body in the electric heat pump-heat and power combined system, established a thermoelectric system.
  • the non-cooperative game model of scheduling is combined with the particle swarm algorithm and iterative algorithm to solve the equilibrium strategy of the game.
  • the final Nash equilibrium strategy is the optimized output plan of each unit, which provides guidance for thermal power scheduling decisions.
  • An optimal control method for an electric heat pump-heat and power combined system includes the following steps:
  • Step 1 Establish the composition structure of the electric heat pump-heat and power combined system, and model the heat/electrical output of each unit;
  • the electric heat pump-heat and power combined system includes two parts, the power supply part and the heating part, as shown in Figure 2.
  • the power supply part is composed of wind turbines, photovoltaic units, power storage systems and electricity users
  • the heating part is composed of cogeneration units, electric heat pumps and heat users.
  • the output of the wind turbine will be constrained by the installed capacity and the actual situation.
  • the maximum value of wind power output at each moment is determined by actual conditions such as weather and environment, and the wind turbine output and wind speed satisfy the following nonlinear relationship:
  • v t is the real-time wind speed at time t
  • vi is the cut -in wind speed of the wind turbine
  • v o is the cut-out wind speed of the wind turbine
  • v r is the rated wind speed of the wind turbine
  • P WZ is the installed capacity value of the wind turbine.
  • the output of photovoltaics will also be constrained by the installed capacity and actual situation.
  • the photovoltaic output is related to the light intensity and temperature, and the photovoltaic output can be expressed by the following formula:
  • ⁇ pv is the power derating coefficient of the unit
  • P PVZ is the installed capacity of photovoltaics
  • At is the actual irradiance of the photovoltaic unit at time t
  • ⁇ T is the power temperature coefficient
  • T stp is the temperature under standard conditions. Since the value of ⁇ T is relatively small, the effect of temperature changes on the output of photovoltaics is approximately 0, so the output of photovoltaic units can be approximately proportional to the actual irradiance A t , namely:
  • the SOC of a battery is the ratio of the battery's remaining charge to the battery's full charge.
  • C e, t is the remaining power of the battery at time t
  • C full is the battery capacity
  • Pe , t as the charging and discharging power of the battery, when Pe , t ⁇ 0, it means that the battery is charging, when Pe , t > 0, it means that the battery is discharging, and the energy storage state of the battery can be expressed as follows:
  • ⁇ is the self-discharge efficiency of the battery
  • ⁇ c and ⁇ d are the charge and discharge efficiencies of the battery, respectively.
  • the entire steam turbine consists of three parts: a low-pressure cylinder, a medium-pressure cylinder, and a high-pressure cylinder.
  • the high-temperature and high-pressure steam generated in the boiler enters the steam turbine to do work, and the extraction steam for heat supply comes from the exhaust steam from the medium-pressure cylinder.
  • (P chp + ⁇ chp Q chp ) can be equivalent to the power of the cogeneration unit:
  • P pc, t is the electric power under pure condensing condition at time t
  • P chp, t is the electric power of the unit at time t
  • Q chp, t is the thermal power of the unit at time t
  • ⁇ chp is the electric-heat conversion coefficient, representing 1W thermal power
  • the electrical power can be converted to ⁇ chp W.
  • the value ranges of the electric power P chp, t and the thermal power Q chp, t are shown in Figure 3.
  • the adjustable area of the cogeneration unit power is the quadrilateral area formed by ABCD. Obviously, when the thermal power increases, the adjustable electric power range Rapid reduction, and the peak shaving ability of the unit is also poor.
  • the electric heat pump can extract low-temperature waste heat from the heating return water of the thermal power plant as low-quality thermal energy and convert it into high-quality thermal energy.
  • Figure 4 depicts the conversion of the basic energy of the electric heat pump. Ideally, according to the first law of thermodynamics, the relationship can be obtained:
  • Q U is the high-quality heat energy converted by the electric heat pump
  • Q D is the low-quality heat energy absorbed from the low-temperature heat source
  • W is the electric energy consumed by the electric heat pump.
  • the heating efficiency (energy efficiency coefficient) ⁇ of the electric heat pump is expressed as follows:
  • Step 2 Model the revenue function and constraint conditions of each unit in the electric heat pump-heat and power combined system
  • the income I w of the wind turbine can be expressed as:
  • I wsell represents the electricity sales revenue of the wind turbine
  • I wa represents the subsidy income of the wind turbine
  • I wm represents the maintenance cost of the wind turbine
  • C sell represents the real-time electricity price at time t
  • C wsu represents the subsidized electricity price of the wind turbine.
  • P ws, t represents the power sales of the wind turbine at time t
  • K wm represents the maintenance coefficient of the wind turbine
  • P wc, t represents the available power of the wind turbine at time t.
  • the revenue function I pv of photovoltaic cells is similar to that of wind turbines:
  • I pvsell represents the photovoltaic electricity sales revenue
  • I pva represents the photovoltaic subsidy income
  • I pvm represents the photovoltaic maintenance cost
  • C pvsu represents the photovoltaic subsidized electricity price
  • P pvs, t represents the photovoltaic electricity sales power at time t
  • K pvm represents the maintenance factor of photovoltaics
  • P pvc represents the available power of photovoltaics at time t.
  • the cogeneration unit and the electric heat pump are regarded as a thermoelectric system.
  • the cogeneration unit is the main component and the core of power supply and heating, while the electric heat pump plays the role of electric heat conversion
  • the auxiliary function of the heat exchanger can solve the mismatch between the heat user demand and the heat supply of the cogeneration unit to a certain extent, thereby improving the adjustment capacity of the entire system.
  • the revenue I chp of the system can be expressed as follows:
  • I ssell is the electricity and heat sales revenue of the thermionic system
  • Isf is the fuel cost of the thermionic system
  • Ism is the maintenance cost of the thermionic system
  • I sa represents the cost of abandoning wind and light that the thermionic system needs to pay
  • C sell-Q t is the unit price of heat energy at time t
  • P chps t is the electricity sales of the cogeneration unit at time t
  • Q load is the heat load demand in the electric heat pump-heat and power combined system at time t
  • C p and C q are the average cost of power generation and heat generation of the cogeneration unit, respectively
  • c f is the unit cost of coal, where 0.123 and 0.1288 are the power equivalent and thermal equivalent of standard coal, respectively, in kg/kWh, ⁇ p and ⁇ q are the power generation efficiency and heating efficiency of the cogeneration unit, respectively
  • K sm1 and K sm2 are the maintenance coefficients of the electrical output and
  • part of the power generation P chp,t of the cogeneration unit P chps, t is used to meet the load demand of electric users, and the other part P pu, t is used as the input power of the electric heat pump, that is, formula (22).
