Micro-grid scheduling method based on source-grid-load-storage multi-agent cooperative game

Abstract

The invention relates to a micro-grid scheduling method based on a source-grid-load-storage multi-agent cooperative game, the main body of micro-grid scheduling comprises a photovoltaic system, a combined cooling heating and power supply system, an electricity storage system, a heat storage system, a cold storage system and a user, and the method comprises the following steps: associating strategy sets of different main bodies through the cooperative game, and constructing a power distribution network source-grid-load-storage cooperative planning model with finite rationality; modeling by taking the highest comprehensive income of each main body of the micro-grid as a target, and determining respective target functions and constraint conditions; initial populations of different game subjects are randomly generated, and the game is evolved until an evolved stable state is reached; and obtaining a micro-grid dispatching scheme. Compared with the prior art, the micro-grid energy management strategy based on the cooperative game theory is adopted, the optimal fitness solution conforming to the real-time output power of the power generation device in the micro-grid can be obtained, and the operation cost of the micro-grid can be effectively controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid dispatching, and in particular to a microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game. Background Art

[0002] As an effective way to improve the efficiency of renewable energy utilization, microgrid systems composed of distributed power sources, loads, energy storage, power distribution and control systems have received increasing attention in ensuring safe and sustainable energy supply. Microgrids are a concept relative to traditional large power grids. They operate in an efficient and flexible grid-connected or isolated grid mode, supplemented by demand response strategies and load flexibility, and play an increasingly important role in absorbing renewable energy. The economic optimization scheduling problem has always been a hot topic of research. With the rapid development of distributed generation resources and energy storage technology, the operation mode of microgrids has become more complex. Therefore, it is necessary to propose a new controllable micro-power output scheduling and allocation strategy for the problem of complex operation mode of microgrids caused by distributed generation resources and energy storage, so as to improve the overall stability and economy of the power grid. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a microgrid scheduling method based on multi-agent cooperative game of source, grid, load and storage.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A microgrid dispatching method based on a multi-agent cooperative game of source, grid, load and storage, wherein the subjects of microgrid dispatching include photovoltaic, combined heat and power system, power storage, heat storage, cold storage and users, and the steps include:

[0006] The strategy sets of different subjects are linked through cooperative games to build a limited rational distribution network source-grid-load-storage collaborative planning model;

[0007] The model is built with the goal of maximizing the comprehensive benefits of each subject in the microgrid, and the respective objective functions and constraints are determined;

[0008] The initial population of different game entities is randomly generated, and the game is evolved until an evolutionary stable state is reached; and a microgrid dispatching plan is obtained.

[0009] As a preferred technical feature, the strategy sets of different entities are associated through cooperative game as follows:

[0010] The cooperative game individuals are multiple entities participating in the management of microgrids, and the microgrid is the large alliance formed by them. The microgrid manager comprehensively considers the properties of each entity, combines the complementarity of micro-sources, energy storage technology, demand-side response management and external factors including electricity prices and costs to decide on reasonable energy management plans and internal electricity prices. The specific game elements are set as follows:

[0011] The system is set up as a single microgrid;

[0012] The main players in the game are photovoltaic, combined heat and power systems, electricity storage, heat storage, cold storage and users.

[0013] Take the profit function as the characteristic function;

[0014] The specific spatial strategy of each game participant needs to be considered: users participate in microgrid management, and the supply plan is decided by the microgrid manager; each micro-source investor will hand over its micro-source scheduling rights to the microgrid manager, who will implement unified scheduling. When making decisions, the microgrid manager must not only ensure the overall benefits, but also the ultimate individual benefits of each micro-source investor and user.

