Distributed optimal scheduling method for multi-member comprehensive energy and shared energy storage

Abstract

The invention relates to the technical field of smart power grids, in particular to a distributed optimal scheduling method for multi-member integrated energy and shared energy storage, and the method comprises the steps: obtaining a distributed optimal scheduling result based on a multi-region integrated energy system cooperative game interaction mechanism which meets a first preset condition and considers shared energy storage; and establishing a multi-subject decision model meeting a second preset condition, thereby establishing a cooperative game model of the multi-region integrated energy system and the shared energy storage operator, and performing distributed solution on the cooperative game model of the multi-region integrated energy system and the shared energy storage operator, and thus, a distributed optimization scheduling result oriented to multi-member comprehensive energy and shared energy storage is obtained. Therefore, the problem that in the related technology, under a multi-member integrated energy system and a shared energy storage architecture, the energy coupling relation is complex, so that the cooperation between the member individual target and the group target is difficult, and a conventional centralized optimization scheduling strategy is difficult to meet the requirements of a new scene is solved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a distributed optimization scheduling method for multi-member integrated energy and shared energy storage. Background Art

[0002] IES (integrated energy system) shows great potential in terms of deep interaction between sources, grids, loads and storage, energy conservation and carbon reduction, and improving system resilience, and is gaining momentum in theoretical research, technology application and engineering promotion. Among them, MRIES (Multi-Regional Integrated energy system) can achieve multi-energy coupling and optimized operation in a larger range through multi-member collaboration, which has attracted the attention of academia and industry. At the same time, with the promotion of shared energy storage mode, the "multi-member integrated energy system and shared energy storage" architecture provides an extremely attractive technical solution for the efficient, flexible and economical operation of MRIES.

[0003] However, in the "multi-member integrated energy system and shared energy storage" system, the investors and operators of the integrated energy system IES members and shared energy storage power stations are different, belonging to different stakeholders and having "individual rationality". Therefore, in the collaborative process of multiple members (including shared energy storage power stations), it is necessary to face the game between individual interests and overall interests, which increases the complexity of the collaborative strategy; at the same time, the data protection and information privacy of each member have also become important factors in testing the practicality of the optimization algorithm. Therefore, under the "multi-member integrated energy system and shared energy storage" architecture, the energy coupling relationship is complex, which makes it difficult to coordinate the individual goals of members with the group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the requirements of new scenarios and urgently needs to be improved. Summary of the invention

[0004] The present application provides a distributed optimization scheduling method for multi-member integrated energy and shared energy storage to solve the problem in related technologies that in a multi-member integrated energy system and a shared energy storage architecture, the energy coupling relationship is complex, resulting in difficulty in coordinating individual members' goals with group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the needs of new scenarios.

[0005] The first aspect of the present application provides a distributed optimization scheduling method for multi-member integrated energy and shared energy storage, comprising the following steps: determining a cooperative game interaction mechanism for a multi-regional integrated energy system taking into account shared energy storage that meets a first preset condition; based on the cooperative game interaction mechanism for the multi-regional integrated energy system, establishing a multi-agent decision-making model that meets a second preset condition; based on the multi-agent decision-making model, establishing a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator, and performing distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage.

[0006] Optionally, in one embodiment of the present application, the establishment of a multi-agent decision-making model that meets the second preset condition includes: establishing a shared energy storage operator model based on the battery loss cost of the shared energy storage operator, the interaction cost between the shared energy storage operator and the upper-level distribution network, and the electricity transaction cost between the shared energy storage operator and the target integrated energy system members; establishing a multi-regional integrated energy system model based on the equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost generated by the interaction between each integrated energy system member and the upper-level energy network, the flexible load scheduling cost of each integrated energy system member, and the electricity transaction cost and network access fee cost between each integrated energy system member and the shared energy storage operator; and using the shared energy storage operator model and the multi-regional integrated energy system model to establish a multi-agent decision-making model that meets the second preset condition.

[0007] Optionally, in one embodiment of the present application, the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is established based on the multi-agent decision-making model, including: determining the target constraints of the multi-agent decision-making model; based on the target constraints, analyzing the multi-agent game interaction to generate a distributed collaborative optimization strategy for the multi-regional integrated energy system taking into account shared energy storage; based on the distributed collaborative optimization strategy of the multi-regional integrated energy system, establishing the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator.

[0008] Optionally, in one embodiment of the present application, the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is:

[0009]

[0010] Among them, C ces,0 is the operating cost before the shared energy storage cooperation, that is, the independent operating cost of the shared energy storage; C ces The operating costs after the cooperation for shared energy storage; is the operating cost of the i-th integrated energy system member before cooperation; C iis the operating cost after cooperation of the i-th integrated energy system member.

[0011] Optionally, in one embodiment of the present application, the distributed solution of the cooperative game model of the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage includes: inputting target data and preset parameters into the cooperative game model of the multi-regional integrated energy system and the shared energy storage operator to solve the first objective problem and the second objective problem in sequence to obtain a solution result; and obtaining the distributed optimization scheduling result for multi-member integrated energy and shared energy storage according to the solution result.

