Shared energy storage capacity optimization configuration method and device

Abstract

The invention provides a shared energy storage capacity optimal configuration method and device, and belongs to the field of shared energy storage. The method comprises the steps that according to energy supply parameters and equipment parameters of an energy supplier, a first target function is established with the maximum energy supply profit of the energy supplier for end-to-end energy transaction as the target, and a robust interval optimization model is established with the minimum worst cost as the target based on the uncertainty of the cost of the energy supplier; according to the energy demand parameter of the energy demand side and the capacity parameter of the shared energy storage, establishing a second target function by taking the minimum energy use cost of the energy demand side for end-to-end energy transaction as a target; according to the first objective function and the second objective function, establishing a bargaining model with the purpose of maximizing the overall income of the end-to-end energy transaction; and based on the robust interval optimization model and the bargaining model, determining the capacity configuration of the shared energy storage under the end-to-end energy transaction. The shared energy storage capacity can be reasonably and accurately configured, and the utilization rate of shared energy storage is improved.

Description

Technical Field

[0001] The present invention relates to the field of shared energy storage technology, and in particular to a method and device for optimizing configuration of shared energy storage capacity. Background Art

[0002] As the demand for distributed energy storage resources continues to increase, shared energy storage has great potential on the demand side. Among them, shared energy storage refers to centralized large-scale independent energy storage power stations invested and built by third parties. In addition to meeting the needs of their own power stations, they also provide services for other new energy power stations. Compared with traditional distributed energy storage, shared energy storage can separate the ownership and use rights of distributed energy storage equipment, thereby integrating idle resources.

[0003] By optimizing the operation of distributed energy storage demand and energy storage resources and realizing shared energy storage, the cost of energy storage can be significantly reduced. Therefore, it is very important to determine the capacity of shared energy storage and achieve mutual benefit and win-win results.

[0004] In the related art, the capacity of shared energy storage is mainly determined from the perspective of energy trading. However, the inventors found that the above methods often ignore the uncertainty in the shared energy storage planning process, making it difficult to accurately and reasonably determine the capacity configuration of shared energy storage. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for optimizing the configuration of shared energy storage capacity, so as to improve the accuracy of the configuration of shared energy storage capacity and ensure the rationality of the configuration of shared energy storage capacity.

[0006] In a first aspect, an embodiment of the present invention provides a method for optimizing configuration of shared energy storage capacity, comprising:

[0007] According to the energy supply parameters and equipment parameters of the energy supplier, a first objective function is established with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, and based on the uncertainty of the cost of the energy supplier, a robust interval optimization model is established with the goal of minimizing the worst cost;

[0008] According to the energy demand parameters of the energy demander and the capacity parameters of the shared energy storage, a second objective function is established with the goal of minimizing the energy use cost of the energy demander in performing end-to-end energy trading;

[0009] According to the first objective function and the second objective function, a bargaining model is established with the goal of maximizing the overall benefit of end-to-end energy trading;

[0010] Based on the robust interval optimization model and bargaining model, the capacity configuration of shared energy storage under end-to-end energy trading is determined.

[0011] In a possible implementation, before establishing the first objective function based on the energy supply parameters and equipment parameters of the energy supplier with the goal of maximizing the energy supply profit of the energy supplier in the end-to-end energy transaction, the method further includes:

[0012] Determine the supply-demand ratio parameter between the energy supplier and the energy demander according to the energy supply of the energy supplier and the energy demand of the energy demander;

[0013] Based on the supply-demand ratio parameter and the preset compensation factor, an energy price model for end-to-end energy trading is determined.

[0014] In a possible implementation, the expression of the first objective function is:

[0015]

[0016] In the formula, F s represents the energy supply profit of the energy supplier participating in the end-to-end energy transaction, R ss represents the revenue from transactions with energy demand parties in end-to-end energy transactions, R sg It represents the revenue from energy suppliers selling energy to the power grid. represents the income of energy suppliers participating in carbon trading, R trad represents the transaction cost of energy suppliers participating in end-to-end energy transactions, R cost It represents the cost of building equipment and generating electricity for energy suppliers. represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, represents the actual power generation of energy supplier s at time t, Represents the unit income coefficient of CO2 in carbon trading, represents the carbon emission coefficient of distributed generation, λ trad represents the transaction cost coefficient of end-to-end energy trading, σ represents the discount rate, represents the variable cost of power generation equipment j at time t′, represents the fixed cost of power generation equipment j at time t′, represents the power generation of power generation equipment j at time t′, represents the capacity of power generation equipment j at time t′, t represents the statistical time in the operation cycle, T represents the set of statistical time, t′ represents the time in the construction cycle of the power generation equipment, T′ represents the set of time in the construction cycle, j represents the power generation equipment, J erepresents the set of all power generation equipment, s represents the energy supplier, b represents the energy demander, and B represents the set of all energy demanders.

[0017] In a possible implementation, the expression of the robust interval optimization model is:

[0018]

[0019] In the formula, G represents the robust optimization problem function, h represents the capacity of the power generation equipment, θ represents the tolerance of cost uncertainty, and N represents the sum of all uncertain costs. N represents the upper limit of N, represents the lower limit of N, represents the variable cost of power generation equipment j at time t′, express The upper limit of express The lower limit of represents the fixed cost of power generation equipment j at time t′, express The upper limit of The lower limit of the representation γ 1j , γ 2j , γ 3j , γ 4j and γ5 represent dual variables, Q represents the power generation of the power generation equipment, j represents the power generation equipment, J e Represents the set of all power generation equipment.

[0020] In a possible implementation, the expression of the second objective function is:

[0021]

[0022] In the formula, F B represents the energy usage cost of the energy demand party participating in end-to-end energy trading, R bs represents the cost of transactions between energy demander b and energy supplier in end-to-end energy transactions, R bg represents the cost of energy demand party b purchasing energy from the grid, represents the price of leasing shared energy storage by energy demand party b, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, It represents the amount of electricity purchased by energy demander b from the power grid at time t, t represents the statistical time in the operation cycle, T represents the set of statistical time, s represents the energy supplier, S represents the set of all energy suppliers, and b represents the energy demander.

