Shared energy storage community energy management method based on nested generalized game

Abstract

The invention provides a shared energy storage community energy management method based on a nested generalized game, and the method comprises the steps: carrying out the modeling of shared energy storage assisted energy transaction interaction into a nested generalized game model, and representing that user subjects in a community participate in two layers of games at the same time: constructing a second layer of games for local energy transaction interaction competition, the first-layer game model is a robust generalized game model based on system uncertainty, receives a shared energy storage strategy from the first-layer game, identifies an uncertain scene causing the most unfavorable consequence according to operation optimization responses of all users, and feeds back the worst scene to the first-layer game; providing an equivalent three-layer optimization model and a corresponding decomposition method for the nested generalized game model, and decomposing the original model into a main problem and a sub-problem; a semi-distributed solving algorithm and an overall iteration solving process based on a column sum constraint generation algorithm are provided for the main problem and the sub-problems, and energy management of the shared energy storage community is achieved. The method has wide application potential.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage, and in particular relates to a shared energy storage community energy management method based on nested generalized games. Background Art

[0002] For small-scale users with limited budgets, shared energy storage systems (CES) provide a more cost-effective solution than investing in independent energy storage. In the actual application of distribution networks, shared energy storage is common in community energy storage systems, and energy storage devices can serve multiple users at the same time. In the existing technology, shared energy storage is allocated to each user according to the initially determined capacity, and then the user independently manages the allocated energy storage capacity. Or an operator manages the entire system to allocate capacity and energy for shared energy storage. With the popularity of renewable energy (RES), more and more prosumers can profit from sharing surplus energy in the community, and local energy sharing and trading have received widespread attention. Combining local energy sharing with shared energy storage management, peak-valley arbitrage can be achieved by coordinating renewable energy generation, adjusting energy storage charging and discharging strategies, and flexibly arbitrage electricity loads, bringing more benefits to small-scale decentralized users and prosumers. In the existing technology, cloud energy storage is also used to support multiple residents in the local market, and the alternating direction multiplier method (ADMM) is used to solve the energy management optimization model. Peer-to-peer (P2P) transactions are also considered in the prior art, and the economic optimization scheduling of microgrid clusters taking into account shared energy storage is studied.

[0003] However, existing studies have failed to fully consider the mutual coupling effects brought about by shared energy storage, local energy sharing and complex nonlinear power flows, or have only treated them as simple power balance constraints; secondly, existing studies have failed to exhaustively characterize the different levels of competition among community users, such as the competition for shared energy storage resources and the interactive competition for local energy sharing considering system uncertainty.

[0004] Although some studies have considered the mutual influence of shared energy storage and local energy trading in distribution networks, there are still some challenges to be addressed. First, there are two different levels of competition among all consumers / users, including resource competition for common energy storage utilization and interactive competition for local energy trading, and system uncertainty is also inevitable in these competitions. Second, the coupling effects caused by shared energy storage, local energy sharing, and complex nonlinear power flows are not considered or are only regarded as simple power balance constraints. These settings will lead to non-negligible errors in the optimization process, and more detailed coupling characteristics should be involved in operational modeling. In addition, fairness plays an important role in the management and operation of shared energy storage, but the guarantee of fairness is often based on sacrificing user privacy. Therefore, in the multi-agent distribution system embedded with shared energy storage, the trade-off between fairness and user privacy is still a difficult problem and needs further exploration. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a shared energy storage community energy management method based on nested generalized games. Based on shared energy storage to assist local energy interactive trading, while considering the bilateral uncertainty of load demand and photovoltaic output during system operation, the coupling influence between multiple subjects is taken into account to determine the shared energy storage charging and discharging strategy and the local energy trading strategy; and a semi-distributed solution algorithm is designed under the framework of the column and constraint generation algorithm to solve the main problem and sub-problems of the generalized nested game.

