A multi-agent game optimization method and device for a renewable energy microgrid

Abstract

The application provides a multi-agent game optimization method and device for a renewable energy micro-grid, and the multi-agent game optimization method for the renewable energy micro-grid comprises the following steps: obtaining interaction data among a micro-grid operator, an energy storage operator and a user, respectively constructing a micro-grid operator model, an energy storage operator model and a load side model based on the interaction data among the micro-grid operator, the energy storage operator and the user; constructing a one-master multi-slave multi-agent game model based on the micro-grid operator model, the energy storage operator model and the load side model; solving the one-master multi-slave multi-agent game model to generate a multi-agent game optimization strategy; and wherein the multi-agent game optimization strategy is used to provide energy for the operation of the renewable energy micro-grid. The method realizes the maximization of multi-agent benefits, solves the problem of the game behavior strategy among the micro-grid, the energy storage and the user, makes the energy supply more economical, and makes the user electricity use more reasonable.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of renewable energy micro-grid technology, and particularly to a multi-agent game optimization method and device for renewable energy micro-grid. BACKGROUND

[0002] The stakeholders in the renewable energy micro-grid include micro-grid operators, users and energy storage operators. According to the characteristics of the micro-grid, it mainly provides sustainable and stable power, tries to deliver power as close as possible, reduces network loss, improves active conversion, maintains competitive advantage in electricity price and increases new energy consumption. Users hope to obtain power that meets the requirements in the micro-grid while trying to reduce electricity costs, otherwise, they would rather choose a more mature power supply in the distribution network. Energy storage is an additional power supply subject in addition to the micro-grid power plant. Due to the randomness of the renewable energy output in the micro-grid, the micro-grid may not be able to meet the user's power demand, and additional energy storage power needs to be purchased.

[0003] Existing researches rarely involve energy storage as an independent stakeholder in power system operation, and lack of research on multi-agent game optimization strategies for micro-grid, energy storage and user stakeholders, which reduces the power supply capacity of renewable energy micro-grid and leads to unreasonable user power consumption. SUMMARY

[0004] Therefore, the technical scheme of the present application mainly solves the defects that the existing technology lacks research on multi-agent game optimization strategies for micro-grid, energy storage and user stakeholders, reduces the power supply capacity of renewable energy micro-grid and leads to unreasonable user power consumption, thereby providing a multi-agent game optimization method and device for renewable energy micro-grid.

[0005] In a first aspect, an embodiment of the present application provides a multi-agent game optimization method for renewable energy micro-grid, comprising:

[0006] Obtaining interaction data between micro-grid operators, energy storage operators and users, and constructing a micro-grid operator model, an energy storage operator model and a load side model based on the interaction data between the micro-grid operators, the energy storage operators and the users;

[0007] Constructing a one-master multi-slave multi-agent game model based on the micro-grid operator model, the energy storage operator model and the load side model;

[0008] Solving the one-master multi-slave multi-agent game model to generate a multi-agent game optimization strategy; wherein the multi-agent game optimization strategy is used to provide energy for the operation of the renewable energy micro-grid.

[0009] The embodiment of the application provides a multi-agent game optimization method for a renewable energy micro-grid, a one-master multi-follower multi-agent game model is constructed according to interactive data among a micro-grid operator, an energy storage operator and users, the micro-grid operator is taken as a leader, the energy storage operator and the users are taken as followers in the one-master multi-follower multi-agent game model, multi-agent benefit maximization is realized, a game behavior strategy related problem among the micro-grid, the energy storage and the users is solved, and the one-master multi-follower multi-agent game model is solved to generate a multi-agent game optimization strategy, the multi-agent game optimization strategy is used to provide energy for operation of the renewable energy micro-grid, the power supply capacity of the renewable energy micro-grid is improved, energy supply is more economical, and user electricity consumption is more reasonable.

[0010] With reference to the first aspect, in a possible implementation manner, the micro-grid operator model, the energy storage operator model and the load side model are respectively constructed based on the interactive data among the micro-grid operator, the energy storage operator and the users, and the method comprises the following steps.

[0011] Micro-grid operator interactive data is acquired from the interactive data among the micro-grid operator, the energy storage operator and the users, a first target function is constructed based on the micro-grid operator interactive data with maximization of daily operation net income as a target;

[0012] Cost constraints, external power purchase power constraints, internal equipment output constraints and power balance constraints are taken as constraint conditions of the first target function, and the micro-grid operator model is constructed based on the first target function and the constraint conditions of the first target function;

[0013] Energy storage operator interactive data is acquired from the interactive data among the micro-grid operator, the energy storage operator and the users, a second target function is constructed based on the energy storage operator interactive data with maximization of daily net income of the energy storage operator as a target;

[0014] Energy storage device operation constraints are taken as constraint conditions of the second target function, and the energy storage operator model is constructed based on the second target function and the constraint conditions of the second target function;

[0015] User interactive data is acquired from the interactive data among the micro-grid operator, the energy storage operator and the users, a third target function is constructed based on the user interactive data with minimization of electricity cost as a target;

[0016] Demand response constraints are taken as constraint conditions of the third target function, and the load side model is constructed based on the third target function and the constraint conditions of the third target function.

[0017] With reference to the first aspect, in a possible implementation form of the first aspect, the first target function is constructed based on the micro-grid operator interaction data, and the first target function is constructed based on the daily net income maximization.

[0018] The electricity selling income of the micro-grid operator is determined based on the electricity selling price of the micro-grid operator, the micro gas turbine output, the wind turbine output and the photovoltaic turbine output in the micro-grid operator interaction data.

[0019] The exchange income of the micro-grid operator with the external power grid is determined based on the electricity selling price of the macro-grid and the electricity power purchased by the micro-grid operator from the macro-grid in the micro-grid operator interaction data.

[0020] The operation and maintenance cost of each device of the micro-grid is determined based on the micro gas turbine operation and maintenance cost, the wind turbine operation and maintenance cost, the photovoltaic turbine operation and maintenance cost, the micro gas turbine output, the wind turbine output and the photovoltaic turbine output in the micro-grid operator interaction data.

[0021] The income of the user after participating in the demand response is determined based on the price of the demand response provided by the user and the amount of the demand response provided by the user in the micro-grid operator interaction data.

[0022] The first target function is constructed based on the electricity selling income of the micro-grid operator, the exchange income of the micro-grid operator with the external power grid, the operation and maintenance cost of each device of the micro-grid, the income of the user after participating in the demand response and the net income of the micro-grid operator.

[0023] With reference to the first aspect, in a possible implementation form of the first aspect, the first target function is constructed based on the micro-grid operator interaction data, and the first target function is constructed based on the daily net income maximization.

[0024] The proportional coefficient, the electricity load prediction value, the wind power output prediction value and the photovoltaic output prediction value are obtained, and the trapezoidal fuzzy parameters of the electricity load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value and the trapezoidal fuzzy parameters of the photovoltaic output prediction value are determined based on the proportional coefficient, the electricity load prediction value, the wind power output prediction value and the photovoltaic output prediction value.

