Active distribution network optimal scheduling method and system considering multi-agent cooperative game

Abstract

The present invention discloses an active distribution network optimal scheduling method and system considering multi-agent cooperative game, which relates to the technical field of distribution network optimal scheduling. The method includes: considering the interaction among multiple market players in the active distribution network, constructing a multi-agent collaborative trading architecture; based on the multi-agent collaborative trading architecture, aiming at minimizing the cost and combining with the constraint conditions, building the operation and revenue models of each agent; among them, as one of the agents, the distributed energy storage operator uses a distributed energy storage allocation strategy based on the SOC balance of distributed energy storage to constrain the operation of distributed energy storage; according to the Nash bargaining theory, integrating the operation and revenue models of multiple agents, constructing a multi-agent cooperative game model; based on the actual energy demand on the user side, solving the multi-agent cooperative game model to obtain the optimal scheduling strategy of energy in the active distribution network, and performing energy scheduling according to the scheduling strategy. The present invention can achieve the balance of charge and discharge power among DES units and improve economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimal scheduling, and in particular, to an active distribution network optimal scheduling method and system considering multi-agent cooperative game. Background Technique

[0002] The statements in this part merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] Under the background of the current increasing energy crisis and environmental pollution, the rapid development of renewable energy generation technology has enabled it to be rapidly popularized globally and widely connected to the active distribution network (ADN) in the form of distributed generation (DG). Under the background of power market reform, power users are encouraged to install distributed generation on the user side, which means that traditional power users can not only consume electric energy but also produce electric energy to participate in power market transactions. Therefore, the power supply mode in which the ownership of distributed resources belongs to the distribution network has been changed. Compared with traditional scheduling methods, the coordinated optimal scheduling of the ADN containing multiple distributed resources can effectively integrate renewable energy (RES) while ensuring the safety, stability, and economic operation of the system. For example, existing research has proposed a multi-stage stochastic programming model for the day-ahead optimal scheduling of active distribution networks, enabling scheduling decisions to more accurately address the uncertainty of renewable energy; proposed applying second-order cone relaxation technology to the day-ahead multi-period optimization model of active distribution networks to achieve effective solution of the model; established a dynamic optimal power flow model at multiple time scales with the goal of maximizing the energy output of renewable energy generation, and reduced the curtailment of wind and light by coordinating the control of energy storage and flexible loads. The above research regards distributed resources as schedulable units, but does not consider that in a market-oriented situation, distributed resources are built and managed by operators, forming independent entities such as distributed energy storage operators (DESO), load aggregators (LA), etc., and each entity can participate in the optimal scheduling according to its own interests. Therefore, the problem of multi-agent cooperation still needs to be solved.

[0004] In addition, the research on the participation of distributed energy storage (DES) in the optimal scheduling of ADN has been widely carried out. With a large number of RES connected to the ADN through DG, the strong randomness and intermittency of its output result in large fluctuations in the grid-connected power, leading to problems such as increased voltage fluctuations and degraded power quality in the system. Distributed energy storage has fast charge and discharge characteristics and can achieve the transfer of energy in time and space, which is an effective means to solve the power fluctuations in the distribution network system with distributed renewable energy. For example, existing research has proposed a novel battery charge and discharge control strategy for the distribution system to optimize the energy trading cost; to alleviate the imbalance problem, the power flexibility of DES is used to provide support to the upper-level grid at the common coupling point, etc. Although existing research has effectively improved the operational stability of the system through DES scheduling, considering the inconsistent charge and discharge levels among distributed energy storages, some DES will be overcharged and over-discharged, thus affecting the lifespan of the energy storage. Existing research has not considered the balance of the SOC (State of Charge) among distributed energy storages, which means that in the actual operation process, there may be a serious inconsistency in the state of charge among energy storages, resulting in overcharging and over-discharging of some energy storages, and further causing problems such as low utilization rate of battery capacity and shortened service life. Summary of the Invention

[0005] To solve the deficiencies of the above-mentioned prior art, the present invention provides an optimal scheduling method and system for an active distribution network considering multi-agent cooperative game, which conducts collaborative optimal scheduling by simultaneously considering the SOC balance of DES and the interests of multiple agents. That is, on the one hand, a DES power distribution strategy based on SOC balance is designed, and DES is divided into a priority charging group and a priority discharging group. Through double-layer allocation between groups and within groups, the balance of charge and discharge power among DES units is achieved, reducing lifespan loss. On the other hand, a trading framework describing the economic interaction among the distribution system operator (DSO), distributed energy storage operator (DESO), and load aggregator (LA) during the energy trading process in the ADN is proposed, and then the Nash bargaining theory is introduced to establish a multi-agent cooperative game model to effectively improve the economic benefits of all participating entities.