[0006] To achieve the above object, the present invention adopts the following technical solution: A method for energy management of a shared energy storage community based on nested generalized games comprises the following steps: Step 1: Model the energy trading interaction assisted by shared energy storage as a nested generalized game model, which represents that the user subjects in the community participate in two levels of games at the same time: the first level of the game determines the shared energy storage strategy, and the second level is the local energy trading interaction competition; Step 2: Construct a second-layer game for the interactive competition of local energy transactions. It is a robust generalized game model based on system uncertainty. It receives the shared energy storage strategy from the first-layer game, optimizes the response based on the operation of all users, identifies the uncertain scenarios that lead to the most unfavorable consequences, and feeds back the worst scenario to the first-layer game. Step 3: Propose an equivalent three-layer optimization model and corresponding decomposition method for the nested generalized game model, and decompose the original model into a main problem and sub-problems; Step 4: A semi-distributed solution algorithm is proposed for the main problem and sub-problems, as well as an overall iterative solution process based on the column and constraint generation algorithm to realize energy management of the shared energy storage community.

[0007] Furthermore, in step 1, the nested generalized game model is as follows: The objective function of each participant depends on the local variable ,in, They correspond to the movable load, energy storage charging, energy storage discharging, local energy trading, and the amount of electricity injected from the public grid of subject i in time period h; the coupling constraints include power flow constraints, local trading balance constraints, and shared storage balance constraints, and are also affected by the strategies of other participants The impact of is the strategy of the ith participant, n is the total number of participants; the collective strategy of all entities is recorded as .

[0008] Furthermore, the optimization goal of each subject is to maximize its benefits or minimize its costs. Considering the mutual influence reflected by the coupling constraints, the first-level game is modeled as a generalized game, as shown in the following formula: (1) in, To consider other participants The feasible domain of the i-th agent of the strategy, By solving the following robust generalized game model reflecting local energy transactions: ; (2) in, is the uncertainty set for all time periods, , represent the system uncertainty caused by PV output and load demand, respectively, , is the set of game participants; the cost of subject i is expressed as ,in, is the cost of electricity purchased by entity i from the large power grid, is the revenue from local energy trading, is the cost of charging and discharging shared energy storage, To share the operating costs of energy storage.

[0009] Furthermore, the local constraints of the nested generalized game model include: 1) Node power conservation: (3) in, is the net load demand of node i, is the node load demand, is the amount of energy storage charge and discharge, is the photovoltaic output of node i, is the photovoltaic output value, is the grid injected power; 2) Load demand constraints: Node load requirements Including translatable loads and fixed load : (4) (5) (6) in, is the shiftable load demand of user i at time h, is the range of values ​​of the translatable load, It is a selectable period for the load to be translated. is the total load power that can be transferred by user i; 3) Energy storage utilization constraints: For user i, the energy storage utilization constraints are as follows: Formula (7) represents the upper limit of the power of energy storage charging and discharging, and Formula (8) indicates that only one of charging or discharging can occur; (7) (8) in, The upper limit of energy storage charging and discharging power; For shared energy storage, the constraints are as follows: (9) (10) (11) in, is the shared energy storage charge and discharge capacity, The amount of energy stored for shared energy storage, is the self-loss rate, charging efficiency and discharging efficiency, [ The upper and lower limits of the power storage capacity; 4) Individual rationality constraints: Assume that users only conduct local energy trading and energy storage charging when the photovoltaic output is sufficient, and conduct local energy trading and energy storage discharging at other times: (12) (13).

[0010] Furthermore, the coupling constraints of the nested generalized game model include: 1) Power flow constraints: Formula (14) to Formula (19) represent the system power flow constraints, where Formula (14) is the node voltage constraint, Formula (15) is the line power constraint, Formula (16) is the grid injection power constraint, Formula (17)-Formula (18) are node injection power expressions, and Formula (19) is the line power expression: (14) (15) (16) (17) (18) (19) in, is the node voltage value, [ are the upper and lower limits of the voltage during operation, is the collection of lines in the system, is the power of the line from node i to j, [ are the upper and lower limits of line power, is the reactive power injected into the grid, / The upper limit of active / reactive power output / injection to the grid, Inject network active / reactive power into nodes, The power angle from node i to j is, is the admittance parameter of the line from node i to j; 2) Local energy trading balance constraints: The local energy trading balance constraint represents the balance between selling power and buying power: (20) 3) Shared energy storage balance constraints: The shared energy storage balance constraint means that the total amount of charging / discharging of users participating in the shared energy storage project is equal to the amount of shared energy storage charging / discharging: (twenty one).