[0012] The second aspect of the present application provides a distributed optimization scheduling device for multi-member integrated energy and shared energy storage, including: a determination module, used to determine a multi-regional integrated energy system cooperative game interaction mechanism taking into account shared energy storage that meets a first preset condition; an establishment module, used to establish a multi-agent decision-making model that meets a second preset condition based on the multi-regional integrated energy system cooperative game interaction mechanism; a processing module, used to establish a multi-regional integrated energy system and shared energy storage operator cooperative game model based on the multi-agent decision-making model, and perform distributed solution on the multi-regional integrated energy system and shared energy storage operator cooperative game model to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage.

[0013] Optionally, in one embodiment of the present application, the establishment module includes: a first establishment unit, used to establish a shared energy storage operator model based on the battery loss cost of the shared energy storage operator, the interaction cost between the shared energy storage operator and the upper-level distribution network, and the electricity transaction cost between the shared energy storage operator and the target integrated energy system members; a second establishment unit, used to establish a multi-regional integrated energy system model based on the equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost generated by the interaction between each integrated energy system member and the upper-level energy network, the flexible load scheduling cost of each integrated energy system member, and the electricity transaction cost and network access fee cost between each integrated energy system member and the shared energy storage operator; a third establishment unit, used to use the shared energy storage operator model and the multi-regional integrated energy system model to establish a multi-agent decision-making model that meets the second preset condition.

[0014] Optionally, in one embodiment of the present application, the processing module includes: a determination unit for determining the target constraints of the multi-agent decision-making model; a generation unit for analyzing the multi-agent game interaction based on the target constraints to generate a distributed collaborative optimization strategy for a multi-regional integrated energy system taking into account shared energy storage; and a fourth establishment unit for establishing a cooperative game model between the multi-regional integrated energy system and a shared energy storage operator based on the distributed collaborative optimization strategy of the multi-regional integrated energy system.

[0015] Optionally, in one embodiment of the present application, the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is:

[0016]

[0017] Among them, C ces,0 is the operating cost before the shared energy storage cooperation, that is, the independent operating cost of the shared energy storage; C ces The operating costs after the cooperation for shared energy storage; is the operating cost of the i-th integrated energy system member before cooperation; C i is the operating cost after cooperation of the i-th integrated energy system member.

[0018] Optionally, in one embodiment of the present application, the processing module includes: a first acquisition unit, used to input target data and preset parameters into the cooperative game model of the multi-regional integrated energy system and shared energy storage operators, so as to solve the first target problem and the second target problem in sequence to obtain a solution result; a second acquisition unit, used to obtain the distributed optimization scheduling result for multi-member integrated energy and shared energy storage according to the solution result.

[0019] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distributed optimization scheduling method for multi-member integrated energy and shared energy storage as described in the above embodiments.

[0020] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned distributed optimization scheduling method for multi-member integrated energy and shared energy storage.

[0021] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned distributed optimization scheduling method for multi-member integrated energy and shared energy storage.

[0022] The embodiment of the present application can establish a multi-agent decision-making model that meets the second condition based on the cooperative game interaction mechanism of the multi-regional integrated energy system taking into account shared energy storage that meets the first condition, thereby establishing a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator, and performing distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage, effectively protecting member information and data privacy, and improving energy utilization efficiency. Thus, the problem in the related technology that the energy coupling relationship is complex under the multi-member integrated energy system and shared energy storage architecture, resulting in difficulty in coordinating individual members' goals with group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the needs of new scenarios is solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a distributed optimization scheduling method for multi-member integrated energy and shared energy storage provided according to an embodiment of the present application;

[0026] Figure 2 This is an architecture diagram for multi-member integrated energy and shared energy storage for a specific embodiment of the present application;

[0027] Figure 3 A schematic diagram of solving a cooperative game in a specific embodiment of the present application;

[0028] Figure 4 A schematic diagram of an internal energy price curve of a multi-region integrated energy system according to a specific embodiment of the present application;

[0029] Figure 5 A schematic diagram of an internal transaction energy curve of a multi-region integrated energy system according to a specific embodiment of the present application;

[0030] Figure 6 A schematic diagram of the internal energy optimization scheduling result of the IES1 system according to a specific embodiment of the present application;

[0031] Figure 7 A schematic diagram of a shared energy storage optimization scheduling result of a specific embodiment of the present application;

[0032] Figure 8A schematic diagram of the structure of a distributed optimization scheduling device for multi-member integrated energy and shared energy storage provided according to an embodiment of the present application;

[0033] Fig. 9 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0035] The following is a description of the distributed optimization scheduling method for multi-member integrated energy and shared energy storage of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that in the related technology mentioned in the background technology center, under the multi-member integrated energy system and shared energy storage architecture, the energy coupling relationship is complex, which leads to difficulties in the coordination of individual goals of members and group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the needs of new scenarios, the present application provides a distributed optimization scheduling method for multi-member integrated energy and shared energy storage, in which a multi-regional integrated energy system cooperative game interaction mechanism taking into account shared energy storage that meets the first condition can be established to establish a multi-agent decision model that meets the second condition, thereby establishing a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator, and performing distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage, effectively protecting member information and data privacy, and improving energy utilization efficiency. Thus, the problem that in the related technology, under the multi-member integrated energy system and shared energy storage architecture, the energy coupling relationship is complex, which leads to difficulties in the coordination of individual goals of members and group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the needs of new scenarios is solved.