[0015] As a preferred technical feature, the modeling is carried out with the goal of maximizing the benefits of each subject in the microgrid to determine the real-time electricity price within the microgrid, which is specifically expressed as follows:

[0016] maxf=W PV +W CCHP +W BS +W HS +W CS -W U

[0017] Among them, W PV is the benefit obtained from photovoltaic power generation, W CCHP The benefits of power generation, heating and cooling for the system, W BS W is the benefit of the energy storage system in storing and releasing electric energy. HS W is the benefit of the thermal energy storage system in storing and releasing thermal energy. CS is the benefit obtained by the cold storage system during the cold storage and release process, W U Electricity expenses for users.

[0018] As a preferred technical feature, the constraints include upper and lower limits of output, energy storage equipment operation constraints, cold and hot power balance constraints, user benefit guarantee constraints and electricity price constraints.

[0019] As a preferred technical feature, the output upper and lower limit constraints include:

[0020] Photovoltaic output constraints:

[0021]

[0022] Among them, P WT (t), P PV (t) are wind power and photovoltaic output power at time t respectively; They are the upper limits of wind power and photovoltaic output power respectively;

[0023] Combined Heat and Power (CHP) Operation Constraints:

[0024]

[0025] in, and They are the lower limits of the operating power of combined heat and power (CHP), power-to-gas (P2G) and electric boiler (EB); and They are the upper limits of the operating power of CHP, P2G and EB respectively; and is the lower limit of the interactive power between the power grid and the gas grid; and To set the upper limit of the interactive power between the power grid and the gas grid; and is the maximum descent and climb value of CHP;

[0026] Heat pump HP operating constraints:

[0027]

[0028] Among them, P h,HP (t) is the output thermal power of the heat pump HP at time t; P HP (t) is the input power of the heat pump HP at time t; η HP is the heat pump HP conversion efficiency; are the upper and lower limits of the heat pump HP input power, respectively;

[0029] Absorption Chiller AC Operation Constraints:

[0030]

[0031] Among them, P c,AC (t) is the output cooling power of the absorption refrigerator AC at time t; P AC (t) is the input heat power of the absorption refrigerator AC at time t; η AC is the conversion efficiency of the absorption chiller; They are the upper and lower limits of the absorption chiller input power respectively;

[0032] Electric refrigerator EC operating constraints:

[0033]

[0034] Among them, P c,EC (t) is the output cooling power of the electric refrigerator EC at time t; P EC (t) is the input power of the electric refrigerator EC at time t; η ECis the conversion efficiency of the electric refrigerator EC; They are the upper and lower limits of the input power of the electric refrigerator EC.

[0035] As a preferred technical feature, the energy storage equipment operation constraints uniformly model the batteries and heat storage tanks included in the system, as shown below:

[0036]

[0037] Where i is the energy type, including electrical energy and thermal energy; P i cha (t), P i dis (t) are the charging and discharging power of the energy storage device at time t; are the charging and discharging efficiency of energy storage equipment respectively; P i cha,max , P i dis,max are the upper limits of charging and discharging power of energy storage equipment; S i (t) is the energy stored in the energy storage device at time t; μ i (t) is a binary variable, when μ i (t) = 0 means energy is released at time t. i When (t) = 1, energy storage is performed at time t; are the upper and lower limits of the energy storage device capacity respectively; S i (1) S i (T) is the energy stored in the energy storage device from the start to the end of the system operation; Δt is the unit time.

[0038] As a preferred technical feature, the cooling and heating power balance constraints include:

[0039] The thermal power balance constraint is expressed as follows:

[0040]

[0041] Among them, P h,Load (t) is the heat load power at time t; P h,WHB (t); P h,EB (t) is the output thermal power of the waste heat boiler at time t; P h,HP (t) is the output thermal power of the heat pump HP at time t; P is the charging and discharging thermal power of the energy storage device at time t; AC (t) is the output power of absorption refrigerator AC at time t;

[0042] The cold power balance constraint is expressed as follows:

[0043] P c,Load (t) = Pc,EC (t)+P c,AC (t)

[0044] Among them, P c,Load (t) is the cooling load power at time t; P c,EC (t) is the output cooling power of the electric refrigerator EC at time t; P c,AC (t) is the output cooling power of AC at time t.

