Collaborative optimization decision-making method for active power distribution network containing rented shared energy storage of producers and diasters

Abstract

The invention discloses a collaborative optimization decision-making method for an active power distribution network including production and consumer renting shared energy storage, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a multi-agent collaborative transaction architecture containing energy storage shared by diasters; based on the multi-agent collaborative transaction architecture, through a multi-agent two-stage collaborative pricing strategy, the bargaining ability of a shared energy storage operator and a producer and a consumer is quantified; taking social benefit maximization and cooperative benefit distribution maximization as targets, and based on preset constraint conditions, constructing a multi-agent collaborative optimization model based on improved Nash bargaining; and optimizing the multi-agent collaborative optimization model in combination with bargaining capabilities of a shared energy storage operator and a producer and a consumer to obtain an optimal energy transaction volume and an optimal energy transaction price. The DDS-production and consumer-SESO collaborative decision-making framework provided by the invention can realize multi-agent cooperation and win-win, and reduce the scheduling difficulty of the power distribution network; the problems of non-uniform income distribution and high optimization scheduling difficulty in the existing electricity market transaction are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network dispatching, and in particular to a collaborative optimization decision-making method for an active distribution network including producer-consumer leasing shared energy storage. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] With the widespread access to user-side renewable energy and distributed energy storage in the distribution system, more and more traditional electricity users are gradually transformed into prosumers with both production and consumption functions. Although the emergence of prosumers can effectively improve local energy utilization, their self-built energy storage faces the problems of high investment cost and low utilization rate. Shared energy storage, as a new energy storage model, provides a new solution to the above problems because of its centralized management and efficient utilization. However, the implementation and promotion of shared energy storage involves multiple stakeholders, including prosumers, shared energy storage operators, distribution system operators, etc. There are differences in the interests of different subjects, which aggravates the complexity of power interaction among multiple subjects. Therefore, coordinating prosumers and distribution networks to adapt to the new shared energy storage system architecture remains a key issue at present.

[0004] In recent years, domestic and foreign scholars have conducted in-depth research on the mechanism design for electricity sharing transactions between producers and consumers; however, they focus on improving the level of local energy consumption, without considering the potential value and impact that shared energy storage can bring when participating in point-to-point transactions between producers and consumers, and the degree of dependence on electricity from the upper-level power grid is still high.

[0005] For the optimization problem of power sharing among prosumers that takes into account the participation of shared energy storage, most of the services are bought and sold through user-led trading models. Although this can give full play to the synergistic and complementary effects between complex entities in the local energy market, it often ignores the constraints on the safe operation of the distribution system, and its feasibility is low in actual operation. In contrast, the trading model led by the distribution system operator can overcome the limitations of the above research by formulating an incentive mechanism to guide users to participate in scheduling. However, although this model is relatively easy to implement and can ensure that the distribution system operator obtains sufficient profits, it may directly hinder the power trading between prosumers and cause damage to the interests of prosumers to a certain extent.

[0006] It can be seen that there is a natural conflict of interest when multiple entities interact with each other, and game theory is an effective way to solve this problem. However, game theory research mainly focuses on the independence of each entity, which may lead to suboptimal equilibrium and does not fully explore the benefits brought by the potential cooperation relationship between entities. Cooperative games will encourage all entities to negotiate together to achieve social optimality.

[0007] In the paper "Incorporating P2P trading into DSO's decision-making: a DSO-prosumers cooperated scheduling framework for transactive distribution system", a distribution system operator-prosumer cooperative scheduling method considering the impact of prosumer point-to-point transactions is proposed based on Nash bargaining theory, ensuring the maximization of social benefits. In the paper "Bargaining-based cooperative energy trading for distribution company and demand response", the energy trading problem between demand response aggregators and distribution system operators is studied, and a cooperative game model is established using the Nash bargaining method, which reduces energy costs and promotes the full utilization of demand-side resources.

[0008] However, most of the above studies adopted the traditional Nash bargaining model, which explored the potential value brought by the collaborative operation among various transaction entities. The optimization process did not take into account the contribution of the participating entities to the improvement of cooperative benefits, resulting in frequent problems of uneven distribution of benefits. Summary of the invention

[0009] In order to address the deficiencies of the prior art, the present invention provides a collaborative optimization decision-making method, system, electronic device, computer-readable storage medium and computer program product for an active distribution network containing prosumers leasing shared energy storage, constructs a multi-agent collaborative transaction framework that considers the interactive impact between distribution system operators, prosumers and shared energy storage operators, establishes a point-to-point power sharing transaction mechanism that takes into account the participation of shared energy storage, and based on this, proposes a two-stage pricing strategy for prosumers leasing shared energy storage, uses bargaining factor indicators to quantify the bargaining power of each subject in the price setting process, establishes an active distribution network game optimization model based on improved Nash bargaining, introduces bargaining factors into the index items to set their weights in the target, and realizes a reasonable distribution of benefits.

[0010] In a first aspect, the present invention provides a collaborative optimization decision-making method for an active distribution network including prosumers leasing shared energy storage;

[0011] A collaborative optimization decision-making method for active distribution network with prosumers leasing shared energy storage, comprising:

[0012] Construct a multi-agent collaborative trading framework involving prosumers sharing energy storage;

[0013] Based on the multi-agent collaborative transaction framework, the bargaining power of shared energy storage operators and prosumers is quantified through a multi-agent two-stage collaborative pricing strategy;

[0014] With the goal of maximizing social benefits and maximizing the distribution of cooperative benefits, a multi-agent collaborative optimization model based on improved Nash bargaining is constructed based on preset constraints;

[0015] The multi-agent collaborative optimization model is optimized in combination with the bargaining power of shared energy storage operators and prosumers to obtain the optimal energy trading volume and the optimal energy trading price.

[0016] In a second aspect, the present invention provides a collaborative optimization decision-making system for active distribution networks including prosumers leasing shared energy storage;

[0017] An active distribution network collaborative optimization decision system with prosumers leasing shared energy storage, comprising:

[0018] The transaction architecture building module is configured to: build a multi-agent collaborative transaction architecture including prosumers sharing energy storage;

[0019] The collaborative pricing module is configured to: quantify the bargaining power of the shared energy storage operator and the prosumer through a multi-agent two-stage collaborative pricing strategy based on the multi-agent collaborative transaction architecture;

[0020] The multi-agent collaborative optimization model building module is configured to: build a multi-agent collaborative optimization model based on improved Nash bargaining based on preset constraints with the goal of maximizing social benefits and maximizing the distribution of cooperative benefits;

[0021] The decision-making module is configured to optimize the multi-agent collaborative optimization model in combination with the bargaining power of the shared energy storage operator and the prosumer to obtain the optimal energy transaction volume and the optimal energy transaction price.