[0023] In a possible implementation, the bargaining model is expressed as:

[0024]

[0025] In the formula, represents the optimal solution of the bargaining model, F S (x) represents the energy supply profit of the energy supplier when the solution of the bargaining model is x, f S represents the initial energy supply profit when the energy supplier negotiation breaks down, F B (x) represents the energy cost of the energy demander when the solution of the bargaining model is x, f B represents the initial energy use cost when the energy demand party's negotiation breaks down, x represents the solution of the bargaining model, X represents the set of solutions of the bargaining model, and st represents the constraints in the bargaining model.

[0026] In a possible implementation, based on the supply-demand ratio parameter and a preset compensation factor, determining an energy price model for end-to-end energy trading includes:

[0027] According to the expression: Determine energy price models for end-to-end energy trading;

[0028] In the formula, represents the energy price of end-to-end energy trading at time t, represents the grid-connected electricity price at time t, η t represents the compensation factor at time t, It represents the supply price of the power grid at time t, and SDR represents the supply-demand ratio parameter.

[0029] In a possible implementation, the constraints of the second objective function include energy balance constraints of energy demanders and shared energy storage capacity constraints.

[0030] In a possible implementation, the energy balance constraint is expressed as:

[0031]

[0032] The expression of the shared energy storage capacity constraint is:

[0033]

[0034] In the formula, represents the energy transaction volume between energy supplier s and energy demander b at time t, represents the amount of energy purchased from the power grid by energy demand party b at time t, represents the discharge amount of energy demand party b at time t, represents the charging capacity of energy demand party b at time t, represents the fixed demand for shared energy storage at time t, Indicates that the shared energy storage is in charging state at time t, Indicates that the shared energy storage is in a discharging state at time t, Indicates the maximum charging and discharging power of the shared energy storage, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the capacity of shared energy storage leased by energy demander b at time t-1, η abs represents the charging efficiency of shared energy storage, η relea represents the charging efficiency of shared energy storage, Δt represents the unit time, represents the capacity of the shared energy storage leased by energy demand party b at the initial moment, E b represents the upper limit of the capacity of shared energy storage leased by energy demand party b, E m represents the total capacity of shared energy storage, s represents the energy supplier, S represents the set of all energy suppliers, b represents the energy demander, and B represents the set of all energy demanders.

[0035] In a second aspect, an embodiment of the present invention provides a device for optimizing configuration of shared energy storage capacity, comprising:

[0036] A first establishing module is used to establish a first objective function based on energy supply parameters and equipment parameters of the energy supplier, with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, and to establish a robust interval optimization model with the goal of minimizing the worst cost based on the uncertainty of the cost of the energy supplier;

[0037] A second establishing module is used to establish a second objective function based on the energy demand parameter of the energy demand party and the capacity parameter of the shared energy storage, with the goal of minimizing the energy use cost of the energy demand party in performing end-to-end energy trading;

[0038] A third establishment module is used to establish a bargaining model based on the first objective function and the second objective function with the goal of maximizing the overall benefit of end-to-end energy trading;

[0039] A determination module is used to determine the capacity configuration of shared energy storage under end-to-end energy trading based on the robust interval optimization model and the bargaining model.

[0040] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0041] In the embodiment of the present invention, a first objective function with the goal of maximizing energy supply profit is established on the energy supplier, and a second objective function with the goal of minimizing energy use cost is established on the energy demander, so that the goals of the energy supplier and the energy demander can be accurately determined; wherein, the uncertainty of the cost of the energy supplier is also considered, and a robust interval optimization model is established with the goal of minimizing the worst cost of the construction and operation cost of the energy supplier, and the construction and operation cost of the energy supplier is added to the consideration of the shared energy storage capacity configuration, and all factors affecting the shared energy storage are fully considered. Finally, a bargaining model with the goal of maximizing overall benefits is established based on the first objective function and the second objective function, and based on the robust interval optimization model and the bargaining model, the capacity optimization plan of the shared energy storage is determined, which can accurately determine the capacity configuration of the shared energy storage, maximize the interests of the energy supplier and the energy demander, and improve the utilization rate of the shared energy storage, and ensure the rationality of the shared energy storage capacity configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is an application scenario diagram of the shared energy storage capacity optimization configuration method provided by an embodiment of the present invention;

[0044] Figure 2 is a flow chart for implementing a method for optimizing configuration of shared energy storage capacity provided by an embodiment of the present invention;

[0045] Figure 3 It is a structural schematic diagram of a shared energy storage capacity optimization configuration device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0047] The inventors of the present invention have found that in order to optimize the configuration of shared energy storage capacity, it is usually considered from the perspective of energy trading, and only the energy trading between energy suppliers and energy demanders is considered, which makes it difficult to reasonably and accurately determine the optimal configuration of shared energy storage capacity.

[0048] In order to improve the accuracy of shared energy storage capacity configuration, in the implementation mode of the present invention, the uncertainty of the cost of the energy supplier is taken into account, and a robust interval optimization model is established with the goal of minimizing the worst-case cost of the energy supplier's construction and operation costs. The construction and operation costs of the energy supplier are added to the consideration of the shared energy storage capacity configuration, and all factors affecting the shared energy storage are fully considered, so as to accurately determine the capacity configuration of the shared energy storage, improve the utilization rate of the shared energy storage, and ensure the rationality of the shared energy storage capacity configuration.

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0050] Considering that the community is a collection of users connected to nodes in a distributed network, information transmission is relatively intensive. In a partially decentralized topology, the community can be selected as a super node for end-to-end energy trading, and the users in the community are ordinary nodes, and the information of ordinary nodes is stored in each super node.

[0051] like Figure 1 As shown in the application scenario diagram of the shared energy storage capacity optimization configuration method, in the end-to-end energy transaction of a partially decentralized topology structure, each user in each community does not directly participate in the energy transaction, but uses the super node corresponding to the overall interests of the community as the energy demander, and conducts transactions between the energy demander and the energy supplier to realize the interaction between the users and the energy supplier in the community, which can reduce the real-time data throughput of network nodes and improve processing efficiency.

