A Microgrid Electric Energy Cooperation Method, System, Electronic Device and Storage Medium Based on Cooperative Game and the Intervention of a Shared Energy Storage Power Station

Abstract

The present invention relates to a microgrid electric energy cooperation method, system, electronic device and storage medium based on cooperative game and the intervention of a shared energy storage power station. The above method includes: calculating the contribution degree of each microgrid user to the entire system according to the purchased electricity quantity and sold electricity quantity of each microgrid user; determining the Nash negotiation breakdown point between the entire microgrid alliance and the shared energy storage power station based on the asymmetric Nash negotiation model, and distinguishing whether the microgrid users participate in the electric energy cooperation with the negotiation breakdown point as the boundary; solving the maximum benefit of the entire microgrid alliance and the shared energy storage power station by the alternating direction method of multipliers; and distributing the benefits according to the contribution degree of each microgrid user. Compared with the prior art, the present invention conforms to the trend of the sharing economy, introduces the shared energy storage power station into the microgrid alliance for electric energy cooperation, and at the same time uses the knowledge of cooperative game in economics to seek the maximum benefit of the participating subjects, which not only effectively alleviates the problem of energy waste, but also improves the economic benefits of microgrid users.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a microgrid electric energy cooperation method, system, electronic device, and storage medium based on cooperative game and shared energy storage power station intervention. Background Art

[0002] A shared energy storage power station can realize the conversion, storage, and utilization of electric energy, which helps to alleviate energy waste and promote the consumption of renewable energy. Since the current investment cost of energy storage power stations is relatively high, a sharing economy-based business model is introduced, which can not only improve the energy utilization rate of each microgrid user but also reduce the cost of microgrid users. Currently, domestic and foreign research on shared energy storage mainly focuses on its operation and scheduling, and there is less research on the trading mode between entities and the bargaining of electric energy. Existing related research uses non-cooperative games for electric energy cooperation between microgrids. Such a method can indeed achieve the goal of maximizing its own interests. However, non-cooperative games establish functions with the goal of each participating entity itself, which will cause each participating entity to blindly pursue economic benefits and lead to disorderly competition in the market. Similarly, there are also some studies that use symmetric Nash bargaining in cooperative games for electric energy cooperation between microgrids. However, symmetric Nash bargaining evenly distributes the benefits of the microgrid alliance to each microgrid, which is obviously unfair to the microgrid entities that have made greater contributions and is likely to trigger market contradictions. Moreover, the above two studies cannot alleviate the serious problem of energy waste at present. In recent years, the rapid development of the sharing economy has made shared energy storage also become a new business model for energy storage in the future. Therefore, researching a microgrid electric energy cooperation method based on cooperative game and shared energy storage power station intervention is of great significance for electric energy cooperation, microgrid users, and alleviating the problem of energy waste under the new business model. The present invention also involves cooperative games, but different from the symmetric Nash bargaining in the above text, the present invention uses asymmetric Nash bargaining in cooperative games to avoid the situation of unfair benefit distribution.

[0003] Currently, the electric energy cooperation in the power market is still in the exploratory stage. Cooperative games can realize the cooperation between microgrids and energy storage power stations. The energy storage power station can store the electric energy that microgrid users have not used up and take it out when needed, which is manifested as the purchase and sale of electric energy by the energy storage power station, so that more energy is utilized rather than discarded, alleviating the problem of energy waste existing in microgrid users. The business model of the shared energy storage power station connects multiple microgrid users and energy storage power stations, which will play an important role in alleviating the problem of energy waste. At the same time, using cooperative games helps to solve the interest contradictions between microgrid users, improve the economic benefits of themselves and energy storage power stations, and achieve a win-win situation. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a microgrid electric energy cooperation method, system, electronic device and storage medium based on cooperative game and the intervention of a shared energy storage power station, which solves the current energy waste problem and, on the basis of solving the problem, finds an electric energy cooperation method that enables all participating entities to obtain economic benefits.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station, the method comprising the following steps:

[0007] Step 1: Calculate the contribution degree of each microgrid user to the entire system according to the electricity purchased and sold by each microgrid user.

