Capacity configuration income analysis method for shared energy storage of new energy field group alliance user side

Abstract

The invention discloses a capacity configuration income analysis method for shared energy storage at a new energy field group alliance user side. The method comprises the following steps: step S1, establishing a shared energy storage capacity two-stage economy analysis architecture; s2, planning a transaction strategy of joint marketing participating in the spot market; s3, balancing, optimizing and deciding an income distribution strategy in the energy storage joint operator; s4, a mixed integer linear programming method is adopted to solve the total income of the joint operator participating in the market transaction; and solving the Nash bargaining equilibrium model, deciding internal virtual interaction electricity prices, and forming a profit distribution pattern through the internal virtual interaction electricity prices.

Description

Technical Field

[0001] The present invention relates to a capacity configuration benefit analysis method, and in particular to a capacity configuration benefit analysis method for user-side shared energy storage of a new energy cluster alliance. Background Art

[0002] With the rapid development of new power systems dominated by new energy, the power market system has been further improved. The diversified exploration and development of new energy storage is of great significance to ensure the safe and reliable energy supply of the system and promote the consumption of new energy. Shared energy storage is a typical application of sharing thinking in the energy system, which can promote the efficient application of energy storage resources and help the low-carbon transformation of the power system. On the one hand, in order to alleviate the peak-shaving pressure of the new power system, my country encourages new energy sites to build or purchase energy storage peak-shaving capabilities; on the other hand, with the maturity and progress of energy storage technology, user-side energy storage has gradually formed a certain scale and developed a commercial scenario of shared application. For example, the combination of shared energy storage with industrial loads, data centers, etc. can optimize the user's electricity curve and improve the user's economy. Especially in the market-oriented environment of power trading, the price peak and valley fluctuations in the spot market have intensified, further promoting the shared energy storage to gradually form a good development trend among industrial and commercial users. Under the above background, considering the synergy of source, grid, load and storage, practicing the sharing win-win thinking, the user-side shared energy storage and the new energy power station cluster are jointly operated to form a new form of new power system development. New energy power stations and shared energy storage operators are independent market players with inconsistent interests. Especially in a complex market environment, a reasonable assessment and analysis of the economic feasibility of user-side energy storage resource aggregation as a joint venture between shared energy storage and new energy stations will be conducive to the sustainable development of the new shared energy storage business model.

[0003] Existing research focuses on introducing game theory models to simulate and analyze the transaction price formation and profit distribution among multiple stakeholders. Among them, the master-slave game and Nash bargaining methods are more common: Literature: Lu Mingfang, Li Xianshan, Li Fei, et al. Photovoltaic power station cluster energy storage leasing configuration strategy based on double-layer game optimization [J]. Proceedings of the CSEE, 2022, 42(16): 5887-5898, based on the master-slave game, a market-based pricing strategy for energy storage and photovoltaic clusters was established; Literature: Shuai Xuanyue, Wang Xiuli, Huang Jing. Optimal configuration of shared energy storage capacity under the interconnection of multi-regional integrated energy systems [J]. Global Energy Internet, 2021, 4(04): 382–392, using the Nash negotiation model to solve the payment negotiation problem between shared energy storage systems and multiple integrated energy systems.

[0004] Existing technologies mostly consider the configuration decision of energy storage capacity in application scenarios where independent energy storage resources provide shared energy storage on the source side or provide shared energy storage on the load side. For new application scenarios where user-side energy storage resources are aggregated to form a joint venture of shared energy storage and new energy station groups, existing technologies cannot effectively support the relevant analysis of achieving reasonable profit distribution among all parties within the joint venture to obtain effective drive. Summary of the invention

[0005] In order to solve the shortcomings of the above-mentioned technology, the present invention provides a capacity configuration benefit analysis method for shared energy storage on the user side of a new energy cluster alliance, so as to solve the benefit distribution problem when load-side energy storage aggregation is used as shared energy storage and cooperates with new energy clusters.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a capacity configuration benefit analysis method for shared energy storage on the user side of a new energy cluster alliance, comprising the following steps:

[0007] Step S1: Establish a two-stage economic analysis framework for sharing energy storage capacity under the joint cooperation mechanism between new energy station clusters and user-side energy storage;