[0036] Specifically, Figure 1 A flow chart of a distributed optimization scheduling method for multi-member integrated energy and shared energy storage provided in an embodiment of the present application.

[0037] like Figure 1 As shown, the distributed optimization scheduling method for multi-member integrated energy and shared energy storage includes the following steps:

[0038] In step S101, a cooperative game interaction mechanism of a multi-regional integrated energy system taking into account shared energy storage that meets a first preset condition is determined.

[0039] It is understandable that the embodiment of the present application can determine the cooperative game interaction mechanism of the multi-regional integrated energy system taking into account the shared energy storage. For example, Figure 2 As shown, it is a diagram of the MRIES collaborative optimization architecture taking into account shared energy storage. The embodiment of the present application can determine the cooperative game interaction mechanism of the multi-regional integrated energy system taking into account shared energy storage based on the Nash negotiation theory, wherein the multi-regional integrated energy system includes multiple IES members and shared energy storage operators, wherein the IES mainly includes CCHP (Combined Cooling, Heating, and Power), GB (Grid Balancing), ISAC (Ice-storage air-conditioners) and other equipment, thereby ensuring that the cooperative game mechanism can motivate each member to actively participate, so that the IES members and the shared energy storage can realize the transfer of electric energy in space through cooperative operation, thereby reducing the operating costs of each IES member and the alliance.

[0040] Among them, in the multi-regional integrated energy system transaction process, the roles and positioning of each subject are different, and different market subjects will optimize in combination with their own profit goals. Therefore, while optimizing its own interests, any subject in the multi-regional integrated energy system will affect the realization of the goals of other market subjects, and there is a relationship of mutual influence and mutual restriction between the subjects. At the same time, since the electricity demand of each IES cannot be fully matched with the output of renewable energy, and shared energy storage can quickly store or release electricity, when the output of renewable energy in IES is excessive, it can choose to sell electricity to shared energy storage or other subjects through the interconnection line, and vice versa, it can purchase electricity from shared energy storage or other subjects; when the electricity demand of IES is too large, shared energy storage sells electricity to IES to ensure the balance of supply and demand, and when there is a surplus of electricity, shared energy storage can exchange electricity with the superior distribution network.

[0041] In step S102, based on the cooperative game interaction mechanism of the multi-regional integrated energy system, a multi-agent decision-making model that meets the second preset condition is established.

[0042] It can be understood that the embodiments of the present application can be based on the cooperative game interaction mechanism of the multi-regional integrated energy system. For example, based on the objective functions of multiple subjects such as IES and shared energy storage operators, a multi-subject decision-making model in the following steps is established, which effectively improves the feasibility of distributed optimization scheduling of multi-member integrated energy and shared energy storage.

[0043] Among them, in one embodiment of the present application, a multi-agent decision-making model that meets the second preset condition is established, including: establishing a shared energy storage operator model based on the battery loss cost of the shared energy storage operator, the interaction cost between the shared energy storage operator and the upper-level distribution network, and the electricity transaction cost between the shared energy storage operator and the target integrated energy system member; establishing a multi-regional integrated energy system model based on the equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost generated by the interaction between each integrated energy system member and the upper-level energy network, the flexible load scheduling cost of each integrated energy system member, and the electricity transaction cost and network access fee cost between each integrated energy system member and the shared energy storage operator; using the shared energy storage operator model and the multi-regional integrated energy system model to establish a multi-agent decision-making model that meets the second preset condition.

[0044] For example, the embodiment of the present application can establish a shared energy storage operator model. Specifically, taking the configuration of a shared energy storage operator in the MRIES system as an example, each IES member and the shared energy storage realize the sharing of electric energy through a two-way energy flow. The shared energy storage utilizes the peak-valley difference of electricity prices to arbitrage and improve energy utilization efficiency and economy. The mathematical model of the shared energy storage operator model is as follows:

[0045] C ces =C ces,op +C ces,grid +C ces,P2C (1)

[0046] Among them, C ces is the total operating cost of the shared energy storage operator; C ces,op is the battery loss cost of the shared energy storage operator; C ces,grid is the interaction cost between the shared energy storage operator and the upper distribution network; C ces,P2C is the electricity transaction cost between the shared energy storage operator and member IES i.

[0047] Among them, the battery loss cost C of the shared energy storage operator is ces,op The specific expression is as follows:

[0048]

[0049] Among them, ε ces is the unit loss cost of the energy storage battery; They are the charging power and discharging power of the shared energy storage at time t respectively.

[0050] The interaction cost C between the shared energy storage operator and the upper distribution network ces,grid The specific expression is as follows:

[0051]

[0052] in, are the electricity purchase price and electricity sales price of shared energy storage and upper-level distribution network at time t respectively; They are the amount of electricity purchased and sold by the shared energy storage to the upper-level distribution network at time t.

[0053] The electricity transaction cost C between the shared energy storage operator and member IES i ces,P2C The specific expression is as follows:

[0054]

[0055] in, is the power transaction power between the shared energy storage and IES i at time t. Indicates that at time t, the shared energy storage inputs electric power from IES i. When , it means that the shared energy storage outputs electric energy to IES i at time t; is the electricity trading price between shared energy storage and IES i at time t.