[0022] In a third aspect, the present invention provides an electronic device;

[0023] An electronic device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium;

[0025] A computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the above-mentioned active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage.

[0026] In a fifth aspect, the present invention provides a computer program product;

[0027] A computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The technical solution provided by the present invention provides a proposed DSO-prosumer-SESO collaborative decision-making framework, which can achieve win-win cooperation among multiple entities; by incorporating P2P transactions into the scheduling process, making full use of the resources of all parties, and cooperatively optimizing the power trading strategy, the overall efficiency of the system is improved.

[0030] 2. The technical solution provided by the present invention establishes a point-to-point electricity sharing transaction mechanism that takes into account the participation of shared energy storage, which can enhance the enthusiasm of producers and consumers and shared energy storage to exchange electricity with each other; a two-stage pricing strategy for producers and consumers to lease shared energy storage is proposed, which can fully consider the bargaining power of each subject in the price setting process and promote the orderly conduct of point-to-point transactions.

[0031] 3. The technical solution provided by the present invention introduces bargaining factor indicators to quantify the bargaining power of each subject, improves the traditional Nash bargaining method, and establishes a multi-subject game optimization model to characterize the transaction behavior between multiple stakeholders and achieve a reasonable distribution of benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 A schematic diagram of the architecture of a multi-agent collaborative transaction architecture provided by an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of the process of a P2P power sharing transaction mechanism provided by an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of a process flow of a pricing strategy for a prosumer leasing shared energy storage provided in an embodiment of the present invention;

[0036] Figure 4 A schematic diagram of a solution process of a multi-agent collaborative optimization model provided by an embodiment of the present invention;

[0037] Figure 5 An example diagram of the system dispatching operation results provided by an embodiment of the present invention, wherein (a) is an example diagram of the purchase and sale of electricity in the SESO retail market, (b) is an example diagram of the total net load of producers and consumers, (c) is an example diagram of the system average voltage, and (d) is an example diagram of the system network loss;

[0038] Figure 6 An example diagram of the power trading results of prosumers provided in an embodiment of the present invention, wherein (a) is an example diagram of the market trading volume of the prosumer alliance, and (b) is an example diagram of the power trading results of prosumers #1-#4;

[0039] Figure 7 An example diagram of power trading results of SESO provided in an embodiment of the present invention;

[0040] Figure 8 An example diagram of the transaction price of producer and consumer #1 provided in an embodiment of the present invention DETAILED DESCRIPTION

[0041] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0043] Embodiment 1

[0044] The complex interactions and interest competition between current prosumers and between them and shared energy storage operators have increased the difficulty of optimizing and regulating active distribution networks. Therefore, the present invention provides a collaborative optimization decision-making method for active distribution networks involving prosumers leasing shared energy storage.

[0045] Next, combine Figure 1-Figure 8 , a method for collaborative optimization decision-making of an active distribution network including prosumers leasing shared energy storage disclosed in this embodiment is described in detail. The method for collaborative optimization decision-making of an active distribution network including prosumers leasing shared energy storage comprises the following steps:

[0046] S1. Construct a multi-agent collaborative transaction architecture involving producers and consumers sharing energy storage.

[0047] like Figure 1 As shown, in this embodiment, the multi-subject collaborative transaction architecture includes four different types of markets and three different types of market entities. The four different types of markets are: wholesale market, backup market, retail market and peer-to-peer (P2P) market. The three different types of market entities are: distribution system operator (DSO), prosumer and shared energy storage operator (SESO). Shared energy storage is centrally funded and constructed by SESO, allowing it to serve the distribution network and prosumers at the same time.

[0048] Next, the market transaction models of various market players are introduced separately:

[0049] In the wholesale market, DSOs can purchase electricity to meet their forecasts of the overall net load of users, and can also sell excess electricity to make a profit. In the reserve market, DSOs can compensate for power imbalances by purchasing reserves to maintain the stable operation of the distribution system. The retail market and the P2P market serve as power trading platforms within the distribution system to provide power services to users. In the retail market, prosumers and SESOs can trade electricity with DSOs at a fixed electricity price. In the P2P market, multiple prosumers and SESOs can negotiate pricing and then trade electricity based on the agreed electricity price. At the same time, DSOs collect network usage fees generated by P2P shared transactions to ensure their own profits.

[0050] In order to explain the power sharing process in detail, in this embodiment, power sharing is divided into two types: direct power sharing and buffer power sharing. The former means that producers and consumers negotiate with each other based on their current power supply and demand levels, and then trade electricity at a negotiated price to achieve local energy sharing. The latter means that when the electricity produced by the producer and consumer cannot be fully consumed through direct power sharing, it will negotiate with the SESO for power trading and store the remaining electricity through shared energy storage. On the contrary, when the producer and consumer is short of electricity and direct power sharing cannot meet its needs, shared energy storage will release the stored electricity. When neither direct power sharing nor buffer sharing can guarantee the energy supply and demand balance of the producer and consumer, the producer and consumer will trade electricity with the DSO.

[0051] It should be noted that there is no direct line connection between the prosumer and the shared energy storage. Therefore, the above two power sharing modes are both implemented through the interconnected lines of the distribution system, and this setting is closer to the real scenario. However, a practical problem faced in this case is that local power sharing will inevitably affect the distribution network flow and even lead to an increase in the operating costs of the DSO. Therefore, the architecture proposed in this embodiment enables the DSO to participate in the P2P power sharing transaction process, and its losses can be modeled as network usage fees paid jointly by buyers and sellers to compensate for the increased operating costs of the DSO in providing power transmission lines during the P2P transaction. Taking into account the P2P power sharing transaction mechanism involving SESO Figure 2 shown.

[0052] S2. Based on the multi-agent collaborative transaction architecture, the bargaining power of shared energy storage operators and prosumers is quantified through a multi-agent two-stage collaborative pricing strategy.