[0025] The confidence level is obtained, and the power balance constraint is constructed based on the trapezoidal fuzzy parameters of the electricity load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value, the trapezoidal fuzzy parameters of the photovoltaic output prediction value and the confidence level.

[0026] With reference to the first aspect, in a possible implementation form of the first aspect, the first target function is constructed based on the micro-grid operator interaction data, and the first target function is constructed based on the daily net income maximization.

[0027] obtain the electricity selling price of the energy storage operator and the discharging amount of the energy storage operator from the energy storage operator interaction data, and determine the income of the energy storage operator from selling electricity to the user based on the electricity selling price of the energy storage operator and the discharging amount of the energy storage operator;

[0028] obtain the charging amount of the energy storage operator from the energy storage operator interaction data, and determine the charging cost of the energy storage operator for purchasing electricity from the micro-grid based on the electricity selling price of the micro-grid operator and the charging amount of the energy storage operator;

[0029] determine the operation and maintenance cost of the energy storage based on the charging cost of the energy storage operator for purchasing electricity from the micro-grid;

[0030] maximize the daily net income of the energy storage operator as the target, and construct the second objective function based on the income of the energy storage operator from selling electricity to the user, the charging cost of the energy storage operator for purchasing electricity from the micro-grid, and the operation and maintenance cost of the energy storage.

[0031] In combination with the first aspect, in another possible implementation manner, the third objective function is constructed based on the user-side interaction data with the target of minimizing the electricity cost, including:

[0032] determine the satisfaction cost of the user based on the day-ahead load forecast value, the actual load after demand response, and the deviation penalty coefficient in the user-side interaction data;

[0033] minimize the electricity cost as the target, and construct the third objective function based on the electricity selling income of the micro-grid operator, the income of the energy storage operator from selling electricity to the user, the income of the user after participating in demand response, and the satisfaction cost of the user.

[0034] In combination with the first aspect, in another possible implementation manner, the one-master multi-slave multi-agent game model is solved to generate the multi-agent game optimization strategy, including:

[0035] the one-master multi-slave multi-agent game model is solved by using a distributed equilibrium solving method of a particle swarm algorithm combined with a CPLEX solver to generate the multi-agent game optimization strategy.

[0036] In the second aspect, the embodiments of the present application further provide a multi-agent game optimization device for a renewable energy micro-grid, including:

[0037] an obtaining module, configured to obtain interaction data among a micro-grid operator, an energy storage operator, and a user, and construct a micro-grid operator model, an energy storage operator model, and a load side model based on the interaction data among the micro-grid operator, the energy storage operator, and the user respectively;

[0038] A construction module is configured to construct a one-master multi-slave multi-agent game model based on the micro-grid operator model, the energy storage operator model and the load-side model.

[0039] A solution module is configured to solve the one-master multi-slave multi-agent game model to generate a multi-agent game optimization strategy, wherein the multi-agent game optimization strategy is used to provide energy for operation of the renewable energy micro-grid.

[0040] In a third aspect, an electronic device is disclosed, including at least one processor, and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the multi-agent game optimization method for renewable energy micro-grid as described in the first aspect or any optional implementation manner of the first aspect.

[0041] In a fourth aspect, a computer readable storage medium is disclosed, which stores a computer program, and the computer program is executed by a processor to implement the steps of the multi-agent game optimization method for renewable energy micro-grid as described in the first aspect or any optional implementation manner of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0043] Figure 1 A flowchart of the multi-agent game optimization method for renewable energy micro-grid provided by the embodiments of the present application;

[0044] Figure 2 An interaction behavior relationship diagram between the multi-agents provided by the embodiments of the present application;

[0045] Figure 3 An interest relationship diagram between the multi-agents provided by the embodiments of the present application;

[0046] Figure 4 A diagram of the distributed equilibrium solution method provided by the embodiments of the present application;

[0047] Figure 5 A flowchart of S101 provided by the embodiments of the present application;

[0048] Figure 6The flowchart of S1011 provided for the embodiment of the present application is shown in the following;

[0049] Figure 7 The flowchart of S1013 provided for the embodiment of the present application is shown in the following;

[0050] Figure 8 The flowchart of S1015 provided for the embodiment of the present application is shown in the following;

[0051] Figure 9 The schematic diagram of typical daily wind power and photovoltaic output and daily load forecasting curve of electricity provided for the embodiment of the present application is shown in the following;

[0052] Figure 10 The schematic diagram of pricing strategy of micro-grid operator provided for the embodiment of the present application is shown in the following;

[0053] Figure 11 The load curve diagram before and after demand response provided for the embodiment of the present application is shown in the following;

[0054] Figure 12 The schematic diagram of power scheduling result provided for the embodiment of the present application is shown in the following;

[0055] Figure 13 The block diagram of a multi-agent game optimization device for renewable energy micro-grid provided for the embodiment of the present application is shown in the following;

[0056] Figure 14 A specific example of the electronic device in the embodiment of the present application is shown in the following. DETAILED DESCRIPTION

[0057] The technical solutions of the present application will be described clearly and completely in the following with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0058] In the description of the present application, it should be noted that the terms "first", "second", "third" are only used for description purpose, and cannot be understood as indicating or implying relative importance. In addition, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood in a broad sense, for example, it can be fixed connection, mechanical connection, or electrical connection; or it can be direct connection, or indirect connection through intermediate medium, or internal connection of two elements, or wireless connection, or wired connection. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0059] The embodiment of the present application provides a multi-agent game optimization method for renewable energy micro-grid, as shown in the following:Figure 1 As shown, it includes:

[0060] S101. Obtain the interaction data between the microgrid operator, energy storage operator and users, and construct the microgrid operator model, energy storage operator model and load-side model based on the interaction data between the microgrid operator, energy storage operator and users respectively.

[0061] Specifically, stakeholders within a renewable energy microgrid are categorized into microgrid operators (MGOs), demand-response users (USERs), and energy storage operators (ESOs). The operational methods and interaction mechanisms of each stakeholder are as follows: Figure 2 As shown, the microgrid includes wind turbines, photovoltaic units, and gas turbines. Users have the ability to reduce and shift their electricity demand, and energy storage is mainly based on battery storage.

[0062] Furthermore, with the support of social capital, microgrid operators act as intermediaries between energy networks and users in the form of energy service companies. They integrate electricity and natural gas to provide power supply services to users, while investing in and operating wind turbines, photovoltaic units, and gas turbines. This improves the operational flexibility of energy services, reduces energy costs, and enhances market competitiveness to obtain higher profits. Users with demand response capabilities can participate in market competition and obtain revenue by reducing or shifting their electricity load, thereby reducing energy costs. Due to the high investment cost of energy storage equipment, the profit objective of energy storage operators is to participate in competition by coordinating and controlling the charging and discharging power of battery energy storage devices at various times and setting discharge prices, thereby generating profits.

[0063] S102. Construct a multi-agent game model with one master and many slaves based on the microgrid operator model, energy storage operator model and load-side model.