[0056] In addition, the embodiment of the present application can also establish an MRIES model for a multi-regional integrated energy system, and each IES member establishes connections with the shared energy storage operator in terms of power and information. When a certain IES member has insufficient dispatchable resources, it can trade power through interconnected power lines to achieve energy sharing and mutual assistance. Taking IES i as an example, the MRIES model is as follows:

[0057]

[0058] Among them, C i The total operating cost of IES i considering the participation of shared energy storage operators; C op,i , C buy,i , C sell,i , C load,i The equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost of each integrated energy system member in interaction with the upper energy network, and the flexible load dispatching cost. Among them, the embodiment of the application assumes that the grid connection fee generated during the power sharing process is borne by the IES members, and the power transaction cost C between IES i and the shared energy storage operator is P2C,i and network costs The specific formula is as follows:

[0059]

[0060] in, is the power transaction power and power transaction price between IES i and shared energy storage at time t; Indicates that at time t, IES i obtains electricity from the shared energy storage. When , it means that at time t, IES i transmits electric energy to the shared energy storage; α ex,i is the grid-connected electricity price between IES i and shared energy storage.

[0061] The operation of each device in the IES will cause certain equipment loss, and the equipment needs to be regularly repaired and maintained. In the embodiment of the present application, it is uniformly converted into equipment operation and maintenance costs, which are expressed as follows:

[0062]

[0063] Among them, f n,i is the operation and maintenance cost coefficient of the internal energy conversion equipment of IES i, n represents the type of equipment; P n,i,t is the output power value of the internal energy conversion device of IES i.

[0064] IES i members can purchase and sell energy from the superior energy network through the interconnection line. The energy price is determined by the superior energy network. The cost of purchasing and selling energy is expressed as follows:

[0065]

[0066] in, are the electricity purchase price and electricity sales price of IES at time t respectively; is the gas purchase price of IES at time t; They are the purchased power and sold power between IES i and the upper-level distribution network at time t respectively.

[0067] In addition, in order to make full use of the flexibility resources of IES, the load can be divided into two parts: rigid load and flexible load. The flexible load can be further divided into two types: transferable load and curtailable load. Since both transfer load and curtailment load need to provide economic compensation, the demand response cost is expressed as follows:

[0068]

[0069] in, are the compensation unit prices for IES transferable electrical load and transferable thermal load respectively; These are the compensation unit prices for the electrical load and thermal load that can be reduced by IES.

[0070] Therefore, the embodiment of the present application can establish a multi-agent decision-making model, effectively improving the feasibility of distributed optimization scheduling of multi-member integrated energy and shared energy storage.

[0071] In step S103, based on the multi-agent decision-making model, a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is established, and the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is solved in a distributed manner to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage.

[0072] It can be understood that the embodiments of the present application can establish a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator in the following steps based on a multi-agent decision-making model, and perform distributed solution on the cooperative game model between a multi-regional integrated energy system and a shared energy storage operator in the following steps to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage, which can make full use of the flexibility resources provided by shared energy storage, take into account the interests of members and overall benefits, realize energy optimization scheduling in a multi-agent game and distributed optimization manner, and improve the comprehensive benefits of energy use.

[0073] Among them, in one embodiment of the present application, based on the multi-agent decision-making model, a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator is established, including: determining the target constraints of the multi-agent decision-making model; based on the target constraints, analyzing the multi-agent game interaction to generate a distributed collaborative optimization strategy for the multi-regional integrated energy system taking into account shared energy storage; based on the distributed collaborative optimization strategy of the multi-regional integrated energy system, a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is established.

[0074] For example, the embodiments of the present application can use the interactive power constraints of the external power grid as the target constraints for the multi-agent decision-making model to establish target constraints. Based on the target constraints, the multi-agent game interaction is analyzed and solved to generate a distributed collaborative optimization strategy for the multi-regional integrated energy system taking into account shared energy storage, thereby achieving optimal scheduling of the multi-regional integrated energy system. Based on the distributed collaborative optimization strategy of the multi-regional integrated energy system, a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is established. This can make full use of the flexibility resources provided by the shared energy storage, take into account the interests of the members and the overall benefits, and achieve energy optimization scheduling in the form of multi-agent game and distributed optimization, thereby improving energy utilization efficiency.

[0075] To ensure the safety of the shared energy storage system, constraints such as state of charge, charging and discharging power must be met, as follows:

[0076]

[0077] Among them, E ces,t is the state of charge of the shared energy storage at time t; E ces,max 、E ces,min are the upper and lower limits of the shared energy storage charge state respectively; η ces,ch , ηces,dis are the charging efficiency and discharging efficiency of shared energy storage respectively; E ces is the battery capacity of shared energy storage; x ces,t is a binary state variable, when x ces,t When the value is 1, it means that the shared energy storage is in charging state; when x ces,t When the value is 0, it means that the shared energy storage is in the discharge state; They are the upper limits of the charging and discharging power of the shared energy storage. In addition, it is also necessary to ensure that the state of charge of the energy storage battery is not lower than the initial state of charge after one scheduling cycle.