[0053] Considering that prosumers and SESOs are different types of entities and have different ways of sharing electricity, in this embodiment, prosumers rent shared energy storage as follows: Figure 3 The two-stage pricing strategy shown.

[0054] As an implementation mode, S2 specifically includes:

[0055] (1) Phase 1: SESO energy storage leasing pricing strategy.

[0056] Multiple prosumers form a prosumer alliance, which aggregates the internal charging and discharging information of the alliance, obtains the total shared energy storage charging and discharging power leasing information, and uploads it to SESO to lease energy storage on demand for power buffering sharing. At the same time, SESO formulates charging and discharging strategies based on the energy storage leasing needs to provide capacity support. When the capacity is insufficient, it can purchase electricity from DSO to meet it. In this process, the formulation of electricity prices is completed by the prosumer alliance and SESO through cooperation and negotiation based on their own bargaining power. Bargaining power reflects the contribution value of each subject in the process of improving the benefits of cooperation, and also represents the right to speak in the bargaining process, that is, the greater the contribution, the greater the bargaining power, and the more favorable the result of electricity price formulation is to oneself to obtain more benefits.

[0057] Therefore, this embodiment takes into account the working status of shared energy storage and the power supply and demand of prosumers, and uses the difference between the rental power required by the prosumer alliance at each moment and the shared power that SESO can provide to quantify the bargaining power of the prosumer alliance and SESO, and introduces bargaining factor indicators to express it. The specific process is as follows:

[0058] (101) Calculate the charging and discharging power that SESO can provide at each moment and the rental power required by the prosumer alliance It is expressed as follows:

[0059]

[0060] Where: is the charge / discharge identifier for shared storage in the retail market, and is the charging / discharging identifier of the shared energy storage and prosumer #i in the P2P market, and is the maximum charge / discharge power of the shared energy storage; is the charge / discharge power of SESO in the retail market during period t; In the process of power buffer sharing, the shared energy storage provides charging / discharging power to the prosumer i. For this process, the charge and discharge identifiers satisfy

[0061] (102) Comparing the above power sizes, if the charging (discharging) power provided by SESO is greater than the charging (discharging) power required by the producer-consumer alliance, it means that SESO has more advantages in the bargaining process, and vice versa. Based on this, the quantified bargaining factors of the two are expressed as follows:

[0062]

[0063] Where: γ Pros / γ SESO is the bargaining factor of the prosumer alliance / SESO; operator [·] + represents max(0,·).

[0064] In order to reduce the complexity of calculation, the above bargaining power is normalized and expressed as:

[0065]

[0066] (2) Second stage: joint pricing strategy among multiple producers and consumers

[0067] Since the renewable energy generation curve of the prosumer does not completely match the load demand curve, the net load power in different periods may be positive or negative, and the net load curves between different prosumers are also different. Therefore, prosumers can formulate a joint pricing strategy to directly share electricity to achieve power mutual assistance, and save the additional payment for charging and discharging through shared energy storage. At this time, it is generally believed that when providing / obtaining the same amount of electrical energy, the supplier contributes more to the alliance than the demander. Therefore, in this embodiment, a nonlinear energy sharing mapping method is used to quantify the bargaining power of prosumers. The specific process is as follows:

[0068] (201) Calculate the total amount of electricity provided by each producer and consumer during the electricity sharing process and the total amount of electrical energy obtained It is expressed as follows:

[0069]

[0070] Where: P i-j,t It is the amount of electricity exchanged from prosumer #i to prosumer #j during direct electricity sharing. A positive value indicates purchase and a negative value indicates sale.

[0071] (202) The nonlinear energy sharing mapping function is constructed using an exponential function with the natural constant e as the base, and the contribution value of each producer and consumer is quantified to represent the bargaining factor λ Pros,i , which is expressed as follows:

[0072]

[0073] Where: The maximum value of electrical energy provided / obtained by each producer and consumer; Pros It is the set of nodes where producers and consumers are located.

[0074] S3. With the goal of maximizing social benefits and maximizing the distribution of cooperative benefits, a multi-agent collaborative optimization model based on improved Nash bargaining is constructed based on preset constraints. Specifically, it includes:

[0075] S301. Construct DSO operation and revenue model, producer-consumer operation and revenue model and SESO operation and revenue model.

[0076] The method proposed in this embodiment aims to design a local energy sharing mechanism involving SESO and incorporate it into the scheduling process. Despite its great potential for cost savings, the architecture of shared energy storage exacerbates the complexity of energy management in the distribution system compared to the "one prosumer, one energy storage" model. Specifically, there is an inherent conflict of interest between DSO, prosumer and SESO. Therefore, in this embodiment, an improved Nash bargaining method is used to coordinate their interests to obtain the optimal scheduling result for the entire distribution system.

[0077] For the bargaining problem, it includes a series of agreements and disagreements. The bargaining disagreement point usually refers to the solution adopted in the most unfavorable scenario. In this embodiment, the optimization result of each entity only trading electricity in the retail market without bargaining transactions is selected as the bargaining disagreement point. In order to model the preferences of DSO, SESO and prosumers, it is necessary to consider the objective functions and constraints in the two cases of agreement and disagreement respectively. Since some objectives and constraints in the two cases are interconnected, in order to improve readability, the interconnected part only shows the model under the agreement, and the disagreement situation can be obtained in the same way.

[0078] Exemplarily, the objective function of the DSO operation and revenue model is expressed as follows:

[0079] 1) Overall objective function:

[0080] For DSO, regardless of the bargaining success or failure scenario, its overall goal is to minimize the total operating cost, which can be expressed as:

[0081]

[0082] Where: superscripts Dag and Ag represent the scenarios of reaching agreement and disagreement in the bargaining process, respectively; WM, BM and RM represent the wholesale market, standby market and retail market, respectively; t is the scheduling time interval; T is the optimization period; and They are the wholesale market transaction cost, reserve market transaction cost and retail market transaction cost of DSO in the bargaining failure / success scenario respectively; in the bargaining success scenario, prosumers and SESOs can trade surplus electricity with each other in the P2P market; It is the network usage fee charged by DSO to buyers and sellers.