  • the power must be balanced during the entire network power transmission process, and this balance characteristic has a decisive impact on the network frequency stability and voltage stability. If the power generated is greater than the required load, the grid frequency will increase, and vice versa, the stability of the power system should depend on the stability of the grid frequency.
  • P load, t is the electric load demand in the electric heat pump-heat and power combined system at time t
  • P e, t is the charging and discharging power of the electric energy storage.
  • Q chp, t is the heating power of the cogeneration unit in the t period
  • Q pu, t is the heating power of the electric heat pump in the t period
  • the actual power supply P ws, t of the wind turbine unit at each moment should be less than or equal to its available power P wc, t
  • the actual power supply power P pvs of the photovoltaic unit at each moment, t should be less than or equal to its available power P pvc,t .
  • the constraints of the power storage equipment during operation include capacity constraints and charge-discharge output constraints.
  • the electric heat pump provides part of the heat load during operation and must also output within the constraints.
  • Step 3 establishing a non-cooperative game model of the electric heat pump-heat and power combined system; that is: according to the operation mode of the electric heat pump-heat and power combined system, a corresponding non-cooperative game model is established for each unit main body;
  • Game theory is a mathematical study of how best decisions are made between contradictions and opposites.
  • the essence of game theory is rational thinking based on systematic thinking. You should use the interests of others wisely and choose the most suitable choice for yourself.
  • Rational choice refers to the choice of an objective function that maximizes the knowledge of game participants, that is, those involved in decision-making are rational and will adopt the optimal strategy to obtain the greatest benefit for themselves with the smallest representative.
  • game player the participant of the game behavior and the main part of the decision-making, that is, the person who makes the decision in the game, there are at least two; strategy set - all the options of each game party in the game process.
  • strategy set all the options of each game party in the game process.
  • a collection of schemes; benefits-benefits are the definite or expected utility of each game party, and the value of the benefits depends not only on the strategies of the individual participants, but also on the strategies of other participants except themselves.
  • Nash equilibrium is an important concept in the system of game theory, which represents a combination of strategies, and this combination of strategies is the set of the best strategies of all players.
  • rational players adopt Nash equilibrium as their strategy, neither player will change his strategy alone, otherwise his payoff will decrease.
  • record u i , s i , and S i as their income, strategy, and strategy set, respectively, if is a Nash equilibrium of the game, then for any s i ⁇ S i , the following equations hold:
  • each unit in the electric heat pump-heat and power combined system is regarded as a gamer, and the output of each unit is the strategy of the corresponding gamer.
  • each unit In the actual operation mode, each unit usually belongs to different operators, that is, the game belongs to non-cooperative game. In this game, since there is no binding agreement between players, each player seeks a strategy that maximizes their own interests to execute.
  • the non-cooperative game model of the electric heat pump-heat and power combined system is as follows:
  • Profit function the profit of each unit I k , k ⁇ w, pv, chp ⁇ .
  • each unit adopts a non-cooperative game mode
  • there is mutual disturbance between the power supply of each unit and the participants of the game will change their decision-making behavior according to the decisions of other participants.
  • Each participant will choose the decision that maximizes its own benefits based on the decisions of other participants, namely:
  • the Nash equilibrium solution of the non-cooperative game model can be obtained by obtaining the solutions of equations (33-35). At this time, the income of each unit can be expressed as
  • Step 4 Combine the particle swarm algorithm and iterative algorithm, solve the established game model, and obtain the thermoelectric scheduling optimization scheme of each unit;
  • Particle swarm optimization is an algorithm used to imitate the foraging behavior of birds and beasts. It combines its own and the flying experience of birds and beasts to search for the best solution.
  • the algorithm solves the space search path by changing the two main parameters, namely the flight direction and the speed.
  • the algorithm is simple in principle and easy to implement, so it is widely used in the scheduling optimization of the system.
  • the strategy combination of each unit is regarded as a particle, and a particle contains two attributes of speed and position.
  • the formula of the particle swarm update algorithm is as follows:
  • ⁇ is the inertia coefficient
  • t is the number of iterations
  • c 1 , c 2 represent acceleration constants
  • r 1 , r 2 represent random numbers in (0, 1)
  • p i, z represent the ith particle
  • p q, z represents the global optimal value of the z-th dimension of all particles
  • the initial value of the particle swarm optimization algorithm is a random particle swarm, which can be determined by estimating each particle.
  • the particles track the global and individual optimal values to update their velocity and position.
  • the fitness function is used to evaluate the pros and cons of the particle's position, and the historical optimal position is updated with the new position, and finally the optimal solution is obtained by satisfying the iterative termination condition.
  • the process of solving the game model is shown in Figure 5.
  • P wc,i , P pvc,i , P chp,i , Q chp,i represent the available power and available thermal power of the wind turbine, photovoltaic, and thermionic systems after the ith round of optimization, respectively.
  • Each unit determines its own power generation and calorific value according to the electric and heating load demand and the conditions of other units, that is, the available power supply/heat supply power, which is the decision value to be optimized, and the power supply/heat supply power is the consumed amount, which is the actual amount.
  • Electricity trading volume In the iterative process, if the decision is no longer changed (equal, or the change is very small, such as less than 1%), it is considered that a Nash equilibrium has been reached. The Nash equilibrium of this problem is bound to exist, so the equilibrium must be reached.
  • the electric heat pump-heat and power combined system described in this patent is equipped with wind turbines, photovoltaics, electricity storage systems, cogeneration units and electric heat pumps, among which the installed capacity of the wind turbines is 3000kW.
  • the installed capacity of photovoltaics is 2000kW
  • the installed capacity of power storage system is 1000kW
  • the installed capacity of cogeneration unit is 400kW.
  • One day T is 24 hours, and each decision-making period is 1 hour.