[0045] As a preferred technical feature, the user benefit guarantee constraint ensures that the user's electricity purchase fee expenditure for participating in microgrid management is less than that before choosing not to participate, and the formula is as follows;

[0046]

[0047] Among them, P U,Load is the electric power of the microgrid, W is the payment fee when choosing to participate in the microgrid management, and W′ is the payment fee when not participating in the microgrid management.

[0048] As a preferred technical feature, the electricity price constraint is specifically that the internal electricity price at each moment shall not be higher than the maximum external electricity price.

[0049] As a preferred technical feature, the method adopts a particle swarm algorithm to solve, randomly generates an initial population of power supply, power grid, energy storage and user game subjects through a multi-agent cooperative game strategy set, and evolves the game until a stable state is reached. The specific steps of the evolutionary game are as follows:

[0050] Combine time-of-use electricity price information and user decision-making behavior to generate photovoltaic and controllable micro-power load curves;

[0051] For different load requirements, analyze the results every 24 hours and find the optimal fitness value by drawing the convergence curve;

[0052] Individuals in the population randomly select strategies from the strategy set until all strategies are selected, and calculate the individual's profit function and payment under the strategy combination.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1) The present invention proposes a microgrid energy management method based on cooperative game theory, and makes scheduling decisions on a microgrid consisting of six entities, namely photovoltaic, combined cooling, heat and power (CCHP), electricity storage, heat storage, cold storage and users, based on multi-agent game. It can simulate the interaction and competition relationship between different participants in the microgrid, and can ensure the overall stability and economy of the power grid under the premise of promoting the consumption of renewable energy.

[0055] 2) The particle swarm algorithm is used to optimize the scheduling of the microgrid according to different load demands and the renewable energy power generation of different micro-sources at the current moment, so as to obtain the optimal fitness solution that meets the real-time output power of the power generation device in the microgrid, which can effectively control the operation cost of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the process of the present invention;

[0057] Figure 2 The uncontrollable photovoltaic power output diagram and the controllable micro power output diagram are shown in Figure 1. a) is the photovoltaic output, and b) is the controllable micro power output.

[0058] Figure 3 is the optimal fitness evolution curve of the function. DETAILED DESCRIPTION

[0059] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0060] Example 1

[0061] The present invention proposes a microgrid dispatching method based on the source-grid-load-storage multi-agent cooperative game. By constructing a game group and game form in a complex microgrid system, an effective solution can be provided for various supply-demand interactions, multi-objective optimization and energy management in the microgrid. The game theory is introduced into the control and optimization of the complex microgrid, which can ensure the overall stability and economy of the power grid under the premise of promoting the consumption of renewable energy. The specific technical scheme is as follows:

[0062] The microgrid system based on the cooperative game of multiple subjects including source, grid, load and storage includes six subjects: photovoltaic, combined cooling, heating and power system (CCHP), electricity storage, heat storage, cold storage and users. Among them, the photovoltaic power generation system includes solar panels (photovoltaic arrays), power electronic devices (mainly controllers and inverters), batteries and some other auxiliary power generation equipment. The CCHP includes micro-turbines, heat exchangers and absorption chillers. The electricity storage equipment is batteries, the heat storage equipment is heat storage tanks and the cold storage equipment is electric chillers.

[0063] In the multi-agent cooperative game within the microgrid system described above, the cooperative game individuals are the multiple entities participating in the microgrid management, and the microgrid is the large alliance formed by them. The microgrid manager comprehensively considers the properties of all parties, combines the complementarity of micro-sources, energy storage technology, demand-side response management and external factors (electricity price factors, cost factors), etc. to decide on reasonable energy management plans and internal electricity prices.