[0064] Specifically, such as Figure 3 As shown, based on the interaction behavior among the various entities, their interests are interconnected and mutually restrictive. When the interests of the microgrid operator, energy storage operator, and adjustable load achieve a balance, the power suppliers, including the upper-level grid, microgrid, and energy storage operator, supply power to users within the region. The expression for the multi-entity game model Ω with one master and many slaves is shown below:

[0065]

[0066] Where MGO stands for microgrid operator, ESO for energy storage operator, USER for user with demand response capability, and S MGO S represents the microgrid operator's strategy. ESO Indicating the energy storage operator's strategy, SUSER represents the user demand response strategy, R MGO represents the net income of the microgrid operator, R ESO represents the daily net income of the energy storage operator, R USER represents the electricity cost of the user.

[0067] Further, the one-leader and multiple-follower multi-agent game model takes the microgrid operator as the leader and the energy storage operator and the user as the followers.

[0068] S103, solving the one-leader and multiple-follower multi-agent game model to generate a multi-agent game optimization strategy; wherein the multi-agent game optimization strategy is used to provide energy for the operation of the renewable energy microgrid.

[0069] Specifically, the one-leader and multiple-follower multi-agent game model is solved by using a distributed equilibrium solving method of particle swarm optimization algorithm combined with CPLEX solver to generate a multi-agent game optimization strategy.

[0070] Further, the one-leader and multiple-follower multi-agent game model is a game model with a hierarchical structure, and the leader with the first-mover advantage first gives its strategy, and the follower then gives the optimal response according to the leader's strategy and transmits the strategy to the leader. Due to the incompleteness of the strategy information, multiple iterations are needed to make the game stable and reach the optimal value of the system; while the traditional centralized optimization method needs to master the detailed information of all participants, such as device parameters, energy preference, etc. However, in the competitive power market, information is not transparent, and each participant needs to optimize separately.

[0071] Further, as shown in Figure 4 , in the distributed equilibrium solving method of particle swarm optimization algorithm combined with CPLEX solver (Particle swarm optimization, PSO-CPLEX is a mathematical solution tool that can help solve the optimal solution or feasible solution of the model), the particle swarm optimization algorithm is used to optimize the electricity selling price of the microgrid operator, and the fitness of the particle is the benefit target of the microgrid operator. The fitness calculation needs to be obtained through the calculation results of the optimization model of each agent, and Yalmip modeling and CPLEX solving tool are used for solving to speed up the algorithm solving speed and ensure the accuracy of the results. The specific steps of the distributed equilibrium solving method are as follows:

[0072] Step 1: The microgrid operator as the leader transmits the initialized electricity selling price and demand response price to the lower-level energy storage operator and user; wherein the electricity selling price of the microgrid operator at time t is multiplied by the group size sizepop to realize the initialization of the electricity selling price particle.

[0073] Step 2: Energy storage operators and users optimize energy storage electricity sales prices, charging and discharging strategies, and user demand response based on the CPLEX solver, according to their own goals of maximizing revenue and minimizing costs, and upload the results to the microgrid operator side.

[0074] Step 3: Based on the feedback results, the microgrid operator re-optimizes the output strategies of wind power, photovoltaics and gas turbines, calculates the objective function obj, and transmits the updated particles, namely the latest electricity sales price and demand response price, to the lower-level energy storage operators and users.

[0075] Step 4: Repeat steps 2 and 3 until the objective functions of each agent no longer change, i.e., the convergence condition is met, and output the game optimization result, i.e., the multi-agent game optimization strategy.

[0076] This embodiment proposes a multi-agent game optimization method for renewable energy microgrids. Based on the interaction data between microgrid operators, energy storage operators, and users, a one-leader, multi-follower multi-agent game model is constructed. This model treats the microgrid operator as the leader and the energy storage operator and users as followers, maximizing the interests of all parties. It solves the game-related issues concerning the behavioral strategies among microgrids, energy storage, and users. Furthermore, by solving the one-leader, multi-follower multi-agent game model, a multi-agent game optimization strategy is generated. This strategy is then used to provide energy for the operation of the renewable energy microgrid, improving its power supply capacity, making energy supply more economical, and enabling users to use electricity more rationally.

[0077] As an optional embodiment of the present invention, such as Figure 5 As shown, S101 above, namely, constructing the microgrid operator model, energy storage operator model, and load-side model based on the interaction data between the microgrid operator, energy storage operator, and user, respectively, includes:

[0078] S1011. Obtain microgrid operator interaction data from the interaction data between microgrid operators, energy storage operators and users, and construct a first objective function based on the microgrid operator interaction data with the goal of maximizing daily net operating revenue.

[0079] Specifically, there are two modes for the grid-connected mode of the micro-grid with new energy power generation as the main power supply on the power distribution side: "full grid-connected" and "self-use, surplus power grid-connected". In the present application, the clean energy generated by the new energy in the micro-grid is preferentially used for local load, and in the case that the local load cannot be completely consumed, energy storage or sale to the superior grid is considered, which conforms to the second power generation grid-connected mode. Therefore, the interest demand of the micro-grid operator is mainly to maximize the daily net income of selling the power generated by the micro-grid to the user, which is the sum of the power selling income and the grid-connected power income minus the operation and maintenance cost. When the micro-grid has surplus power, the grid-connected power income is positive, and when the micro-grid needs to purchase power from the grid, the grid-connected power income is negative.

[0080] Further, the micro-grid operator formulates a price strategy on the basis of the output plan of wind power, photovoltaic and other new energy and energy storage and the satisfaction of user power demand, with the goal of maximizing daily operation net income, and the expression of the first target function is as follows:

[0081]

[0082] Wherein, R e is the power selling income of the micro-grid operator, R exc is the exchange income with the external grid, is the operation and maintenance cost of each device of the micro-grid, R dr is the income obtained by the user after participating in demand response.

[0083] S1012, the cost constraint, the external power purchase power constraint, the internal device output constraint and the power balance constraint are taken as the constraint conditions of the first target function, and the micro-grid operator model is constructed based on the first target function and the constraint conditions of the first target function.

[0084] Specifically, in order to prevent the increase of user power cost and the degradation of optimization problem, the time-of-use price formulated by the micro-grid operator should make the power cost not increase, and should not be higher than the power selling price of the distribution network, and the corresponding cost constraint condition is as follows:

[0085]

[0086] In the above formula, is the power selling price of the micro-grid operator at t moment, is the power selling price of the large grid.

[0087] Further, the external power purchase power constraint is expressed as follows:

[0088]

[0089] In the above formula, and are the minimum and maximum tie-line power respectively, is the electricity power purchased by the micro-grid operator from the large power grid at time t.

[0090] Further, the internal device output constraint is expressed as follows:

[0091]

[0092] In the above formula, is the maximum installed capacity of the micro gas turbine, is the output of the micro gas turbine at time t.

[0093] S1013, obtain the energy storage operator interaction data from the interaction data between the micro-grid operator, the energy storage operator and the user, maximize the daily net income of the energy storage operator as the target, and construct a second objective function based on the energy storage operator interaction data.