[0008] Step 2: Establish an asymmetric Nash bargaining model, which includes the problem of maximizing the benefits of the microgrid alliance and the problem of internal benefit distribution within the microgrid alliance; calculate the Nash bargaining breakdown point between the entire microgrid alliance and the shared energy storage power station, and use the bargaining breakdown point as the boundary to distinguish whether the microgrid users participate in electricity cooperation, so as to obtain the microgrid users participating in electricity cooperation.

[0009] Step 3: On the premise that the interactive electricity between the microgrid alliance and the energy storage power station reaches balance, the microgrid alliance and the energy storage power station reach a cooperation consensus, establish a distributed optimization operation model for the shared energy storage power station and each microgrid user, and use the alternating direction method of multipliers for iterative calculation to solve the problem of maximizing the benefits of the microgrid alliance, and obtain the maximum benefit value between the microgrid alliance and the energy storage power station.

[0010] Step 4: Take the contribution degree of each microgrid user to the entire system as the bargaining power of each microgrid user, and establish an internal interest distribution model.

[0011] Step 5: On the premise that both the microgrid users and the energy storage power station reach a consensus on the unit price of the interactive electricity, conduct cooperation, establish a distributed iterative model for internal interest distribution according to the internal interest distribution model, use the alternating direction method of multipliers for iterative calculation of interest distribution, solve the problem of internal benefit distribution within the microgrid alliance, determine the transaction electricity price between the microgrid users and the energy storage power station, and complete the revenue distribution based on the transaction electricity price.

[0012] To optimize the above technical solutions, the specific measures taken also include:

[0013] Further, Step 1 is specifically:

[0014] Calculate the electric energy sold by the i-th microgrid user and the electric energy purchased

[0015]

[0016]

[0017] Wherein, P i-j,t is the interactive power between the i-th microgrid user and the j-th energy storage power station at time t, and T is the total number of time periods;

[0018] Calculate the contribution degree X of the i-th microgrid user i :

[0019]

[0020] Wherein, is the maximum value of the sold electric energy; is the maximum value of the purchased electric energy.

[0021] Furthermore, in step 2, the constraint conditions of the asymmetric Nash bargaining model are:

[0022]

[0023] Wherein, U PES represents the revenue of the energy storage power station after participating in the electric energy cooperation, represents the revenue of the energy storage power station before participating in the electric energy cooperation; U n represents the revenue of the microgrid user after participating in the electric energy cooperation, represents the revenue of the microgrid user before participating in the electric energy cooperation, is also called the negotiation breakdown point in the Nash bargaining model, and N is the total number of microgrid users.

[0024] Furthermore, step 3 is specifically as follows:

[0025] Step 3.1: For both the microgrid alliance and the energy storage power station, when the following formula conditions are met, the energy storage power station reaches a consensus with each microgrid user;

[0026] P E,n (t) + P n,E (t) = 0

[0027] Wherein, P E,n (t) is the interactive power between the energy storage power station and the microgrid user, and P n,E (t) is the interactive power between the microgrid user and the energy storage power station;

[0028] Step 3.2: Establish a distributed optimal operation model for the shared energy storage power station;

[0029]

[0030] Wherein, C inv,w is the daily average investment and maintenance cost of the energy storage power station, and λ lρ is the Lagrange multiplier of the distributed optimal operation model of the shared energy storage power station. l ρ is the penalty factor; n is the ordinal number of the microgrid users, and N is the total number of microgrid users; t is the time period, and T is the total number of time periods.

[0031] Step 3.3: Establish the distributed optimal operation model of the microgrid users.

[0032]

[0033] In the formula, C inv,n is the daily average investment cost of the microgrid users, and λ l is the Lagrange multiplier of the distributed optimal operation model of the shared energy storage power station, and ρ l is the penalty factor, t is the time period, and T is the total number of time periods.