[0064] The game elements mentioned above are specifically set as follows: the system is set as a single microgrid; there are n = 6 game participants in the grid (photovoltaic, CCHP, power storage, heat storage, cold storage and users); the characteristic function is the profit function; the specific spatial strategy of each game participant needs to consider the following two points: 1. Users participate in microgrid management, and the power supply plan is decided by the microgrid manager; 2. Each micro-source investor will hand over its micro-source scheduling rights to the microgrid manager, who will perform unified scheduling. This requires the microgrid manager to ensure not only the overall benefits when making decisions, but also the ultimate individual benefits of each micro-source investor and user.

[0065] Dispatch method implemented by microgrid system:

[0066] Step 1: Model the microgrid with the goal of maximizing the benefits of each subject (including users), determine the real-time electricity price within the microgrid, set the dispatch time to 1h, and the calculation formula is as follows:

[0067] maxf=W PV +W CCHP +W BS +W HS +W CS -W U

[0068] Among them, W PV Refers to the benefits of photovoltaic power generation, W CCHP It refers to the benefits of power generation, heating and cooling of the system, W BS It refers to the benefits obtained by the power storage system in storing and releasing electric energy, W HS It refers to the benefits obtained by the heat storage system in storing and releasing heat energy, W CS It refers to the benefits obtained by the cold storage system during the cold storage and release process. U It refers to the user benefit (electricity expenditure);

[0069] Step 2: Input constraints, including output upper and lower limits, energy storage equipment operation constraints, cold and hot power balance constraints, user benefit constraints, and electricity price constraints:

[0070] The output upper and lower limit constraints include photovoltaic output constraints, and the formula is as follows:

[0071]

[0072] Among them, P WT (t), P PV (t) are wind power and photovoltaic output power at time t respectively; They are the upper limits of wind power and photovoltaic output power respectively;

[0073] The output upper and lower limit constraints also include CHP operation constraints, the formula is as follows:

[0074]

[0075] in, and They are the lower limits of operating power for cogeneration combined heat and power (CHP), power to gas (P2G) and electric boiler (EB); and They are the upper limits of the operating power of CHP, P2G and EB respectively; and is the lower limit of the interactive power between the power grid and the gas grid; and To set the upper limit of the interactive power between the power grid and the gas grid; and is the maximum descent and climb value of CHP;

[0076] The output upper and lower limit constraints also include the heat pump HP operation constraints, the formula is as follows:

[0077]

[0078] Among them, P h,HP (t) is the output thermal power of the heat pump HP at time t; P HP (t) is the input power of the heat pump HP at time t; η HP is the heat pump HP conversion efficiency; are the upper and lower limits of the heat pump HP input power, respectively;

[0079] The output upper and lower limit constraints also include the absorption chiller AC operation constraints, the formula is as follows:

[0080]

[0081] Among them, P c,AC (t) is the output cooling power of the absorption refrigerator AC at time t; P AC (t) is the input heat power of the absorption refrigerator AC at time t; η AC is the conversion efficiency of the absorption chiller; They are the upper and lower limits of the absorption chiller input power respectively;

[0082] The output upper and lower limit constraints also include the electric refrigerator EC operation constraints, the formula is as follows:

[0083]

[0084] Among them, P c,EC(t) is the output cooling power of the electric refrigerator EC at time t; P EC (t) is the input power of the electric refrigerator EC at time t; η EC is the conversion efficiency of the electric refrigerator EC; are the upper and lower limits of the input power of the electric refrigerator EC respectively;

[0085] Energy storage equipment operation constraints. The models of electric energy storage equipment and thermal energy storage equipment are similar. The batteries and thermal storage tanks included in the system are modeled uniformly. The formula is as follows:

[0086]

[0087] Where i is the energy type, including electrical energy and thermal energy; P i cha (t), P i dis (t) are the charging and discharging power of the energy storage device at time t; are the charging and discharging efficiencies of the energy storage device; P i cha,max , P i dis,max are the upper limits of charging and discharging power of energy storage equipment; S i (t) is the energy stored in the energy storage device at time t; μ i )t) is a binary variable, when μ i (t) = 0 means energy is released at time t. i When (t) = 1, energy storage is performed at time t; are the upper and lower limits of the energy storage device capacity respectively; S i (1) S i (T) is the energy stored in the energy storage device from the start to the end of the system operation; Δt is the unit time;