[0083] It is worth mentioning that the above-mentioned "cost" is not a single operating cost in the traditional sense, but covers a wider range of cost and benefit considerations, including the direct power purchase cost of DSO in the wholesale market and the reserve market, as well as the transaction volume in the retail market, that is, formula (8). If the point-to-point market between producers and consumers and between producers and consumers and SESO is considered, the "cost" here should also include the network usage fee collected during the point-to-point market transaction, that is, formula (9). Among them, the transaction volume in the objective function, positive values ​​represent expenditures, while negative values ​​represent income (i.e., benefits). All statements about "cost" below conform to this definition.

[0084] 2) Wholesale market transaction costs:

[0085]

[0086] Where: is the purchase / sale electricity price of DSO in the wholesale market during period t; It is the amount of electricity purchased (positive value) or sold (negative value) by DSO in the wholesale market in the success scenario.

[0087] 3) Alternative market transaction costs

[0088]

[0089] Where: is the reserve purchase price of DSO in the reserve market during period t; It is the positive / negative spinning reserve in the scenario of successful bargaining.

[0090] 4) Retail market transaction costs:

[0091]

[0092] Where: is the electricity price purchased / sold by DSO from prosumers and SESO in the retail market during period t; Cons It is the collection of nodes where traditional electricity users are located; is the amount of electricity purchased (positive value) or sold (negative value) by prosumer #i in the retail market in the success scenario; are the charging / discharging power of SESO in the retail market under the successful bargaining scenario; P Cons,i,t The amount of electricity purchased for traditional electricity users.

[0093] 5) Network usage fee:

[0094]

[0095] Where: Pros-i is the set of nodes where all other prosumers, excluding prosumer #i, are located; s represents the node where the shared energy storage is located; μ is the power transmission cost coefficient; the operator |·| represents taking the absolute value of the variable ·; is the amount of electricity exchanged from prosumer #i to prosumer #j during direct electricity sharing, with a positive value indicating purchase and a negative value indicating sale; During the power buffer sharing process, the shared energy storage provides charging / discharging power to the prosumer #i.

[0096] The constraints of the DSO operation and revenue model are as follows:

[0097] 1) Power flow constraints

[0098] In this embodiment, a branch power flow model is used to describe the power flow distribution of the distribution network, which is expressed as follows:

[0099]

[0100] Where: K / I is the set of downstream / upstream nodes of node j; P j,t / Q j,t is the injected active / reactive power of node j; P ij,t / Q ij,t is the active / reactive power of the head node of branch ij; r ij / x ij is the resistance / reactance of branch ij; l ij,t is the square of the current amplitude of branch ij; v j,t is the square of the voltage amplitude at node i, Q Cons,i,t Reactive power for traditional electricity users; is the planned reactive output power of prosumer #i in the successful bargaining scenario.

[0101] 2) System operation safety constraints

[0102]

[0103] Where: are the upper and lower bounds of the variable .

[0104] 3) Alternative constraints

[0105] The power reserve set to deal with the internal renewable energy output forecast error of the prosumer in the successful bargaining scenario is as follows:

[0106]

[0107] Where: ε Re,Up,Ag / ε Re,Down,Ag are the ratio coefficients of the total positive / negative rotation reserve required in the bargaining success scenario to the absolute value of the predicted net load of all prosumers, respectively; is the predicted net load of prosumer #i in the success scenario.

[0108] 4) One-way constraints on power trading

[0109] In order to prevent prosumers from making profits by purchasing electricity at a low price and selling it at a high price, restrictions are imposed on prosumers’ electricity transactions, and prosumers are not allowed to conduct two-way electricity trading at the same time, as shown below:

[0110]

[0111] Where: M is a fairly large positive integer; is a 0-1 variable representing the net load status of prosumer #i in period t. It indicates that the net load of prosumer #i is positive, that is, the local power generation cannot meet the load demand. At this time, it is only allowed to purchase electricity from other prosumers, SESO or DSO, and is prohibited from selling electricity, and vice versa.

[0112] 5) Bargaining prerequisites

[0113]

[0114] Formula (20) is the prerequisite for DSO to participate in Nash bargaining, that is, the operating cost in the bargaining success scenario should be less than or equal to the operating cost in the bargaining failure scenario.

[0115] The objective function of the prosumer operation and revenue model is expressed as follows:

[0116] 1) Overall objective function

[0117] For prosumer #i, the overall goals in the bargaining success and bargaining failure scenarios are as follows:

[0118]

[0119] In the bargaining failure scenario, prosumer #i only trades electricity with DSO in the retail market. This is the transaction cost of producer-consumer #i in this case. In the scenario of successful bargaining, producer-consumer #i can also trade electricity with other producers-consumers and SESO in the P2P market. is the total transaction cost of prosumer #i, Transaction costs for P2P electricity sharing.

[0120] 2) Retail market transaction costs

[0121]

[0122] 3) P2P power sharing transaction costs

[0123]

[0124] The transaction cost of P2P electricity sharing consists of the direct electricity sharing cost Shared costs with electricity buffer The former refers to the fees and network usage fees paid for power exchanges with other prosumers, and the latter refers to the costs and network usage fees paid for power exchanges with SESO. i-j,t / π i-s,t It is the electricity transaction fee paid by prosumer #i to prosumer #i / SESO #s. A positive value indicates buying and a negative value indicates selling.

[0125] The constraints of the prosumer operation and revenue model are expressed as follows:

[0126] 1) Renewable energy output constraints

[0127]

[0128] Where: The upper limit of the active power that can be exported by the local renewable energy of prosumer #i; is the planned active output power of prosumer #i in time period t in the scenario of bargaining failure; is the planned active / reactive output power of prosumer #i in the successful bargaining scenario; is the rated power of the inverter. In the scenario of failed bargaining, the prosumer will increase the active power output as much as possible to meet the local power demand and sell the surplus power to make a profit. In contrast, in the scenario of successful bargaining, the prosumer can cooperate with the DSO to provide reactive power to adjust the power flow of the distribution system.

[0129] 2) P2P transaction constraints

[0130]

[0131]

[0132] Equations (27) and (28) respectively represent the conditional relationship between the amount of electricity traded and the payment of both parties in P2P power sharing transactions. The explanation of the relevant variables has been mentioned in the previous article.