[0034] Step 3.4: Establish the distributed algorithm for the problem of maximizing the benefits of the microgrid alliance, and the iteration formula is as follows:

[0035]

[0036] In the formula, the superscript k represents the iteration number.

[0037] When the following formula is satisfied, the iteration stops, and the maximum value of the benefits between the microgrid alliance and the energy storage power station is obtained.

[0038]

[0039] In the formula, ψ is the convergence accuracy of the problem of maximizing the benefits of the microgrid alliance.

[0040] Furthermore, Step 4 is specifically as follows:

[0041] The internal interest distribution model is as follows:

[0042]

[0043] In the formula, is the electricity cost before energy cooperation, and C i is the electricity sharing cost, X i is the contribution degree of the i-th microgrid user, and C inv,w is the daily average investment and maintenance cost of the energy storage power station.

[0044] Transform the maximization problem in the internal interest distribution model into the following minimization problem:

[0045]

[0046] In the formula, is the electricity cost before energy cooperation, and C i is the electricity sharing cost, Xi is the contribution degree of the i-th microgrid user, C inv,w is the daily average investment and maintenance cost of the energy storage power station.

[0047] Further, step 5 is specifically as follows:

[0048] Step 5.1: When the microgrid user and the energy storage power station reach a consensus on the transaction unit price in the following formula, subsequent electricity cooperation is carried out;

[0049]

[0050] In the formula, K i-j,t represents the transaction unit price of electricity quantity P expected by microgrid user i; K i-j,t represents the transaction unit price of electricity quantity P expected by microgrid user j; j-i,t j-i,t

[0051] Step 5.2: Establish a distributed iterative model for internal benefit distribution;

[0052]

[0053] In the formula, ω l is the Lagrange multiplier of the distributed iterative model for internal benefit distribution, and e l is the penalty parameter;

[0054] For the transaction unit price of electricity, the following iteration is established:

[0055] K i-j,t (k + 1) = argminL i [ω l (k), K i-j,t (k + 1), K j-i,t (k)]

[0056] K j-i,t (k + 1) = argminL i [ω l (k), K i-j,t (k), K j-i,t (k + 1)]

[0057] In the formula, L i [] represents the Lagrange function in the distributed iterative model for internal benefit distribution, and k is the number of iterations;

[0058]

[0059] When is satisfied, the iteration stops;

[0060] In the formula, ψ’ represents the convergence accuracy of the internal benefit distribution problem within the microgrid alliance;​​

[0061] Obtain the transaction electricity price between the microgrid users and the energy storage power station, and complete the revenue distribution based on the transaction electricity price.

[0062] The present invention also proposes a microgrid electric energy cooperation system based on cooperative game and the intervention of a shared energy storage power station. The system includes a modeling module, a judgment module, and a calculation module;

[0063] The modeling module is used to establish an asymmetric Nash bargaining model, establish a distributed optimal operation model for the shared energy storage power station and each microgrid user, establish an internal interest distribution model, and establish a distributed iterative model for internal interest distribution;

[0064] The judgment module is used to distinguish whether the microgrid users participate in the electric energy cooperation with the negotiation breakdown point as the boundary, judge whether the interactive electricity quantity between the microgrid alliance and the energy storage power station reaches balance, and judge whether the microgrid users and the energy storage power station reach a consensus on the unit price of the interactive electricity quantity;

[0065] The calculation module is used to calculate the contribution degree of each microgrid user to the entire system according to the electricity quantity purchased and sold by each microgrid user, calculate the Nash negotiation breakdown point between the entire microgrid alliance and the shared energy storage power station, use the alternating direction method of multipliers for iterative calculation to solve the problem of maximizing the microgrid alliance benefit, obtain the maximum benefit value between the microgrid alliance and the energy storage power station, use the alternating direction method of multipliers for iterative calculation of interest distribution, solve the problem of internal benefit distribution in the microgrid alliance, and determine the transaction electricity price between the microgrid users and the energy storage power station.

[0066] The present invention also proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station.

[0067] The present invention also proposes a computer-readable storage medium storing a computer program, and the computer program causes the computer to execute the above-mentioned microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station.

