An optimal configuration method for microgrid cluster sharing energy storage based on mixed game

Abstract

The application discloses a kind of optimal configuration method of microgrid cluster sharing energy storage based on mixed game, it is related to new energy technology field, including the following steps: source load data of microgrid cluster is collected as input data;Build mixed game microgrid cluster sharing energy storage optimal configuration model, the model minimizes microgrid annual operating cost as objective function, combined with constraint condition, optimal price is solved using Stackelberg game model, the cost of microgrid cluster cooperation game is apportioned using demand-based Shapley value method;Based on price information and microgrid cluster leasing demand, the optimal configuration scheme is obtained by solving Cplex solver.This application is based on Stackelberg game theory, establishes the master-slave game model with shared energy storage operator as leader and microgrid cluster as follower, so as to form mixed game framework to solve optimal leasing price.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to an optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory. Background Technology

[0002] Against the backdrop of an escalating global energy crisis, accelerating the construction of the energy internet has become a crucial pathway to promoting energy system reform and improving energy efficiency. With a high proportion of distributed renewable energy sources connected to the grid, multi-microgrid and energy storage systems, as an important component of the energy internet, can utilize energy storage systems to time-shift energy output. Because renewable energy output fluctuates, these systems absorb energy during periods of high output and release it during periods of low output, effectively promoting the absorption of distributed energy resources, ensuring microgrid stability, and playing a significant role in achieving energy conservation, emission reduction, and energy structure transformation.

[0003] However, in microgrids, self-built energy storage often incurs high costs, making it impossible for microgrids to configure energy storage that meets their own needs, resulting in resource waste. Furthermore, self-built energy storage is only used for a short period each day, and this period causes significant fluctuations in electricity demand, which greatly impacts the lifespan of the energy storage and leads to a decline in overall economic efficiency. With the continuous development of the sharing economy, the application of shared energy storage can improve the economic efficiency of microgrid systems, promote the consumption of new energy sources, effectively avoid resource waste, and extend the lifespan of the entire system.

[0004] CN118589562A presents a multi-microgrid shared energy storage optimization method and medium based on the Shapley value method. It establishes a life-cycle daily cost and shared energy storage optimization configuration model, primarily aimed at promoting the local consumption of distributed renewable energy. However, in practical applications, excessively pursuing renewable energy consumption rates may negatively impact system stability and economics. Regarding the calculation of cost allocation weights for each microgrid using the improved Shapley value method based on the shared energy storage optimization configuration model, the model may lead to unfair allocation results due to varying energy interaction needs between different microgrids and shared energy storage. Furthermore, it cannot guarantee that the established shared energy storage optimization configuration model can meet the microgrid leasing needs, and it does not provide a pricing method for microgrid leasing of shared energy storage. Therefore, with a high proportion of distributed renewable energy connected to the grid, how to formulate a reasonable configuration and pricing scheme for shared energy storage to promote maximum renewable energy consumption, improve overall security and stability, and rationally allocate the resulting costs among microgrids has become an urgent problem to be solved. Summary of the Invention

[0005] In view of the above-mentioned shortcomings and deficiencies of the existing technology, the present invention provides an optimization configuration method for shared energy storage in microgrid clusters based on hybrid game theory. This method is based on Stackelberg game theory, establishes a master-slave game model with the shared energy storage operator as the leader and the microgrid cluster as the follower, and introduces the Shapley value method based on demand degree to establish a cooperative game model among multiple microgrids, thereby forming a hybrid game framework to solve the optimal lease price and shared energy storage configuration scheme, so as to minimize the annual cost of the microgrid.