[0008] Step S2: for phase 1, simulate the trading strategy of new energy and user-side shared energy storage participating in the spot market based on mixed integer linear optimization programming;

[0009] Step S3: for phase 2, the profit distribution strategy within the new energy and user-side shared energy storage joint venture is determined based on the Nash bargaining equilibrium optimization decision;

[0010] Step S4: For phase 1, a mixed integer linear programming method is used to solve the problem, and the total revenue of the new energy station and the user-side shared energy storage consortium participating in the market transaction is simulated and calculated;

[0011] For the second stage, the Nash bargaining equilibrium model of the new energy and shared energy storage joint venture is solved based on the ADMM algorithm, and the internal virtual interactive electricity price is decided, and the profit distribution pattern is formed through the internal virtual interactive electricity price.

[0012] Preferably, the two stages in step S1 include:

[0013] Phase 1: Simulate the participation of new energy and user-side shared energy storage in the market transaction under the electricity spot market environment, and decide on the optimal bidding strategy and shared energy storage charging and discharging strategy for the participation of new energy station groups in the market;

[0014] Phase 2: Based on the benefits of the consortium’s participation in the market obtained from the simulation in Phase 1, the Nash bargaining theory is further adopted to conduct an analysis of the internal interactive game between the new energy and user-side shared energy storage consortium, decide on the profit distribution plan for the new energy and user-side shared energy storage aggregators to achieve Nash equilibrium, and analyze the effectiveness of the economic drive of the user-side energy storage aggregators to participate in the consortium.

[0015] Preferably, in step S2, user-side energy storage resources are aggregated to form shared energy storage and are jointly operated with a group of new energy stations. The joint venture participates in the electricity spot market as a new market entity, and a market clearing model involving new energy stations + user-side shared energy storage joint ventures is established, and the market benefits of the joint venture are simulated and calculated.

[0016] Preferably, in step S2, a market clearing model is established with the goal of maximizing the social welfare of the entire system, and the objective function is as follows:

[0017]

[0018] Among them, the electricity sales revenue of the whole system is:

[0019]

[0020] Where: is the bidding price of the load in the quotation segment d in time period t; is the winning bid power of the load in the quotation segment d in time period t;

[0021] Cost expenditure of purchasing thermal power generation:

[0022]

[0023] Where: is the bidding price of thermal power unit g in the quotation period h during time period t; is the winning bid power of thermal power unit g in bid period h during time period t;

[0024] System backup cost expenditure:

[0025]

[0026] Where: They are the upper and lower spare capacities required by the system in period t respectively;

[0027] Cost expenditure for purchasing electricity generated by new energy stations:

[0028]

[0029] Where: t is the winning bid price of renewable energy at time t; P i,t is the winning bid power of new energy station i at time t.

[0030] Preferably, in step S2, the following constraints are established for the objective function:

[0031] System power balance constraints:

[0032]

[0033] Alternative constraints:

[0034]

[0035]

[0036] They are the upper and lower standby powers of thermal power units, They are the upper and lower reserve powers of the shared energy storage respectively;

[0037] Power constraints of thermal power units:

[0038]

[0039] Where: They are the upper and lower limits of the output of thermal power units respectively;

[0040] Power constraints for interaction between new energy and user-side shared energy storage:

[0041]

[0042] Where: P i,t is the winning bid power of the new energy station i at time t; The predicted output of the new energy station i at time t; and are the discharge and charging power of the new energy station i interacting with the user-side shared energy storage at time t; ν i,t is a 0-1 variable, which constrains the power purchase and sale status of the new energy station at each moment to be only one; M is a maximum value; P N The rated power of the shared energy storage on the user side;

[0043] User-side shared energy storage charge state constraints:

[0044]

[0045]

[0046]

[0047]

[0048] Where: γ t is a 0-1 variable, constraining the shared energy storage to be in one charging and discharging state at each moment; are the power purchased and sold by the new energy station cluster to the shared energy storage at time t;

[0049] is the remaining power of the shared energy storage at time t; η c and η d are the charging efficiency and discharging efficiency of shared energy storage respectively; E N is the rated capacity of the shared energy storage;

[0050] SOC min SOC is the lower limit of energy storage charge state; max It is the upper limit of energy storage charge state.