[0094] Specifically, the interest appeal of the energy storage operator is to realize price difference arbitrage between the micro-grid operator and the user through "low charging and high discharging", and the energy storage operator maximizes its own income to optimize the output of the energy storage device, and the expression of the second objective function is as follows:

[0095]

[0096] In the above formula, R dis is the income of the energy storage operator selling electricity to the user, C cha is the charging cost of the energy storage operator purchasing electricity from the micro-grid, is the operation and maintenance cost of the energy storage, which is set as α times of the charging cost.

[0097] S1014, take the energy storage device operation constraint as the constraint condition of the second objective function, and construct the energy storage operator model based on the second objective function.

[0098] Specifically, the model control variable in the second objective function is the charging and discharging power of the energy storage device at each period, which needs to meet the basic constraints of the energy storage device, i.e. the energy storage device operation constraint, including the output constraint and the initial and final state consistency constraint; at the same time, since the user masters the quotation information of the micro-grid operator, in order to ensure the success of the electricity bidding, the price of the energy storage operator should be lower than the quotation of the micro-grid operator at the same period, and the highest quotation of the energy storage operator is 95% of the micro-grid operator, and the energy storage device operation constraint is expressed as follows:

[0099]

[0100]

[0101]

[0102]

[0103] In the above formula, and respectively, the installed capacity of the energy storage device at time t and time t-1, δ is the self-loss rate of the energy storage device, η ch and η dis respectively, the charging efficiency and the discharging efficiency, Δt is the charging time (the value is 1), and respectively, the minimum and maximum charging and discharging power of the energy storage device, and respectively, the minimum installed capacity and the maximum installed capacity of the energy storage device, and respectively, the discharging power and the charging power of the energy storage device at time t.

[0104] S1015, obtain user-side interaction data from interaction data between microgrid operators, energy storage operators and users, and construct a third objective function based on the user-side interaction data, with the goal of minimizing electricity cost.

[0105] Specifically, the power users are mainly industrial users with stable power load demand, and it is assumed that the users are rational and can independently adjust the power load demand with the goal of minimizing their own energy cost; the operation cost is the cost of purchasing electricity from microgrid and energy storage operators and the satisfaction cost caused by participating in demand response; the income is the subsidy income obtained by participating in demand response; the objective function of the load side is to minimize the electricity cost, and the demand response amount can be optimized based on the given electricity selling price of the microgrid operator and the energy storage operator and the demand response incentive subsidy. The specific expression is as follows:

[0106]

[0107] In the above formula, is the cost of the user purchasing electricity from the microgrid operator (i.e., the electricity selling income of the microgrid operator) is the cost of the user purchasing electricity from the energy storage operator (i.e., the electricity selling income of the energy storage operator to the user), C sat is the satisfaction cost of the user, R dr is the income obtained by the user after participating in demand response.

[0108] S1016, take the demand response constraint as a constraint condition of the third objective function, and construct a load side model based on the third objective function and the constraint condition of the third objective function.

[0109] Specifically, the constraint condition of the load side user is mainly the demand response constraint, so as to ensure that the user has the minimum impact on the user's own production plan after participating in demand response.

[0110]

[0111] In the above formula, represents the maximum demand response load.

[0112] As an optional embodiment of the present application, as shown in the above S1011, i.e., the above taking the maximization of daily operation net income as the target, a first target function is constructed based on the micro-grid operator interaction data, including: Figure 6

[0113] S10111, based on the micro-grid operator interaction data, the micro-grid operator's electricity selling price, the micro gas turbine output, the wind turbine output and the photovoltaic turbine output of the micro-grid operator are determined to determine the micro-grid operator's electricity selling income.

[0114] Specifically, the calculation formula of the micro-grid operator's electricity selling income R e is as follows:

[0115]

[0116] In the above formula, and are the electricity selling price and the electricity selling quantity of the micro-grid operator at time t, and are the output of the micro gas turbine, the wind turbine and the photovoltaic turbine at time t, and T is the total scheduling duration, which is 24h (hours).

[0117] S10112, based on the micro-grid operator interaction data, the electricity selling price of the large power grid and the electricity power purchased by the micro-grid operator from the large power grid are determined to determine the exchange income of the micro-grid operator and the external power grid.

[0118] Specifically, the calculation formula of the exchange income R exc of the micro-grid operator and the external power grid is as follows:

[0119]

[0120] In the above formula, is the electricity selling price of the large power grid, is the electricity power purchased by the micro-grid operator from the large power grid at time t.

[0121] S10113, based on the micro-grid operator interaction data, the micro gas turbine operation and maintenance cost, the wind turbine operation and maintenance cost, the photovoltaic turbine operation and maintenance cost, the micro gas turbine output, the wind turbine output and the photovoltaic turbine output are determined to determine the operation and maintenance cost of each device of the micro-grid.

[0122] Specifically, the calculation formula of the operation and maintenance cost of each device of the micro-grid ​The calculation formula of the user's income after participating in the demand response is as follows:

[0123]

[0124] In the above formula, c MT , c WT and c PV are the operation and maintenance costs of the micro gas turbine, the wind turbine generator and the photovoltaic generator respectively.

[0125] S10114, determine the user's income after participating in the demand response based on the price of the user's demand response and the amount of the user's demand response in the micro-grid operator interaction data.

[0126] Specifically, the user's income after participating in the demand response The calculation formula of the user's income after participating in the demand response is as follows:

[0127]

[0128] In the above formula, c is the price of the user's demand response, is the amount of the user's demand response at time t.

[0129] S10115, maximize the daily net income, based on the electricity sales income of the micro-grid operator, the exchange income of the micro-grid operator and the external power grid, the operation and maintenance costs of each device of the micro-grid, the user's income after participating in the demand response and the net income of the micro-grid operator, a first target function is constructed, as shown in formula (2).

[0130] As an optional embodiment of the present application, the above S1011, that is, the above first target function is constructed based on the micro-grid operator interaction data with the goal of maximizing the daily net income, further includes:

[0131] S10116, obtain the proportional coefficient, the electric load prediction value, the wind power output prediction value and the photovoltaic output prediction value, and determine the trapezoidal fuzzy parameters of the electric load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value and the trapezoidal fuzzy parameters of the photovoltaic output prediction value based on the proportional coefficient, the electric load prediction value, the wind power output prediction value and the photovoltaic output prediction value respectively.

[0132] Specifically, since the wind power and photovoltaic output in the micro-grid and the user's electricity demand have prediction errors, in order to ensure the safe and reliable operation of the power system, the uncertainty of the prediction error needs to be modeled; the wind power and photovoltaic output on the source side and the uncertainty on the load side are modeled based on fuzzy chance constraints, the fuzzy parameters of the uncertainty parameter prediction can be represented by a trapezoidal function or a triangular function, and the expression of the membership function is as follows:

[0133]

[0134] In the formula, π(q f ) is a membership function, q fi (i=1, 2, 3, 4) is a membership parameter of a trapezoidal function, which determines the shape of the membership function, and the calculation formula is as follows:

[0135] q fi = λ i q f0 (18)

[0136] In the formula, λ i is a proportional coefficient, which can be determined according to historical data, q f0 is a parameter prediction value, when λ2=λ3=1, i.e., q f2 =q f3 =q f0 , the fuzzy parameter is a triangular function.