[0078] The power interaction between shared energy storage and the upper distribution network meets the following constraints:

[0079]

[0080] The power balance constraints of the shared energy storage system are as follows:

[0081]

[0082] IES operation needs to meet the following constraints. First, the interactive power constraint with the upper-level distribution network is as follows:

[0083]

[0084] Among them, P buy,i,max , P sell,i,max They are the upper limits of electricity purchase and sales from IES i to the superior distribution network.

[0085] Secondly, IES i and the shared energy storage operator interact with each other on power constraints:

[0086]

[0087] in, They are the upper and lower limits of the interaction between IES i and the shared energy storage power.

[0088] Finally, the IES i system satisfies the electrical, thermal, and cooling power balance constraints, as shown in the following equations:

[0089]

[0090] in, is the cooling load forecast value of IES i at time t.

[0091] Next, since each IES member and the shared energy storage operator belong to different stakeholders, the embodiment of the present application can regard the collaborative operation of MRIES and the shared energy storage operator as a cooperative game. MRIES and the shared energy storage operator form a cooperative alliance by seeking collaborative cooperation, which can reduce the total operating cost of the alliance while taking into account individual rationality. Each IES member in the alliance can share electricity with other members, shared energy storage and upper-level distribution networks. Among them, the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is:

[0092]

[0093] Among them, C ces,0 is the operating cost before the shared energy storage cooperation, that is, the independent operating cost of the shared energy storage; C ces The operating costs after the cooperation for shared energy storage; is the operating cost of the i-th integrated energy system member before cooperation; C i is the operating cost after cooperation of the i-th integrated energy system member.

[0094] Therefore, the embodiment of the present application can achieve mutual benefit and win-win results between the multi-member integrated energy system and the shared energy storage operator, and improve energy utilization efficiency, while protecting the information privacy of each IES member and the shared energy storage operator.

[0095] Optionally, in one embodiment of the present application, a distributed solution is performed on the cooperative game model of the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage, including: inputting target data and preset parameters into the cooperative game model of the multi-regional integrated energy system and the shared energy storage operator to solve the first target problem and the second target problem in sequence to obtain a solution result; and obtaining a distributed optimization scheduling result for multi-member integrated energy and shared energy storage according to the solution result.

[0096] For example, since formula (15) is a non-convex nonlinear optimization problem and is difficult to solve directly, the embodiment of the present application can convert the original model into the MRIES-shared energy storage alliance benefit maximization problem and the MRIES-shared energy storage alliance electricity payment benefit maximization problem. By solving the above sub-problems in turn, the electricity trading power and electricity trading price between IES members and shared energy storage can be obtained.

[0097] Then, if Figure 3 As shown, the embodiment of the present application can be an optimization scheduling program compiled in the MATLAB 2018b environment, and the Cplex solver and the Mosek solver are called by the Yalmip toolbox to perform distributed solution on the above model. Figure 3For the specific game model solution steps and flow chart, after inputting data and preset parameters, the improved adaptive ADMM (alternating direction multiplier method) algorithm is adopted to sequentially solve subproblems 1 and 2 to obtain the equilibrium solution of the cooperative game, thereby obtaining the distributed optimization scheduling results for multi-member integrated energy and shared energy storage, so as to achieve mutual benefit and win-win results between multi-member integrated energy systems and shared energy storage operators, and improve energy utilization efficiency.

[0098] For example, the embodiments of the present application can verify the effectiveness of the game strategy based on the example analysis in the following steps.

[0099] First, if Figure 4 As shown, it is the internal energy price curve, then, Figure 5 As shown, this is the internal transaction energy curve of the system. Figure 5 In (a), during the hours 00:00-05:00, 12:00-15:00 and 17:00-22:00, due to IES 3 has a high renewable energy generation capacity, and has surplus electricity to exchange electricity with shared energy storage; in the periods of 05:00-12:00 and 18:00-21:00, the power level of IES1's load is high, while the output of renewable energy and other energy conversion equipment is insufficient to meet the load demand, resulting in a power shortage. IES1 meets its electricity demand by purchasing electricity from shared energy storage; in the period of 17:00-22:00, the load power of IES2 surges, and a large amount of electricity is purchased from shared energy storage, and the power purchased is greater than the power purchased by IES1 from shared energy storage during the same period; in the period of 12:00-14:00, IES2 sells electricity to shared energy storage due to the large photovoltaic power generation at noon to absorb the excess photovoltaic power generation; in summary, considering that IES members share electricity with shared energy storage can promote the local consumption of renewable energy and reduce operating costs.

[0100] Secondly, in Figure 5 In (b), “IES1-shared energy storage electricity price” represents the transaction electricity price between member IES1 and shared energy storage. The transaction electricity prices between other members are expressed in a similar way. Figure 5 (b) It can be seen that the transaction electricity prices between IES members and shared energy storage in each period are between the purchase and sale prices of the distribution network. In other words, IES members can purchase electricity from the energy storage system at a price lower than the purchase price from the grid, and can also sell electricity to the shared energy storage at a price higher than the sale price to the grid. Therefore, the use of a cooperative game strategy based on Nash negotiation can further mobilize the cooperation enthusiasm of each alliance member and effectively improve the operating economy of the system.