[0051] Preferably, in step S3, stage 2 is based on the total revenue obtained by the consortium participating in spot market transactions analyzed and decided in stage 1, considers the dynamic power interaction between the various entities within the consortium, constructs a Nash bargaining equilibrium game model between the new energy site group and the user-side shared energy storage aggregator, determines the charging and discharging energy interaction price between the new energy site and the load-side shared energy storage, and then calculates the revenue distribution pattern of the various entities within the consortium.

[0052] Preferably, in step S3, the maximum Nash product solution of the utility increments of each party before and after the joint venture between the new energy station and the user-side shared energy storage aggregator is used as the Nash bargaining solution, and the Nash bargaining equilibrium optimization model is as follows:

[0053]

[0054] Where: U SES Shared energy storage cooperation benefits for users, U is the aggregate cost of shared energy storage at the user side; i To maximize the benefits of cooperation for new energy stations, is the maximum profit of new energy station i when it does not participate in the cooperation;

[0055] The Nash product is converted into a logarithmic sum form using a logarithmic function. The converted Nash bargaining model is as follows:

[0056]

[0057] Where: is the interactive power that the user-side shared energy storage and the new energy station i expect to achieve in period t, and They are the initial income of the new energy station i and the shared energy storage on the user side without considering the internal virtual interactive price settlement, which is obtained by simulation calculation based on the market transaction strategy of the consortium in stage 1; δi,t It is the virtual interaction price reached between the user-side shared energy storage and the new energy station i in period t.

[0058] Preferably, in step S4, the nonlinear terms in the objective function of stage one are linearized and solved using a mixed integer linear programming method;

[0059] In the objective function of the optimization model for clearing the power grid at the next stage, there is a nonlinear term ρ t P i,t , use the strong duality theorem to perform linear transformation, and convert ρ t Equal to the dual variable λ of the power balance constraint of the lower model t , the dual problem of the lower objective function is expressed as follows:

[0060]

[0061] Linearize the dual variables in the transformation model for the strong duality theorem, is the upper limit of the quoted power of the load in the quoted segment d;

[0062] According to the strong duality theory, the optimal solution of the lower objective function is equal to the optimal solution of its dual problem. The nonlinear terms in the lower objective function are linearized:

[0063]

[0064] in, is the bidding price of the load in the quotation segment d in time period t; is the winning bid power of the load in quotation segment d in time period t.

[0065] Preferably, in step S4, in stage 2: using the alternating direction multiplier algorithm ADMM to solve the Nash bargaining game model, the Lagrange multiplier ζ is introduced i,t , penalty factor φ i,t , the augmented Lagrangian function of the Nash bargaining function is constructed as follows:

[0066]

[0067] The ADMM algorithm decomposes the augmented Lagrangian function of the above Nash bargaining function into interactive electricity price negotiation sub-problems for solution, and obtains the distributed optimization sub-problems of shared energy storage and new energy stations as follows:

[0068] A. Interactive electricity price optimization sub-problem model of new energy stations:

[0069]

[0070] B. Interactive electricity price optimization sub-problem model for shared energy storage:

[0071]

[0072] Preferably, the specific solution steps of stage 2 are as follows:

[0073] 1) Initialize various parameters and set the maximum number of iterations k max , set the initial virtual interaction price δ between the new energy station cluster and shared energy storage i,t , Initialize the Lagrange multiplier ζ i,t and penalty factor φ i,t ;

[0074] 2) Each new energy station obtains the initial interactive electricity price Solve the interactive electricity price optimization sub-problem model of the new energy station group to obtain the expected transaction price for shared energy storage

[0075] 3) Shared energy storage obtains prices from wind and solar power stations And solve the interactive electricity price optimization sub-problem model of user-side shared energy storage to obtain the expected interactive price for the new energy station

[0076] 4) According to the expected interaction price of the new energy station and the user-side shared energy storage, the Lagrange multiplier iteration is performed:

[0077]

[0078] 5) Compare whether the result reaches the convergence accuracy or the maximum number of iterations. If the convergence accuracy is not reached, return to step 2 to continue iterating; if the convergence accuracy is met, the calculation ends and the result is output.