[0052] Among them, the energy supplier is equipped with power generation equipment, such as distributed photovoltaic equipment and decentralized wind power equipment, etc. The energy supplier can also set up corresponding energy storage equipment to provide energy stably within a certain period of time.

[0053] In addition, in end-to-end energy trading, centralized energy storage resources are distributed in the distribution network. The centralized energy storage resources can be owned by the third-party operator of the shared energy storage or by the distribution network. Figure 1 As shown, energy demanders can lease the capacity of shared energy storage and, by setting up energy storage equipment, facilitate more flexible energy management and energy use.

[0054] Figure 2 The implementation flow chart of the shared energy storage capacity optimization configuration method provided by the embodiment of the present invention is detailed as follows:

[0055] Step S201, based on the energy supply parameters and equipment parameters of the energy supplier, a first objective function is established with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, and based on the uncertainty of the cost of the energy supplier, a robust interval optimization model is established with the goal of minimizing the worst cost.

[0056] In this embodiment, the energy supplier obtains income by selling energy. Therefore, for the energy supplier in each community, the goal of end-to-end energy trading is to maximize profits.

[0057] The energy supply parameters may be parameters related to energy provided by the energy supplier, and the equipment parameters may be parameters related to power generation equipment installed at the energy supplier.

[0058] Here, considering that the energy suppliers have different power generation technologies and the different locations and environments of power generation equipment, the construction and operation costs of power generation equipment will fluctuate, leading to uncertainty in the costs of energy suppliers. Therefore, a robust interval optimization model is established with the goal of minimizing the worst cost of construction and operation of equipment at the energy supplier to optimize the uncertain costs.

[0059] Step S202 , based on the energy demand parameters of the energy demander and the capacity parameters of the shared energy storage, a second objective function is established with the goal of minimizing the energy use cost of the energy demander in performing end-to-end energy trading.

[0060] In this embodiment, energy demanders purchase energy for use and lease shared energy storage for energy management. Therefore, for energy demanders in each community, the goal of end-to-end energy trading is to minimize costs.

[0061] Among them, the energy demand parameters may be related parameters that the energy demander needs to purchase energy, and the capacity parameters of shared mutual energy may be related parameters of the capacity of leasing shared energy storage.

[0062] Step S203: establishing a bargaining model based on the first objective function and the second objective function with the goal of maximizing the overall benefit of the end-to-end energy transaction.

[0063] In this embodiment, in the end-to-end energy transaction, the energy supplier expects to increase the energy price to maximize profits, while the energy demander expects to trade at a lower price to reduce the cost of energy use. Therefore, in order to coordinate the interests of both parties and ensure the maximization of the overall interests of the energy supplier and the energy demander, a bargaining model based on bargaining game can be established according to the first objective function and the second objective function established in the above steps.

[0064] Step S204, based on the robust interval optimization model and the bargaining model, determine the capacity configuration of the shared energy storage under the end-to-end energy transaction.

[0065] In this embodiment, the uncertainty of the equipment cost of the energy supplier is considered through a robust interval optimization model, and the cooperation agreement between the energy supplier and the energy demander is considered through a bargaining model, and an optimization solution is performed. The corresponding data of the energy supplier and the energy demander when the overall benefit is maximized can be determined, so that the demand of the energy demander for the shared energy storage capacity can be obtained, and then the capacity configuration of the shared energy storage under the end-to-end transaction can be determined.

[0066] The embodiment of the present invention establishes a first objective function with the goal of maximizing energy supply profit on the energy supplier side, and establishes a second objective function with the goal of minimizing energy use cost on the energy demand side, which can accurately determine the goals of the energy supplier and the energy demand side; wherein, the uncertainty of the cost of the energy supplier is also considered, and a robust interval optimization model is established with the goal of minimizing the worst cost of the construction and operation cost of the energy supplier, and the construction and operation cost of the energy supplier is added to the consideration of the shared energy storage capacity configuration, and all factors affecting the shared energy storage are fully considered. Finally, a bargaining model with the goal of maximizing overall benefits is established based on the first objective function and the second objective function, and based on the robust interval optimization model and the bargaining model, the capacity optimization plan of the shared energy storage is determined, which can accurately determine the capacity configuration of the shared energy storage, maximize the interests of the energy supplier and the energy demand side, and improve the utilization rate of the shared energy storage, and ensure the rationality of the shared energy storage capacity configuration.

[0067] In some embodiments, before establishing a first objective function based on the energy supply parameters and equipment parameters of the energy supplier with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, it may also include: determining the supply-demand ratio parameters between the energy supplier and the energy demander based on the energy supply of the energy supplier and the energy demander; and determining the energy price model for end-to-end energy trading based on the supply-demand ratio parameters and preset compensation factors.

[0068] In end-to-end energy transactions, the energy price of energy transactions between energy suppliers and energy demanders is uncertain. Therefore, in this embodiment, the energy price in end-to-end energy transactions may be further considered.

[0069] Here, when determining the energy price in end-to-end energy trading, the supply-demand ratio between energy suppliers and energy demanders is taken into consideration to guide energy suppliers to participate in end-to-end energy trading. The compensation for energy suppliers is also considered through compensation factors to ensure the benefits of energy suppliers and enable energy suppliers to actively participate in end-to-end energy trading.

[0070] Optionally, according to the energy supply of the energy supplier and the energy demand of the energy demander, the supply-demand ratio parameter between the energy supplier and the energy demander is determined, which can be: according to the expression: Determine the supply-demand ratio parameter between the energy supplier and the energy demander; where SDR(t) represents the supply-demand ratio parameter between the energy supplier and the energy demander at time t, S total,t represents the total energy supply of all energy suppliers in the end-to-end energy transaction at time t, D total,t represents the total demand of all energy demanders in the end-to-end energy transaction at time t, represents the energy supply of the i-th energy supplier in the end-to-end energy transaction at time t, represents the energy demand of the j-th energy demander in the end-to-end energy transaction at time t, Δt represents the unit time, i represents the i-th energy supplier, j represents the j-th energy demander, S represents the total number of energy suppliers, and B represents the total number of energy demanders.