[0133] 3) Local energy balance constraints

[0134] For each prosumer, the power flow should be balanced at any time. Since prosumers can trade electricity in the retail market and the P2P market, the total transaction volume should be consistent with the local net load.

[0135]

[0136] Where: It is the net load forecast of prosumer #i in time period t in the scenario of successful bargaining.

[0137] 4) Bargaining prerequisites

[0138] Each producer and consumer strives to make the cost after bargaining less than or equal to the cost before bargaining.

[0139]

[0140] The objective function of SESO operation and revenue model is expressed as follows:

[0141] 1) Overall objective function

[0142] In both cases of participating in bargaining and not participating in bargaining, SESO's goal is to maximize revenue, which is equivalent to minimizing costs in the above-mentioned cost structure, as shown below:

[0143]

[0144] Where: is the cost of SESO to trade electricity with DSO in the retail market during period t under the bargaining failure scenario; The cost of SESO conducting power buffer sharing transactions with prosumers in the P2P market in the scenario of successful bargaining; Reduce the degradation cost of charging and discharging shared energy storage.

[0145] 2) Retail market costs

[0146]

[0147] 3) P2P power sharing transaction costs

[0148]

[0149] 4) Shared energy storage degradation costs

[0150]

[0151] Where: is the degradation cost coefficient of shared energy storage.

[0152] It is worth mentioning that the degradation cost coefficient introduced in this embodiment is a comprehensive indicator to characterize the performance degradation and loss of energy storage equipment during the charging and discharging process as well as the maintenance cost. Specifically, the charging and discharging amounts of each period are added up during the scheduling cycle and multiplied by the energy storage degradation cost coefficient to quantify the charging and discharging costs of energy storage and incorporate them into the overall benefit calculation of the system. This relatively simplified processing method is intended to further focus on the design and optimization of the trading mechanism.

[0153] The constraints of SESO operation and revenue model are as follows:

[0154] 1) Constraints on charging and discharging behavior

[0155]

[0156]

[0157] Where: is the maximum charge / discharge power of the shared energy storage. Equations (38) and (39) constrain the charging and discharging behavior of the shared energy storage in the scenarios of failed and successful bargaining, respectively, that is, SESO is prohibited from charging and discharging for the same market participant at the same time to earn the price difference.

[0158] 2) Dynamic power constraints

[0159]

[0160] Where: is the power consumption of shared energy storage in time period t under the scenario of bargaining failure / success; η c / ηd is the charge / discharge efficiency of shared energy storage.

[0161] 3) Power limit constraints

[0162] The amount of shared energy storage must always be kept within its permitted limits to prevent any overcharging or overdischarging.

[0163]

[0164] Where: It is the minimum / maximum power limit of shared energy storage.

[0165] 4) Bargaining prerequisites

[0166] For SESO, the cost of a successful agreement should not exceed the cost of a failed bargaining scenario.

[0167]

[0168] S302. Based on the DSO operation and revenue model, the prosumer operation and revenue model and the SESO operation and revenue model, a multi-agent collaborative optimization model based on improved Nash bargaining is constructed.

[0169] In the active distribution network system, DSO, SESO and each producer and consumer are independent and rational individuals. Each subject will choose to prioritize protecting its own interests when interacting. Therefore, the interaction between the subjects is full of complex game relationships that are both competitive and cooperative. In this environment, the key to cooperation is not only to increase the benefits of the cooperative alliance, but also to reasonably determine the distribution of benefits. As a key branch of cooperative game theory, Nash bargaining theory is often used to characterize the cooperative behavior among multiple stakeholders. Therefore, based on the DSO operation and revenue model, the producer and consumer operation and revenue model, and the SESO operation and revenue model, the multi-agent collaborative decision-making problem considering P2P power sharing transactions (referred to as the original problem) can be modeled as follows:

[0170]

[0171] In the original problem, both energy transaction volume and energy transaction price are used as optimization variables. There is a product term between the two. It is essentially a mixed integer non-convex optimization problem and is difficult to solve directly. In order to reduce the complexity of problem solving, model (38) is transformed into two sub-problems. Sub-problem 1 is the objective function of maximizing social benefits, and sub-problem 2 is the objective function of maximizing cooperative benefit distribution.

[0172] In subproblem 1, DSO, prosumers, and SESO jointly optimize the power flow of the distribution system to improve resource utilization efficiency and minimize the total operating cost of the system in one day. By solving subproblem 1, the energy transaction volume of the participants in the cooperative alliance can be obtained. In subproblem 2, DSO, prosumers, and SESO negotiate the energy transaction price in order to pursue the fairness of the transaction. By solving subproblem 2, a Nash bargaining solution with Pareto efficiency can be obtained.

[0173] S4. Combine the bargaining power of shared energy storage operators and prosumers to optimize the multi-agent collaborative optimization model, obtain the optimal energy trading volume and the benefits of each entity, that is, the trading volume and benefits, and dispatch the distribution network according to the trading volume and benefits. The specific process is as follows:

[0174] 1) Sub-problem 1: Maximizing social benefits

[0175]

[0176] Solving subproblem 1 can optimize the following variables: P ij,t , Q ij,t , l ij,t 、v j,t ,

[0177] 2) Sub-problem 2: Distribution of cooperative benefits

[0178] In order to promote the reasonable distribution of benefits after cooperation among various market players, the Nash bargaining method is improved based on the above-mentioned bargaining power index. Different from the traditional Nash bargaining model in which the index items of each participant are all 1, the improved bargaining model uses the bargaining factor index as an index item to represent its weight in the target. It is worth mentioning that the electricity price of the DSO considered in this embodiment in the retail market is a fixed time-of-use electricity price. Therefore, only SESO and prosumers participate in the bargaining in sub-problem 2. Participants negotiate according to the size of their respective bargaining power and finally determine the energy transaction price that all parties to the bargaining are satisfied with. The specific process is as follows:

[0179] First, we construct the first-stage cooperative benefit distribution model based on the quantitative expression (4) of the bargaining power between prosumers and SESOs.

[0180]

[0181] Then, based on the obtained prosumer alliance benefits, the benefits are redistributed among the prosumers, and the second-stage cooperative benefit distribution model is constructed according to the quantitative expression (7) of the bargaining power of each prosumer.