[0011] Furthermore, the step 3 comprises: Through the penalty representation of coupling constraints, the proposed nested generalized game model, namely, equation (1) and equation (2), is reconstructed into a three-level optimization problem. Each user needs to solve the following optimization problem: (twenty two) in, is the local feasible region of the i-th user, which is used to characterize the penalty term of the coupling constraint state. The calculation method is as follows: (twenty three) (twenty four) (25) (26) (27) (28) in, is the vector of all node voltages, is the vector of all line powers; the four penalty terms correspond to voltage constraints, line power constraints, shared energy storage constraints, and local energy trading constraints, and the weights are expressed as ; The equivalent model in formula (22) is further decomposed into the main problem and sub-problems for further solution.

[0012] Furthermore, the main problem is expressed as: (29) The sub-problem is expressed as: (30) in, It can be regarded as an auxiliary variable, and k is the uncertain scene number.

[0013] Furthermore, the step 4 comprises: A semi-distributed iterative solution algorithm is proposed for the main problem and sub-problems, in which the penalty term at time t is linearly expressed based on the coupling constraint as follows: ; ; ; .

[0014] Furthermore, for the main problem, the semi-distributed algorithm iterative solution algorithm includes: Step 4.1. Input: Known parameters , , , let t=1; Step 4.2. Initialize user subject variables; Step 4.3. When the convergence condition is not met, execute: t=t+1, and the system operator performs power flow calculation, determines the penalty term value and Jacobian matrix, and sends them to all users; Step 4.4. For each user, execute: Each of , Execution: Solve the optimization model in the sub-problem; Step 4.6. Keep the user subject variable results and uncertainty scenarios corresponding to the maximum value of the objective function; Step 4.7. For each user, execute: ; Step 4.8. Output the shared energy storage utilization strategy results.

[0015] Beneficial effects: The present invention takes into account the bilateral uncertainty of load demand and photovoltaic output during system operation and the coupling influence between multiple subjects to determine the shared energy storage charging and discharging strategy and the local energy trading strategy. Based on the semi-distributed solution algorithm under the proposed column and constraint method framework, the user cost optimization results of the 10-node community test system are as follows: Figure 2 and Figure 3 As shown, it can be seen that the overall solution process reaches convergence after 2 iterations. The present invention provides an effective method for solving the problem of energy sharing and fair use of shared energy storage within the community, which can improve energy utilization efficiency, promote the development of green energy, and meet the current needs of environmental protection and energy conservation. It not only enriches the local energy sharing framework based on shared energy storage in theory, but also provides an effective practical solution with broad application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a diagram of the multi-agent interaction framework for the shared energy storage community; Figure 2 Cost-optimized results for users; Figure 3 Energy storage charging and discharging strategy results diagram; where (a) is the energy storage charging policy and (b) is the energy storage discharging policy. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0018] The present invention proposes a shared energy storage community energy management method based on nested generalized games, which is a method to strategically enhance local energy interactive transactions by utilizing public shared energy storage among all participants and solves the challenges brought by the inherent uncertainty of the system. The shared energy storage control center also acts as a central coordinator for local energy transactions, serving as a natural platform for energy transactions. Figure 1 The energy flow state (solid line) of this shared energy storage-assisted local energy trading scheme is shown, including power exchange between the community and the grid, local energy trading between all users in the community, and power exchange between users and shared energy storage.