  • the wind speed and irradiance data of a typical day are selected for analysis, and combined with the output expressions of wind turbines and photovoltaics, the typical day is obtained.
  • the generated power and photovoltaic power generation curves of wind turbines are shown in Figures 8 and 9, and the selected typical daily electricity load and thermal load forecast curves are shown in Figures 10 and 11.
  • the invention adopts the cyclic iteration calculation of particle swarm algorithm, taking the acceleration constants c 1 and c 2 as 1.3, the allowable error ⁇ as 0.05%, the particle swarm size N as 100, and the maximum iteration times as 100.
  • Each unit in the system plays a non-cooperative game with the goal of maximizing its own profit function.
  • the data related to the corresponding working equipment and the electricity price in the calculation example are shown in Table 1 and Table 2. Considering the reality, when selecting the available power, its decision space is discrete, and the power supply decision of each unit must be an integer kW.
  • the income results of each unit are: the typical daily total income of wind turbines is 18830.563 yuan; the typical daily total income of photovoltaics is 6159.171 yuan; the typical daily total income of cogeneration units is 9639.859 yuan.
  • the present invention no longer regards all the units in the electric heat pump-heat and power combined system as a whole for optimization, but treats each unit as a different subject, and each subject aims at maximizing its own interests.
  • a non-cooperative game model of thermoelectric dispatching is established, which is beneficial to deal with the diversity of subjects in the electric heat pump-heat and power combined system;
  • the present invention combines the particle swarm algorithm and the iterative algorithm to solve the game model, wherein the particle swarm algorithm simulates the process of each subject searching for the optimal solution under the given conditions, and the iterative algorithm simulates the effect of each subject on other subjects Change the decision-making response; by solving the obtained Nash equilibrium solution strategy, the optimal scheme of dispatching output of each unit can be determined, thus providing guidance for the decision-making of thermoelectric dispatching; this output scheme can not only meet the load demand of thermoelectric users, but also make each subject are satisfied.

Abstract

提供了一种电热泵-热电联合系统的优化调控方法及系统，涉及能源运营技术领域，能够将各个机组看成不同的主体，以每个主体自身利益最大化为目标进行调控，不仅能够满足热电用户的负荷需求，还能够使得各个机组主体对其收益最大程度的满意；该方法包括S1、建立电热泵-热电联合系统的组成结构框架，并建立各机组的出力模型；S2、建立电热泵-热电联合系统中各机组的收益函数模型；S3、建立电热泵-热电联合系统的非合作博弈模型；S4、采用粒子群算法对博弈模型进行求解，获得各机组的热电调度优化方案。提供的技术方案适用于电热泵-热电联合系统调控的过程中。

Description

一种电热泵-热电联合系统的优化调控方法及系统 【技术领域】
本发明涉及能源运营技术领域，尤其涉及一种电热泵-热电联合系统的优化调控方法及系统。
【背景技术】
在能源结构发生深刻变革的国际环境中，全球能源转型正在深入推进，风电、光伏等清洁能源的开发利用也得到了大力的发展，是实现可持续发展的关键。在能源转型的全球趋势下，热电联产机组与风力发电、光伏发电相结合的热电联合系统是一种有效的能源开发利用形式。但在供暖期，热电联产机组出力大，而热电联产“以热定电”的运行方式，极大地降低了热电联产机组的调节能力，限制了电力系统的灵活性，从而导致了弃风弃光现象较严重。