[0182]

[0183] Finally, take the logarithm of equations (46) and (47), and then take their inverses to transform the product maximum problem into the sum minimum problem, which can be expressed as follows:

[0184]

[0185] Substituting the energy transaction volume obtained from subproblem 1 into the above formula, we can get the energy transaction price and the distribution of cooperative benefits. At this point, the original non-convex problem has been transformed into two linear subproblems and can be solved efficiently.

[0186] For example, after the original problem is linearly transformed, the original non-convex model is transformed into a linearized model that can be solved by conventional techniques. On the MATLAB platform, using the Yalmip modeling tool, the above two sub-problems and corresponding constraints are input, and the Mosek solver is called to solve the problem. Its internal logic is as follows: Figure 4 As shown, this is not an improvement point of this embodiment and will not be described in detail here.

[0187] Embodiment 2

[0188] This embodiment discloses an active distribution network collaborative optimization decision system including prosumer leasing shared energy storage, including:

[0189] The transaction architecture building module is configured to: build a multi-agent collaborative transaction architecture including prosumers sharing energy storage;

[0190] The collaborative pricing module is configured to: quantify the bargaining power of the shared energy storage operator and the prosumer through a multi-agent two-stage collaborative pricing strategy based on the multi-agent collaborative transaction architecture;

[0191] The multi-agent collaborative optimization model building module is configured to: build a multi-agent collaborative optimization model based on improved Nash bargaining based on preset constraints with the goal of maximizing social benefits and maximizing the distribution of cooperative benefits;

[0192] The decision-making module is configured to optimize the multi-agent collaborative optimization model in combination with the bargaining power of the shared energy storage operator and the prosumer to obtain the optimal energy transaction volume and the optimal energy transaction price.

[0193] It should be noted here that the above-mentioned transaction architecture building module, collaborative pricing module, multi-agent collaborative optimization model building module and decision-making module correspond to the steps in Example 1, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0194] Embodiment 3

[0195] A calculation example is provided to verify the performance of the method provided in Example 1.

[0196] Basic description of the case

[0197] The method proposed in this embodiment uses an improved IEEE-33 node distribution system for simulation analysis. The example system includes 4 prosumers (nodes 13, 21, 24 and 29) and 1 shared energy storage (node ​​8). For the convenience of description, the 4 prosumers are named prosumer #1-prosumer #4 in ascending order according to the node number. Prosumers #1 and #2 mainly use photovoltaic power generation, while prosumers #3 and #4 mainly use wind power generation. The scheduling time interval is 1h, the optimization cycle is 24h, and the power transactions between each market participant and the DSO adopt time-of-use electricity prices, see Appendix A Table A1. For DSO, the wholesale market purchase price is set to 0.8 times the retail market sales price, and the reserve purchase price for all periods of the reserve market is set to 0.9 yuan / kWh. The modeling tool for the example simulation is Yalmip, the operating environment is MATLAB 2020a, the called solver is Mosek, and the computer hardware configuration is Intel(R)Core(TM)i5-11400 2.60GHz, 16G RAM.

[0198] In order to verify the effectiveness of the collaborative optimization decision-making method for active distribution network with prosumers leasing shared energy storage proposed in Example 1, three schemes are set for comparative analysis:

[0199] Option 1: Prosumers and SESOs only trade electricity with DSOs in the retail market and do not engage in P2P electricity sharing transactions.

[0200] Solution 2: Considering that prosumers lease shared energy storage, prosumers and SESOs can conduct electricity sharing transactions in the P2P market, and can also conduct electricity transactions with DSOs in the retail market. The traditional Nash bargaining method is used to model and solve the problem.

[0201] Scheme 3: Based on the electricity trading model of Scheme 2, an improved Nash bargaining method based on a two-stage pricing strategy of prosumers leasing shared energy storage is used to model and solve the problem.

[0202] In order to verify the effectiveness of the method proposed in Example 1 in improving the economic benefits of various market entities, Schemes 1, 2 and 3 are optimized and solved respectively. The comparison of the total costs of each entity under the three schemes is shown in Table 1. A positive cost indicates expenditure, and a negative cost indicates income.

[0203] The income distribution under the method proposed in Example 1 is shown in Table 2.

[0204] Table 1 Comparison of costs of various entities under different schemes (yuan)

[0205] Market players Total cost of option 1 Total cost of option 2 Total cost of option 3 DSO -1569.51 -2239.32 -2239.32 SESO -99.53 -145.66 -195.57 Prosumer #1 226.17 180.04 201.05 Prosumer #2 447.91 401.79 418.84 Prosumer #3 -142.93 -189.06 -183.69 Prosumer #4 -322.27 -368.39 -385.35

[0206] When considering P2P power sharing transactions and multi-party cooperative bargaining, the overall economic benefits of the system are improved by 923.88 yuan. For DSO, its total dispatch cost is significantly reduced. Compared with Scheme 1, the total dispatch cost of Scheme 3 is reduced by 669.81 yuan, of which the wholesale market cost is reduced by 35.38 yuan and the reserve market cost is reduced by 865.06 yuan. This is because Scheme 3 enables DSO, prosumers and SESO to cooperate in optimizing the distribution system flow, promote the maximum utilization of local energy, and thus reduce the wholesale market's electricity purchase cost. At the same time, in Scheme 1, DSO cannot obtain the planned net load information of prosumers and can only purchase reserves based on historical information. In contrast, in Scheme 3, prosumers participate in dispatching, and DSO can directly obtain the planned net load information of prosumers and more accurately evaluate the reserve capacity based on this, reducing the reserve purchase cost in the reserve market. However, it can be found that the retail market cost in Scheme 3 has increased instead of decreased. This is because the introduction of the P2P market in the dispatching framework will weaken the trading ability of prosumers and SESOs in the retail market, resulting in an increase in the retail market cost of DSO. In order to compensate DSO for its losses in maintaining P2P transactions, electricity buyers and sellers in the P2P market need to pay DSO a network usage fee to adjust the distribution of benefits. It is worth mentioning that the total cost of DSO in Schemes 2 and 3 is equal. This is because DSO trades at a fixed electricity price in each market. When the energy trading volume is determined, its income is also determined. That is, DSO income is not redistributed in Scheme 3.