[0101] In the embodiment of the present application, in order to compare and analyze the impact of the IES low-carbon economic optimization scheduling strategy of multi-agent game on the economic and environmental benefits of each subject in the IES, two different types of example scenarios can be selected to calculate the benefits of each subject and compare them respectively:

[0102] Scenario 1: There is no power sharing between IES members and shared energy storage. Scenario 2: There is power sharing between IES members and shared energy storage.

[0103] Among them, Table 1 is the operating cost table of each entity under different scenarios. The profit scheduling results of each entity in the above scenarios are shown in Table 1:

[0104] Table 1

[0105]

[0106] Comparing the data of scenario 1 and scenario 2, it can be seen that after cooperative operation, the operating costs of members IES1, IES2, and IES 3 were reduced by 419.3, 417.3, and 419.9 yuan respectively, the shared energy storage cost was reduced by 418.2 yuan, and the total operating cost of the alliance was reduced by 1674.7 yuan, a reduction of about 3.0%. The above results show that through cooperative operation, each IES member and shared energy storage can reduce operating costs; and the share of reduced operating costs of alliance members accounts for about 1 / 4 of the total increased benefits. This shows that the benefits obtained through cooperative operation are equally shared by IES members and shared energy storage, taking into account the interests of each entity and the alliance.

[0107] Again, the embodiment of the present application can perform supply and demand balance analysis, taking IES1 as an example, Figure 6 As shown in the figure, it is the result diagram of internal energy optimization scheduling of IES1 system, where: Figure 6 (a) is the supply and demand balance diagram of power optimization scheduling after cooperative game optimization, Figure 6 (b) is the supply and demand balance diagram of thermal energy optimization scheduling after cooperative game optimization. Figure 6 (c) is the supply and demand balance diagram of cold energy optimal scheduling after cooperative game optimization, as shown in Figure 6As shown in (a), considering the advantages of low cost and environmental protection, renewable energy is consumed first. When renewable energy cannot meet the user's electricity demand, it is satisfied through GT (Gas Turbine), shared energy storage and upper-level distribution network interaction. During the 00:00-05:00 and 22:00-24:00 periods, when the electricity load level is low and the electricity price is at a low point, IES1 meets the power balance by purchasing a large amount of electricity from the upper-level distribution network and GT power generation; during the 07:00-18:00 period, when the electricity load level is high and the photovoltaic power is sufficient, the power balance is met through interaction with shared energy storage and GT power, reducing the demand for energy purchase from the upper-level distribution network. Through the cooperative operation mechanism with shared energy storage, the renewable energy consumption rate of each member in the alliance reached 100%, indicating that cooperative operation can effectively improve the renewable energy consumption of each member.

[0108] Furthermore, if Figure 6 As shown in (b), in most periods, the heat load demand of IES1 is borne by GT alone. During the period from 11:00 to 16:00, GT cannot meet the heat load demand alone, so it is borne by GT and GB together. Figure 6 As shown in (c), during the time periods of 0:00-5:00 and 23:00-24:00, the cooling load level of IES1 is low, and EC alone meets the cooling demand. During the rest of the time periods, EC and AC cooperate with each other to ensure the balance of cooling supply and demand. By analyzing the optimization scheduling results of IES1, it can be seen that the three types of loads, electricity, heat, and cooling, are coupled with each other, and each energy supply equipment meets the balance of supply and demand through synergy and complementarity, which improves the renewable energy consumption rate and realizes the low-carbon economy and efficient operation of the system.

[0109] In addition, if Figure 7 As shown in the figure, the shared energy storage scheduling results and the state of charge diagram are shown. In the period of 00:00-05:00, due to the excess power of renewable energy in IES 3, the shared energy storage interacts with IES 3 to store energy during this period, so the state of charge of the shared energy storage continues to increase, reaching a peak of 0.9; in the period of 05:00-12:00, due to the small capacity of IES1's distributed photovoltaic power generation device, the shared energy storage supplies energy to IES1 during this period, and the state of charge drops to 0.59; in the period of 12:00-16:00, IES2 and IES 3 transmit their internal surplus energy to the shared energy storage, and the state of charge rises to a peak of 0.9 again. The shared energy storage returns to the initial state of charge of 0.2 at 22:00, thus completing a complete scheduling cycle. In summary, the shared energy storage realizes the transfer of electric energy in time and space through its electric energy translation characteristics, and improves the utilization rate of energy storage equipment.

[0110] It can be seen that based on the Nash negotiation theory, considering the low-carbon economy of the system under the dual-carbon background, a comprehensive energy distributed collaborative optimization strategy for virtual power plants taking into account shared energy storage is proposed, and a decision-making model for each subject is built. The cooperative game model in the embodiment of this application is also helpful in analyzing the energy interaction between different market decision-making subjects and providing parameter support information for relevant agencies such as the government in actual engineering.

[0111] According to the distributed optimization scheduling method for multi-member integrated energy and shared energy storage proposed in the embodiment of the present application, a multi-agent decision-making model that meets the second condition can be established based on the cooperative game interaction mechanism of the multi-regional integrated energy system taking into account shared energy storage that meets the first condition, thereby establishing a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator, and performing distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage, effectively protecting member information and data privacy, and improving energy utilization efficiency. Thus, the problem in the related technology that under the multi-member integrated energy system and shared energy storage architecture, the energy coupling relationship is complex, which leads to difficulty in coordinating individual goals of members with group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the needs of new scenarios is solved.