[0079] In order to solve the problem of reasonable distribution of benefits after the user-side shared energy storage alliance new energy cluster, the present invention establishes a cost-benefit game analysis model of user-side shared energy storage of the new energy cluster alliance. First, a simulation model of the market bidding strategy of user-side shared energy storage and new energy joint ventures is constructed to optimize the market benefits of the joint venture; secondly, the benefits of the internal entities of the new energy joint user-side shared energy storage are distributed, and a new energy + user-side shared energy storage joint venture internal benefit distribution model based on Nash bargaining is constructed. The ADMM algorithm is used to optimize the internal benefit distribution of user-side shared energy storage and new energy sites, ensuring that both new energy sites and user-side shared energy storage resources in the alliance can obtain effective economic drive.

[0080] The present invention is aimed at the application scenario of aggregating shared energy storage on the user side and jointly participating in the market with a group of new energy stations: the load side aggregates energy storage to form shared energy storage and cooperates with a cluster of new energy power stations. On the one hand, new energy + shared energy storage and other market players jointly participate in market clearing; on the other hand, the new energy power station cluster shares the income with the shared energy storage on the user side. By automatically generating an operating strategy that satisfies the aggregation and joint participation of new energy stations and shared energy storage resources on the user side in the market, the operating strategy and market revenue simulation calculation of the aggregated capacity of energy storage on the user side of the new energy station joint venture can be realized, providing a decision-making reference for the participation of energy storage aggregators on the user side of the new energy station joint venture in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A two-stage economic analysis architecture diagram for configuring the shared energy storage capacity of the present invention.

[0082] Figure 2 The present invention is a flow chart for solving the overall model. DETAILED DESCRIPTION

[0083] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0084] The present invention establishes a capacity configuration benefit analysis method for user-side shared energy storage of a new energy cluster alliance, and the specific contents include:

[0085] S1: Establish a two-stage economic analysis framework for sharing energy storage capacity under the joint cooperation mechanism between new energy station clusters and user-side energy storage;

[0086] The present invention is based on the electricity spot market environment, and is aimed at the application scenario of user-side shared energy storage formed by aggregating user-side energy storage and jointly operating with new energy stations, to build a basic framework for economic analysis of user-side shared energy storage configuration capacity.

[0087] The business interaction relationship between the multiple entities in the joint venture in the power market environment includes: first, the user-side energy storage aggregator and the new energy power station cluster establish a joint venture mechanism to provide user-side shared energy storage services to the new energy station group, and the joint venture and other market entities jointly participate in the power spot market bidding; second, the new energy power station cluster and the user-side energy storage aggregator distribute the market benefits of the joint venture, and the user-side energy storage aggregator obtains the benefits of shared energy storage services.

[0088] The basic framework of the capacity economic analysis of user-side shared energy storage configuration includes two stages. Figure 1 shown.

[0089] Phase 1: Simulate the participation of new energy and user-side shared energy storage in market transactions under the electricity spot market environment, and decide on the optimal bidding strategy and shared energy storage charging and discharging strategy for the participation of new energy station groups in the market.

[0090] Phase 2: Based on the benefits of the consortium’s participation in the market obtained from the simulation in Phase 1, the Nash bargaining theory is further adopted to conduct an analysis of the internal interactive game between the new energy and user-side shared energy storage consortium, decide on the profit distribution plan for the new energy and user-side shared energy storage aggregators to achieve Nash equilibrium, and analyze the effectiveness of the economic drive of the user-side energy storage aggregators to participate in the consortium.

[0091] S2: Simulate the trading strategy of renewable energy and user-side shared energy storage participating in the spot market based on mixed integer linear optimization programming;

[0092] User-side energy storage resources are aggregated to form shared energy storage and jointly operated with new energy stations. The joint venture participates in the electricity spot market as a new market entity. A market clearing model involving new energy stations + user-side shared energy storage joint ventures is established, which can simulate and calculate the market benefits of the joint venture.

[0093] 1. Establish the objective function:

[0094] With the goal of maximizing the social welfare of the entire system, a market clearing model is established, and the objective function is as follows:

[0095]

[0096] 1) Revenue from electricity sales of the entire system:

[0097]

[0098] Where: is the bidding price of the load in the quotation segment d in time period t; is the winning bid power of the load in quotation segment d in time period t.

[0099] 2) Cost expenditure of purchasing thermal power generation:

[0100]

[0101] Where: is the bidding price of thermal power unit g in the quotation period h during time period t; It is the winning bid power of thermal power unit g in quotation period h during time period t.