目前主要通过在热电联合系统中增设蓄热罐、电热转换装置(如电热泵)，优化系统结构和性能等方法来解耦以热定电的约束，在此基础上，可以通过热电调度优化来进一步地促进风电、光电消纳。实现发电、供热的机组相互协调运行，合理地调度热电两种能量，优化热电联产机组与风电机组、光伏的出力，这对于满足系统内多元化用能需求、提升能源利用效率和减少用能时的环境污染具有重要意义。
目前的热电联合系统调度优化方法将系统内各个机组聚合成一个整体，以系统整体经济效益最优、系统整体运行成本最低等目标建立热电联合调度模型。但这些方法没有考虑到各个机组可能属于不同的主体，不同主体的决策行为相互影响，即系统整体最优与各个主体所追求的自身收益最优可能存在着冲突，因此这些方法的调度优化方案对于各个主体而言可能不是最优解，这将导致各个主体背离该优化方案而寻求其他方案以获取更高的回报。
博弈论作为一种先进的优化方法，主要研究多个相互影响的独立主体之间复杂的行为，适用于解决多主体多目标的优化问题。
因此，有必要研究一种电热泵-热电联合系统的优化调控方法及系统来应对现有技术的不足，以解决或减轻上述一个或多个问题。
【发明内容】
有鉴于此，本发明提供了一种电热泵-热电联合系统的优化调控方法及系统，能够将各个机组看成不同的主体，以每个主体自身利益最大化为目标进行调控，不仅能够满足热电用 户的负荷需求，还能够使得各个机组主体对其收益最大程度的满意。
一方面，本发明提供一种电热泵-热电联合系统的优化调控方法，其特征在于，所述方法的步骤包括：
S1、建立电热泵-热电联合系统的组成结构框架，并建立各机组的出力模型；
S2、建立电热泵-热电联合系统中各机组的收益函数模型；
S3、建立电热泵-热电联合系统的非合作博弈模型；
S4、采用粒子群算法对博弈模型进行求解，获得各机组的热电调度优化方案。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，采用粒子群算法对博弈模型进行求解的过程包括：
S41、输入相关参数；
S42、初始化种群初值；
S43、计算相应的收益函数；
S44、根据收益更新种群；
S45、计算适应度函数；
S46、判断所求结果是否为纳什均衡解；若是，求解完成，否则，返回到S44。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，步骤S3中博弈模型的建立以电热供需平衡、机组运行条件为约束，以电热泵-热电联合系统中各主体利益最大化为优化目标。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，电热泵-热电联合系统包括风电机组、光伏机组、储电系统、热电机组和电热泵。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，风电机组的出力模型为：
Figure PCTCN2021080499-appb-000001
其中，v t为t时刻的实时风速，v i为风电机组的切入风速，v o为风电机组的切出风速，v r为风电机组的额定风速，P WZ为风电机组的装机容量值；
风电机组的收益函数模型为：
I w＝I wsell+I wa-I wm；
其中，I w风电机组的收益，I wsell表示风电机组的售电收入，I wa表示风电机组的补贴收入，I wm表示风电机组的维护成本。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，光伏机组的出力模型为：
Figure PCTCN2021080499-appb-000002
其中，α pv为光伏机组的功率降额系数，P PVZ为光伏的装机容量，A t为t时刻光伏机组的实际辐照度，A s为标准条件下的辐照度，α T为功率温度系数，T stp为标准条件下的温度，T为实时温度；
光伏机组的收益函数模型为：
I pv＝I pvsell+I pva-I pvm；
其中，I pv为光伏机组的收益，I pvsell表示光伏机组的售电收入，I pva表示光伏机组的补贴收入，I pvm表示光伏机组的维护成本。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，储电系统的出力模型为：
Figure PCTCN2021080499-appb-000003
其中，C e，t+1为t+1时刻蓄电池的剩余电量，C e，t为t时刻蓄电池的剩余电量，α为蓄电池的自放电效率，β c和β d分别为蓄电池的充放电效率，P e，t为蓄电池充放电功率，Δt为充放电时长。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，热电机组的出力模型为：
P pc，t＝P chp，t+α chpQ chp，t；
其中，P pc，t为t时刻纯凝工况电功率，P chp，t为t时刻热电机组的电功率，Q chp，t为t时刻热电机组的热功率，α chp为电热转换系数；
热电机组的收益函数模型为：
I chp＝I ssell-I sf-I sm-I sa；
其中，I chp为热电机组的收益，I ssell为热电机组的售电售热收入，I sf为热电机组的燃料成本，I sm为热电机组的维护费用，I sa表示热电机组需要支付的弃风弃光成本。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，电热泵的出力模型为：
Figure PCTCN2021080499-appb-000004
Q pu＝χ·P pu；
其中，χ为电热泵的供热效率，Q U是通过电热泵转换的热能，W是电热泵消耗的电能，Q pu为电热泵的制热量，P pu为电热泵的输入功率。
另一方面，本发明提供一种电热泵-热电联合系统的优化调控装置，其特征在于，所述装置用于实现如上任一所述的调控方法；
所述装置包括控制模块和通信模块；所述通信模块分别与电热泵-热电联合系统的各个机组连接，用于采集各个机组的数据以及向各个机组发送调控指令；所述控制模块用于存储各个机组的出力模型、收益函数模型、约束条件以及博弈模型，并求解各模型得到最优调控方案。
与现有技术相比，本发明可以获得包括以下技术效果：
本发明不再将电热泵-热电联合系统中的所有机组视为一个整体来进行优化，而是将各个机组看成不同的主体，每个主体以自身利益最大化为目标，因此以博弈论的思想来看待该多主体决策的优化问题，并考虑各个主体之间的相互作用，建立了热电调度的非合作博弈模 型，这有利于应对电热泵-热电联合系统的主体多样性；
本发明结合了粒子群算法与迭代算法来对博弈模型进行求解，其中粒子群算法模拟了各主体在既定条件下搜寻最优解的过程，而迭代算法模拟了每个主体对其他主体更改决策的响应；通过求解得到的纳什均衡解，即可确定各个机组调度出力的最优方案，该出力方案不仅能够满足热电用户的负荷需求，还能使得各个主体都满意。
当然，实施本发明的任一产品并不一定需要同时达到以上所述的所有技术效果。
【附图说明】
为了更清楚地说明本发明实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1是本发明一个实施例提供的电热泵-热电联合系统的优化调控方法流程图；