[0137] Further, the present application adopts a trapezoidal fuzzy parameter to represent fuzzy variables such as wind power, photovoltaic power and load prediction, and the specific expression is as follows:

[0138]

[0139] In the formula, q , q and q are trapezoidal fuzzy parameters of the load prediction value, the wind power prediction value and the photovoltaic power prediction value respectively; are trapezoidal membership parameters of the load prediction value, the wind power prediction value and the photovoltaic power prediction value respectively; and are prediction values of the load, the wind power and the photovoltaic power respectively.

[0140] S10118, a confidence level is obtained, and a power balance constraint is constructed based on the trapezoidal fuzzy parameter of the electric load prediction value, the trapezoidal fuzzy parameter of the wind power prediction value, the trapezoidal fuzzy parameter of the photovoltaic power prediction value and the confidence level.

[0141] Specifically, the microgrid needs to meet the electricity demand of the user and ensure full consumption of new energy such as wind power and photovoltaic power, and based on the uncertain modeling of formula (20), the corresponding power balance constraint condition can be expressed as shown in the following formula:

[0142]

[0143] In the formula, Cr{} is the possibility of an event, α p is a confidence level, and are the discharge power and the charging power of the energy storage at t.

[0144] Furthermore, by transforming the opportunity constraints into clear equivalence classes, we perform clear equivalence class processing on equation (23) to solve the uncertainty model. The clear equivalence class processing is expressed as follows:

[0145]

[0146] In the above formula, The membership parameter for the predicted electrical load value. The membership parameter for the predicted wind power output. The membership parameter for the predicted photovoltaic power output. Let t be the amount of charging done by the energy storage operator. Let t be the discharge amount of the energy storage operator. The demand response quantity provided to users at time t.

[0147] As an optional embodiment of the present invention, such as Figure 7 As shown, S1013 above, which aims to maximize the daily net revenue of energy storage operators and constructs a second objective function based on the interaction data of energy storage operators, includes:

[0148] S10131. Obtain the electricity sales price and discharge volume of the energy storage operator from the interaction data of the energy storage operator, and determine the revenue of the energy storage operator from selling electricity to users based on the electricity sales price and discharge volume of the energy storage operator.

[0149] Specifically, the revenue R from energy storage operators selling electricity to users dis The calculation formula is as follows:

[0150]

[0151] In the above formula, Let t be the electricity price sold by the energy storage operator. Let t be the discharge amount of the energy storage operator at time t.

[0152] S10132. Obtain the charging volume of the energy storage operator from the interaction data of the energy storage operator, and determine the charging cost of the energy storage operator purchasing electricity from the microgrid based on the electricity sales price of the microgrid operator and the charging volume of the energy storage operator.

[0153] Specifically, the charging cost C for energy storage operators purchasing electricity from microgrids. cha The calculation formula is as follows:

[0154]

[0155] In the above formula, Let t be the amount of charging done by the energy storage operator.

[0156] S10133, determine the operation and maintenance cost of the energy storage based on the charging cost of the energy storage operator purchasing electricity from the micro-grid.

[0157] Specifically, the operation and maintenance cost of the energy storage The calculation formula is as follows:

[0158]

[0159] In the above formula, Set to α times the charging cost, and α is set to 10%.

[0160] S10134, maximize the daily net income of the energy storage operator, and construct a second objective function based on the revenue of the energy storage operator selling electricity to users, the charging cost of the energy storage operator purchasing electricity from the micro-grid, and the operation and maintenance cost of the energy storage. The second objective function is formula (6) shown above.

[0161] As an optional embodiment of the present application, as shown in Figure 8 The above S1015, that is, the above third objective function is constructed based on the user-side interaction data with the lowest electricity cost as the target, including:

[0162] S10151, determine the user's satisfaction cost based on the day-ahead electricity load forecast value, the actual load after demand response, and the deviation penalty coefficient in the user-side interaction data.

[0163] Specifically, the calculation formula of the user's satisfaction cost C sat The calculation formula is as follows:

[0164]

[0165] In the above formula, a is the day-ahead electricity load forecast value of the user, the deviation penalty coefficient of the actual load after demand response When is closer to sat , C is smaller, and the user's satisfaction cost is smaller, which means that when the load after the user participates in the demand response is closer to the day-ahead electricity load forecast value, the user's satisfaction is higher.

[0166] S10152, construct a third objective function based on the electricity selling revenue of the micro-grid operator, the revenue of the energy storage operator selling electricity to users, the revenue after the user participates in demand response, and the user's satisfaction cost with the lowest electricity cost as the target. The second objective function is formula (11) shown above.

[0167] Specifically, in the expression of the second objective function, is the cost of the user purchasing electricity from the micro-grid operator, This refers to the cost for users to purchase electricity from energy storage operators.

[0168] The following specific example illustrates a multi-agent game optimization method for renewable energy microgrids.

[0169] Example 1:

[0170] Taking a northern industrial park as an example, this paper analyzes a multi-agent game model with one master and many slaves using simulation to verify the effectiveness of the proposed model. It assumes a 24-hour scheduling cycle and a 1-hour unit scheduling time. The day-ahead forecast curves for wind and solar power output and electricity load on a typical day are shown below. Figure 9 As shown, the confidence level α p =0.95, the trapezoidal membership parameters are shown in Table 1 below. Wind power and photovoltaic output are more difficult to predict than load, so the membership parameters of wind power and photovoltaic have a larger expansion range. The initial price parameters of microgrid operators are shown in Table 2 below, and the parameters of each device are shown in Table 3 below. It is assumed that the unit power operation and maintenance cost of micro gas turbine and energy storage device is 0.18 yuan / kW and 0.2 yuan / kW respectively, and the unit power operation and maintenance cost of wind power and photovoltaic units is 0.25 yuan / kW (unit: yuan / kilowatt).

[0171] Table 1

[0172] Fuzziness parameter ​ ​ ​ ​ Wind power, PV 0.6 0.9 1.1 1.4 Load 0.9 0.95 1.05 1.1

[0173] Table 2

[0174]

[0175] Table 3

[0176] Equipment Capacity parameter Gas turbine capacity / kW 500 Interconnection line capacity / kW 500 Energy storage installed capacity / kWh 1000 Energy storage installed power / kW 500 Charge-discharge efficiency 0.9 / 0.95 Self-loss rate 0.005

[0177] The optimization iteration process for microgrid operators, energy storage operators, and users converged in the 40th iteration. The revenues for microgrid operators and energy storage operators were 7619.11 yuan and 2644.89 yuan, respectively, while the energy cost for users remained stable at 13564.65 yuan. The pricing strategy for microgrid operators is as follows: Figure 10 As shown, the load curve and demand response results after the user-side demand response are respectively as follows: Figure 11 As shown, the results of power optimization scheduling are as follows: Figure 12 As shown.