[0102] 3) System backup cost expenditure:

[0103]

[0104] Where: They are the upper and lower spare capacities required by the system in period t respectively.

[0105] 4) Cost expenditure of purchasing power generated by new energy stations:

[0106]

[0107] Where: t is the winning bid price of renewable energy at time t; P i,t is the winning bid power of new energy station i at time t.

[0108] 2. Establish constraints on the objective function:

[0109] 1) System power balance constraints:

[0110]

[0111] 2) Alternative constraints:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] Where: They are the upper and lower standby powers of thermal power units, They are the upper backup and lower backup power of the shared energy storage respectively.

[0119] 3) Power constraints of thermal power units:

[0120]

[0121] Where: They are the upper and lower limits of the output of thermal power units respectively.

[0122] 4) Interaction power constraints between new energy and user-side shared energy storage:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Where: P i,t is the winning bid power of the new energy station i at time t; The predicted output of the new energy station i at time t; and are the discharge and charging power of the new energy station i interacting with the user-side shared energy storage at time t; ν i,t is a 0-1 variable, which constrains the power purchase and sale status of the new energy station at each moment to be only one; M is a maximum value. N It is the rated power of the shared energy storage on the user side.

[0129] 5) User-side shared energy storage charge state constraints:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Where: γ t is a 0-1 variable, constraining the shared energy storage to be in one charging and discharging state at each moment; They are respectively the power purchased and sold by the new energy station cluster to the shared energy storage at time t.

[0138] is the remaining power of the shared energy storage at time t; η c and η d are the charging efficiency and discharging efficiency of shared energy storage respectively; E N is the rated capacity of the shared energy storage.

[0139] SOC min SOC is the lower limit of energy storage charge state; max It is the upper limit of energy storage charge state.

[0140] The above-mentioned market clearing model involving new energy stations and user-side shared energy storage consortiums is a linear optimization model, which can be solved using a mixed integer linear programming method to simulate and calculate the total revenue of new energy stations and user-side shared energy storage consortiums participating in market transactions.

[0141] S3: Revenue distribution strategy within the new energy and user-side shared energy storage joint venture based on Nash bargaining equilibrium optimization decision;

[0142] In the second stage, based on the total revenue obtained by the consortium from participating in spot market transactions analyzed and decided in the first stage, the dynamic power interaction between the various entities within the consortium is considered, and a Nash bargaining equilibrium game model between the new energy station group and the user-side shared energy storage aggregator is constructed to decide the charging and discharging energy interaction price between the new energy station and the load-side shared energy storage, and then calculate the revenue distribution pattern of the various entities within the consortium.

[0143] The mechanism of the Nash bargaining equilibrium solution is a solution that all game entities can accept and maximize the utility of all parties. Therefore, the invention optimizes the decision-making of the profit distribution strategy within the new energy and user-side shared energy storage joint venture based on the Nash bargaining equilibrium, which can ensure that all members of the joint venture can obtain more profits than before the cooperation, realize effective economic drive for all parties in the cooperation, reflect the rationality of the distribution of cooperative profits, improve the stability of the cooperative joint venture, and promote the reasonable development and configuration of the user-side shared energy storage capacity.

[0144] The maximum Nash product solution of the utility increment of each party before and after the joint venture between the new energy station and the user-side shared energy storage aggregator is adopted as the Nash bargaining solution. The Nash bargaining equilibrium optimization model is as follows:

[0145]

[0146] Where: U SES The user-side shared energy storage cooperation benefits, U S 0 E * S U is the aggregate cost of shared energy storage at the user side; i is the maximum benefit of the new energy station i when participating in the cooperation, U i 0* It is the maximum profit of new energy station i when it does not participate in the cooperation.

[0147] The above Nash bargaining model is a non-convex nonlinear problem. The logarithmic function can be used to convert the Nash product of the new energy station and the user-side shared energy storage operator into a logarithmic sum form, which can greatly reduce the difficulty of solving. The converted Nash bargaining model is as follows:

[0148]

[0149] Where: is the interactive power that the user-side shared energy storage and the new energy station i expect to achieve in period t, is the initial income of the new energy station i without considering the internal virtual interaction price settlement, The initial revenue of shared energy storage on the user side without considering the internal virtual interaction price settlement is obtained by simulation calculation based on the market transaction strategy of the consortium in stage 1. i,t It is the virtual interaction price reached between the user-side shared energy storage and the new energy station i in period t.