图2是本发明一个实施例提供的电热泵-热电联合系统构成框图；
图3是本发明一个实施例提供的热电联产机组特性曲线图；
图4是本发明一个实施例提供的电热泵能量转换关系图；
图5是本发明一个实施例提供的粒子群算法求解博弈模型流程图；
图6是本发明一个实施例提供的典型日风速曲线；
图7是本发明一个实施例提供的典型日辐照度曲线；
图8是本发明一个实施例提供的典型日风电机组发电功率曲线；
图9是本发明一个实施例提供的典型日光伏发电功率曲线；
图10是本发明一个实施例提供的典型日电负荷需求曲线；
图11是本发明一个实施例提供的典型日热负荷需求曲线；
图12是本发明一个实施例提供的风电机组决策曲线；
图13是本发明一个实施例提供的光伏决策曲线；
图14是本发明一个实施例提供的热电联产机组供电决策曲线；
图15是本发明一个实施例提供的热电联产机组供热决策曲线；
图16是本发明一个实施例提供的电热泵供热曲线。
【具体实施方式】
为了更好的理解本发明的技术方案，下面结合附图对本发明实施例进行详细描述。
应当明确，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实 施例，都属于本发明保护的范围。
在本发明实施例中使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本发明。在本发明实施例和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。
电热泵-热电联合系统中各个机组可能属于不同的主体，且不同主体的决策行为相互影响，即系统整体最优与各个主体所追求的自身收益最优可能存在着冲突，因此电热泵-热电联合系统的热电调度问题是一个多主体参与的决策优化问题。基于电热泵-热电联合系统中各个机组的运行出力特性及收益函数，如何对各机组的调度决策进行优化，并获得相应的出力方案，是本专利要解决的技术问题。
为了解决该问题，本发明针对涉及热电联产机组、风力机组、光伏等多个主体的电热泵-热电联合系统，基于建立的机组出力模型及其收益函数模型，以博弈论的思想充分考虑各主体的行为以及相应的利益，同时考虑各行为之间的相互影响，以电热供需平衡、机组运行条件为约束，以电热泵-热电联合系统中各主体的利益最大化为优化目标，建立了热电调度的非合作博弈模型，并结合粒子群算法与迭代算法对博弈的均衡策略进行求解，最后得到的纳什均衡策略即为优化后的各机组出力方案，从而为热电调度决策提供指导。
一种电热泵-热电联合系统的优化调控方法，如图1所示，包括以下步骤：
步骤1、建立电热泵-热电联合系统的组成结构框架，并对各机组的热/电出力进行建模；
1-1)电热泵-热电联合系统包括两部分，供电部分和供热部分，如图2所示。其中，供电部分由风电机组、光伏机组、储电系统和电用户组成，而供热部分由热电联产机组、电热泵和热用户组成。
1-2)风电机组出力模型
在电热泵-热电联合系统中，风电机组的出力会受到装机规模和实际情况的约束。当装机容量确定时，各个时刻风电出力的最大值是由天气、环境等实际情况所决定，风机出力与风速满足下列非线性关系：
Figure PCTCN2021080499-appb-000005
式中，v t为t时刻的实时风速，v i为风电机组的切入风速，v o为风电机组的切出风速，v r为风电机组的额定风速，P WZ为风电机组的装机容量值。当实时风速小于切入风速或大于切出风速时，风电机组处于停机状态，当实时风速大于切入风速且小于额定风速时，实时功率与风速满足一次函数关系式，当实时风速大于额定风速且小于切出风速时，实时出力值就等于装机容量值。
1-3)光伏出力模型
类似地，光伏的出力也会受到装机规模和实际情况的约束。当装机容量确定时，光伏的出力与光照强度、温度有关，光伏出力可由下式表示：
Figure PCTCN2021080499-appb-000006
式中，α pv为机组的功率降额系数，P PVZ为光伏的装机容量，A t为t时刻光电机组的实际辐照度，A s为标准条件下的辐照度，α T为功率温度系数，T stp为标准条件下的温度。由于α T的值相对非常小，温度变动对光伏的出力影响近似为0，因此光电机组的出力可近似正比于实际辐照度A t，即：
Figure PCTCN2021080499-appb-000007
1-4)储电系统出力模型
电池的SOC是电池剩余电量和电池满电量之比。
Figure PCTCN2021080499-appb-000008
式中，C e，t为t时刻蓄电池的剩余电量，C full为蓄电池容量。
定义P e，t为蓄电池充放电功率，当P e，t≤0时，表示蓄电池正在充电，当P e，t＞0时，表示蓄电池正在放电，蓄电池的储能状态可表示如下：
Figure PCTCN2021080499-appb-000009
式中，α为蓄电池的自放电效率，β c和β d分别为蓄电池的充放电效率。
1-5)热电联产机组出力模型
在本发明专利选取的抽凝式机组中，整个汽轮机由三部分组成：低压缸、中压缸、高压缸。锅炉中产生的高温高压蒸汽进入汽轮机作功，供热抽汽来自中压缸排汽，中压缸其余蒸汽投入低压缸做功后又进入凝汽器冷凝，再返回锅炉实现重复运用。基于此工作原理，可将(P chp+α chpQ chp)等效为热电联产机组的功率：
P pc，t＝P chp，t+α chpQ chp，t      (6)
式中，P pc，t为t时刻纯冷凝工况电功率，P chp，t为t时刻机组的电功率，Q chp，t为t时刻机组的热功率，α chp为电热转换系数，表示1W热功率可调换为α chpW的电功率。电功率P chp，t和热功率Q chp，t的取值范围如图3所示，热电联产机组功率的可调节区域为ABCD构成的四边形区域，显然在热功率增加时，可调节的电功率范围迅速减少，机组的调峰能力也较差。
1-6)电热泵出力模型
电热泵可以从热电厂的供热回水中提取低温余热作为低品质热能，并将它转换成高品质热能。图4描述了电热泵基本能量的转换。在理想情况下，根据热力学第一定律，可得关 系式：
Q U＝Q D+W      (7)
式中，Q U是通过电热泵转换的高品质热能，Q D是从低温热源吸收的低品质热能，W是电热泵消耗的电能。电热泵的供热效率(能效系数)χ表达如下：
Figure PCTCN2021080499-appb-000010
由上式可得，电热泵的热电转换关系可表示为：Q pu＝χ·P pu，Q pu为电热泵的制热量，P pu为电热泵的输入功率。
步骤2、对电热泵-热电联合系统中各机组的收益函数和约束条件进行建模；
2-1)风电机组收益函数
风电机组的收益I w可以表示为：
I w＝I wsell+I wa-I wm      (9)
Figure PCTCN2021080499-appb-000011
Figure PCTCN2021080499-appb-000012
Figure PCTCN2021080499-appb-000013
式中，I wsell表示风电机组的售电收入，I wa表示风电机组的补贴收入，I wm表示风电机组的维护成本，C sell，t表示t时刻的实时电价，C wsu表示风电机组的补贴电价，P ws，t表示t时刻风电机组的售电功率，K wm表示风电机组的维护系数，P wc，t表示t时刻风电机组的可供电功率。