[0006] To achieve the above objectives, the main technical solutions adopted by the present invention include: An optimal configuration method for shared energy storage in a microgrid cluster based on hybrid game theory includes the following steps: The source-load data of the microgrid cluster is collected as input data; a hybrid game-theoretic microgrid cluster shared energy storage optimization configuration model is constructed, with the objective function being the minimization of the annual operating cost of the microgrid. Combined with constraints, the Stackelberg game model is used to solve for the optimal electricity price, and the Shapley value method based on demand degree is used to allocate the cost of the cooperative game of the microgrid cluster. Based on the electricity price information and the microgrid cluster leasing demand, the optimal configuration scheme is obtained through the Cplex solver. The hybrid game theory microgrid cluster shared energy storage optimization configuration model is as follows: ; In the formula, To minimize the annual cost of the microgrid, the model considers six factors. It is the cost of microgrid leasing. It is the power exchange cost after the microgrids form a cooperative network. It is the cost of assessing the deviation in microgrid power generation prediction. It is the assessment cost of wind and solar power curtailment in microgrids. It is the revenue from selling electricity through the microgrid. It is the benefit of interaction between the microgrid and the power grid; Microgrid leasing costs are calculated using the following formula: ; in, It is the cost of microgrid leasing. It is the price for renting shared energy storage capacity during time period t; It refers to the shared energy storage capacity leased during time period t; The price for shared energy storage capacity during time period t; It is the charging power of shared energy storage leased during time period t; H is the discharge power of the shared energy storage leased during time period t, where H is the total number of days in a year and T is the time period. The power exchange cost after the microgrids form a cooperative network is calculated using the following formula: ; in, It is the power exchange cost after the microgrids form a cooperative network. and These represent the power prices that a microgrid buys or sells to other microgrids during time period t. and These represent the power purchased or sold from other microgrids during time period t; The cost of assessing the deviation in microgrid power generation forecasts is calculated using the following formula: ; ; In the formula, It is the cost of assessing the deviation of microgrid power generation prediction. That is, if the actual power generation of the microgrid minus the predicted power generation is greater than or less than 5% of the predicted power generation of the microgrid, this part is defined as the deviation power. It is the price for assessing prediction deviations; Power needs to be assessed during time period t; It is the deviation at time t; This is the predicted power output of the microgrid during time period t; The actual power output of the microgrid during time period t; It is the price for assessing prediction deviations; Power needs to be assessed during time period t; The cost of wind and solar power curtailment in microgrids is calculated using the following formula: ; In the formula, This refers to the assessment cost of wind and solar power curtailment in microgrids. The assessment of energy curtailment in microgrids aims to reduce energy curtailment. It is the assessment price for wind and solar power curtailment; It is the power of wind and solar power curtailed at time t; The revenue from selling electricity through a microgrid is calculated using the following formula: ; In the formula, This refers to the revenue from selling electricity from the microgrid. The microgrid sells electricity to the grid and to users within the microgrid; this portion specifically refers to the revenue sold to users within the microgrid. It is the price of the microgrid's load sold during time period t; It is the fixed load of the microgrid during time period t; It is the mobile load of the microgrid during time period t; The benefits of interaction between the microgrid and the power grid are calculated using the following formula: ; In the formula, It refers to the revenue generated from the interaction between the microgrid and the power grid, meaning that the microgrid can sell electricity to the power grid and purchase electricity from the power grid when there is a power shortage. and These are the electricity purchase and sale prices from the grid during time period t; and These represent the power purchased and sold from the power grid during time period t.

[0007] Furthermore, the source-load data of the microgrid cluster includes power generation data from wind turbines and photovoltaics, as well as load data that the microgrid needs to handle.

[0008] Furthermore, the constraints include power balance constraints, leased capacity constraints, transferable load constraints, energy curtailment constraints, and charge / discharge mutual exclusion constraints.

[0009] Furthermore, to ensure the overall power balance of the system, making the total charging equal to the total discharging, the power balance constraint is set as follows: ; in, The actual power output of the microgrid during time period t. and These are the power purchased and sold from the grid during time period t. It is the charging power of shared energy storage during the t-period rental period. and These represent the power purchased or sold from other microgrids during time period t. It is the fixed load of the microgrid during time period t. The mobile load of the microgrid during time period t It is the power of wind and solar power curtailment at time t.

[0010] Furthermore, the leased capacity constraint is as follows: ; In the formula, It is the initial leased capacity of a scheduling cycle, i.e., the leased capacity at time 0. This refers to the leased capacity at the end of the scheduling cycle, i.e., at 24:00; this part constrains the initial leased shared energy storage capacity to be equal to the leased shared energy storage capacity at the end of the scheduling cycle. and These are the maximum and minimum values ​​of the leased capacity, respectively. The leased capacity of the microgrid and shared energy storage at time t is a constraint that the leased capacity of the microgrid cannot exceed the leased capacity of the shared energy storage. It refers to the shared energy storage capacity leased by the microgrid at time t-1. It is an interval period. It is the shared energy storage charging and discharging efficiency. and It refers to the shared energy storage charging and discharging power.