[0019] Since there is competition for shared energy storage resource utilization and local energy trading interaction among users in the community, the shared energy storage-assisted energy trading interaction scheme is modeled as a nested generalized game, and the user entities in the community participate in multiple games at the same time. In the nested generalized game proposed in this invention, the first-level game refers to the competition for shared energy storage resource utilization ( Figure 1 The shared energy storage (CES) in the system aims to consider the worst operating conditions caused by system uncertainty and determine the day-ahead charging and discharging strategy of shared energy storage. The second-layer game is designed for interactive competition in local energy transactions and is expressed as a robust game model based on system uncertainty ( Figure 1 The goal of this robust game is to identify the uncertain scenarios that lead to the most adverse consequences based on the operational responses of all users (1st user to nth user) and to feed back the worst scenarios to the first layer. It can be seen that the generalized game at two levels is realized through multilateral information interaction ( Figure 1 The two layers (dashed line) influence each other, and the Robin game is embedded in the shared energy storage utilization game on the first layer. The Robin game needs to convey the worst case scenario to the shared energy storage and receive the shared energy storage strategy from the first layer game.

[0020] A method for energy management of a shared energy storage community based on nested generalized games of the present invention comprises the following steps: Step 1: Model the energy trading interaction assisted by shared energy storage as a nested generalized game model, which represents that the user subjects in the community participate in two levels of games at the same time: the first level of the game determines the shared energy storage strategy, and the second level is the local energy trading interaction competition; Step 2: Construct a second-layer game for the interactive competition of local energy transactions. It is a robust generalized game model based on system uncertainty. It receives the shared energy storage strategy from the first-layer game, optimizes the response based on the operation of all users, identifies the uncertain scenarios that lead to the most unfavorable consequences, and feeds back the worst scenario to the first-layer game. Step 3: Propose an equivalent three-layer optimization model and corresponding decomposition method for the nested generalized game model, and decompose the original model into a main problem and sub-problems; Step 4: A semi-distributed solution algorithm is proposed for the main problem and sub-problems, as well as an overall iterative solution process based on the column and constraint generation algorithm to realize energy management of the shared energy storage community.

[0021] Specifically, in step 1, the nested generalized game model is: The objective function of each participant depends on the local variable , They correspond to the movable load, energy storage charging, energy storage discharging, local energy trading and the amount of electricity injected from the public grid of subject i in period h. Since the coupling constraints include power flow constraints, local trading balance constraints and shared storage balance constraints, they are also affected by the strategies of other participants. The impact of is the strategy of the ith participant, and n is the total number of participants). The collective strategy of all entities is recorded as It is worth noting that the power flow constraint is expressed as an inequality constraint, while the local energy trading conservation and shared energy storage balance constraints are expressed as equality constraints. The optimization goal of each subject is to maximize its benefits or minimize its costs. Considering the mutual influence reflected by the coupling constraints, the first-level game is modeled as a generalized game (as shown in expression (1)).

[0022] (1) in, To consider other participants The feasible domain of the i-th agent of the strategy. It is obtained by solving the following robust generalized game (second-level game) that reflects local energy trading.

[0023] ; (2) in, is the uncertainty set for all time periods ( ), respectively representing the system uncertainty caused by PV output and load demand, , is the set of game participants. The cost of subject i can be expressed as ,in, is the cost of electricity purchased by entity i from the large power grid, is the revenue from local energy trading, is the cost of charging and discharging shared energy storage, To share the operating costs of energy storage.

[0024] (2) The local constraints of the nested generalized game model are: 1) Node power conservation: (3) in, is the net load demand of node i, is the node load demand, is the amount of energy storage charge and discharge, is the photovoltaic output of node i, is the photovoltaic output value, is the grid injected power.

[0025] 2) Load demand constraints: Node load requirements Including translatable loads and fixed load : (4) (5) (6) in, is the shiftable load demand of user i at time h, is the range of values ​​of the translatable load, It is a selectable period for the load to be translated. is the total load power that can be transferred by user i.

[0026] 3) Energy storage utilization constraints: For user i, the energy storage utilization constraints are as follows: Formula (7) represents the upper limit of the power of energy storage charging and discharging, and Formula (8) indicates that only one of charging or discharging can occur.