2-2)光伏收益函数
光伏电池的收益函数I pv与风电机组相似：
I pv＝I pvsell+I pva-I pvm      (13)
Figure PCTCN2021080499-appb-000014
Figure PCTCN2021080499-appb-000015
Figure PCTCN2021080499-appb-000016
式中，I pvsell表示光伏的售电收入，I pva表示光伏的补贴收入，I pvm表示光伏的维护成本，C pvsu表示光伏的补贴电价，P pvs，t表示t时刻光伏的售电功率，K pvm表示光伏的维护系数，P pvc，t表示t时刻光伏的可供电功率。
2-3)将热电联产机组和电热泵看成一个热电子系统，在该系统中，热电联产机组是主要是组成部分，也是供电与供热的核心，而电热泵则起到电热转换的辅助作用，可以在一定程度上解决热用户需求与热电联产机组的供热量的不匹配，从而提高整个系统的调节能力。该系统的收益I chp可以表示如下：
I chp＝I ssell-I sf-I sm-I sa      (17)
Figure PCTCN2021080499-appb-000017
Figure PCTCN2021080499-appb-000018
Figure PCTCN2021080499-appb-000019
Figure PCTCN2021080499-appb-000020
式中，I ssell为热电子系统的售电售热收入，I sf为热电子系统的燃料成本，I sm为热电子系统的维护费用，I sa表示热电子系统需要支付的弃风弃光成本，C sell-Q，t表示t时刻热能的单价，P chps，t为t时刻热电联产机组的售电量，Q load，t为电热泵-热电联合系统内t时段的热负荷需求，C p和C q分别为热电联产机组发电平均成本和发热平均成本，c f为单位煤成本，式中的0.123和0.1288分别为标准煤的电力当量和热力当量，单位为kg/kWh，η p和η q分别为热电联产机组的发电效率和发热效率，K sm1和K sm2分别为热电子系统电出力和热出力的维护系数，μ w表示弃风的惩罚因子，μ pv为弃光的惩罚因子。其中，热电联产机组的发电量P chp，t中的一部分P chps，t用于满足电用户的负荷需求，另一部分P pu，t作为电热泵的输入功率，即式(22)。
P chp，t＝P chps，t+P pu，t      (22)。
2-4)约束条件
2-4-1)供电平衡约束
在整个网络电力传输过程中功率必须保持平衡，这一平衡特性对网络频率稳定性和电压稳定性具有决定性影响。如果发电的功率大于所需的负载，则电网频率会随之增加，相反会随之减少，电力系统的稳定性应取决于网络频率的稳定性。
P chps，t+P ws，t+P pvs，t+P e，t＝P load，t      (23)
式中，P load，t为t时刻电热泵-热电联合系统内的电负荷需求，P e，t为电储能的充放电功率。
2-4-2)供热平衡约束
在供热系统中，必须保持消费者和热源供求之间的平衡。供热温度随着热用户需求量的减少而上升，反之亦然，供热的质量在某种程度上取决于供热温度，因此确保有必要调度的结果与供热需求一致。本发明不考虑传输引起的热损失，供热平衡约束如下：
Q chp，t+Q pu，t＝Q load，t    (24)
式中，Q chp，t为热电联产机组在t时段的供热功率，Q pu，t为t时段内电热泵的制热功率。
2-4-3)机组出力约束
在电热泵-热电联合系统的优化调度中，风电机组每一时刻的实际供电功率P ws，t都应小于等于其可供电功率P wc，t，光伏机组每一时刻的实际供电功率P pvs，t都应小于等于其可供电功率P pvc，t。储电设备在工作时受到的约束包含了容量约束和充放电出力约束，电热泵在运行过程中提供部分热负荷，也必须在约束范围内出力。
0≤P ws，t≤P wc，t        (25)
0≤P pvs，t≤P pvc，t      (26)
SOC min≤SOC≤SOC max    (27)
|P e，t|≤|P e，t，max|        (28)
P chp，min≤P chp，t≤P chp，max    (29)
Q chp，min≤Q chp，t≤Q chp，max    (30)
P pu，min≤P pu，t≤P pu，max      (31)
式中，|P e，t，max|为储电设备充放电功率的最大值，P chp，max和P chp，min分别为热电联产机组的供电功率上下限，Q chp，max和Q chp，min分别为热电联产机组的供热功率上限和下限，P pu，max和P pu，min是电热泵输入功率的上下限。其中，热电联产机组的电功率P chp，t和热功率Q chp，t还应在图3所示的四边形ABCD之中。
步骤3、建立电热泵-热电联合系统的非合作博弈模型；即：根据电热泵-热电联合系统的运行模式，对各个机组主体建立了相应的非合作博弈模型；
3-1)博弈论与电热泵-热电联合系统
博弈论是一门数学研究理论，研究如何在矛盾和对立之间作出最佳决策。博弈论的本质是基于系统思维上的理性思考，应当明智地利用他人的利益，为自己选择最适合的选择。理性选择是指一个目标函数在博弈参与者有认知的情况下极大化的选择，即参与决策的人都具有理性并会采纳最优策略，以最小的代表为自己取得最大的收益。
博弈论的基本要素为：博弈者--博弈行为的参预者和决策主要部分，也就是在博弈中制定决策的人，至少有两个；策略集合--各博弈方在博弈过程中所有可选方案的集合；收益-收益是各个博弈方的确定效用或期望效用，收益的值除了取决于参与者个人主体的策略，还取决于除自身外其他参与者的策略。
纳什均衡是博弈论体系中一个重要的概念，它表示一种策略的组合，这种策略的组合是所有博弈者的最佳策略的集合。当理性的博弈者采取了纳什均衡作为他们的策略，任一博弈者均不会独自更改自己的策略，否则他的收益会下降。对于任一博弈者i，记u i、s i、S i分别为其收益、策略、策略集合，若
Figure PCTCN2021080499-appb-000021
为博弈的一个纳什均衡，则对于任意的s i∈S i，下式都成立：
Figure PCTCN2021080499-appb-000022
将电热泵-热电联合系统中的各机组主体都视为一个博弈者，各机组的出力即为相应博弈者的策略，在实际运行模式中，各个机组通常属于不同的运营者，即该博弈属于非合作博弈。在该博弈下，各博弈者之间由于没有具有约束力的协议，各自寻求使得自身利益最大化的策略去执行。
3-2)电热泵-热电联合系统的非合作博弈模型
电热泵-热电联合系统的非合作博弈模型如下：
(1)参与者：风电机组、光伏机组和热电子系统(包含热电联产机组和电热泵)
(2)策略集：风电机组的可供电功率P wc，t，光伏的可供电功率P pvc，t，热电联产机组的供电功率P chp，t与供热功率Q chp，t。
(3)收益函数：各个机组的收益I k，k∈{w，pv，chp}。