[0011] Peak-valley electricity pricing can lead to new electricity consumption habits among users, resulting in transferable loads—loads shifted from peak pricing periods to other periods. To prevent excessive transferable loads from impacting grid stability, transferable load constraints are set as follows: ; in, It is the fixed load of the microgrid during time period t. This represents the movable load of the microgrid during time period t. The transferable load is 40% of the total load of the microgrid during time period t, and the sum of the transferable loads is 0.

[0012] Furthermore, to prevent energy wastage from exceeding the system's available energy, the energy wastage constraint is set as follows: ; in, It is the power of wind and solar power curtailed at time t. The actual power output of the microgrid during time period t. It is the fixed load of the microgrid during time period t. The mobile load of the microgrid during time period t It is the charging power of shared energy storage leased during time period t.

[0013] Furthermore, to ensure that the system cannot charge and discharge simultaneously, a mutual exclusion constraint for charging and discharging is set as follows: ; In the formula, and These represent the power purchased or sold from other microgrids during time period t. and These are the upper and lower limits of the power that a microgrid sells to other microgrids; and These are the upper and lower limits of the power that a microgrid can purchase from other microgrids; and It is a binary variable, that is and The value can only be 0 or 1, as defined in the formula. ,represent and It cannot be 1 at the same time, meaning that a microgrid cannot charge and discharge other microgrids simultaneously.

[0014] Furthermore, the Stackelberg game model is as follows: ; The above game model contains three elements: participants, strategies, and payoffs. Here, G is the total set of game frameworks, L is the leader's strategy, F is the follower's strategy, and R represents the payoff. SESO It is a leader-shared energy storage strategy, FMG For follower microgrid strategies, R SESO It is the revenue from shared energy storage, R MG It is the revenue of microgrids; Specifically, it is expressed as follows: Participants: SESO, MG1, ..., MGn are the participants in this game, and the set of participants is represented as: .

[0015] Trading Strategy: The leader SESO's strategy involves 24-hour rental prices (including rental capacity price and rental power price) and the allocation of rental capacity and arbitrage capacity, which can be represented as a vector. The strategy for microgrids is to represent the rental demand (including rental capacity demand and rental power demand) at each time point as a vector. .

[0016] Benefits: The benefits for each participant are defined by the objective function in the microgrid.

[0017] Furthermore, in the cost allocation of the microgrid cluster cooperative game using the demand-based Shapley value method, the allocated cost is: ; Among them, I i Costs allocated to each microgrid The price for shared energy storage capacity leased during time period t. Ω represents the price of shared energy storage capacity leased during time period t, Ω represents the total leased capacity of each microgrid during each time period, and Ψ represents the total leased capacity of each microgrid during each time period. C Leasing charging power to each microgrid at different times, Ψ D For each microgrid, the rental discharge power is provided at each time period. C For microgrid i to send power to other microgrids, ζ D For microgrid j to send power to other microgrids; Suppose there are n microgrids in a microgrid cluster, and the total capacity demand for leasing in each time period is a set: ; The total power demand for rentals at different times is a set: ; ; The microgrid interaction power at each time period is a set: ; ; in, It is a time period for leasing shared energy storage capacity. It is the charging power of shared energy storage during the t-period rental period. It is the discharge power of the shared energy storage leased during time period t. and These represent the power purchased or sold from other microgrids during time period t.

[0018] The beneficial effects of this invention are: This invention proposes an optimized configuration method for shared energy storage in microgrid clusters based on hybrid game theory. This method, based on Stackelberg game theory, establishes a master-slave game model with the shared energy storage operator as the leader and the microgrid cluster as the followers. It also introduces the Shapley value method based on demand to establish a cooperative game model among multiple microgrids, thus forming a hybrid game framework to solve for the optimal lease price and shared energy storage configuration scheme. Through this hybrid game method, the absorption of renewable energy is maximized while ensuring the overall economic efficiency and security of the system, based on actual demand. Microgrids can perform power sharing, forming a microgrid cluster for cooperative game playing, reducing dependence on shared energy storage. The demand-based Shapley value method allocates lease costs according to the actual lease demand of each microgrid. Each microgrid only reports its own energy deficit and does not provide other information, effectively protecting the data privacy of each microgrid user. By allocating costs based on the demand of each microgrid, the fairness of the final allocation result is improved.