[0068] The beneficial effects of the present invention are as follows: The present invention conforms to the trend of the development of the sharing economy today, provides a new business model for energy storage power stations. The energy storage power station can store the electric energy that microgrid users have not used up and take it out when needed. The manifestation is the purchase and sale of electricity by the energy storage power station, enabling more energy to be utilized rather than discarded, alleviating the problem of energy waste among microgrid users, and improving the utilization rate of renewable energy. While reducing the phenomenon of "wind and light abandonment", it improves the economic benefits of participants, reduces internal contradictions among participants, and achieves win-win results. The present invention applies the asymmetric Nash negotiation in cooperative game, avoiding the situation of unfair interest distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the transaction mode between the shared energy storage power station and microgrid users;

[0070] Figure 2 It is a flowchart of a microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station;

[0071] Figure 3 It is a line chart of the interactive electricity quantity between Microgrid 1 (MG1) and the energy storage power station;

[0072] Figure 4 It is a line chart of the interactive electricity quantity between Microgrid 2 (MG2) and the energy storage power station;

[0073] Figure 5 It is a line chart of the interactive electricity quantity between Microgrid 3 (MG3) and the energy storage power station;

[0074] Figure 6 It is a diagram of the distributed iterative simulation results of the electricity cost when the shared energy storage power station (ESS) intervenes in the microgrid;

[0075] Figure 7 It is a diagram of the simulation results of the total benefit value when the microgrid alliance cooperates with the energy storage power station;

[0076] Figure 8 It is a diagram of the simulation results of the interactive electricity quantity between the microgrid and the energy storage power station. SPECIFIC EMBODIMENTS

[0077] Now, the present invention will be further described in detail with reference to the accompanying drawings.

[0078] In one embodiment, the present invention proposes a microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station. The purpose of the electric energy cooperation is to maximize the interests of the microgrid alliance and the shared energy storage power station participating in the electric energy cooperation. Then, the economic benefits obtained are distributed to microgrid users according to the contribution degree of the microgrid users. The transaction mode is as Figure 1 shown.

[0079] The flowchart of this method is asFigure 2 As shown in the figure, it specifically includes the following steps:

[0080] Step 1: Calculate the contribution degree of each microgrid user to the entire system according to the electricity purchased and sold by each microgrid user;

[0081] Step 2: Establish an asymmetric Nash bargaining model, which includes the problem of maximizing the benefits of the microgrid alliance and the problem of internal benefit distribution within the microgrid alliance; calculate the Nash bargaining breakdown point between the entire microgrid alliance and the shared energy storage power station, and use the bargaining breakdown point as the boundary to distinguish whether the microgrid users participate in electricity cooperation, and obtain the microgrid users participating in electricity cooperation;

[0082] Step 3: On the premise that the interactive electricity between the microgrid alliance and the energy storage power station reaches balance, the microgrid alliance and the energy storage power station reach a cooperation consensus, establish a distributed optimization operation model for the shared energy storage power station and each microgrid user, and use the alternating direction method of multipliers for iterative calculation to solve the problem of maximizing the benefits of the microgrid alliance and obtain the maximum benefit value between the microgrid alliance and the energy storage power station;

[0083] Step 4: Take the contribution degree of each microgrid user to the entire system as the bargaining power of each microgrid user and establish an internal interest distribution model;

[0084] Step 5: On the premise that both the microgrid users and the energy storage power station reach a consensus on the unit price of the interactive electricity, conduct cooperation, establish a distributed iterative model for internal interest distribution according to the internal interest distribution model, use the alternating direction method of multipliers for iterative calculation of interest distribution, solve the problem of internal benefit distribution within the microgrid alliance, determine the transaction electricity price between the microgrid users and the energy storage power station, and complete the revenue distribution based on the transaction electricity price.