当各机组采取不合作的博弈方式时，各机组的供电功率之间存在相互扰动，博弈的参与者会根据其他参与者的决策改变自身的决策行为。各个参与者会基于其他参与者的决策选择使自身收益最大的决策，即：
Figure PCTCN2021080499-appb-000023
Figure PCTCN2021080499-appb-000024
Figure PCTCN2021080499-appb-000025
式中，
Figure PCTCN2021080499-appb-000026
表示以P wc为变量，I w取得最大值时的P wc；
Figure PCTCN2021080499-appb-000027
同理。
求得式(33-35)的解即可得到该非合作博弈模型的纳什均衡解
Figure PCTCN2021080499-appb-000028
此时各机组的收益可以表示为
Figure PCTCN2021080499-appb-000029
Figure PCTCN2021080499-appb-000030
步骤4、结合粒子群算法与迭代算法，对建立的博弈模型进行求解，获得各机组的热电调度优化方案；
粒子群算法是一种用来模仿鸟兽觅食行径的算法，它结合了自己与鸟兽的翔行经历，以搜索最佳的解决方案。该算法通过改变两个主要的参数即飞行方向和速度，从而实现求解空间搜索路径，该算法原理简单、实现容易，因此被广泛用于系统的调度优化。
在非合作博弈模型的求解过程中，将各个机组的策略组合看做粒子，一个粒子包含速度与位置两种属性。粒子群更新算法的公式如下：
Figure PCTCN2021080499-appb-000031
Figure PCTCN2021080499-appb-000032
其中：ω表示惯性系数；t表示迭代次数；
Figure PCTCN2021080499-appb-000033
表示第i个粒子第t次迭代的第z维速度；c 1、c 2表示加速常数；r 1、r 2表示处于(0，1)中的随机数；p i，z表示第i个粒子第z维的个体最优值；p q，z表示全体粒子第z维的全局最优值；
Figure PCTCN2021080499-appb-000034
表示第i个粒子第t次迭代的第z维位置。粒子群优化算法的初始值为随机粒子群，可以通过对各个粒子的估计来确定。在迭代过程中，粒子对全局和个体的最优值追踪，以更新自己的速度和位置。利用适应度函数来评价粒子所 处位置的优劣，并结合新的位置来更新历史最优位置，最后满足迭代终止条件而得到最优解。求解博弈模型的过程如图5所示。
对于非合作博弈模型，采用粒子群算法迭代求解的具体步骤如下：
(1)初始化设备运行参数和限制参数，并且设定参数的上下限约束；
(2)设定均衡点初值(P wc，0，P pvc，0，P chp，0，Q chp，0)；
(3)各博弈参与者按照一定的顺序独立优化；
(4)第i轮的优化结果是(P wc，i，P pvc，i，P chp，i，Q chp，i)，则经过第i+1轮迭代优化后得到的优化结果为(P wc，i+1，P pvc，i+1，P chp，i+1，Q chp，i+1)，并满足下列关系式：
Figure PCTCN2021080499-appb-000035
Figure PCTCN2021080499-appb-000036
Figure PCTCN2021080499-appb-000037
式中，P wc，i、P pvc，i、P chp，i、Q chp，i分别表示第i轮优化后的风电机组、光伏、热电子系统的可供电功率和可供热功率。
(5)判断所找出的解是否为纳什均衡解。如果参与者在整轮博弈中都没有改变自己的策略，整个过程终止。
Figure PCTCN2021080499-appb-000038
式中，
Figure PCTCN2021080499-appb-000039
的分别是纳什均衡解对应的风电机组、光伏、热电子系统的可供电功率和可供热功率。各机组根据电热负荷需求以及其他机组的情况，确定自己的发电发热量，即为可供电/供热功率，为待优化的决策值，而供电/供热功率为被消纳的量，为实际电热交易成交量。在迭代过程中，决策不再改变(相等，或者变化幅度很小，如小于1％)，即认为达到了纳什均衡。该问题的纳什均衡是必然存在的，所以一定会达到均衡。
(6)根据纳什均衡解确定各个机组的出力后，可得到电热泵-热电联合系统的优化调度方案。
实施例1：
以本专利所述的电热泵-热电联合系统(如图2)为例，配置了风电机组、光伏、储电系统、热电联产机组和电热泵等设备，其中风电机组的装机容量为3000kW，光伏的装机容量为2000kW，储电系统的装机容量为1000kW，热电联产机组的装机容量为400kW。一天T为24小时，每个决策时段为1小时，选取某典型日的风速和辐照度数据(如图6、7)加以分析，并结合风电机组和光伏的出力表达式，得到了典型日的风电机组发电功率和光伏发电功率曲线(如图8、9)，所选取的典型日中电负荷和热负荷预测曲线如图10和11所示。
本发明采用了粒子群算法循环迭代计算，取加速常数c 1、c 2为1.3，允许误差Δ设为0.05％，粒子群规模N为100，最大迭代次数设为100。系统中各机组都以使自身的收益函 数最大为目标进行非合作博弈。算例中涉及相应工作设备的数据以及电价如表1和表2所示。考虑到实际，在选择可供电功率时，其决策空间是离散的，各机组的供电决策必须是整数kW。
表1 各机组运行参数
Figure PCTCN2021080499-appb-000040
表2 其他相关参数
Figure PCTCN2021080499-appb-000041
基于该算例仿真中的具体数据，运用上述模型对该电热泵-热电联合系统的供电决策问题进行建模并在MATLAB中计算。在该非合作博弈模型下，在某典型日求解出各个机组主体的供电决策和供热决策的结果如图12-16所示。
在该非合作博弈均衡解下，各个机组的收益结果为：风电机组的典型日总收益为18830.563元；光伏的典型日总收益为6159.171元；热电联产机组的典型日总收益为9639.859元。
由图14和15可知，由于系统中的热电联产机组将会按照“以热定电”的模式运作，首先满足热负荷，这限制了其电力调峰能力，故供电决策相对稳定，而热电联产机组与风电机组、光伏机组协同互补运行。风电机组和光伏机组为了获得更多收益，会尽可能地提供更多的可供电量，但若该值太大，会导致机组的维护成本增加以及弃风弃光量增加，热电联产机组的惩罚成本也会相应地增加。而热电联产机组为了使自己的利益最大化，又会尽可能减少弃风弃光的现象，这就形成了相互博弈的局面。如图16所示，当热电联产机组的供热出力不足时，会由电热泵提供不足的热能，但是系统中电热泵的供热需要依靠热电联产机组增大供电量来做功。
本发明的优势在于：
(1)本发明不再将电热泵-热电联合系统中的所有机组视为一个整体来进行优化，而是将各个机组看成不同的主体，每个主体以自身利益最大化为目标，因此以博弈论的思想来看待该多主体决策的优化问题，并考虑各个主体之间的相互作用，建立了热电调度的非合作博弈模型，这有利于应对电热泵-热电联合系统的主体多样性；