[0150] The overall solution process of the two-stage game analysis model of new energy + shared energy storage is as follows: Figure 2 shown.

[0151] S4: The mixed integer linear programming method is used to solve the first stage, and the total revenue of the new energy station and the user-side shared energy storage consortium participating in the market transaction is simulated and calculated.

[0152] In the objective function of the optimization model for clearing the power grid at the next stage, there is a nonlinear term ρ t P i,t , the strong duality theorem can be used for linear transformation, and ρ t Equal to the dual variable λ of the power balance constraint of the lower model t , the dual problem of the lower objective function is expressed as follows:

[0153]

[0154] Linearize the dual variables in the transformation model for the strong duality theorem, is the upper limit of the quoted power of the load in the quoted segment d;

[0155] According to the strong duality theory, the optimal solution of the lower objective function is equal to the optimal solution of its dual problem. The nonlinear terms in the lower objective function can be linearized:

[0156]

[0157]

[0158] in, is the bidding price of the load in the quotation segment d in time period t; is the winning bid power of the load in quotation segment d in time period t.

[0159] As shown in the above formula, the nonlinear terms in the objective function of stage one can be linearized and solved using the mixed integer linear programming method.

[0160] Phase 2: Solve the Nash bargaining equilibrium model of the new energy and shared energy storage consortium based on the ADMM algorithm, decide on the internal virtual interactive electricity price, and form a profit distribution pattern through the internal virtual interactive electricity price.

[0161] The use of the alternating direction multiplier algorithm (ADMM) for solving the problem has the advantages of fast solution speed and good convergence performance. The introduction of the Lagrange multiplier ζ i,t , penalty factor φ i,t , the augmented Lagrangian function of the Nash bargaining function is constructed as follows:

[0162]

[0163] The ADMM algorithm can decompose the augmented Lagrangian function of the above Nash bargaining function into interactive electricity price negotiation sub-problems for solution, and obtain the distributed optimization sub-problems of shared energy storage and new energy stations as follows:

[0164] 1) Interactive electricity price optimization sub-problem model of new energy stations:

[0165]

[0166] 2) Interactive electricity price optimization sub-problem model for shared energy storage:

[0167]

[0168]

[0169] 1) Initialize various parameters and set the maximum number of iterations k max , set the initial virtual interaction price δ between the new energy station cluster and shared energy storage i,t , Initialize the Lagrange multiplier ζ i,t and penalty factor φ i,t .

[0170] 2) Each new energy station obtains the initial interactive electricity price Solve the interactive electricity price optimization sub-problem model of the new energy station group to obtain the expected transaction price for shared energy storage

[0171] 3) Shared energy storage obtains prices from wind and solar power stations And solve the interactive electricity price optimization sub-problem model of user-side shared energy storage to obtain the expected interactive price for the new energy station

[0172] 4) According to the expected interaction price of the new energy station and the user-side shared energy storage, the Lagrange multiplier iteration is performed:

[0173]

[0174] 5) Compare whether the result reaches the convergence accuracy or the maximum number of iterations. If the convergence accuracy is not reached, return to step 2 to continue iterating; if the convergence accuracy is met, the calculation ends and the result is output.

[0175] The present invention proposes an internal profit distribution method for the joint venture between new energy stations and user-side shared energy storage based on Nash bargaining equilibrium, which can be used to analyze whether the joint venture cooperation between new energy stations and user-side energy storage aggregators has effective economic driving force, while achieving overall incentive compatibility, promoting the sustainable development of the business model of the joint venture between new energy stations and user-side shared energy storage, and supporting the improvement of the cost recovery mechanism of the user-side distributed energy storage aggregation capacity.

[0176] The above implementation modes are not limitations of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also belong to the protection scope of the present invention.