[0071] Optionally, based on the supply-demand ratio parameter, the energy price model for end-to-end energy trading can be determined as follows: Determine the energy price model for end-to-end energy trading; where, represents the energy price of end-to-end energy trading at time t, represents the grid-connected electricity price at time t, η t represents the compensation factor at time t, It represents the supply price of the power grid at time t, and SDR represents the supply-demand ratio parameter.

[0072] In some embodiments, the expression of the first objective function may be:

[0073]

[0074] In the formula, F s represents the energy supply profit of the energy supplier participating in the end-to-end energy transaction, R ss represents the revenue from transactions with energy demand parties in end-to-end energy transactions, R sg represents the revenue of energy suppliers from selling energy to the grid, R trad represents the transaction cost of energy suppliers participating in end-to-end energy transactions, R cost It represents the cost of building equipment and generating electricity for energy suppliers. represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, represents the actual power generation of energy supplier s at time t, λ trad represents the transaction cost coefficient of end-to-end energy trading, σ represents the discount rate, represents the variable cost of power generation equipment j at time t′, represents the fixed cost of power generation equipment j at time t′, represents the power generation of power generation equipment j at time t′, represents the capacity of power generation equipment j at time t′, t represents the statistical time in the operation cycle, T represents the set of statistical time, t′ represents the time in the construction cycle of the power generation equipment, T′ represents the set of time in the construction cycle, j represents the power generation equipment, J e represents the set of all power generation equipment, s represents the energy supplier, b represents the energy demander, and B represents the set of all energy demanders.

[0075] In this embodiment, the profit of the energy supplier is determined by considering the revenue and transaction costs of the energy supplier participating in end-to-end energy transactions, the revenue from selling energy to the power grid, and the cost of the energy supplier to build and operate power generation equipment.

[0076] Here, the energy supply parameters of the energy supplier may include: energy price of end-to-end energy trading, energy trading volume of end-to-end energy trading, grid-connected electricity price, actual power generation and other parameters, and equipment parameters may include equipment variable cost, fixed cost, power generation and other parameters.

[0077] Alternatively, compared to traditional coal-fired power generation, renewable energy produces almost no carbon emissions during production and operation. In order to achieve low-carbon electricity, in addition to selling energy, energy suppliers of renewable energy can also provide carbon emission rights through end-to-end energy transactions to increase the profits of energy suppliers.

[0078] Correspondingly, the expression of the first objective function can also be:

[0079]

[0080] In the formula, F s represents the energy supply profit of the energy supplier participating in the end-to-end energy transaction, R ss represents the revenue from transactions with energy demand parties in end-to-end energy transactions, R sg It represents the revenue from energy suppliers selling energy to the power grid. represents the income of energy suppliers participating in carbon trading, R trad represents the transaction cost of energy suppliers participating in end-to-end energy transactions, R cost It represents the cost of building equipment and generating electricity for energy suppliers. represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, represents the actual power generation of energy supplier s at time t, Represents the unit income coefficient of CO2 in carbon trading, represents the carbon emission coefficient of distributed generation, λ trad represents the transaction cost coefficient of end-to-end energy trading, σ represents the discount rate, represents the variable cost of power generation equipment j at time t′, represents the fixed cost of power generation equipment j at time t′, represents the power generation of power generation equipment j at time t′, represents the capacity of power generation equipment j at time t′, t represents the statistical time in the operation cycle, T represents the set of statistical time, t′ represents the time in the construction cycle of the power generation equipment, T′ represents the set of time in the construction cycle, j represents the power generation equipment, J e represents the set of power generation equipment, s represents the energy supplier, b represents the energy demander, and B represents the set of all energy demanders.

[0081] In some embodiments, energy suppliers need to consider the uncertainty of the cost of building and operating equipment. As the cost vector of uncertain cost parameters, we have:

[0082]

[0083] In the formula, Ω represents the set of cost vectors, ω represents the cost vector, represents the upper limit of the variable cost of power generation equipment j at time t′, represents the lower limit of the variable cost of power generation equipment j at time t′, represents the upper limit of the fixed cost of power generation equipment j at time t′, represents the lower limit of the fixed cost of power generation equipment j at time t′, N represents the sum of all uncertain cost parameters, represents an upper bound on the sum of all uncertain cost parameters, N represents the lower bound of the sum of all uncertain cost parameters, and θ represents the tolerance of uncertain costs.

[0084] Here, we can assume that capacity is an exogenous parameter, so the cost of energy suppliers to build equipment and generate electricity in the first objective function is It can be transformed into the following problem:

[0085]

[0086]

[0087]

[0088] In the formula, g(h) represents the worst-case cost when the equipment capacity is h, h represents the capacity of the power generation equipment, Q represents the power generation of the power generation equipment, α represents the variable cost, β represents the fixed cost, and a j represents the first impairment coefficient of power generation equipment j, b j represents the second impairment coefficient of power generation equipment j, represents t′+τ j The capacity of newly constructed power generation equipment at a given time, τ j represents the construction duration of power generation equipment j, d t′ represents the third impairment coefficient of power generation equipment j at time t′, ρ represents the maximum operating time of the power generation equipment, ∈ j represents the capacity utilization rate of power generation equipment j, Represents the capacity of power generation equipment j at time t′.

[0089] The optimal solution (α*, β*) of the above maximization problem model can be regarded as the most undesirable cost scenario under capacity h. and is the production constraint of the above maximization problem model, that is, the sum of intermediate demand and final demand should not exceed total output.

[0090] The above robust optimization model can be converted into a deterministic optimization problem based on dual transformation.