[0088] The cold and hot power balance constraints include the hot power balance constraints, and the power is as follows:

[0089]

[0090] Among them, P h,Load (t) is the heat load power at time t; P h,WHB (t); P h,EB (t) is the output thermal power of the waste heat boiler at time t; P h,HP (t) is the output thermal power of the heat pump HP at time t; P is the charging and discharging thermal power of the energy storage device at time t; AC (t) is the output power of absorption refrigerator AC at time t;

[0091] The cold and hot power balance constraint also includes the cold power balance constraint, and the formula is as follows:

[0092] P c,Load (t) = P c,EC (t)+P c,AC (t)

[0093] Among them, P c,Load (t) is the cooling load power at time t; P c,EC (t) is the output cooling power of the electric refrigerator EC at time t; P c,AC (t) is the output cooling power of the absorption refrigerator AC at time t.

[0094] To ensure the user benefit constraint, the user's electricity purchase fee expenditure will be less than that before choosing not to participate in the microgrid management. The formula is as follows;

[0095]

[0096] Among them, P U,Load is the electric power of the microgrid, W is the payment fee when choosing to participate in the microgrid management, and W′ is the payment fee when not participating in the microgrid management;

[0097] The electricity price constraint is that the internal electricity price at any moment shall not be higher than the maximum external electricity price. The formula is as follows:

[0098] e t,max ≤R max

[0099] Step 3: Use the particle swarm algorithm to solve the problem. The initial population of power supply, power grid, energy storage and user game entities is randomly generated through a multi-agent cooperative game strategy set. The game is evolved until a stable state is reached. The specific steps of the evolutionary game are as follows:

[0100] S3.1: Combine time-of-use electricity price information and user decision-making behavior to generate photovoltaic and controllable micro-power load curves;

[0101] S3.2: For different load demands, analyze the results every hour for 24 hours, simplify all equal and unequal constraints, and find the optimal fitness value by drawing the convergence curve;

[0102] S3.3: Individuals in the population randomly select strategies from the strategy set until all strategies are selected, and calculate the individual's payoff function and individual payment under the strategy combination.

[0103] The present invention takes into account multiple microgrid operation participants including source, network, load and storage, proposes a microgrid energy management strategy based on cooperative game theory, and adopts particle swarm algorithm to optimize the scheduling of microgrid according to different load demands and the renewable energy power generation of different micro-sources at the current moment, so as to obtain the optimal fitness solution that meets the real-time output power of the power generation device in the microgrid, and can effectively control the operation cost of the microgrid.

[0104] Example 2

[0105] As one of the specific implementation examples of the present invention, this embodiment is as follows:

[0106] Step 1: Link the strategy sets of different entities through cooperative games to build a limited rational distribution network source-grid-load-storage collaborative planning model;

[0107] Step 2: Model the network with the goal of maximizing the comprehensive benefits of each entity in the network, and determine their respective objective functions and constraints;

[0108] Step 3: Randomly generate the initial population of different game entities and evolve the game until the evolutionary stable state is reached.

[0109] Based on the typical daily load data collection in the selected grid in summer, the solar power output range is concentrated between 7 and 18 hours after sunrise, with a maximum output power of 130kW, and the maximum output power mainly occurs between 10 and 14 hours. The hourly forecast of solar power output shows a relatively stable trend as a whole. The output forecast of controllable micro power sources in each period is shown in the attached figure. Figure 2 shown.

[0110] See the instruction manual Figure 3 ,The simplified PSO algorithm is used to analyze the results of each hour of 24h, and all equality and inequality constraints are simplified. Figure 3 The convergence curve shown in shows the optimal fitness value.