[0178] Depend on Figure 10It can be seen that the pricing strategy of microgrid operators is similar to the initial price trend, both adopting a time-of-use pricing mechanism to guide users to use electricity rationally. During peak load periods of 9:00-11:00 and 17:00-20:00, the electricity price of the distribution network is high, at 0.71 yuan / kWh; followed by the price during normal periods of 7:00-8:00, 12:00-16:00, and 21:00-22:00, at 0.59 yuan / kWh; the price during other off-peak periods is the lowest, at 0.34 yuan / kWh. The electricity price of energy storage operators is based on the grid's on-grid price, with the final optimized result being 0.55 yuan / kWh. The demand response price implemented by users is determined according to the different proportions of load response by users, increasing from 1 yuan / kWh to 2 yuan / kWh.

[0179] Depend on Figure 11 The changes in load curves before and after user participation in demand response reveal that, under the time-of-use pricing and demand response incentive subsidies issued by microgrid operators, the optimized load curve exhibits "peak shaving and valley filling" characteristics. During the off-peak hours of 1:00-6:00, users increased their electricity load through load shifting based on the time-of-use pricing signals. During the period of 11:00-24:00, users' electricity load was significantly lower than before optimization, with the highest load reduction occurring during the peak hours of 17:00-20:00. This indicates that under the dual demand response incentive mechanisms of time-of-use pricing and subsidies, users' demand response significantly increased, resulting in a more significant "shaping" effect on the load curve.

[0180] Depend on Figure 12The power dispatch results show that, considering the environmental friendliness of wind and solar power, the microgrid fully absorbs the output of wind and solar power, with micro gas turbines, energy storage units, and the external power grid serving as supplements. When the output of new energy sources such as wind and solar power within the microgrid is insufficient, it compensates for the lack of new energy sources, thereby ensuring a balance between power supply and demand. During the off-peak hours of 1:00-6:00, users' power demand is mainly met by the output of micro gas turbines and wind turbines. At the same time, energy storage units are charging during this period, and users increase their power load. During the period of 7:00-16:00, solar power output is high and wind power output is low. At this time, users' power demand is mainly met by wind and solar power. The power supply and demand are balanced by using wind turbines and micro gas turbines to meet demand, while also guiding users to reduce their electricity load by issuing demand response signals. During the period from 17:00 to 21:00, users' electricity demand is high and photovoltaic power is not outputting. At this time, the electricity demand is mainly met by wind turbines, micro gas turbines, the external power grid, and energy storage units. At the same time, users reduce their electricity load based on time-of-use pricing signals and incentive subsidy signals, thereby achieving a balance between power supply and demand during this period. During the period from 22:00 to 24:00, users' electricity demand is at its lowest. At this time, the power demand is mainly met by wind power output, while micro gas turbine output and user demand response are used as supplements.

[0181] This invention also discloses a multi-agent game optimization device for renewable energy microgrids, such as... Figure 13 As shown, it includes:

[0182] The acquisition module 131 is used to acquire the interaction data between the microgrid operator, the energy storage operator, and the user, and to construct the microgrid operator model, the energy storage operator model, and the load-side model based on the interaction data between the microgrid operator, the energy storage operator, and the user, respectively; for details, please refer to the relevant description of step S101 in the above method embodiment.

[0183] Module 132 is used to construct a multi-agent game model with one master and many slaves based on the microgrid operator model, energy storage operator model and load side model; for details, please refer to the relevant description of step S102 in the above method embodiment.

[0184] The solution module 133 is used to solve the multi-agent game model with one master and many slaves, and generate a multi-agent game optimization strategy; wherein, the multi-agent game optimization strategy is used to provide energy for the operation of renewable energy microgrids; for details, please refer to the relevant description of step S103 in the above method embodiment.

[0185] The application provides a multi-agent game optimization device for a renewable energy micro-grid, a one-master multi-follower multi-agent game model is constructed according to interactive data among a micro-grid operator, an energy storage operator and users, the micro-grid operator is taken as a leader, and the energy storage operator and the users are taken as followers in the one-master multi-follower multi-agent game model, multi-agent benefit maximization is realized, a problem related to game behavior strategies among the micro-grid, the energy storage and the users is solved, and the one-master multi-follower multi-agent game model is solved to generate a multi-agent game optimization strategy, the multi-agent game optimization strategy is used to provide energy for operation of the renewable energy micro-grid, the power supply capacity of the renewable energy micro-grid is improved, energy supply is more economical, and user electricity consumption is more reasonable.

[0186] As an optional embodiment of the application, the acquisition module 131 comprises: a first construction submodule, which is configured to acquire micro-grid operator interactive data from interactive data among a micro-grid operator, an energy storage operator and users, construct a first objective function based on the micro-grid operator interactive data, and take daily operation net income maximization as a target; a second construction submodule, which is configured to take cost constraints, external power purchase power constraints, internal equipment output constraints and power balance constraints as constraint conditions of the first objective function, and construct a micro-grid operator model based on the first objective function and the constraint conditions of the first objective function; a third construction submodule, which is configured to acquire energy storage operator interactive data from the interactive data among the micro-grid operator, the energy storage operator and the users, construct a second objective function based on the energy storage operator interactive data, and take daily net income maximization of the energy storage operator as a target; a fourth construction submodule, which is configured to take energy storage equipment operation constraints as constraint conditions of the second objective function, and construct an energy storage operator model based on the second objective function and the constraint conditions of the second objective function; a fifth construction submodule, which is configured to acquire user interactive data from the interactive data among the micro-grid operator, the energy storage operator and the users, construct a third objective function based on the user interactive data, and take minimum electricity cost as a target; and a sixth construction submodule, which is configured to take demand response constraints as constraint conditions of the third objective function, and construct a load side model based on the third objective function and the constraint conditions of the third objective function.

[0187] As an optional implementation of the present application, the first construction sub-module comprises: a first determination unit configured to determine the electricity selling revenue of the micro-grid operator based on the electricity selling price of the micro-grid operator, the micro-turbine output, the wind turbine output and the photovoltaic turbine output in the micro-grid operator interaction data; a second determination unit configured to determine the exchange revenue of the micro-grid operator with the external power grid based on the electricity selling price of the macro-grid and the electricity power purchased by the micro-grid operator from the macro-grid in the micro-grid operator interaction data; a third determination unit configured to determine the operation and maintenance cost of each device of the micro-grid based on the micro-turbine operation and maintenance cost, the wind turbine operation and maintenance cost, the photovoltaic turbine operation and maintenance cost, the micro-turbine output, the wind turbine output and the photovoltaic turbine output in the micro-grid operator interaction data; a fourth determination unit configured to determine the revenue of the user after participating in the demand response based on the price of the demand response provided by the user and the amount of the demand response provided by the user in the micro-grid operator interaction data; and a first construction unit configured to construct a first objective function based on the electricity selling revenue of the micro-grid operator, the exchange revenue of the micro-grid operator with the external power grid, the operation and maintenance cost of each device of the micro-grid, the revenue of the user after participating in the demand response and the net revenue of the micro-grid operator with the maximization of the daily net revenue as the target.