[0027] (7) (8) in, is the upper limit of the energy storage charging and discharging power. For shared energy storage, the constraints are as follows: (9) (10) (11) in, is the shared energy storage charge and discharge capacity, The amount of energy stored for shared energy storage, is the self-loss rate, charging efficiency and discharging efficiency, [ The upper and lower limits of the power storage capacity.

[0028] 4) Individual rationality constraints: It is assumed that users only conduct local energy trading (selling) and energy storage charging when photovoltaic output is sufficient, and conduct local energy trading (buying) and energy storage discharging at other times.

[0029] (12) (13) (3) The coupling constraints of the nested generalized game model are: 1) Power flow constraints: Formula (14) to Formula (19) represent the system power flow constraints, where Formula (14) is the node voltage constraint, Formula (15) is the line power constraint, Formula (16) is the grid injection power constraint, Formula (17)-Formula (18) are node injection power expressions, and Formula (19) is the line power expression.

[0030] (14) (15) (16) (17) (18) (19) in, is the node voltage value, [ are the upper and lower limits of the voltage during operation, is the collection of lines in the system, is the power of the line from node i to j, [ are the upper and lower limits of line power, is the reactive power injected into the grid, / The upper limit of active / reactive power output / injection to the grid, Inject network active / reactive power into nodes, The power angle from node i to j is, is the admittance parameter of the line from node i to j.

[0031] 2) Local energy trading balance constraints: The local energy trading balance constraint represents the balance between sell power and buy power.

[0032] (20) 3) Shared energy storage balance constraints: The shared energy storage balancing constraint means that the total charging / discharging amount of users participating in the shared energy storage project is equal to the shared energy storage charging / discharging amount.

[0033] (twenty one) Specifically, the step 3 includes: Through the penalty representation of coupling constraints, the proposed generalized nested game model (expression (1) and expression (2)) is reconstructed into a three-level optimization problem. Each user needs to solve the following optimization problem: (twenty two) in, is the local feasible region of the i-th user, which is used to characterize the penalty term of the coupling constraint state. The calculation method is as follows: (twenty three) (twenty four) (25) (26) (27) (28) in, is the vector of all node voltages, is the vector of all line powers. The four penalty terms correspond to voltage constraints, line power constraints, shared energy storage constraints, and local energy trading constraints, and the weights are expressed as The equivalent model in formula (22) can be further decomposed into the main problem and sub-problems for further solution.

[0034] The main problem is expressed as: (29) The sub-problem is expressed as: (30) in, It can be regarded as an auxiliary variable, and k is the uncertain scene number.

[0035] Specifically, step 4 includes: A semi-distributed iterative solution algorithm is proposed for the main problem and sub-problems, where the penalty term at time t can be linearly expressed based on the coupling constraints as follows: ; ; ; ; Taking the main problem as an example, the semi-distributed algorithm iterative solution algorithm process is as follows: 1. Input: , , Equal known parameters, let t=1; 2. Initialize user subject variables; When the convergence condition is not met, execute: 3. t=t+1: The system operator performs power flow calculations, determines the penalty term value and Jacobian matrix, and sends them to all users; 4. For each user, execute: 5. For Each of ,implement: 6. Solve the optimization model in the sub-problem; 7. Keep the user subject variable results and uncertainty scenarios corresponding to the maximum value of the objective function 8. For each user, execute: 9. ; 10. Output: Shared energy storage utilization strategy results.

[0036] Example: The specific implementation steps of the present invention are the iterative solution process of the overall problem: 1) Carry out mathematical modeling and basic parameter setting for the shared energy storage community to be optimized.

[0037] 2) Set the optimization target lower limit , upper limit , set the uncertain set to empty and the number of iterations to iter=1.

[0038] 3) Solve the optimization main problem and obtain the optimal value of the objective function And the corresponding optimal solution , using the solution obtained renew The value of .