[0091] First, for any variable cost α and fixed cost β, the internal minimization problem is a deterministic linear programming model with decision variables x. Since the linear programming problem is feasible and bounded, the strong duality holds. Under the optimality condition, the original problem and the dual problem have the same objective function value. The dual variables λ and π can be defined, corresponding to the two constraints respectively, then the above optimization model can be transformed into

[0092]

[0093]

[0094] In the formula, C represents the cost function, λ t′ and represents the dual variable of the power generation cost constraint, σ represents the discount rate, θ represents the tolerance for cost uncertainty, N represents the sum of all uncertain costs, N represents the upper limit of N, represents the lower limit of N, represents the variable cost of power generation equipment j, express The upper limit of express The lower limit of represents the fixed cost of power generation equipment j, express The upper limit of express The lower limit of J e Represents a collection of power generation equipment.

[0095] By solving the above transformed optimization model, we can determine the minimization of cost under any given capacity h. Further, assuming that capacity h is also a decision variable, its set can be expressed as follows:

[0096]

[0097] In the formula, Φ represents the collection of power generation equipment capacity, represents the updated capacity of power generation equipment j at time t′, c j represents the capacity of the initial power generation equipment j, η j represents the annual depreciation rate of power generation equipment j, represents t′+τ j The cumulative capacity of power generation equipment j after new construction and elimination at any time, represents t′+τ j The capacity of the newly constructed power generation equipment j at the moment, represents t′+τ j -q j The capacity of power generation equipment j that expires at the end of its life, q j represents the life of power generation equipment j, represents the upper limit of the capacity of power generation equipment j.

[0098] Based on this, we can further obtain the minimum-maximum model to determine the worst-case cost minimization, which is expressed as follows:

[0099] g * =min h∈Φ g(h)=min h∈Φ max α,β,λ,π {C(α,β,λ,π;h)|st};

[0100] Where g represents the worst-case cost to be minimized. The robust optimization problem in the above formula can be converted into a deterministic optimization problem through dual transformation.

[0101] For any (α,β,λ,π), the inner minimization problem is a programming model of a deterministic linear problem. Since the linear programming problem is feasible and bounded, the strong duality holds. Therefore, the primal problem and its dual problem have the same optimal objective function value.

[0102] The dual variable γ corresponding to each inequality in the above constraints 1j , γ 2j , γ 3j , γ 4j and γ5, augmenting the outer minimization with the dual of the inner maximization, so that the interval dynamic robust optimization can be determined to minimize the worst-case cost, that is:

[0103]

[0104] Where ω={ω1,…,ω T′}, ω represents the cost vector, ω1 represents the cost vector at the first moment, ω T′ represents the cost vector at the time T′, Y={y={x,h}′}, y represents the combination of the capacities of power generation equipment corresponding to different costs, and Y represents the set composed of y.

[0105] Based on this, by using the dual variables and the above series of expressions for transformation, we can get the robust interval optimization model, which can be expressed as:

[0106]

[0107] In the formula, G represents the robust optimization problem function, h represents the capacity of the power generation equipment, θ represents the tolerance of cost uncertainty, and N represents the sum of all uncertain costs. N represents the upper limit of N, represents the lower limit of N, represents the variable cost of power generation equipment j, express The upper limit of express The lower limit of represents the fixed cost of power generation equipment j, express The upper limit of express The lower limit of γ 1j , γ 2j , γ 3j , γ 4j and γ5 represent dual variables, Q represents the power generation of the power generation equipment, j represents the power generation equipment, J e Represents the set of all power generation equipment.

[0108] Accordingly, the constraints of the above robust interval optimization model can be as follows:

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] In the formula, a j represents the first impairment coefficient of power generation equipment j, represents the power generation of power generation equipment j at time t′, d t′ represents the third loss factor of the power generation equipment at time t′, b j represents the second impairment coefficient of power generation equipment j, represents the updated capacity of power generation equipment j at time t′, represents t′+τ j The cumulative capacity of power generation equipment j after new construction and elimination at any time, represents t′+τ j The capacity of the newly constructed power generation equipment j at the time, represents t′+τ j -q j The capacity of power generation equipment j that expires at the end of its life, q j represents the life of power generation equipment j, represents the capacity of power generation equipment j at time t′, represents the upper capacity limit of power generation equipment j at time t′, c j represents the initial equipment capacity of power generation equipment j, η j represents the annual depreciation rate of power generation equipment j, t′ represents the time in the construction period of power generation equipment, ρ represents the maximum operating time of power generation equipment, ∈ j represents the capacity utilization rate of power generation equipment j, σ represents the discount rate, represents the energy transaction volume between energy supplier s and energy demander b at time t, represents the power supply of energy supplier s at time t, j represents the power generation equipment, J e Represents a collection of power generation equipment.

[0119] In some embodiments, the expression of the second objective function is:

[0120]

[0121] In the formula, F B represents the energy usage cost of energy demanders participating in end-to-end energy trading, R bs represents the cost of transactions between energy demander b and energy supplier in end-to-end energy transactions, R bg represents the cost of energy demand party b purchasing energy from the grid, represents the price of leasing shared energy storage by energy demand party b, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, It represents the amount of electricity purchased by energy demander b from the power grid at time t, t represents the statistical time in the operation cycle, T represents the set of statistical time, s represents the energy supplier, S represents the set of all energy suppliers, and b represents the energy demander.

[0122] In this embodiment, the total cost of the energy demander is determined by considering the cost of the energy demander participating in end-to-end energy trading, the cost of purchasing electricity from the power grid, and the cost of leasing shared energy storage.

[0123] Here, the energy demand parameters of the energy demander may include: energy price of end-to-end energy trading, energy trading volume of end-to-end energy trading, on-grid electricity price, electricity purchase volume and other parameters; the capacity parameters of shared energy storage may include parameters such as the capacity and price of leasing shared energy storage and other parameters.

[0124] Optionally, the constraints of the second objective function include energy balance constraints of energy demanders and shared energy storage capacity constraints.