[0019] This invention aims to minimize the annual operating cost of microgrid clusters, with constraints including power balance, leased capacity, transferable load, energy curtailment, and charge / discharge mutual exclusion. An optimal configuration model for shared energy storage within the microgrid cluster is constructed. This method utilizes shared energy storage in the microgrid cluster, eliminating the need for advance capacity demand forecasting. Configuration is performed in real-time based on the microgrid cluster's leasing needs, reducing the cost of self-built energy storage, maximizing the utilization of shared energy storage capacity, and avoiding resource waste. This promotes the maximum integration of renewable energy sources and ensures the system's safety, stability, and economic efficiency. Attached Figure Description

[0020] Figure 1 This is a framework diagram of the present invention; Figure 2 This is a graph showing the power output curve of a microgrid cluster in a specific embodiment of the present invention. Figure 3 This is a microgrid cluster load curve collected in a specific embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the implementation process of the present invention; Figure 5 This is a diagram of the solution architecture of the present invention; Figure 6 This is a diagram of energy curtailment in a microgrid cluster according to a specific embodiment of the present invention. Detailed Implementation

[0021] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] This invention provides an optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, comprising the following main components: Figure 1 As shown, the power grid interacts with the shared energy storage microgrid through the power grid dispatch and trading center. The shared energy storage operator, as the leader in the Stackelberg game, sets the rental price and configures the shared energy storage capacity based on the microgrid cluster's leasing demand through the shared energy storage dispatch center. The microgrid cluster, as a follower, can promote power sharing through inter-microgrid cooperation, thereby facilitating renewable energy consumption, reducing dependence on shared energy storage, and lowering microgrid costs. Based on the shared energy storage leasing price determined by the genetic algorithm, the microgrid leasing demand, and renewable energy output fluctuations, the solution is obtained by minimizing the annual cost, considering factors such as leasing costs, energy interaction costs, deviation assessment costs, and electricity sales revenue. The method of this invention includes the following steps: Step 1: Collect source and load data of the microgrid cluster.

[0023] Collect hourly power generation data from wind turbines and solar panels within the microgrid cluster, as well as load data that the microgrid needs to handle. For example... Figure 2 , Figure 3 As shown, this embodiment includes three microgrids within the regions MG1, MG2, and MG3, simulating a microgrid cluster.

[0024] Microgrid MG1 has an installed capacity of 600kW photovoltaic and 900kW wind turbines; microgrid MG2 has an installed capacity of 750kW photovoltaic; and microgrid MG3 has an installed capacity of 750kW wind turbines. The assessment cost per unit power prediction deviation is 0.60 yuan / (kW•h). The assessment cost per unit of wind and solar curtailment power is based on tiered pricing, as shown in Table 1. The electricity purchase and sale prices between the microgrid and the grid are shown in Table 2, with the price of selling electricity to the grid being equal to the price of selling electricity to loads within the microgrid. The electricity purchase and sale prices between microgrids are equal, as shown in Table 3. Figure 4 Process and Figure 5 Simulation calculations are performed using the solution architecture diagram. First, the parameters of the genetic algorithm are initialized. The genetic algorithm randomly generates prices for each time period based on the leasing demand reported by the microgrid at each time period. The solver solves for the optimal configuration scheme of shared energy storage based on the leasing price, leasing demand, and renewable energy curtailment. The microgrid adjusts the leasing demand in a timely manner according to the price, generates new leasing demand, and performs the next iteration until the optimal solution is reached, at which point the iteration ends.

[0025] Table 1. Assessment Cost Table for Unit Wind and Solar Curtailment Power .

[0026] Table 2. Electricity Purchase and Sale Prices between Microgrids and the Grid .

[0027] Table 3: Electricity Purchase and Sale Prices Between Microgrids .

[0028] Step 2: The rental price includes the rental capacity price and the rental power price, expressed as follows: ; in The time period during which shared energy storage provides leasing services. It is the total rental price of shared energy storage at time t. The price of shared energy storage leasing capacity at time t. It is the shared energy storage leasing power price at time t.