[0085] Specifically, Step 1 is as follows:

[0086] Calculate the electric energy sold by the i-th microgrid user and the electric energy purchased

[0087]

[0088]

[0089] where P i-j,t is the interactive electricity between the i-th microgrid user and the j-th energy storage power station at time t, and T is the total number of time periods;

[0090] Calculate the contribution degree Xi of the i-th microgrid user i :

[0091]

[0092] where is the maximum value of the sold electric energy; The maximum value of the purchased electric energy.

[0093] The interactive electricity quantities between Microgrid 1, Microgrid 2, and Microgrid 3 and the energy storage power station within 24 hours are as Figure 3 , Figure 4 and Figure 5 shown.

[0094] For microgrid users to participate in electricity cooperation based on the asymmetric Nash negotiation, it is necessary to determine the negotiation breakdown point and meet relevant constraints. The constraint conditions of the asymmetric Nash negotiation model in Step 2 are:

[0095]

[0096] In the formula, U PES represents the revenue of the energy storage power station after participating in electricity cooperation, represents the revenue of the energy storage power station before participating in electricity cooperation; U n represents the revenue of microgrid users after participating in electricity cooperation, represents the revenue of microgrid users before participating in electricity cooperation, is also called the negotiation breakdown point in the Nash negotiation model, and N is the total number of microgrid users.

[0097] Step 3 is specifically as follows:

[0098] Step 3.1: For both the microgrid alliance and the energy storage power station, when P E,n (t) + P n,E (t) = 0, the shared energy storage power station and each microgrid user reach a consensus;

[0099] In the formula, P E,n (t) is the interactive electricity quantity between the energy storage power station and microgrid users, and P n,E (t) is the interactive electricity quantity between microgrid users and the energy storage power station;

[0100] Step 3.2: Establish a distributed optimal operation model for the shared energy storage power station;

[0101]

[0102] In the formula, C inv,w is the daily average investment and maintenance cost of the energy storage power station, λ l is the Lagrange multiplier of the distributed optimal operation model of the shared energy storage power station, ρ l is the penalty factor; n is the ordinal number of microgrid users, N is the total number of microgrid users; t is the time period, and T is the total number of time periods;

[0103] Step 3.3: Establish a distributed optimal operation model for microgrid users;

[0104]

[0105] Wherein, C inv,n is the daily average investment cost of microgrid users, λ l is the Lagrange multiplier of the distributed optimization operation model of the shared energy storage power station, ρ l is the penalty factor, t is the time period, and T is the total number of time periods;

[0106] Step 3.4: Establish a distributed algorithm for the problem of maximizing the benefits of the microgrid alliance, and the iteration formula is as follows:

[0107]

[0108] Wherein, the superscript k represents the iteration number;

[0109] When the following formula is satisfied, the iteration stops, and the maximum benefit value between the microgrid alliance and the energy storage power station is obtained;

[0110]

[0111] Wherein, ψ is the convergence accuracy of the problem of maximizing the benefits of the microgrid alliance.

[0112] As Figure 6 and Figure 7 shown, through the iterative calculation of the alternating multiplier method, the final solution of the maximum benefit value between the microgrid alliance and the energy storage power station is obtained.

[0113] Step 4 is specifically as follows:

[0114] The internal interest distribution model is as follows:

[0115]

[0116] Wherein, is the electricity cost before energy cooperation, C i is the electricity sharing cost, X i is the contribution degree of the i-th microgrid user, C inv,w is the daily average investment and maintenance cost of the energy storage power station;

[0117] In order to facilitate the solution, the maximization problem in the internal interest distribution model is transformed into the following minimization problem:

[0118]

[0119] Wherein, is the electricity cost before energy cooperation, C i is the electricity sharing cost, X i is the contribution degree of the i-th microgrid user, C inv,w is the daily average investment and maintenance cost of the energy storage power station.