[0111] The microgrid entities under typical load conditions in summer are analyzed to clarify the output of different entities. While ensuring the maximum benefit of the microgrid, the benefits of each entity are shown in Table 1.

[0112] Table 1 Benefits of each entity

[0113] Entity income Photovoltaic CCHP Power Storage Heat storage Cold Storage user Cooperative Game 720.35 3577.14 77.55 40.14 152.56 5946.88 Non-cooperative game 694.59 3014.84 75.49 16.85 45.44 5718.65

[0114] It can be found that, except for the user subject, the benefits of each subject in the microgrid in the cooperative game scenario are higher than those in the non-cooperative game scenario to varying degrees. This is because in the non-cooperative mode, there is a waste of some resources. For example, the cold and heat storage system does not know when there is excess energy in the combined heat and power system, which makes it difficult to control the charging and discharging energy nodes of the cold and heat storage system to achieve the optimal state. Therefore, in the process of cooperative game of microgrid subjects, the benefits of the overall microgrid can be effectively improved.

[0115] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A microgrid dispatching method based on multi-agent cooperative game of source, grid, load and storage. It is characterized in that The main bodies of microgrid dispatch include photovoltaic, combined heat and power system, power storage, heat storage, cold storage and users. The steps include: The strategy sets of different subjects are linked through cooperative games to build a limited rational distribution network source-grid-load-storage collaborative planning model; The model is built with the goal of maximizing the comprehensive benefits of each subject in the microgrid, and the respective objective functions and constraints are determined; The initial population of different game entities is randomly generated, and the game is evolved until an evolutionary stable state is reached; and a microgrid dispatching plan is obtained.

2. According to claim 1, a microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game, It is characterized in that The strategy sets of different subjects are specifically associated through cooperative game: The cooperative game individuals are multiple entities participating in the management of microgrids, and the microgrid is the large alliance formed by them. The microgrid manager comprehensively considers the properties of each entity, combines the complementarity of micro-sources, energy storage technology, demand-side response management and external factors including electricity prices and costs to decide on reasonable energy management plans and internal electricity prices. The specific game elements are set as follows: The system is set up as a single microgrid; The main players in the game are photovoltaic, combined heat and power systems, electricity storage, heat storage, cold storage and users. Take the profit function as the characteristic function; The specific spatial strategy of each game participant needs to be considered: users participate in microgrid management, and the supply plan is decided by the microgrid manager; each micro-source investor will hand over its micro-source scheduling rights to the microgrid manager, who will implement unified scheduling. When making decisions, the microgrid manager must not only ensure the overall benefits, but also the ultimate individual benefits of each micro-source investor and user.

3. According to claim 1, a microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game, It is characterized in that The modeling is based on the goal of maximizing the benefits of each subject in the microgrid, and the real-time electricity price inside the microgrid is determined. The specific expression is as follows: maxf=W PV +W CCHP +W BS +W HS +W CS -IN U Among them, W PV is the benefit obtained from photovoltaic power generation, W CCHP The benefits of power generation, heating and cooling for the system, W BS W is the benefit of the energy storage system in storing and releasing electric energy. HS W is the benefit of the thermal energy storage system in storing and releasing thermal energy. CS is the benefit obtained by the cold storage system during the cold storage and release process, W U Electricity expenses for users.

4. According to claim 1, a microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game, It is characterized in that The constraints include upper and lower limits of output, energy storage equipment operation constraints, cold and hot power balance constraints, user benefit guarantee constraints and electricity price constraints.