[0039] 4) Planning solution based on the solution of the main problem , solve the optimization sub-problem and obtain the optimal value of the objective function (including the objective function value in the planning stage) and the corresponding uncertain scenario solution results ,use renew The value of .

[0040] 5) If (indicating that the difference between LB and UB is small enough), the solution process ends and goes to 7), otherwise proceed to the next step.

[0041] 6) Add to collection In the main optimization problem, Corresponding variables, add corresponding constraints, let iter=iter+1 and go to 3).

[0042] 7) Summarize and analyze the optimization results.

[0043] The present invention is applied to a 10-node community test system using programming software. The optimization solver used is Gurobi 9.5, and the computer processor is Intel Core-i7 @2.5 GHz. Based on typical daily data, the final shared energy storage capacity is configured to be 2MWh, and the energy storage power is 0.35MW. According to the calculation results, the verification of the test system reached convergence after two rounds of iterations ( Figure 2 ). According to the optimization results of the energy storage charging and discharging strategy, it can be seen that the energy storage is mainly charged during the peak photovoltaic output period and discharged during the peak load period ( Figure 3 , where (a) is the energy storage charging policy and (b) is the energy storage discharging policy).

Claims

1. A method for energy management of a shared energy storage community based on nested generalized games, characterized in that: The steps include: Step 1: Model the energy trading interaction assisted by shared energy storage as a nested generalized game model, which represents that the user subjects in the community participate in two levels of games at the same time: the first level of the game determines the shared energy storage strategy, and the second level is the local energy trading interaction competition; Step 2: Construct a second-layer game for the interactive competition of local energy transactions. It is a robust generalized game model based on system uncertainty. It receives the shared energy storage strategy from the first-layer game, optimizes the response based on the operation of all users, identifies the uncertain scenarios that lead to the most unfavorable consequences, and feeds back the worst scenario to the first-layer game. Step 3: Propose an equivalent three-layer optimization model and corresponding decomposition method for the nested generalized game model, and decompose the original model into a main problem and sub-problems; Step 4: A semi-distributed solution algorithm is proposed for the main problem and sub-problems, as well as an overall iterative solution process based on the column and constraint generation algorithm to realize energy management of the shared energy storage community.

2. The energy management method for a shared energy storage community based on nested generalized games according to claim 1, characterized in that: In step 1, the nested generalized game model is as follows: The objective function of each participant depends on the local variable ,in, They correspond to the movable load, energy storage charging, energy storage discharging, local energy trading, and the amount of electricity injected from the public grid of subject i in time period h; the coupling constraints include power flow constraints, local trading balance constraints, and shared storage balance constraints, and are also affected by the strategies of other participants The impact of is the strategy of the ith participant, n is the total number of participants; the collective strategy of all entities is recorded as .

3. The energy management method for a shared energy storage community based on nested generalized games according to claim 2 is characterized in that: The optimization goal of each subject is to maximize its benefits or minimize its costs. Considering the mutual influence reflected by the coupling constraints, the first-level game is modeled as a generalized game, as shown in the following formula: （1） in, To consider other participants The feasible domain of the i-th agent of the strategy, By solving the following robust generalized game model reflecting local energy transactions: ； （2） in, is the uncertainty set for all time periods, , represent the system uncertainty caused by PV output and load demand, respectively, , is the set of game participants; the cost of subject i is expressed as ,in, is the cost of entity i buying electricity from the large power grid, is the revenue from local energy trading, is the cost of charging and discharging shared energy storage, To share the operating costs of energy storage.