[0207] Compared with Scheme 1, the costs of SESO and prosumers in Scheme 3 are reduced to varying degrees. For SESO, the energy storage degradation cost increases in Scheme 3, and network usage fees need to be paid, but the revenue in the retail market and P2P market increases significantly, and the total revenue increases by 96.04 yuan. This is because SESO participates in retail market transactions and P2P power sharing transactions at the same time. While the utilization rate of energy storage is improved, its life also decreases, resulting in an increase in degradation costs, but SESO can more flexibly choose trading partners that are beneficial to itself to increase income. For prosumers, prosumers #1 and #2 are in a state of power shortage as a whole, and need to purchase electricity, with a positive cost; prosumers #3 and #4 are in a state of surplus power as a whole, and need to sell electricity, with a negative cost. Compared with Scheme 1, the total costs of the four prosumers are reduced by 11.10%, 6.49%, 28.51% and 19.57% respectively. This is because the electricity price in the retail market is fixed and the DSO's electricity sales price is higher than the electricity purchase price. In Scheme 3, prosumers can give priority to the P2P market to obtain profitable transaction quotations.

[0208] Table 2 Benefit distribution of Scheme 3

[0209]

[0210] Combined with Table 1 and Table 2, the utility of the improved Nash bargaining method based on the two-stage pricing strategy of prosumer leasing shared energy storage proposed in Example 1 in the reasonable distribution of benefits is analyzed. In Scheme 2, the revenue increase (cost reduction) of SESO and each prosumer is basically equal, about 46.13 yuan. This method is obviously unreasonable when the contribution of each subject to the overall benefit improvement in cooperation is different. In Scheme 3, the contribution value of each subject is considered in the negotiation pricing process and quantified by the bargaining factor. It can be seen from Table 2 that SESO has a larger bargaining factor than the prosumer alliance, that is, SESO tends to allocate more benefits to SESO in the joint pricing process of SESO and the prosumer alliance. Therefore, SESO's benefits are further improved compared with Scheme 2. As for prosumers, their bargaining power varies. Prosumers #1 and #2 have less new energy power generation and need to accept external electricity. Therefore, their bargaining factors are smaller, 0.5874 and 0.6625 respectively, and ultimately they are allocated less benefits in the power sharing transaction process.

[0211] In order to verify the effectiveness of the active distribution network coordinated adjustment decision-making method proposed in Example 1 in optimizing the power distribution system flow and improving the system operation status, Figure 5 The comparison of system dispatching and operation results of Schemes 1 and 3 is shown. In Scheme 1, there is no cooperative relationship between the subjects. The prosumers will maintain the maximum predicted power output of renewable energy and sell the excess power to DSO in the retail market. At the same time, SESO only exchanges power with DSO in the retail market, guided by the time-of-use electricity price, and earns the price difference by buying low and selling high. In contrast, in Scheme 3, prosumers and SESO can directly trade power with each other. At this time, SESO plays a dual role: on the one hand, it can trade with DSO. Although the total amount of electricity exchanged is reduced, it realizes the optimal distribution of energy and cooperates with DSO to optimize the power flow to reduce the operating cost of DSO; on the other hand, it can store the excess power of prosumers and release electricity for prosumers in a power shortage state, and indirectly realize power sharing between prosumers. At the same time, SESO uses its energy time-shifting characteristics to move power between prosumers, making its total net load curve more stable, thereby alleviating the power supply pressure of the distribution network with large load peak-valley difference.

[0212] Figure 5 (c) is the average voltage of all nodes in each period within the optimization cycle. Compared with Scheme 1, the voltage in Scheme 3 during peak load and low load periods is improved, which is closer to the rated voltage level of the system. This is because the proposed method can guide prosumers to reduce the active power output of renewable energy, and then provide more sufficient reactive power through self-matched inverters, thus realizing reactive power compensation nearby. The voltage increase is also conducive to reducing system network losses, such as Figure 5As shown in (d), further calculation shows that the loss reduction rate of the proposed method can reach 30.4%, which is also an important factor in improving the overall efficiency of the system.

[0213] In order to explore the impact of power generation and load of prosumers on electricity trading in the retail market and P2P market, equations (50) and (51) are used to calculate the electricity trading volume in the retail market and P2P market respectively. The calculation results are shown in the figure below: Figure 6 As shown in (a), Figure 6 (b) shows the specific electricity trading results of four prosumers.

[0214]

[0215] analyze Figure 6 It can be seen that there are power transactions in the P2P market and the retail market in each period of the optimization cycle. The former is because the four prosumers are equipped with wind power and photovoltaic power respectively. Wind power generation is mainly concentrated in the night time period, and photovoltaic power generation is only carried out during the daytime. The power generation periods are just complementary, and the power generation is sufficient to meet their own load requirements. Therefore, there is excess local renewable energy power generation for P2P transactions. In addition, shared energy storage can act as a power carrier, ensuring that there is power participating in P2P transactions in each period. The latter is because the distribution network is the medium for P2P market participants to conduct power transactions. In order to ensure that the interests of DSO are not lost, both parties of P2P transactions need to pay network usage fees to DSO. At this time, both parties will weigh the benefits of P2P transactions and retail market transactions, and seek the most favorable transaction form for themselves. At the same time, it also proves that it is difficult to achieve real-time balance of local electricity by relying solely on P2P transactions, and DSO intervention is required to maintain the normal operation of P2P transactions. It is worth mentioning that there are two peak trading periods in the retail market at 11:00-14:00 and 18:00-22:00, and the causes are different. The former is because the prosumers have excess photovoltaic production capacity, and the excess power generation is sold to DSO to make a profit; the latter is because the total load is large, and the local renewable energy generation and SESO storage power cannot meet the demand, so they can only purchase electricity from the retail market to ensure the balance between supply and demand.

[0216] Combination Figure 6 , Figure 7Analysis shows that the charging and discharging behavior of SESO is mainly related to the retail market electricity price and the net load of prosumers. SESO charges during the period of low electricity price in the retail market and discharges during the period of high electricity price, which can balance the load of the distribution system and earn the price difference. Prosumers #1 and #2 sell excess electricity to SESO, prosumers #3 and #4 through photovoltaic power generation from 9:00 to 15:00. Starting from 15:00, the photovoltaic output begins to gradually decrease, and the wind power generation is also small at this time, and the power purchase of prosumers #3 and #4 in the retail market gradually increases. From 20:00 to 24:00, the wind power generation of prosumers #3 and #4 dominates, meeting local electricity demand while selling excess electricity to SESO and other prosumers. Therefore, the proposed method can make P2P transactions between prosumers and between prosumers and SESO more flexible, rather than just limited to transactions with DSO in the retail market.