[0112] Next, a distributed optimization scheduling device for multi-member integrated energy and shared energy storage proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0113] Figure 8 It is a block diagram of a distributed optimization scheduling device for multi-member integrated energy and shared energy storage according to an embodiment of the present application.

[0114] like Figure 8 As shown, the distributed optimization scheduling device 10 for multi-member integrated energy and shared energy storage includes: a determination module 100, an establishment module 200 and a processing module 300.

[0115] Specifically, the determination module 100 is used to determine a cooperative game interaction mechanism of a multi-regional integrated energy system taking into account shared energy storage that meets a first preset condition.

[0116] A module 200 is established to establish a multi-agent decision-making model that meets the second preset condition based on the cooperative game interaction mechanism of the multi-regional integrated energy system.

[0117] Processing module 300 is used to establish a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator based on a multi-agent decision-making model, and to perform distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage.

[0118] Optionally, in one embodiment of the present application, the establishing module 200 includes: a first establishing unit, a second establishing unit and a third establishing unit.

[0119] Among them, the first establishment unit is used to establish a shared energy storage operator model based on the battery loss cost of the shared energy storage operator, the interaction cost between the shared energy storage operator and the upper-level distribution network, and the electricity transaction cost between the shared energy storage operator and the target integrated energy system members.

[0120] The second establishment unit is used to establish a multi-regional integrated energy system model based on the equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost generated by the interaction between each integrated energy system member and the superior energy network, the flexible load scheduling cost of each integrated energy system member, and the electricity transaction cost and grid access fee cost between each integrated energy system member and the shared energy storage operator.

[0121] The third establishing unit is used to establish a multi-agent decision-making model that meets the second preset condition by utilizing the shared energy storage operator model and the multi-regional integrated energy system model.

[0122] Optionally, in one embodiment of the present application, the processing module 300 includes: a determining unit, a generating unit and a fourth establishing unit.

[0123] Among them, the determination unit is used to determine the target constraint conditions of the multi-agent decision-making model.

[0124] The generation unit is used to analyze the multi-agent game interaction based on the target constraints to generate a distributed collaborative optimization strategy for a multi-regional integrated energy system taking into account shared energy storage.

[0125] The fourth establishment unit is used to establish a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator based on the distributed collaborative optimization strategy of the multi-regional integrated energy system.

[0126] Optionally, in one embodiment of the present application, the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is:

[0127]

[0128] Among them, C ces,0 is the operating cost before the shared energy storage cooperation, that is, the independent operating cost of the shared energy storage; C ces The operating costs after the cooperation for shared energy storage; is the operating cost of the i-th integrated energy system member before cooperation; C i is the operating cost after cooperation of the i-th integrated energy system member.

[0129] Optionally, in one embodiment of the present application, the processing module 300 includes: a first acquisition unit and a second acquisition unit.

[0130] Among them, the first acquisition unit is used to input the target data and preset parameters into the cooperative game model of the multi-regional integrated energy system and the shared energy storage operator, so as to solve the first target problem and the second target problem in turn to obtain the solution result.

[0131] The second acquisition unit is used to obtain distributed optimization scheduling results for multi-member integrated energy and shared energy storage according to the solution results.

[0132] It should be noted that the aforementioned explanation of the embodiment of the distributed optimization scheduling method for multi-member integrated energy and shared energy storage is also applicable to the distributed optimization scheduling device for multi-member integrated energy and shared energy storage of this embodiment, and will not be repeated here.

[0133] According to the distributed optimization scheduling device for multi-member integrated energy and shared energy storage proposed in the embodiment of the present application, a multi-agent decision-making model that meets the second condition can be established based on the cooperative game interaction mechanism of the multi-regional integrated energy system taking into account shared energy storage that meets the first condition, thereby establishing a cooperative game model between the multi-regional integrated energy system and the shared energy storage operator, and performing distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage, effectively protecting member information and data privacy, and improving energy utilization efficiency. Thus, the problem in the related technology that the energy coupling relationship is complex under the multi-member integrated energy system and shared energy storage architecture, resulting in difficulty in coordinating individual members' goals with group goals, making it difficult for conventional centralized optimization scheduling strategies to meet the needs of new scenarios is solved.

[0134] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0135] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0136] When the processor 902 executes the program, the distributed optimization scheduling method for multi-member integrated energy and shared energy storage provided in the above embodiment is implemented.

[0137] Furthermore, the electronic device further comprises:

[0138] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0139] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0140] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0141] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0142] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0143] The processor 902 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0144] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned distributed optimization scheduling method for multi-member integrated energy and shared energy storage.

[0145] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned distributed optimization scheduling method for multi-member integrated energy and shared energy storage.

[0146] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0147] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0148] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0149] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0150] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0152] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0153] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A distributed optimization scheduling method for multi-member integrated energy and shared energy storage, characterized in that: The following steps are involved: Determine a cooperative game interaction mechanism for a multi-regional integrated energy system taking into account shared energy storage that meets the first precondition; Based on the cooperative game interaction mechanism of the multi-regional integrated energy system, a multi-agent decision-making model that meets the second preset condition is established; Based on the multi-agent decision-making model, a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator is established, and the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is solved in a distributed manner to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage.