[0125] The expression of energy balance constraint is:

[0126]

[0127] The expression of shared energy storage capacity constraint is:

[0128]

[0129] In the formula, represents the energy transaction volume between energy supplier s and energy demander b at time t, represents the amount of energy purchased from the power grid by energy demand party b at time t, represents the discharge amount of energy demand party b at time t, represents the charging capacity of energy demand party b at time t, represents the fixed demand for shared energy storage at time t, Indicates that the shared energy storage is in charging state at time t, Indicates that the shared energy storage is in a discharging state at time t, Indicates the maximum charging and discharging power of the shared energy storage, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the capacity of shared energy storage leased by energy demander b at time t-1, η abs represents the charging efficiency of shared energy storage, η relea represents the charging efficiency of shared energy storage, Δt represents the unit time, represents the capacity of the shared energy storage leased by energy demand party b at the initial moment, E b represents the upper limit of the capacity of shared energy storage leased by energy demand party b, E m represents the total capacity of shared energy storage, s represents the energy supplier, S represents the set of all energy suppliers, b represents the energy demander, and B represents the set of all energy demanders.

[0130] In some embodiments, the bargaining model is expressed as:

[0131]

[0132] In the formula, represents the optimal solution of the bargaining model, F S (x) represents the energy supply profit of the energy supplier when the solution of the bargaining model is x, that is, the first objective function, f S represents the initial energy supply profit when the energy supplier negotiation breaks down, F B (x) represents the energy use cost of the energy demander when the solution of the bargaining model is x, that is, the second objective function, f B represents the initial energy use cost when the energy demand party's negotiation breaks down, x represents the solution of the bargaining model, X represents the set of solutions of the bargaining model, and st represents the constraints in the bargaining model.

[0133] In this embodiment, a bargaining model is established through the first objective function, the second objective function, and the profit of the energy demander and the cost of the energy supplier when the negotiation breaks down. The bargaining model can be optimized and solved to obtain the corresponding Nash equilibrium solution to maximize the overall benefits.

[0134] Here, F S (x) can be directly substituted into the first objective function, F B (x) can be directly substituted by the second objective function. In the subsequent optimization process, the first objective function and the second objective function are used to perform calculations to obtain the corresponding optimal solution.

[0135] Optionally, in order to facilitate solving the bargaining model, the above formula can be converted into logarithmic form:

[0136]

[0137] Optionally, based on the robust interval optimization model and the bargaining model, the capacity configuration of shared energy storage under end-to-end energy trading is determined, and the robust interval optimization model can be used as the constraint of the first objective function to determine the constraint of the second objective function; the bargaining model is optimized and solved according to the constraints of the first objective function, the second objective function, the robust interval optimization model and the second objective function to determine the optimal solution of the bargaining model; based on the capacity of the shared energy storage leased by each energy demand party in the optimal solution of the bargaining model, the capacity configuration of the shared energy storage under end-to-end energy trading is obtained.

[0138] Here, the optimization solution of the bargaining model can be solved by a solver. The capacity of the shared energy storage leased by each energy demand party in the optimal solution is the optimal capacity of the shared energy storage leased by each energy demand party when conducting end-to-end energy transactions. Therefore, the capacity of the shared energy storage can be optimally configured according to the optimal capacity to improve the utilization rate of the energy storage equipment.

[0139] The embodiment of the present invention establishes a first objective function with the goal of maximizing energy supply profit on the energy supplier side, and establishes a second objective function with the goal of minimizing energy use cost on the energy demand side, which can accurately determine the goals of the energy supplier and the energy demand side; wherein, the uncertainty of the cost of the energy supplier is also considered, and a robust interval optimization model is established with the goal of minimizing the worst cost of the construction and operation cost of the energy supplier, and the construction and operation cost of the energy supplier is added to the consideration of the shared energy storage capacity configuration, and all factors affecting the shared energy storage are fully considered. Finally, a bargaining model with the goal of maximizing overall benefits is established based on the first objective function and the second objective function, and based on the robust interval optimization model and the bargaining model, the capacity optimization plan of the shared energy storage is determined, which can accurately determine the capacity configuration of the shared energy storage, maximize the interests of the energy supplier and the energy demand side, and improve the utilization rate of the shared energy storage, and ensure the rationality of the shared energy storage capacity configuration. Among them, an energy price model is established based on the supply-demand ratio and compensation factors between energy suppliers and energy demanders. Considering the dynamically changing energy prices in end-to-end energy transactions can make the optimal solution of the bargaining model more reasonable, thereby ensuring the rationality and robustness of the shared energy storage capacity configuration.

[0140] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0141] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0142] Figure 3 The structure diagram of the shared energy storage capacity optimization configuration device provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0143] like Figure 3 As shown, the shared energy storage capacity optimization configuration device 30 includes:

[0144] A first establishing module 31 is used to establish a first objective function based on energy supply parameters and equipment parameters of the energy supplier with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, and to establish a robust interval optimization model with the goal of minimizing the worst cost based on the uncertainty of the cost of the energy supplier;

[0145] A second establishing module 32 is used to establish a second objective function based on the energy demand parameter of the energy demand party and the capacity parameter of the shared energy storage, with the goal of minimizing the energy use cost of the energy demand party in performing end-to-end energy trading;

[0146] A third establishing module 33 is used to establish a bargaining model based on the first objective function and the second objective function with the goal of maximizing the overall benefit of end-to-end energy trading;

[0147] Determination module 34, used to determine the capacity configuration of shared energy storage under end-to-end energy trading based on the robust interval optimization model and the bargaining model

[0148] In a possible implementation, the shared energy storage capacity optimization configuration device 30 further includes a price module, which is used to:

[0149] Determine the supply-demand ratio parameter between the energy supplier and the energy demander according to the energy supply of the energy supplier and the energy demand of the energy demander;

[0150] Based on the supply-demand ratio parameters and the preset compensation factors, the energy price model for end-to-end energy trading is determined.