[0029] The objective function for minimizing the annual cost of microgrids is: ; In the formula, The goal is to minimize the overall objective across all factors considered in the model. The model considers six factors, among which... It is the cost of microgrid leasing; It is the power exchange cost after the microgrids form a cooperative relationship; It is the cost of assessing the deviation in microgrid power generation prediction; It is the assessment cost of wind and solar power curtailment in microgrids; It is the revenue from selling electricity through the microgrid; It is the benefit of interaction between the microgrid and the power grid.

[0030] ; in, This represents the microgrid leasing cost, where the microgrid leases shared energy storage capacity based on its own needs. To ensure fairness for users with different electricity consumption habits, the charging and discharging power of each lease is also considered. In the formula, It is the price for renting shared energy storage capacity during time period t; It refers to the shared energy storage capacity leased during time period t; The price for shared energy storage capacity during time period t; It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t.

[0031] ; in, This represents the power exchange cost after microgrids form a cooperative network. It considers the power mutual assistance between microgrids, meaning that when there is surplus energy, priority is given to considering whether other microgrids need it, thus promoting the consumption of new energy and reducing microgrid costs. In the formula, and These represent the power prices that a microgrid buys or sells to other microgrids during time period t. and These represent the power purchased or sold from other microgrids during time period t.

[0032] ; ; in, This is the cost of assessing the microgrid power generation forecast deviation. Specifically, if the actual microgrid power generation minus the forecasted power generation is greater than or less than 5% of the forecasted power generation, this portion is defined as the deviation power. In the formula, It is the deviation at time t; This is the predicted power output of the microgrid during time period t; The actual power output of the microgrid during time period t; It is the price for assessing prediction deviations; Power needs to be assessed during the t-period.

[0033] ; in, This refers to the assessment cost of wind and solar power curtailment in microgrids. It assesses the energy curtailment in microgrids and aims to reduce energy curtailment. In the formula, It is the assessment price for wind and solar power curtailment; It is the power of wind and solar power curtailment at time t.

[0034] ; in This refers to the revenue from electricity sales by the microgrid. The microgrid sells electricity to both the grid and users within the microgrid; this portion specifically refers to the revenue sold to users within the microgrid. In the formula, It is the price at which the microgrid sells its load during time period t. It is the fixed load of the microgrid during time period t; It is the mobile load of the microgrid during time period t.

[0035] ; in, This refers to the revenue generated through interaction between the microgrid and the power grid; that is, the microgrid can sell electricity to the grid and purchase electricity from the grid when there is a power shortage. In the formula, and These are the electricity purchase and sale prices from the grid during time period t; and These represent the power purchased and sold from the power grid during time period t.

[0036] The following are the constraints of the above model: Power balance constraints: ; Rental capacity constraints: ; In the formula, It is the initial leased capacity of a scheduling cycle, i.e., the leased capacity at time 0. This refers to the leased capacity at the end of the scheduling cycle, i.e., at 24:00. This part of the constraint is that the initial leased shared energy storage capacity is equal to the leased shared energy storage capacity at the end of the scheduling cycle. and These are the maximum and minimum values ​​of the leased capacity, respectively. The leased capacity of the microgrid and shared energy storage at time t is a constraint that the leased capacity of the microgrid cannot exceed the leased capacity of the shared energy storage. It refers to the shared energy storage capacity leased by the microgrid at time t-1. These are time intervals, each lasting one hour. It is the shared energy storage charging and discharging efficiency. and It refers to the shared energy storage charging and discharging power.

[0037] Transferable load constraints: ; The transferable load is 40% of the total load of the microgrid during time period t, and the sum of the transferable loads is 0.

[0038] Energy attrition constraint: ; Charge and discharge mutual exclusion constraint: ; In the formula, and These are the upper and lower limits of the power that a microgrid sells to other microgrids; and These are the upper and lower limits of the power that a microgrid can purchase from other microgrids; and It is a binary variable, that is and The value can only be 0 or 1, as defined in the formula. ,represent and It cannot be 1 at the same time, meaning that a microgrid cannot charge and discharge other microgrids simultaneously.