[0188] As an optional implementation of the present application, the first construction sub-module further comprises: a fifth determination unit configured to obtain a proportionality coefficient, an electricity load prediction value, a wind power output prediction value and a photovoltaic output prediction value, and determine the trapezoidal fuzzy parameters of the electricity load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value and the trapezoidal fuzzy parameters of the photovoltaic output prediction value based on the proportionality coefficient, the electricity load prediction value, the wind power output prediction value and the photovoltaic output prediction value; and a second construction unit configured to obtain a confidence level, and construct a power balance constraint based on the trapezoidal fuzzy parameters of the electricity load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value, the trapezoidal fuzzy parameters of the photovoltaic output prediction value and the confidence level.

[0189] As an optional implementation of the present application, the third construction sub-module comprises: a sixth determination unit configured to obtain the electricity selling price of the energy storage operator and the discharge amount of the energy storage operator from the energy storage operator interaction data, and determine the revenue of the energy storage operator selling electricity to the user based on the electricity selling price of the energy storage operator and the discharge amount of the energy storage operator; a seventh determination unit configured to obtain the charge amount of the energy storage operator from the energy storage operator interaction data, and determine the charging cost of the energy storage operator purchasing electricity from the micro-grid based on the electricity selling price of the micro-grid operator and the charge amount of the energy storage operator; an eighth determination unit configured to determine the operation and maintenance cost of the energy storage based on the charging cost of the energy storage operator purchasing electricity from the micro-grid; and a third construction unit configured to construct a second objective function based on the revenue of the energy storage operator selling electricity to the user, the charging cost of the energy storage operator purchasing electricity from the micro-grid and the operation and maintenance cost of the energy storage with the maximization of the daily net revenue of the energy storage operator as the target.

[0190] As an optional embodiment of the present invention, the fifth construction submodule includes: a ninth determining unit, used to determine the user satisfaction cost based on the day-ahead electricity load forecast, the actual load after demand response, and the deviation penalty coefficient in the user terminal interaction data; and a fourth construction unit, used to construct a third objective function with the goal of minimizing electricity costs, based on the electricity sales revenue of the microgrid operator, the revenue of the energy storage operator selling electricity to the user, the revenue of the user after participating in demand response, and the user satisfaction cost.

[0191] In addition, embodiments of the present invention also provide an electronic device, such as... Figure 14 As shown, the electronic device may include a processor 110 and a memory 120, wherein the processor 110 and the memory 120 may be connected via a bus or other means. Figure 14 For example, the connection is via a bus. Furthermore, the electronic device also includes at least one interface 130, which can be a communication interface or other interface; this embodiment does not impose any limitations on this.

[0192] The processor 110 can be a central processing unit (CPU). The processor 110 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0193] The memory 120, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the video synthesis method in this embodiment of the invention. The processor 110 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 120, thereby implementing a multi-agent game optimization method for renewable energy microgrids as described in the above method embodiment.

[0194] The memory 120 can include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the data storage area can store data created by the processor 110, etc. In addition, the memory 120 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 120 can optionally include a memory disposed remotely with respect to the processor 110, which can be connected to the processor 110 through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0195] In addition, the at least one interface 130 is used for communication between the electronic device and an external device, such as communication with a server, etc. Optionally, the at least one interface 130 can also be used to connect peripheral input, output devices, such as a keyboard, a display screen, etc.

[0196] The one or more modules are stored in the memory 120, and when executed by the processor 110, perform the following steps: Figure 1 A multi-agent game optimization method for a renewable energy micro-grid in an embodiment shown.

[0197] The above electronic device specific details can be referred to Figure 1 The corresponding related description and effects in the embodiment shown are understood, and will not be repeated here.

[0198] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of each method. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above types of memories.

[0199] Obviously, the above embodiments are only examples for clearly illustrating, and not limiting the implementation. For those skilled in the art, on the basis of the above description, other different forms of changes or variations can also be made. Here, all the implementations do not need to be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A multi-agent game optimization method for a renewable energy microgrid, characterized in that, The method comprises the following steps: acquiring interaction data among a micro-grid operator, an energy storage operator and a user, and constructing a micro-grid operator model, an energy storage operator model and a load side model based on the interaction data among the micro-grid operator, the energy storage operator and the user; constructing a one-leader-multiple-follower multi-agent game model based on the micro-grid operator model, the energy storage operator model and the load side model; the one-leader-multiple-follower multi-agent game model takes the micro-grid operator as a leader and the energy storage operator and the user as followers; solving the one-leader-multiple-follower multi-agent game model to generate a multi-agent game optimization strategy; the multi-agent game optimization strategy is used to provide energy for operation of a renewable energy micro-grid; the step of constructing the micro-grid operator model, the energy storage operator model and the load side model based on the interaction data among the micro-grid operator, the energy storage operator and the user comprises the following steps: acquiring micro-grid operator interaction data from the interaction data among the micro-grid operator, the energy storage operator and the user, constructing a first objective function based on the micro-grid operator interaction data, and taking maximum daily net income as a target; taking cost constraints, external power purchase power constraints, internal equipment output constraints and power balance constraints as constraint conditions of the first objective function, and constructing the micro-grid operator model based on the first objective function and the constraint conditions of the first objective function; acquiring energy storage operator interaction data from the interaction data among the micro-grid operator, the energy storage operator and the user, constructing a second objective function based on the energy storage operator interaction data, and taking maximum daily net income of the energy storage operator as a target; taking energy storage device operation constraints as constraint conditions of the second objective function, and constructing the energy storage operator model based on the second objective function and the constraint conditions of the second objective function; acquiring user interaction data from the interaction data among the micro-grid operator, the energy storage operator and the user, constructing a third objective function based on the user interaction data, and taking minimum electricity cost as a target; taking demand response constraints as constraint conditions of the third objective function, and constructing the load side model based on the third objective function and the constraint conditions of the third objective function; the step of constructing the first objective function based on the micro-grid operator interaction data and taking maximum daily net income as a target comprises the following steps: acquiring a proportionality coefficient, an electric load prediction value, a wind power output prediction value and a photovoltaic output prediction value, determining trapezoidal fuzzy parameters of the electric load prediction value, trapezoidal fuzzy parameters of the wind power output prediction value and trapezoidal fuzzy parameters of the photovoltaic output prediction value based on the proportionality coefficient, the electric load prediction value, the wind power output prediction value and the photovoltaic output prediction value respectively; acquiring a confidence level, and constructing the power balance constraint based on the trapezoidal fuzzy parameters of the electric load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value, the trapezoidal fuzzy parameters of the photovoltaic output prediction value and the confidence level; the step of solving the one-leader-multiple-follower multi-agent game model to generate a multi-agent game optimization strategy comprises the following steps: A distributed equilibrium solving method using a particle swarm algorithm combined with a CPLEX solver is used to solve a multi-agent game model of one master and multiple slaves, and an optimization strategy of the multi-agent game is generated; The second objective function is constructed based on the interaction data of the energy storage operator, with the goal of maximizing the daily net income of the energy storage operator, including: The selling price of the energy storage operator and the discharge capacity of the energy storage operator are obtained from the interaction data of the energy storage operator, and the revenue of the energy storage operator from selling electricity to users is determined based on the selling price of the energy storage operator and the discharge capacity of the energy storage operator; The charging capacity of the energy storage operator is obtained from the interaction data of the energy storage operator, and the charging cost of the energy storage operator for purchasing electricity from the microgrid is determined based on the selling price of the microgrid operator and the charging capacity of the energy storage operator; The operation and maintenance cost of the energy storage is determined based on the charging cost of the energy storage operator for purchasing electricity from the microgrid; The second objective function is constructed based on the revenue of the energy storage operator from selling electricity to users, the charging cost of the energy storage operator for purchasing electricity from the microgrid, and the operation and maintenance cost of the energy storage, with the goal of maximizing the daily net income of the energy storage operator; The third objective function is constructed based on the user interaction data, with the goal of minimizing the electricity cost, including: The satisfaction cost of the user is determined based on the day-ahead load forecast value, the actual load after demand response, and the deviation penalty coefficient in the user interaction data; The third objective function is constructed based on the selling revenue of the microgrid operator, the revenue of the energy storage operator from selling electricity to users, the revenue of the user after participating in demand response, and the satisfaction cost of the user, with the goal of minimizing the electricity cost. 2.The multi-agent game optimization method for a renewable energy microgrid of claim 1, wherein, The first objective function is constructed based on the microgrid operator interaction data, with the goal of maximizing the daily net income, including: The selling revenue of the microgrid operator is determined based on the selling price of the microgrid operator, the output of the micro gas turbine, the output of the wind turbine, and the output of the photovoltaic unit in the microgrid operator interaction data; The exchange revenue of the microgrid operator with the external grid is determined based on the selling price of the large grid and the electric power purchased from the large grid in the microgrid operator interaction data; The operation and maintenance cost of each device of the microgrid is determined based on the operation and maintenance cost of the micro gas turbine, the operation and maintenance cost of the wind turbine, the operation and maintenance cost of the photovoltaic unit, the output of the micro gas turbine, the output of the wind turbine, and the output of the photovoltaic unit in the microgrid operator interaction data; The revenue of the user after participating in demand response is determined based on the price of demand response provided by the user and the amount of demand response provided by the user in the microgrid operator interaction data; The first objective function is constructed based on the selling revenue of the microgrid operator, the exchange revenue of the microgrid operator with the external grid, the operation and maintenance cost of each device of the microgrid, the revenue of the user after participating in demand response, and the net income of the microgrid operator, with the goal of maximizing the daily net income.