[0120] Step 5 is specifically as follows:

[0121] Step 5.1: When the microgrid users and the energy storage power station reach a consensus on the transaction unit price in the following formula, subsequent electricity cooperation is carried out;

[0122]

[0123] In the formula, K i-j,t represents the expected transaction electricity price per unit of electricity P for microgrid user i; K i-j,t represents the expected transaction electricity price per unit of electricity P for microgrid user j; j-i,t j-i,t

[0124] Step 5.2: Establish a distributed iterative model for internal benefit distribution;

[0125]

[0126] In the formula, ω l is the Lagrange multiplier of the distributed iterative model for internal benefit distribution, and e l is the penalty parameter;

[0127] For the transaction electricity price per unit, the following iteration is established:

[0128] K i-j,t (k + 1) = argminL i [ω l (k), K i-j,t (k + 1), K j-i,t (k)]

[0129] K j-i,t (k + 1) = argminL i [ω l (k), K i-j,t (k), K j-i,t (k + 1)]

[0130] In the formula, L i [] represents the Lagrange function in the distributed iterative model for internal benefit distribution, and k is the number of iterations;

[0131]

[0132] When is satisfied, the iteration stops;

[0133] In the formula, ψ’ represents the convergence accuracy of the internal benefit distribution problem within the microgrid alliance;

[0134] Obtain the transaction electricity price between the microgrid users and the energy storage power station, and complete the revenue distribution based on the transaction electricity price. ​​

[0135] Substituting cost data, the results of this method are compared with those of the direct transaction model without cooperation and the symmetric Nash negotiation model, as shown in Table 1, which verifies the rationality and effectiveness of the cooperation model in this method. As can be seen from the table, after the negotiation, the benefits of each microgrid have been significantly improved.

[0136] Table 1 Data results comparison table

[0137]

[0138] The microgrid power cooperation method based on cooperative game and shared energy storage power station intervention in this embodiment can make renewable energy more fully utilized and bring economic benefits to microgrid users. One of the characteristics of this method is to enable the participating entities to obtain economic benefits by conducting power cooperation through cooperative game. All microgrid users constitute the microgrid alliance as a whole, and adopt the cooperative game method to avoid the problem of disorderly market competition. Therefore, the interests of the entire microgrid alliance are maximized first, and then the distribution of interests is considered. The interests are distributed according to the standard of contribution, so that all microgrid users within the alliance can share the results fairly.

[0139] The microgrid power cooperation method based on cooperative game and shared energy storage power station intervention can be applied to the power system, which not only improves the utilization rate and economy of renewable energy, but also brings economic benefits to the participating entities, especially for the current serious waste and absorption problems of renewable energy, and achieves a win-win situation for all parties.

[0140] In another embodiment, the present invention proposes a microgrid power cooperation system based on cooperative game and shared energy storage power station intervention, the system comprising a modeling module, a judgment module and a calculation module;

[0141] The modeling module is used to establish an asymmetric Nash negotiation model, a distributed optimization operation model of a shared energy storage power station and each microgrid user, an internal benefit distribution model, and a distributed iterative model of internal benefit distribution;

[0142] The judgment module is used to distinguish whether microgrid users participate in the power cooperation based on the negotiation breakdown point, to judge whether the interactive power between the microgrid alliance and the energy storage power station is balanced, and to judge whether the microgrid users and the energy storage power station have reached a consensus on the unit price of interactive power;

[0143] The calculation module is used to calculate the contribution degree of each microgrid user to the entire system according to the electricity purchased and sold by each microgrid user, to calculate the Nash bargaining breakdown point between the entire microgrid alliance and the shared energy storage power station, to perform iterative calculations using the alternating direction method of multipliers to solve the problem of maximizing the benefits of the microgrid alliance, to obtain the maximum benefit value between the microgrid alliance and the energy storage power station, to perform iterative calculations of benefit distribution using the alternating direction method of multipliers, to solve the problem of internal benefit distribution in the microgrid alliance, and to determine the transaction electricity price between the microgrid user and the energy storage power station.

[0144] In another embodiment, the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the microgrid power cooperation method based on cooperative game and the intervention of a shared energy storage power station as described above is implemented.

[0145] In another embodiment, the present invention provides a computer-readable storage medium storing a computer program, and the computer program causes a computer to execute the microgrid power cooperation method based on cooperative game and the intervention of a shared energy storage power station as described above.