4. The energy management method for a shared energy storage community based on nested generalized games according to claim 1, characterized in that: The local constraints of the nested generalized game model include: 1) Node power conservation: （3） in, is the net load demand of node i, is the node load demand, is the amount of energy storage charge and discharge, is the photovoltaic output of node i, is the photovoltaic output value, is the grid injected power; 2) Load demand constraints: Node load requirements Including translatable loads and fixed load : （4） （5） （6） in, is the shiftable load demand of user i at time h, is the range of values ​​of the translatable load, It is a selectable period for the load to be translated. is the total load power that can be transferred by user i; 3) Energy storage utilization constraints: For user i, the energy storage utilization constraints are as follows: Formula (7) represents the upper limit of the power of energy storage charging and discharging, and Formula (8) indicates that only one of charging or discharging can occur; （7） （8） in, The upper limit of energy storage charging and discharging power; For shared energy storage, the constraints are as follows: （9） （10） （11） in, is the shared energy storage charge and discharge capacity, The amount of energy stored for shared energy storage, is the self-loss rate, charging efficiency and discharging efficiency, [ The upper and lower limits of the power storage capacity; 4) Individual rationality constraints: Assume that users only conduct local energy trading and energy storage charging when the photovoltaic output is sufficient, and conduct local energy trading and energy storage discharging at other times: （12） （13）。 5. The energy management method for a shared energy storage community based on nested generalized games according to claim 1, characterized in that: The coupling constraints of the nested generalized game model include: 1) Power flow constraints: Formula (14) to Formula (19) represent the system power flow constraints, where Formula (14) is the node voltage constraint, Formula (15) is the line power constraint, Formula (16) is the grid injection power constraint, Formula (17)-Formula (18) are node injection power expressions, and Formula (19) is the line power expression: （14） （15） （16） （17） （18） （19） in, is the node voltage value, [ are the upper and lower limits of the voltage during operation, is the collection of lines in the system, is the power of the line from node i to j, [ are the upper and lower limits of line power, is the reactive power injected into the grid, / The upper limit of active / reactive power output / injection to the grid, Inject network active / reactive power into nodes, The power angle from node i to j is, is the admittance parameter of the line from node i to j; 2) Local energy trading balance constraints: The local energy trading balance constraint represents the balance between selling power and buying power: （20） 3) Shared energy storage balance constraints: The shared energy storage balance constraint means that the total amount of charging / discharging of users participating in the shared energy storage project is equal to the amount of shared energy storage charging / discharging: （21）。 6. The energy management method for a shared energy storage community based on nested generalized games according to claim 3 is characterized in that: The step 3 comprises: Through the penalty representation of coupling constraints, the proposed nested generalized game model, namely, equation (1) and equation (2), is reconstructed into a three-level optimization problem. Each user needs to solve the following optimization problem: （22） in, is the local feasible region of the i-th user, which is used to characterize the penalty term of the coupling constraint state. The calculation method is as follows: （23） （24） （25） （26） （27） （28） in, is the vector of all node voltages, is the vector of all line powers; the four penalty terms correspond to voltage constraints, line power constraints, shared energy storage constraints, and local energy trading constraints, and the weights are expressed as ; The equivalent model in formula (22) is further decomposed into the main problem and sub-problems for further solution.

7. The energy management method for a shared energy storage community based on nested generalized games according to claim 6, characterized in that: The main problem is expressed as: （29） The sub-problem is expressed as: （30） in, It can be regarded as an auxiliary variable, and k is the uncertain scene number.

8. The energy management method for a shared energy storage community based on nested generalized games according to claim 7, characterized in that: The step 4 comprises: A semi-distributed iterative solution algorithm is proposed for the main problem and sub-problems, in which the penalty term at time t is linearly expressed based on the coupling constraint as follows: ； ； ； 。 9. The energy management method for a shared energy storage community based on nested generalized games according to claim 8, characterized in that: For the main problem, the semi-distributed algorithm iterative solution algorithm includes: Step 4.

1. Input: Known parameters , , , let t=1; Step 4.

2. Initialize user subject variables; Step 4.

3. When the convergence condition is not met, execute: t=t+1, and the system operator performs power flow calculation, determines the penalty term value and Jacobian matrix, and sends them to all users; Step 4.

4. For each user, execute: Each of , Execution: Solve the optimization model in the sub-problem; Step 4.

6. Keep the user subject variable results and uncertainty scenarios corresponding to the maximum value of the objective function; Step 4.

7. For each user, execute: ； Step 4.

8. Output the shared energy storage utilization strategy results.

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