[0217] In order to further analyze the relationship between the power trading behaviors of each entity and the negotiated electricity price, taking prosumer #1 as an example, the bargaining results with other alliance participants are as follows: Figure 8 As shown in the figure. As can be seen from the figure, the electricity prices for each period negotiated and formulated by prosumer #1 and other entities are all within the envelope range of the DSO electricity sales and purchase prices in the retail market, which just guarantees the benefits of all parties in the P2P transaction. The reason for the transaction between prosumers #1 and #3 is analyzed as follows: During the period of 9:00-15:00, prosumer #1 has excess capacity, while prosumer #3 is in a state of power shortage. At this time, prosumer #1 sells excess electricity to prosumer #3 at a price higher than the DSO electricity purchase price, ensuring its own benefits; and for prosumer #3, the electricity purchase price at prosumer #1 is lower than the DSO electricity sales price. Based on the principle of cost minimization, prosumer #3 also prefers to trade electricity with prosumer #1. In this way, the two parties reach a consensus on P2P transactions. It should be noted that this bargaining process seems to damage the interests of DSO, but in fact it is compensated by paying network usage fees, optimizing power flow, reducing standby costs, etc., and finally achieving a win-win situation for all parties.

[0218] Embodiment 4

[0219] Embodiment 4 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage are completed.

[0220] Embodiment 5

[0221] Embodiment 5 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage are completed.

[0222] Embodiment 6

[0223] Embodiment 6 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage.

[0224] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0225] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A collaborative optimization decision-making method for active distribution network with shared energy storage leased by prosumers, characterized in that: include: Construct a multi-agent collaborative trading framework involving prosumers sharing energy storage; Based on the multi-agent collaborative transaction framework, the bargaining power of shared energy storage operators and prosumers is quantified through a multi-agent two-stage collaborative pricing strategy; With the goal of maximizing social benefits and maximizing the distribution of cooperative benefits, a multi-agent collaborative optimization model based on improved Nash bargaining is constructed based on preset constraints; The multi-agent collaborative optimization model is optimized in combination with the bargaining power of shared energy storage operators and prosumers to obtain the optimal energy trading volume and the optimal energy trading price.

2. The active distribution network collaborative optimization decision-making method with prosumers leasing shared energy storage as claimed in claim 1, characterized in that: The multi-subject collaborative transaction architecture includes multiple different types of markets and multiple different types of market entities. The types of markets include wholesale markets, backup markets, retail markets, and peer-to-peer markets. The types of market entities include distribution system operators, prosumers, and shared energy storage operators.

3. The active distribution network collaborative optimization decision-making method with prosumers leasing shared energy storage as claimed in claim 1, characterized in that: The above-mentioned multi-agent two-stage collaborative pricing strategy is used to quantify the bargaining power of shared energy storage operators and prosumers, including: In the first stage, the bargaining power of the prosumer alliance and the shared energy storage operator is quantified based on the working status of the shared energy storage and the power supply and demand of the prosumers. In the second stage, the bargaining power of prosumers is quantified through a nonlinear energy sharing mapping method.

4. The active distribution network collaborative optimization decision-making method with prosumer leasing shared energy storage as claimed in claim 3 is characterized in that: The described method of combining the working status of shared energy storage and the electricity supply and demand of prosumers to quantify the bargaining power of the prosumer alliance and the shared energy storage operator is as follows: respectively calculating the charging and discharging power that the shared energy storage operator can provide at each moment and the rental power required by the prosumer alliance and comparing them to calculate the bargaining factor.

5. The active distribution network collaborative optimization decision-making method with prosumer leasing shared energy storage as claimed in claim 3 is characterized in that: The method of quantifying the bargaining power of producers and consumers through the nonlinear energy sharing mapping method is specifically as follows: calculating the total amount of electric energy provided and the total amount of electric energy obtained by each producer and consumer during the electric energy sharing process, and constructing a nonlinear energy sharing mapping function to quantify the contribution value of each producer and consumer as a bargaining factor.

6. The active distribution network collaborative optimization decision-making method with prosumers leasing shared energy storage as claimed in claim 1, characterized in that: The above-mentioned optimization of the multi-agent collaborative optimization model by combining the bargaining power of shared energy storage operators and prosumers to obtain the optimal energy transaction volume and the optimal energy transaction price includes: Initialize basic parameters, solve the objective function of maximizing social benefits, and obtain the optimal energy trading volume; Based on the bargaining power between prosumers and shared energy storage operators, combined with the maximization of cooperative benefit distribution, a first-stage cooperative benefit distribution model is constructed to obtain the benefits of the prosumer alliance and distribute them to each prosumer; According to the bargaining power of each producer and consumer, a second-stage cooperative profit distribution model is constructed; according to the optimal energy trading volume and the second-stage cooperative profit distribution model, the optimal energy trading price is obtained.

7. An active distribution network collaborative optimization decision system with prosumers leasing shared energy storage, characterized in that: include: The transaction architecture building module is configured to: build a multi-agent collaborative transaction architecture including prosumers sharing energy storage; The collaborative pricing module is configured to: quantify the bargaining power of the shared energy storage operator and the prosumer through a multi-agent two-stage collaborative pricing strategy based on the multi-agent collaborative transaction architecture; The multi-agent collaborative optimization model building module is configured to: build a multi-agent collaborative optimization model based on improved Nash bargaining based on preset constraints with the goal of maximizing social benefits and maximizing the distribution of cooperative benefits; The decision-making module is configured to optimize the multi-agent collaborative optimization model in combination with the bargaining power of the shared energy storage operator and the prosumer to obtain the optimal energy transaction volume and the optimal energy transaction price.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the active distribution network collaborative optimization decision-making method involving producer-consumer leasing shared energy storage as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the collaborative optimization decision-making method for an active distribution network involving producer-consumer leasing shared energy storage as described in any one of claims 1-6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the collaborative optimization decision-making method for an active distribution network involving producer-consumer leasing shared energy storage as described in any one of claims 1-6 are implemented.

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