(2)本发明结合了粒子群算法与迭代算法来对博弈模型进行求解，其中粒子群算法模拟了各主体在既定条件下搜寻最优解的过程，而迭代算法模拟了每个主体对其他主体更改决策的响应；通过求解得到的纳什均衡解策略，可以确定各个机组调度出力的最优方案，从而为热电调度决策提供指导；该出力方案不仅能够满足热电用户的负荷需求，还能使得各个主体都满意。
以上对本申请实施例所提供的一种电热泵-热电联合系统的优化调控方法及系统，进行了详细介绍。以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。
如在说明书及权利要求书当中使用了某些词汇来指称特定组件。本领域技术人员应可理解，硬件制造商可能会用不同名词来称呼同一个组件。本说明书及权利要求书并不以名称的差异来作为区分组件的方式，而是以组件在功能上的差异来作为区分的准则。如在通篇说明书及权利要求书当中所提及的“包含”、“包括”为一开放式用语，故应解释成“包含/包括但不限定于”。“大致”是指在可接收的误差范围内，本领域技术人员能够在一定误差范围内解决所述技术问题，基本达到所述技术效果。说明书后续描述为实施本申请的较佳实施方式，然所述描述乃以说明本申请的一般原则为目的，并非用以限定本申请的范围。本申请的保护范围当视所附权利要求书所界定者为准。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的商品或者系统不仅包括那些要素，而且还包括没有明确列 出的其他要素，或者是还包括为这种商品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个......”限定的要素，并不排除在包括所述要素的商品或者系统中还存在另外的相同要素。
应当理解，本文中使用的术语“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
上述说明示出并描述了本申请的若干优选实施例，但如前所述，应当理解本申请并非局限于本文所披露的形式，不应看作是对其他实施例的排除，而可用于各种其他组合、修改和环境，并能够在本文所述申请构想范围内，通过上述教导或相关领域的技术或知识进行改动。而本领域人员所进行的改动和变化不脱离本申请的精神和范围，则都应在本申请所附权利要求书的保护范围内。

Claims (10)

  1. 一种电热泵-热电联合系统的优化调控方法，其特征在于，所述方法的步骤包括：
    S1、建立电热泵-热电联合系统的组成结构框架，并建立各机组的出力模型；
    S2、建立电热泵-热电联合系统中各机组的收益函数模型；
    S3、建立电热泵-热电联合系统的非合作博弈模型；
    S4、采用粒子群算法对博弈模型进行求解，获得各机组的热电调度优化方案。
  2. 根据权利要求1所述的电热泵-热电联合系统的优化调控方法，其特征在于，采用粒子群算法对博弈模型进行求解的过程包括：
    S41、输入相关参数；
    S42、初始化种群初值；
    S43、计算相应的收益函数；
    S44、根据收益更新种群；
    S45、计算适应度函数；
    S46、判断所求结果是否为纳什均衡解；若是，求解完成，否则，返回到S44。
  3. 根据权利要求1所述的电热泵-热电联合系统的优化调控方法，其特征在于，步骤S3中博弈模型的建立以电热供需平衡、机组运行条件为约束，以电热泵-热电联合系统中各主体利益最大化为优化目标。
  4. 根据权利要求1所述的电热泵-热电联合系统的优化调控方法，其特征在于，电热泵-热电联合系统包括风电机组、光伏机组、储电系统、热电机组和电热泵。
  5. 根据权利要求4所述的电热泵-热电联合系统的优化调控方法，其特征在于，风电机组的出力模型为：
    Figure PCTCN2021080499-appb-100001
    其中，v t为t时刻的实时风速，v i为风电机组的切入风速，v o为风电机组的切出风速，v r为风电机组的额定风速，P WZ为风电机组的装机容量值；
    风电机组的收益函数模型为：
    I w＝I wsell+I wa-I wm；
    其中，I w风电机组的收益，I wsell表示风电机组的售电收入，I wa表示风电机组的补贴收入，I wm表示风电机组的维护成本。
  6. 根据权利要求4所述的电热泵-热电联合系统的优化调控方法，其特征在于，光伏机组的出力模型为：
    Figure PCTCN2021080499-appb-100002
    其中，α pv为光伏机组的功率降额系数，P PVZ为光伏的装机容量，A t为t时刻光伏机组的实际辐照度，A s为标准条件下的辐照度，α T为功率温度系数，T stp为标准条件下的温度，T为实时温度；
    光伏机组的收益函数模型为：
    I pv＝I pvsell+I pva-I pvm；
    其中，I pv为光伏机组的收益，I pvsell表示光伏机组的售电收入，I pva表示光伏机组的补贴收入，I pvm表示光伏机组的维护成本。
  7. 根据权利要求4所述的电热泵-热电联合系统的优化调控方法，其特征在于，储电系统的出力模型为：
    Figure PCTCN2021080499-appb-100003
    其中，C e,t+1为t+1时刻蓄电池的剩余电量，C e,t为t时刻蓄电池的剩余电量，α为蓄电池的自放电效率，β c和β d分别为蓄电池的充放电效率，P e,t为蓄电池充放电功率，Δt为充放电时长。
  8. 根据权利要求4所述的电热泵-热电联合系统的优化调控方法，其特征在于，热电机组的出力模型为：
    P pc,t＝P chp,t+α chpQ chp,t；
    其中，P pc,t为t时刻纯凝工况电功率，P chp,t为t时刻热电机组的电功率，Q chp,t为t时刻热电机组的热功率，α chp为电热转换系数；
    热电机组的收益函数模型为：
    I chp＝I ssell-I sf-I sm-I sa；
    其中，I chp为热电机组的收益，I ssell为热电机组的售电售热收入，I sf为热电机组的燃料成本，I sm为热电机组的维护费用，I sa表示热电机组需要支付的弃风弃光成本。
  9. 根据权利要求4所述的电热泵-热电联合系统的优化调控方法，其特征在于，电热泵的出力模型为：
    Figure PCTCN2021080499-appb-100004
    Q pu＝χ·P pu；
    其中，χ为电热泵的供热效率，Q U是通过电热泵转换的热能，W是电热泵消耗的电能，Q pu为电热泵的制热量，P pu为电热泵的输入功率。
  10. 一种电热泵-热电联合系统的优化调控装置，其特征在于，所述装置用于实现如权利要求1-9任一所述的调控方法；
    所述装置包括控制模块和通信模块；所述通信模块分别与电热泵-热电联合系统的各个机组连接，用于采集各个机组的数据以及向各个机组发送调控指令；所述控制模块用于存储各个机组的出力模型、收益函数模型、约束条件以及博弈模型，并求解各模型得到最优调控方案。
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