[0151] In a possible implementation, the expression of the first objective function is:

[0152]

[0153] In the formula, F s represents the energy supply profit of the energy supplier participating in the end-to-end energy transaction, R ss represents the revenue from transactions with energy demand parties in end-to-end energy transactions, R sg It represents the revenue from energy suppliers selling energy to the power grid. represents the income of energy suppliers participating in carbon trading, R trad represents the transaction cost of energy suppliers participating in end-to-end energy transactions, R cost It represents the cost of building equipment and generating electricity for energy suppliers. represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, represents the actual power generation of energy supplier s at time t, Represents the unit income coefficient of CO2 in carbon trading, represents the carbon emission coefficient of distributed generation, λ trad represents the transaction cost coefficient of end-to-end energy trading, σ represents the discount rate, represents the variable cost of power generation equipment j at time t′, represents the fixed cost of power generation equipment j at time t′, represents the power generation of power generation equipment j at time t′, represents the capacity of power generation equipment j at time t′, t represents the statistical time in the operation cycle, T represents the set of statistical time, t′ represents the time in the construction cycle of the power generation equipment, T′ represents the set of time in the construction cycle, j represents the power generation equipment, J e represents the set of all power generation equipment, s represents the energy supplier, b represents the energy demander, and B represents the set of all energy demanders.

[0154] In one possible implementation, the robust interval optimization model is expressed as:

[0155]

[0156] In the formula, G represents the robust optimization problem function, h represents the capacity of distributed generation equipment, θ represents the tolerance of cost uncertainty, and N represents the sum of all uncertain costs. N represents the upper limit of N, represents the lower limit of N, represents the variable cost of power generation equipment j at time t′, express The upper limit of express The lower limit of represents the fixed cost of power generation equipment j at time t′, Represents β j t′ The upper limit of The lower limit of the representation γ 1j, γ 2j , γ 3j , γ 4j and γ5 represent dual variables, Q represents the power generation of the power generation equipment, j represents the power generation equipment, J e Represents the set of all power generation equipment.

[0157] In a possible implementation, the expression of the second objective function is:

[0158]

[0159] In the formula, F B represents the energy usage cost of energy demanders participating in end-to-end energy trading, R bs represents the cost of transactions between energy demander b and energy supplier in end-to-end energy transactions, R bg represents the cost of energy demand party b purchasing energy from the grid, represents the price of leasing shared energy storage by energy demand party b, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, It represents the amount of energy purchased by energy demander b from the power grid at time t, t represents the statistical time in the operation cycle, T represents the set of statistical time, s represents the energy supplier, S represents the set of all energy suppliers, and b represents the energy demander.

[0160] In one possible implementation, the bargaining model is expressed as:

[0161]

[0162] In the formula, represents the optimal solution of the bargaining model, F S (x) represents the energy supply profit of the energy supplier when the solution of the bargaining model is x, f S represents the initial energy supply profit when the energy supplier negotiation breaks down, F B (x) represents the energy cost of the energy demander when the solution of the bargaining model is x, f B represents the initial energy use cost when the energy demand party's negotiation breaks down, x represents the solution of the bargaining model, X represents the set of solutions of the bargaining model, and st represents the constraints in the bargaining model.

[0163] In a possible implementation, the price module is specifically used to:

[0164] According to the expression: Determine energy price models for end-to-end energy trading;

[0165] In the formula, represents the energy price of end-to-end energy trading at time t, represents the grid-connected electricity price at time t, η t represents the compensation factor at time t, It represents the supply price of the power grid at time t, and SDR represents the supply-demand ratio parameter.

[0166] In a possible implementation, the constraints of the second objective function include energy balance constraints of energy demanders and shared energy storage capacity constraints.

[0167] In one possible implementation, the energy balance constraint is expressed as:

[0168]

[0169] The expression of shared energy storage capacity constraint is:

[0170]

[0171] In the formula, represents the energy transaction volume between energy supplier s and energy demander b at time t, represents the amount of energy purchased from the power grid by energy demand party b at time t, represents the discharge amount of energy demand party b at time t, represents the charging capacity of energy demand party b at time t, represents the fixed demand for shared energy storage at time t, Indicates that the shared energy storage is in charging state at time t, Indicates that the shared energy storage is in a discharging state at time t, Indicates the maximum charging and discharging power of the shared energy storage, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the capacity of shared energy storage leased by energy demander b at time t-1, η abs represents the charging efficiency of shared energy storage, η relea represents the charging efficiency of shared energy storage, Δt represents the unit time, represents the capacity of the shared energy storage leased by energy demand party b at the initial moment, E b represents the upper limit of the capacity of shared energy storage leased by energy demand party b, E m represents the total capacity of shared energy storage, s represents the energy supplier, S represents the set of all energy suppliers, b represents the energy demander, and B represents the set of all energy demanders.

[0172] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0173] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0174] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0175] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of shared energy storage capacity, characterized in that: include: According to the energy supply parameters and equipment parameters of the energy supplier, a first objective function is established with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, and based on the uncertainty of the cost of the energy supplier, a robust interval optimization model is established with the goal of minimizing the worst cost; According to the energy demand parameters of the energy demander and the capacity parameters of the shared energy storage, a second objective function is established with the goal of minimizing the energy use cost of the energy demander in performing end-to-end energy trading; According to the first objective function and the second objective function, a bargaining model is established with the goal of maximizing the overall benefit of end-to-end energy trading; Based on the robust interval optimization model and bargaining model, the capacity configuration of shared energy storage under end-to-end energy trading is determined.

2. The method for optimizing the configuration of shared energy storage capacity according to claim 1, characterized in that: Before establishing the first objective function based on the energy supply parameters and equipment parameters of the energy supplier with the goal of maximizing the energy supply profit of the energy supplier in the end-to-end energy transaction, the method further includes: Determine the supply-demand ratio parameter between the energy supplier and the energy demander according to the energy supply of the energy supplier and the energy demand of the energy demander; Based on the supply-demand ratio parameter and the preset compensation factor, an energy price model for end-to-end energy trading is determined.