3. A multi-agent game optimization device for a renewable energy microgrid, characterized in that, including: The acquisition module is configured to acquire interaction data among the micro-grid operator, the energy storage operator, and the user, and construct a micro-grid operator model, an energy storage operator model, and a load-side model based on the interaction data among the micro-grid operator, the energy storage operator, and the user. The construction module is configured to construct a one-leader-multiple-follower multi-agent game model based on the micro-grid operator model, the energy storage operator model, and the load-side model, with the micro-grid operator as the leader and the energy storage operator and the user as the followers. The solution module is configured to solve the one-leader-multiple-follower multi-agent game model to generate a multi-agent game optimization strategy, which is used to provide energy for operation of the renewable energy micro-grid. The acquisition module includes a first construction submodule configured to acquire micro-grid operator interaction data from the interaction data among the micro-grid operator, the energy storage operator, and the user, construct a first objective function based on the micro-grid operator interaction data, and maximize daily net revenue as an objective; a second construction submodule configured to take cost constraints, external power purchase constraints, internal equipment output constraints, and power balance constraints as constraint conditions of the first objective function, and construct a micro-grid operator model based on the first objective function and the constraint conditions of the first objective function; a third construction submodule configured to acquire energy storage operator interaction data from the interaction data among the micro-grid operator, the energy storage operator, and the user, construct a second objective function based on the energy storage operator interaction data, and maximize daily net revenue of the energy storage operator as an objective; a fourth construction submodule configured to take energy storage device operation constraints as constraint conditions of the second objective function, and construct an energy storage operator model based on the second objective function and the constraint conditions of the second objective function; a fifth construction submodule configured to acquire user-side interaction data from the interaction data among the micro-grid operator, the energy storage operator, and the user, construct a third objective function based on the user-side interaction data, and minimize electricity cost as an objective; and a sixth construction submodule configured to take demand response constraints as constraint conditions of the third objective function, and construct a load-side model based on the third objective function and the constraint conditions of the third objective function. The first construction submodule includes a fifth determination unit configured to acquire a proportionality coefficient, an electric load prediction value, a wind power output prediction value, and a photovoltaic output prediction value, determine trapezoidal fuzzy parameters of the electric load prediction value, trapezoidal fuzzy parameters of the wind power output prediction value, and trapezoidal fuzzy parameters of the photovoltaic output prediction value based on the proportionality coefficient, the electric load prediction value, the wind power output prediction value, and the photovoltaic output prediction value, respectively; and a second construction unit configured to acquire a confidence level, and construct a power balance constraint based on the trapezoidal fuzzy parameters of the electric load prediction value, the trapezoidal fuzzy parameters of the wind power output prediction value, the trapezoidal fuzzy parameters of the photovoltaic output prediction value, and the confidence level. The solution module is specifically configured to solve the one-leader-multiple-follower multi-agent game model by using a distributed equilibrium solving method of a particle swarm algorithm combined with a CPLEX solver to generate the multi-agent game optimization strategy. The third construction submodule comprises: a sixth determination unit, configured to acquire the electricity selling price of the energy storage operator and the discharging capacity of the energy storage operator from the energy storage operator interaction data, and determine the revenue of the energy storage operator from selling electricity to the user based on the electricity selling price of the energy storage operator and the discharging capacity of the energy storage operator; a seventh determination unit, configured to acquire the charging capacity of the energy storage operator from the energy storage operator interaction data, and determine the charging cost of the energy storage operator for purchasing electricity from the microgrid based on the electricity selling price of the microgrid operator and the charging capacity of the energy storage operator; an eighth determination unit, configured to determine the operation and maintenance cost of the energy storage based on the charging cost of the energy storage operator for purchasing electricity from the microgrid; and a third construction unit, configured to construct a second objective function based on the revenue of the energy storage operator from selling electricity to the user, the charging cost of the energy storage operator for purchasing electricity from the microgrid, and the operation and maintenance cost of the energy storage, with the goal of maximizing the daily net revenue of the energy storage operator; The fifth construction submodule comprises: a ninth determination unit, configured to determine the satisfaction cost of the user based on the day-ahead electricity load prediction value, the actual load after demand response, and the deviation penalty coefficient in the user-side interaction data; and a fourth construction unit, configured to construct a third objective function based on the electricity selling revenue of the microgrid operator, the revenue of the energy storage operator from selling electricity to the user, the revenue of the user after participating in demand response, and the satisfaction cost of the user, with the goal of minimizing the electricity cost.

4. An electronic device, comprising: The device comprises a processor and a memory, wherein the memory is coupled to the processor; The memory stores computer readable program instructions, and when the instructions are executed by the processor, the method of claim 1 or 2 is implemented.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of claim 1 or 2.

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