5. According to claim 4, a microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game, It is characterized in that The output upper and lower limit constraints include: Photovoltaic output constraints: Among them, P WT (t), P PV (t) are wind power and photovoltaic output power at time t respectively; They are the upper limits of wind power and photovoltaic output power respectively; Combined Heat and Power (CHP) Operation Constraints: in, and They are the lower limits of the operating power of combined heat and power (CHP), power-to-gas (P2G) and electric boiler (EB); and They are the upper limits of the operating power of CHP, P2G and EB respectively; and is the lower limit of the interactive power between the power grid and the gas grid; and To set the upper limit of the interactive power between the power grid and the gas grid; and is the maximum descent and climb value of CHP; Heat pump HP operating constraints: Among them, P h,HP (t) is the output thermal power of the heat pump HP at time t; P HP (t) is the input power of the heat pump HP at time t; η HP is the heat pump HP conversion efficiency; are the upper and lower limits of the heat pump HP input power, respectively; Absorption Chiller AC Operation Constraints: Among them, P c,AC (t) is the output cooling power of the absorption refrigerator AC at time t; P AC (t) is the input heat power of the absorption refrigerator AC at time t; η AC is the conversion efficiency of the absorption chiller; They are the upper and lower limits of the absorption chiller input power respectively; Electric refrigerator EC operating constraints: Among them, P c,EC (t) is the output cooling power of the electric refrigerator EC at time t; P EC (t) is the input power of the electric refrigerator EC at time t; η EC is the conversion efficiency of the electric refrigerator EC; They are the upper and lower limits of the input power of the electric refrigerator EC respectively.

6. A microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game according to claim 4, It is characterized in that The energy storage equipment operation constraints uniformly model the batteries and heat storage tanks included in the system, as shown below: Among them, i is the energy type, including electrical energy and thermal energy; are the charging and discharging power of the energy storage device at time t respectively; are the charging and discharging efficiencies of the energy storage device, respectively; are the upper limits of charging and discharging power of energy storage equipment; S i (t) is the energy stored in the energy storage device at time t; μ i (t) is a binary variable, when μ i (t) = 0 means energy is released at time t. i When (t) = 1, energy storage is performed at time t; are the upper and lower limits of the energy storage device capacity respectively; S i (1) S i (T) is the energy stored in the energy storage device from the start to the end of the system operation; Δt is the unit time.

7. A microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game according to claim 4, It is characterized in that The cooling and heating power balance constraints include: The thermal power balance constraint is expressed as follows: Among them, P h,Load (t) is the heat load power at time t; P h,WHB (t); P h,EB (t) is the output thermal power of the waste heat boiler at time t; P h,HP (t) is the output thermal power of the heat pump HP at time t; P is the charging and discharging thermal power of the energy storage device at time t; AC (t) is the output power of absorption refrigerator AC at time t; The cold power balance constraint is expressed as follows: P c,Load (t)=P c,EC (t)+P c,AC (t) Among them, P c,Load (t) is the cooling load power at time t; P c,EC (t) is the output cooling power of the electric refrigerator EC at time t; P c,AC (t) is the output cooling power of AC at time t.

8. A microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game according to claim 7, It is characterized in that The aforementioned constraint of guaranteeing user benefits ensures that the user's electricity purchase fee expenditure for participating in microgrid management is less than that before choosing not to participate. The formula is as follows; Among them, P U,Load is the electric power of the microgrid, W is the payment fee when choosing to participate in the microgrid management, and W′ is the payment fee when not participating in the microgrid management.

9. A microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game according to claim 4, It is characterized in that The electricity price constraint is specifically that the internal electricity price at any moment shall not be higher than the maximum external electricity price.

10. A microgrid dispatching method based on source-grid-load-storage multi-agent cooperative game according to claim 1, It is characterized in that The method adopts the particle swarm algorithm to solve, randomly generates the initial population of power supply, power grid, energy storage and user game subjects through a multi-agent cooperative game strategy set, and evolves the game until a stable state is reached. The specific steps of the evolutionary game are as follows: Combine time-of-use electricity price information and user decision-making behavior to generate photovoltaic and controllable micro-power load curves; For different load requirements, analyze the results every 24 hours and find the optimal fitness value by drawing the convergence curve; Individuals in the population randomly select strategies from the strategy set until all strategies are selected, and calculate the individual's profit function and payment under the strategy combination.

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