3. The method for optimizing the configuration of shared energy storage capacity according to claim 1, characterized in that: The expression of the first objective function is: In the formula, F s represents the energy supply profit of the energy supplier participating in the end-to-end energy transaction, R ss represents the revenue from transactions with energy demand parties in end-to-end energy transactions, R sg It represents the revenue from energy suppliers selling energy to the power grid. represents the income of energy suppliers participating in carbon trading, R trad represents the transaction cost of energy suppliers participating in end-to-end energy transactions, R cost It represents the cost of building equipment and generating electricity for energy suppliers. represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, represents the actual power generation of energy supplier s at time t, Represents the unit income coefficient of CO2 in carbon trading, represents the carbon emission coefficient of distributed generation, λ trad represents the transaction cost coefficient of end-to-end energy trading, σ represents the discount rate, represents the variable cost of power generation equipment j at time t′, represents the fixed cost of power generation equipment j at time t′, represents the power generation of power generation equipment j at time t′, represents the capacity of power generation equipment j at time t′, t represents the statistical time in the operation cycle, T represents the set of statistical time, t′ represents the time in the construction cycle of the power generation equipment, T′ represents the set of time in the construction cycle, j represents the power generation equipment, J e represents the set of all power generation equipment, s represents the energy supplier, b represents the energy demander, and B represents the set of all energy demanders.

4. The method for optimizing the configuration of shared energy storage capacity according to claim 3, characterized in that: The expression of the robust interval optimization model is: In the formula, G represents the robust optimization problem function, h represents the capacity of the power generation equipment, θ represents the tolerance of cost uncertainty, and N represents the sum of all uncertain costs. N represents the upper limit of N, represents the lower limit of N, represents the variable cost of power generation equipment j at time t′, express The upper limit of express The lower limit of represents the fixed cost of power generation equipment j at time t′, express The upper limit of The lower limit of the representation γ 1j , γ 2j , γ 3j , γ 4j and γ5 represent dual variables, Q represents the power generation of the power generation equipment, j represents the power generation equipment, J e Represents the set of all power generation equipment.

5. The method for optimizing the configuration of shared energy storage capacity according to claim 1, characterized in that: The expression of the second objective function is: In the formula, F B represents the energy usage cost of the energy demand party participating in end-to-end energy trading, R bs represents the cost of transactions between energy demander b and energy supplier in end-to-end energy transactions, R bg represents the cost of energy demand party b purchasing energy from the grid, represents the price of leasing shared energy storage by energy demand party b, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the energy price of end-to-end energy trading at time t, represents the energy transaction volume between energy supplier s and energy demander b at time t, Δt represents the unit time, represents the grid-connected electricity price at time t, It represents the amount of electricity purchased by energy demander b from the power grid at time t, t represents the statistical time in the operation cycle, T represents the set of statistical time, s represents the energy supplier, S represents the set of all energy suppliers, and b represents the energy demander.

6. The method for optimizing the configuration of shared energy storage capacity according to claim 1, characterized in that: The expression of the bargaining model is: In the formula, represents the optimal solution of the bargaining model, F S (x) represents the energy supply profit of the energy supplier when the solution of the bargaining model is x, f S represents the initial energy supply profit when the energy supplier negotiation breaks down, F B (x) represents the energy cost of the energy demander when the solution of the bargaining model is x, f B represents the initial energy use cost when the energy demand party's negotiation breaks down, x represents the solution of the bargaining model, X represents the set of solutions of the bargaining model, and st represents the constraints in the bargaining model.

7. The method for optimizing the configuration of shared energy storage capacity according to claim 2, characterized in that: Based on the supply-demand ratio parameter and the preset compensation factor, an energy price model for end-to-end energy trading is determined, including: According to the expression: Determine energy price models for end-to-end energy trading; In the formula, represents the energy price of end-to-end energy trading at time t, represents the grid-connected electricity price at time t, η t represents the compensation factor at time t, It represents the supply price of the power grid at time t, and SDR represents the supply-demand ratio parameter.

8. The method for optimizing the configuration of shared energy storage capacity according to any one of claims 1 to 7, characterized in that: The constraints of the second objective function include energy balance constraints of energy demanders and shared energy storage capacity constraints.

9. The method for optimizing the configuration of shared energy storage capacity according to claim 8, characterized in that: The expression of the energy balance constraint is: The expression of the shared energy storage capacity constraint is: In the formula, represents the energy transaction volume between energy supplier s and energy demander b at time t, represents the amount of energy purchased from the power grid by energy demand party b at time t, represents the discharge amount of energy demand party b at time t, represents the charging capacity of energy demand party b at time t, represents the fixed demand for shared energy storage at time t, Indicates that the shared energy storage is in charging state at time t, Indicates that the shared energy storage is in a discharging state at time t, Indicates the maximum charging and discharging power of the shared energy storage, represents the capacity of shared energy storage leased by energy demand party b at time t, represents the capacity of shared energy storage leased by energy demander b at time t-1, η abs represents the charging efficiency of shared energy storage, η relea represents the charging efficiency of shared energy storage, Δt represents the unit time, represents the capacity of the shared energy storage leased by energy demand party b at the initial moment, E b represents the upper limit of the capacity of shared energy storage leased by energy demand party b, E m represents the total capacity of shared energy storage, s represents the energy supplier, S represents the set of all energy suppliers, b represents the energy demander, and B represents the set of all energy demanders.

10. A device for optimizing the configuration of shared energy storage capacity, characterized in that: include: A first establishing module is used to establish a first objective function based on energy supply parameters and equipment parameters of the energy supplier, with the goal of maximizing the energy supply profit of the energy supplier in end-to-end energy trading, and to establish a robust interval optimization model with the goal of minimizing the worst cost based on the uncertainty of the cost of the energy supplier; A second establishing module is used to establish a second objective function based on the energy demand parameter of the energy demand party and the capacity parameter of the shared energy storage, with the goal of minimizing the energy use cost of the energy demand party in performing end-to-end energy trading; A third establishment module is used to establish a bargaining model based on the first objective function and the second objective function with the goal of maximizing the overall benefit of end-to-end energy trading; A determination module is used to determine the capacity configuration of shared energy storage under end-to-end energy trading based on the robust interval optimization model and the bargaining model.

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