Claims

1. A capacity configuration benefit analysis method for user-side shared energy storage in a new energy cluster alliance, characterized by: The following steps are involved: Step S1: Establish a two-stage economic analysis framework for sharing energy storage capacity under the joint cooperation mechanism between new energy station clusters and user-side energy storage; Step S2: for phase 1, simulate the trading strategy of new energy and user-side shared energy storage participating in the spot market based on mixed integer linear optimization programming; Step S3: for phase 2, the profit distribution strategy within the new energy and user-side shared energy storage joint venture is determined based on the Nash bargaining equilibrium optimization decision; Step S4: For phase 1, a mixed integer linear programming method is used to solve the problem, and the total revenue of the new energy station and the user-side shared energy storage consortium participating in the market transaction is simulated and calculated; For the second stage, the Nash bargaining equilibrium model of the new energy and shared energy storage joint venture is solved based on the ADMM algorithm, and the internal virtual interactive electricity price is decided, and the profit distribution pattern is formed through the internal virtual interactive electricity price.

2. The capacity configuration benefit analysis method of the new energy cluster alliance user-side shared energy storage according to claim 1 is characterized by: The two stages in step S1 include: Phase 1: Simulate the participation of new energy and user-side shared energy storage in the market transaction under the electricity spot market environment, and decide on the optimal bidding strategy and shared energy storage charging and discharging strategy for the participation of new energy station groups in the market; Phase 2: Based on the benefits of the consortium’s participation in the market obtained from the simulation in Phase 1, the Nash bargaining theory is further adopted to conduct an analysis of the internal interactive game between the new energy and user-side shared energy storage consortium, decide on the profit distribution plan for the new energy and user-side shared energy storage aggregators to achieve Nash equilibrium, and analyze the effectiveness of the economic drive of the user-side energy storage aggregators to participate in the consortium.

3. The capacity configuration benefit analysis method of the new energy cluster alliance user-side shared energy storage according to claim 1 is characterized by: In step S2, user-side energy storage resources are aggregated to form shared energy storage and are jointly operated with a group of new energy stations. The joint venture participates in the electricity spot market as a new market entity, and a market clearing model involving new energy stations + user-side shared energy storage joint ventures is established to simulate and calculate the market benefits of the joint venture.

4. The capacity configuration benefit analysis method for shared energy storage at the user side of a new energy cluster alliance according to claim 3 is characterized by: In step S2, a market clearing model is established with the goal of maximizing the social welfare of the entire system, and the objective function is as follows: Among them, the electricity sales revenue of the whole system is: Where: is the bidding price of the load in the quotation segment d in time period t; is the winning bid power of the load in the quotation segment d in time period t; Cost expenditure of purchasing thermal power generation: Where: is the bidding price of thermal power unit g in the quotation period h during time period t; is the winning bid power of thermal power unit g in bid period h during time period t; System backup cost expenditure: Where: They are the upper and lower spare capacities required by the system in period t respectively; Cost expenditure for purchasing electricity generated by new energy stations: Where: t is the winning bid price of renewable energy at time t; P i,t is the winning bid power of new energy station i at time t.

5. The capacity configuration benefit analysis method for shared energy storage at the user side of the new energy cluster alliance according to claim 2 is characterized by: In step S2, the following constraints are established for the objective function: System power balance constraints: Alternative constraints: Where: They are the upper and lower standby powers of thermal power units, They are the upper and lower reserve powers of the shared energy storage respectively; Power constraints of thermal power units: Where: They are the upper and lower limits of the output of thermal power units respectively; Power constraints for interaction between new energy and user-side shared energy storage: Where: P i,t is the winning bid power of the new energy station i at time t; The predicted output of the new energy station i at time t; and are the discharge and charging power of the new energy station i interacting with the user-side shared energy storage at time t; ν i,t is a 0-1 variable, which constrains the power purchase and sale status of the new energy station at each moment to be only one; M is a maximum value; P N The rated power of the shared energy storage on the user side; User-side shared energy storage charge state constraints: Where: γ t is a 0-1 variable, constraining the shared energy storage to be in one charging and discharging state at each moment; P t b , are the power purchased and sold by the new energy station cluster to the shared energy storage at time t; is the remaining power of the shared energy storage at time t; η c and η d are the charging efficiency and discharging efficiency of shared energy storage respectively; E N is the rated capacity of the shared energy storage; SOC min SOC is the lower limit of energy storage charge state; max It is the upper limit of energy storage charge state.