[0039] Step 3: In this embodiment, the leasing demand of the microgrid cluster is obtained based on the source-load curve of the microgrid cluster, and the optimal configuration capacity of shared energy storage is finally calculated. This example selects two scenarios for comparison, as shown in Table 4.

[0040] Table 4. Comparison of the two scenarios .

[0041] Scenario 1 considers the Stackelberg game theory method in relation to the microgrid. Scenario 2 is the hybrid game theory configuration method proposed in this invention. The configuration results and economic benefits are shown in Table 5.

[0042] Table 5. Configuration Results and Economic Benefits .

[0043] Scenario 1 establishes a master-slave game between shared energy storage and individual microgrids. Scenario 2, the hybrid game method proposed in this invention, shows that the annual revenue of shared energy storage is slightly lower than in Scenario 1 because the microgrids in Scenario 1 do not form a cooperative game and cannot perform power sharing, so they can only adopt a leasing model. However, the annual revenue of the microgrid cluster in Scenario 2 increases significantly, indicating that the cooperative game effectively reduces the dependence on shared energy storage, promotes the consumption of new energy sources, and maximizes the overall system revenue.

[0044] In this embodiment, the energy curtailment of each microgrid is as follows: Figure 6 As shown in the figure, scenario 2 has less energy curtailment than scenario 1, indicating that the method of the present invention can reduce the energy curtailment assessment cost of microgrids and better promote the consumption of new energy.

[0045] In summary, by applying the optimized configuration method for shared energy storage in microgrid clusters based on hybrid game theory proposed in this invention, an optimized configuration model for shared energy storage in microgrids can be constructed. Based on the leasing needs of microgrid clusters, appropriate energy storage can be rationally configured, thereby promoting the local consumption of distributed new energy sources. In terms of the economics of microgrid clusters, the costs associated with constructing shared energy storage can be reduced, and assessment costs can be lowered through leasing, thereby improving overall economic efficiency.

[0046] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any modifications, alterations, substitutions, and variations made by those skilled in the art to the above embodiments are within the scope of the present invention.

Claims

1. An optimal configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, characterized in that, Includes the following steps: The source-load data of the microgrid cluster is collected as input data; a hybrid game-theoretic microgrid cluster shared energy storage optimization configuration model is constructed, with the objective function being the minimization of the annual operating cost of the microgrid. Combined with constraints, the Stackelberg game model is used to solve for the optimal electricity price, and the Shapley value method based on demand degree is used to allocate the cost of the cooperative game of the microgrid cluster. Based on the electricity price information and the microgrid cluster leasing demand, the optimal configuration scheme is obtained through the Cplex solver. The hybrid game theory microgrid cluster shared energy storage optimization configuration model is as follows: ； In the formula, To minimize the annual cost of microgrids, It is the cost of microgrid leasing. It is the power exchange cost after the microgrids form a cooperative network. It is the cost of assessing the deviation in microgrid power generation prediction. It is the assessment cost of wind and solar power curtailment in microgrids. It is the revenue from selling electricity through the microgrid. It is the benefit of interaction between the microgrid and the power grid; Microgrid leasing costs are calculated using the following formula: ; in, It is the cost of microgrid leasing. It is the price for renting shared energy storage capacity during time period t; It refers to the shared energy storage capacity leased during time period t; The price for shared energy storage capacity during time period t; It is the charging power of shared energy storage leased during time period t; H is the discharge power of the shared energy storage leased during time period t, where H is the total number of days in a year and T is the time period. The power exchange cost after the microgrids form a cooperative network is calculated using the following formula: ； in, It is the power exchange cost after the microgrids form a cooperative network. and These represent the power prices that a microgrid buys or sells to other microgrids during time period t. and These represent the power purchased or sold from other microgrids during time period t; The cost of assessing the deviation in microgrid power generation forecasts is calculated using the following formula: ； ； In the formula, It is the cost of assessing the deviation in microgrid power generation prediction. It is the price for assessing prediction deviations; Power needs to be assessed during time period t; It is the deviation at time t; This is the predicted power output of the microgrid during time period t; The actual power output of the microgrid during time period t; It is the price for assessing prediction deviations; Power needs to be assessed during time period t; The cost of wind and solar power curtailment in microgrids is calculated using the following formula: ； In the formula, It is the assessment cost of wind and solar power curtailment in microgrids. It is the assessment price for wind and solar power curtailment; It is the power of wind and solar power curtailed at time t; The revenue from selling electricity through a microgrid is calculated using the following formula: ； In the formula, It is the revenue from selling electricity through the microgrid. It is the price of the microgrid's load sold during time period t; It is the fixed load of the microgrid during time period t; It is the mobile load of the microgrid during time period t; The benefits of interaction between the microgrid and the power grid are calculated using the following formula: ； In the formula, It is the benefit of interaction between the microgrid and the power grid. and These are the electricity purchase and sale prices from the grid during time period t; and These represent the power purchased and sold from the power grid during time period t.

2. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 1, is characterized in that: The source-load data of the microgrid cluster includes power generation data from wind turbines and photovoltaics, as well as load data that the microgrid needs to handle.

3. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 1, is characterized in that: The constraints include power balance constraints, leased capacity constraints, transferable load constraints, energy curtailment constraints, and charge / discharge mutual exclusion constraints.

4. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 3, is characterized in that... The power balance constraint is: ； in, The actual power output of the microgrid during time period t. and These are the power purchased and sold from the grid during time period t. It is the charging power of shared energy storage during the t-period rental period. and These represent the power purchased or sold from other microgrids during time period t. It is the fixed load of the microgrid during time period t. The mobile load of the microgrid during time period t It is the power of wind and solar power curtailment at time t.

5. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 3, is characterized in that... The leased capacity constraint is: ； In the formula, It is the initial leased capacity of a scheduling cycle, i.e., the leased capacity at time 0. It is the leased capacity at the end of the scheduling cycle, i.e., 24 hours. and These are the maximum and minimum values ​​of the leased capacity, respectively. It is the shared energy storage capacity of the microgrid leased at time t. It refers to the shared energy storage capacity leased by the microgrid at time t-1. It is an interval period. It is the shared energy storage charging and discharging efficiency. and It refers to the shared energy storage charging and discharging power.

6. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 3, is characterized in that... The transferable load constraint is: ； in, It is the fixed load of the microgrid during time period t. It is the mobile load of the microgrid during time period t.

7. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 3, is characterized in that... The energy release constraint is: ； in, It is the power of wind and solar power curtailed at time t. The actual power output of the microgrid during time period t. It is the fixed load of the microgrid during time period t. The mobile load of the microgrid during time period t It is the charging power of shared energy storage leased during time period t.

8. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 3, is characterized in that... The charge / discharge mutual exclusion constraint is: ； In the formula, and These represent the power purchased or sold from other microgrids during time period t. and These are the upper and lower limits of the power that a microgrid sells to other microgrids; and These are the upper and lower limits of the power that a microgrid can purchase from other microgrids; and It is a binary variable.

9. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 1, is characterized in that: The Stackelberg game model is as follows: ； Where G is the total set of game frameworks, L is the leader strategy, F is the follower strategy, R represents the payoff, and L SESO It is a leader-shared energy storage strategy, F MG For follower microgrid strategies, R SESO It is the revenue from shared energy storage, R MG It is the revenue of the microgrid, and the set of participants is represented as ; The leader SESO's strategy is based on 24-hour rental prices and the allocation of rental capacity and arbitrage capacity; the microgrid's strategy is based on rental demand at any given time, and the revenue of each participant is defined by the objective function in the microgrid.

10. The optimized configuration method for shared energy storage in a microgrid cluster based on hybrid game theory, as described in claim 1, is characterized in that: In the cost allocation of microgrid cluster cooperative game using the demand-based Shapley value method, the allocated cost is: ； Among them, I i Costs allocated to each microgrid The price for shared energy storage capacity leased during time period t. Ω represents the price of shared energy storage capacity leased during time period t, Ω represents the total leased capacity of each microgrid during each time period, and Ψ represents the total leased capacity of each microgrid during each time period. C Leasing charging power to each microgrid at different times, Ψ D For each microgrid, the rental discharge power is provided at each time period. C For microgrid i to send power to other microgrids, ζ D For microgrid j to send power to other microgrids; ； ； ； ； ； in, It is a time period for leasing shared energy storage capacity. It is the charging power of shared energy storage during the t-period rental period. It is the discharge power of the shared energy storage leased during time period t. and These represent the power purchased or sold from other microgrids during time period t.

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