2. The method according to claim 1, characterized in that The establishing of a multi-agent decision-making model that meets the second preset condition includes: A shared energy storage operator model is established based on the shared energy storage operator's battery loss cost, the interaction cost between the shared energy storage operator and the upper-level distribution network, and the electricity transaction cost between the shared energy storage operator and the target integrated energy system members. A multi-regional integrated energy system model is established based on the equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost generated by the interaction between each integrated energy system member and the superior energy network, the flexible load dispatch cost of each integrated energy system member, the power transaction cost and grid fee cost between each integrated energy system member and the shared energy storage operator; The shared energy storage operator model and the multi-region integrated energy system model are used to establish a multi-agent decision-making model that meets the second preset condition.

3. The method according to claim 1, characterized in that The multi-regional integrated energy system and shared energy storage operator cooperative game model is established based on the multi-agent decision-making model, including: Determining target constraints of the multi-agent decision-making model; Based on the target constraints, the multi-agent game interaction is analyzed to generate a distributed collaborative optimization strategy for a multi-region integrated energy system taking into account shared energy storage; Based on the distributed collaborative optimization strategy of the multi-regional integrated energy system, a cooperative game model between the multi-regional integrated energy system and shared energy storage operators is established.

4. The method according to claim 1, characterized in that The cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is: Among them, C ces,0 is the operating cost before the shared energy storage cooperation, that is, the independent operating cost of the shared energy storage; C ces is the operating cost after the shared energy storage cooperation; C i 0 is the operating cost of the i-th integrated energy system member before cooperation; C i is the operating cost after cooperation of the i-th integrated energy system member.

5. The method according to claim 1, characterized in that The distributed solution of the cooperative game model of the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage includes: Inputting the target data and preset parameters into the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to solve the first target problem and the second target problem in sequence to obtain a solution result; The distributed optimization scheduling result for multi-member integrated energy and shared energy storage is obtained according to the solution result.

6. A distributed optimization scheduling device for multi-member integrated energy and shared energy storage, characterized in that: include: A determination module, used to determine a cooperative game interaction mechanism of a multi-regional integrated energy system taking into account shared energy storage that meets a first preset condition; Establishing a module for establishing a multi-agent decision-making model that meets the second preset condition based on the cooperative game interaction mechanism of the multi-regional integrated energy system; A processing module is used to establish a cooperative game model between a multi-regional integrated energy system and a shared energy storage operator based on the multi-agent decision-making model, and to perform distributed solution on the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to obtain a distributed optimization scheduling result for multi-member integrated energy and shared energy storage.

7. The device according to claim 6, characterized in that The establishment module includes: A first establishing unit is used to establish a shared energy storage operator model based on the battery loss cost of the shared energy storage operator, the interaction cost between the shared energy storage operator and the upper distribution network, and the electric energy transaction cost between the shared energy storage operator and the target integrated energy system member; The second establishment unit is used to establish a multi-regional integrated energy system model based on the equipment maintenance cost of each integrated energy system member, the energy purchase and sale cost generated by the interaction between each integrated energy system member and the superior energy network, the flexible load dispatching cost of each integrated energy system member, and the power transaction cost and network fee cost between each integrated energy system member and the shared energy storage operator; The third establishing unit is used to establish a multi-agent decision-making model that meets the second preset condition by using the shared energy storage operator model and the multi-regional integrated energy system model.

8. The device according to claim 6, characterized in that The processing module comprises: A determination unit, used to determine the target constraint conditions of the multi-agent decision-making model; A generation unit, configured to analyze the multi-agent game interaction based on the target constraint conditions to generate a distributed collaborative optimization strategy for a multi-regional integrated energy system taking into account shared energy storage; The fourth establishing unit is used to establish a cooperative game model between the multi-regional integrated energy system and a shared energy storage operator based on the distributed collaborative optimization strategy of the multi-regional integrated energy system.

9. The device according to claim 6, characterized in that The cooperative game model between the multi-regional integrated energy system and the shared energy storage operator is: Among them, C ces,0 is the operating cost before the shared energy storage cooperation, that is, the independent operating cost of the shared energy storage; C ces The operating costs after the cooperation for shared energy storage; is the operating cost of the i-th integrated energy system member before cooperation; C i is the operating cost after cooperation of the i-th integrated energy system member.

10. The device according to claim 6, characterized in that The processing module comprises: A first acquisition unit is used to input the target data and preset parameters into the cooperative game model between the multi-regional integrated energy system and the shared energy storage operator to solve the first target problem and the second target problem in sequence to obtain a solution result; The second acquisition unit is used to obtain the distributed optimization scheduling result for multi-member integrated energy and shared energy storage according to the solution result.

11. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distributed optimization scheduling method for multi-member integrated energy and shared energy storage as described in any one of claims 1 to 5.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a distributed optimization scheduling method for multi-member integrated energy and shared energy storage as described in any one of claims 1 to 5.

13. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the distributed optimization scheduling method for multi-member integrated energy and shared energy storage as described in any one of claims 1 to 5.

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