[0146] In the embodiments disclosed in the present application, the computer storage medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0147] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0148] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station, characterized in that The method includes the following steps: Step 1: Calculate the contribution degree of each microgrid user to the entire system according to the electricity purchased and sold by each microgrid user. Step 2: Establish an asymmetric Nash bargaining model, which includes the problem of maximizing the benefits of the microgrid alliance and the problem of internal benefit distribution within the microgrid alliance; calculate the Nash bargaining breakdown point between the entire microgrid alliance and the shared energy storage power station, and distinguish whether the microgrid users participate in electricity cooperation based on the bargaining breakdown point to obtain the microgrid users participating in electricity cooperation. Step 3: On the premise that the interaction electricity between the microgrid alliance and the energy storage power station reaches balance, the microgrid alliance and the energy storage power station reach a cooperation consensus, establish a distributed optimization operation model for the shared energy storage power station and each microgrid user, and use the alternating direction method of multipliers for iterative calculation to solve the problem of maximizing the benefits of the microgrid alliance and obtain the maximum benefit value between the microgrid alliance and the energy storage power station. Step 4: Use the contribution degree of each microgrid user to the entire system as the bargaining power of each microgrid user and establish an internal interest distribution model. Step 5: On the premise that both the microgrid users and the energy storage power station reach a consensus on the unit price of the interaction electricity, conduct cooperation, establish a distributed iterative model for internal interest distribution according to the internal interest distribution model, use the alternating direction method of multipliers for iterative calculation of interest distribution, solve the problem of internal benefit distribution within the microgrid alliance, determine the transaction electricity price between the microgrid users and the energy storage power station, and complete the revenue distribution based on the transaction electricity price.

2. The microgrid power cooperation method based on cooperative game and the intervention of a shared energy storage power station according to claim 1, wherein Specifically, Step 1 is as follows: Calculate the electric energy sold by the i-th microgrid user and the electric energy purchased Where, P i-j,t is the interactive power between the i-th microgrid user and the j-th energy storage power station at time t, and T is the total number of time periods; Calculate the contribution degree X of the i-th microgrid user i : Wherein, is the maximum value of the sold electric energy; is the maximum value of the purchased electric energy.

3. The microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station according to claim 2, wherein In Step 2, the constraint conditions of the asymmetric Nash bargaining model are as follows: Wherein, U PES represents the revenue of the energy storage power station after participating in the electric energy cooperation, represents the revenue of the energy storage power station before participating in the electric energy cooperation; U n represents the revenue of the microgrid users after participating in the electric energy cooperation, represents the revenue of the microgrid users before participating in the electric energy cooperation, is also called the negotiation breakdown point in the Nash negotiation model, and N is the total number of microgrid users.

4. The microgrid electric energy cooperation method based on cooperative game and shared energy storage power station intervention according to claim 3, wherein Specifically, Step 3 is as follows: Step 3.1: For both the microgrid alliance and the energy storage power station, when the following conditions are met, the energy storage power station reaches a consensus with each microgrid user. P E,n (t) + P n,E (t) = 0 where P E,n (t) is the interactive power between the energy storage power station and the microgrid users, and P n,E (t) is the interactive power between the microgrid users and the energy storage power station; Step 3.2: Establish a distributed optimization operation model for the shared energy storage power station. Where, C inv,w is the daily average investment and maintenance cost of the energy storage power station, λ l is the Lagrange multiplier of the distributed optimal operation model of the shared energy storage power station, ρ l is the penalty factor; n is the ordinal number of the microgrid users, N is the total number of the microgrid users; t is the time period, T is the total number of time periods; Step 3.3: Establish a distributed optimization operation model for the microgrid users. where, C inv,n is the daily average investment cost of microgrid users, λ l is the Lagrange multiplier of the distributed optimal operation model of the shared energy storage power station, ρ l is the penalty factor, t is the time period, and T is the total number of time periods; Step 3.4: Establish a distributed algorithm for the problem of maximizing the benefits of the microgrid alliance, and the iterative formula is as follows: In the formula, the superscript k represents the number of iterations. When the following conditions are met, the iteration stops and the maximum benefit value between the microgrid alliance and the energy storage power station is obtained. In the formula, ψ is the convergence accuracy of the problem of maximizing the benefits of the microgrid alliance.