6. The capacity configuration benefit analysis method for shared energy storage at the user side of a new energy cluster alliance according to claim 1 is characterized by: In step S3, based on the total revenue obtained by the consortium from participating in spot market transactions analyzed and decided in stage one, stage two considers the dynamic power interaction between the various entities within the consortium, constructs a Nash bargaining equilibrium game model between the new energy station group and the user-side shared energy storage aggregator, decides the charging and discharging energy interaction price between the new energy station and the load-side shared energy storage, and then calculates the revenue distribution pattern of the various entities within the consortium.

7. The capacity configuration benefit analysis method for shared energy storage at the user side of a new energy cluster alliance according to claim 6 is characterized by: In step S3, the maximum Nash product solution of the utility increments of each party before and after the joint venture between the new energy station and the user-side shared energy storage aggregator is used as the Nash bargaining solution. The Nash bargaining equilibrium optimization model is as follows: Where: U SES Shared energy storage cooperation benefits for users, U is the aggregate cost of shared energy storage at the user side; i To maximize the benefits of cooperation for new energy stations, is the maximum profit of new energy station i when it does not participate in the cooperation; The Nash product is converted into a logarithmic sum form using a logarithmic function. The converted Nash bargaining model is as follows: Where: is the interactive power that the user-side shared energy storage and the new energy station i expect to achieve in period t, and They are the initial income of the new energy station i and the shared energy storage on the user side without considering the internal virtual interactive price settlement, which is obtained by simulation calculation based on the market transaction strategy of the consortium in the first stage; δ i,t It is the virtual interaction price reached between the user-side shared energy storage and the new energy station i in period t.

8. The capacity configuration benefit analysis method for shared energy storage at the user side of a new energy cluster alliance according to claim 1 is characterized by: In step S4, the nonlinear terms in the objective function of stage 1 are linearized and solved using a mixed integer linear programming method; In the objective function of the optimization model for clearing the power grid at the next stage, there is a nonlinear term ρ t P i,t , use the strong duality theorem to perform linear transformation, and convert ρ t Equal to the dual variable λ of the power balance constraint of the lower model t , the dual problem of the lower objective function is expressed as follows: λ up，t , down，t , Linearize the dual variables in the transformation model for the strong duality theorem, is the upper limit of the quoted power of the load in the quoted segment d; According to the strong duality theory, the optimal solution of the lower objective function is equal to the optimal solution of its dual problem. The nonlinear terms in the lower objective function are linearized: in, is the bidding price of the load in the quotation segment d in time period t; is the winning bid power of the load in quotation segment d in time period t.

9. The capacity configuration benefit analysis method for shared energy storage at the user side of a new energy cluster alliance according to claim 1 is characterized by: In step S4, for stage 2: using the alternating direction multiplier algorithm ADMM to solve the Nash bargaining game model, the Lagrange multiplier ζ is introduced i,t , penalty factor φ i,t , the augmented Lagrangian function of the Nash bargaining function is constructed as follows: The ADMM algorithm decomposes the augmented Lagrangian function of the above Nash bargaining function into interactive electricity price negotiation sub-problems for solution, and obtains the distributed optimization sub-problems of shared energy storage and new energy stations as follows: A. Interactive electricity price optimization sub-problem model of new energy stations: B. Interactive electricity price optimization sub-problem model for shared energy storage:

10. The capacity configuration benefit analysis method of the new energy cluster alliance user-side shared energy storage according to claim 9 is characterized by: The specific solution steps of the second stage are as follows: 1) Initialize various parameters and set the maximum number of iterations k max , set the initial virtual interaction price δ between the new energy station cluster and shared energy storage i,t , Initialize the Lagrange multiplier ζ i,t and penalty factor φ i,t ; 2) Each new energy station obtains the initial interactive electricity price Solve the interactive electricity price optimization sub-problem model of the new energy station group to obtain the expected transaction price for shared energy storage 3) Shared energy storage obtains prices from wind and solar power stations And solve the interactive electricity price optimization sub-problem model of user-side shared energy storage to obtain the expected interactive price for the new energy station 4) According to the expected interaction price of the new energy station and the user-side shared energy storage, the Lagrange multiplier iteration is performed: 5) Compare whether the result reaches the convergence accuracy or the maximum number of iterations. If the convergence accuracy is not reached, return to step 2 to continue iterating; if the convergence accuracy is met, the calculation ends and the result is output.

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