5. The microgrid power cooperation method based on cooperative game and shared energy storage power station intervention according to claim 4, wherein Specifically, Step 4 is as follows: The internal interest distribution model is as follows: Wherein, is the electricity cost before energy cooperation, C i is the electricity sharing cost, X i is the contribution degree of the i-th microgrid user, C inv,w is the daily average investment and maintenance cost of the energy storage power station; Transform the maximization problem in the internal interest distribution model into the following minimization problem: Wherein, is the electricity cost before energy cooperation, C i is the electricity sharing cost, X i is the contribution degree of the i-th microgrid user, C inv,w is the daily average investment and maintenance cost of the energy storage power station.

6. The microgrid electric energy cooperation method based on cooperative game and the intervention of a shared energy storage power station according to claim 5, wherein, Specifically, Step 5 is as follows: Step 5.1: When the microgrid users and the energy storage power station reach a consensus on the transaction unit price as shown in the following formula, conduct subsequent electricity cooperation. where, K i-j,t represents the unit price of the transaction power quantity expected by microgrid user i for the power quantity P i-j,t ; K j-i,t represents the unit price of the transaction power quantity expected by microgrid user j for the power quantity P j-i,t ; Step 5.2: Establish a distributed iterative model for internal benefit distribution. where ω l is the Lagrange multiplier of the distributed iterative model for internal benefit distribution, and e l is the penalty parameter; For the transaction electricity unit price, establish the following iteration: K i-j,t (k + 1) = arg min L i [ω l (k), K i-j,t (k + 1), K j-i,t (k)] K j-i,t (k + 1) = arg min L i [ω l (k), K i-j,t (k), K j-i,t (k + 1)] where L i [] represents the Lagrangian function in the distributed iterative model of internal benefit distribution, and k is the number of iterations; When is satisfied, the iteration stops; In the formula, ψ’ represents the convergence accuracy of the problem of internal benefit distribution within the microgrid alliance. Obtain the transaction electricity price between the microgrid users and the energy storage power station, and complete the revenue distribution based on the transaction electricity price.

7. A microgrid power cooperation system based on cooperative game and the intervention of a shared energy storage power station, characterized in that: The system includes a modeling module, a judgment module, and a calculation module. The modeling module is used to establish an asymmetric Nash bargaining model, establish a distributed optimization operation model for the shared energy storage power station and each microgrid user, establish an internal interest distribution model, and establish a distributed iterative model for internal interest distribution. The judgment module is used to distinguish whether the microgrid users participate in the electric energy cooperation with the negotiation breakdown point as the boundary, to judge whether the interactive power between the microgrid alliance and the energy storage power station reaches balance, and to judge whether both the microgrid users and the energy storage power station reach a consensus on the unit price of the interactive power. The calculation module is used to calculate the contribution degree of each microgrid user to the whole system according to the purchased and sold power of each microgrid user, to calculate the Nash negotiation breakdown point between the whole microgrid alliance and the shared energy storage power station, to use the alternating direction method of multipliers for iterative calculation to solve the problem of maximizing the microgrid alliance benefit, and to obtain the maximum benefit value between the microgrid alliance and the energy storage power station, and to use the alternating direction method of multipliers for iterative calculation of benefit distribution to solve the problem of internal benefit distribution of the microgrid alliance and determine the transaction electricity price between the microgrid users and the energy storage power station.

8. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for microgrid electric energy cooperation based on cooperative game and the intervention of a shared energy storage power station as described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium storing a computer program, where the computer program causes a computer to execute the method for microgrid electric energy cooperation based on cooperative game and the intervention of a shared energy storage power station as described in any one of claims 1-6.

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