[0006] In the first aspect, the present invention provides an optimal scheduling method for an active distribution network considering multi-agent cooperative game.

[0007] An optimal scheduling method for an active distribution network considering multi-agent cooperative game includes:

[0008] Considering the interaction among multiple market players in the active distribution network, constructing a multi-agent collaborative trading architecture;

[0009] Based on the multi-agent collaborative trading architecture, with the goal of minimizing costs and combining constraint conditions, an operation and revenue model for each agent is established. Among them, as one of the agents, the distributed energy storage operator uses a distributed energy storage allocation strategy based on the balance of the state of charge (SOC) of the distributed energy storage to constrain the operation of the distributed energy storage.

[0010] According to the Nash bargaining theory, by integrating the operation and revenue models of multiple agents, a multi-agent cooperative game model is constructed.

[0011] Based on the actual energy demand on the user side, the multi-agent cooperative game model is solved to obtain an optimized energy dispatch strategy for the active distribution network, and energy dispatch is carried out according to the dispatch strategy.

[0012] In a second aspect, the present invention provides an optimized dispatch system for an active distribution network considering multi-agent cooperative games.

[0013] An optimized dispatch system for an active distribution network considering multi-agent cooperative games includes:

[0014] An optimized dispatch model construction module, which is used to consider the interaction between multiple market agents in the active distribution network and construct a multi-agent collaborative trading architecture; based on the multi-agent collaborative trading architecture, with the goal of minimizing costs and combining constraint conditions, an operation and revenue model for each agent is established. Among them, as one of the agents, the distributed energy storage operator uses a distributed energy storage allocation strategy based on the balance of the state of charge (SOC) of the distributed energy storage to constrain the operation of the distributed energy storage; according to the Nash bargaining theory, by integrating the operation and revenue models of multiple agents, a multi-agent cooperative game model is constructed.

[0015] A model solving and dispatch module, which is used to solve the multi-agent cooperative game model based on the actual energy demand on the user side to obtain an optimized energy dispatch strategy for the active distribution network, and carry out energy dispatch according to the dispatch strategy.

[0016] In a third aspect, the present invention further provides an electronic device, including: a memory for storing executable instructions; a processor for implementing the above-mentioned optimized dispatch method for an active distribution network considering multi-agent cooperative games when executing the executable instructions stored in the memory.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to implement the above-mentioned optimized dispatch method for an active distribution network considering multi-agent cooperative games when executing the executable instructions.

[0018] Fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when a processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned active distribution network optimal scheduling method considering multi-agent cooperative game is realized.

[0019] The above one or more technical solutions have the following beneficial effects:

[0020] 1. The present invention provides an active distribution network optimal scheduling method and system considering multi-agent cooperative game. By simultaneously considering the SOC balance of DES and the interests of multiple agents for collaborative optimal scheduling, on the one hand, a DES power distribution strategy based on SOC balance is designed, dividing DES into a priority charging group and a priority discharging group, and through double-layer distribution between groups and within groups, the balance of charge and discharge power among DES units is realized, reducing life loss; on the other hand, a trading framework describing the economic interaction among the distribution system operator DSO, distributed energy storage operator DESO, and load aggregator LA during the energy trading process in the ADN is proposed, and then the Nash bargaining theory is introduced to establish a multi-agent cooperative game model to effectively improve the economic benefits of all participating entities.

[0021] 2. The present invention proposes a DES power distribution strategy based on SOC balance, dividing DES into a priority charging group and a priority discharging group. The optimal allocation of the total power command between groups is determined through upper-layer distribution, and the relative balance of the distributed energy storage SOC within the group is realized through lower-layer distribution. Compared with the traditional energy storage scheduling strategy, this strategy can ensure the dynamic relative balance of each energy storage SOC during the scheduling process, effectively improve the utilization rate of DES, and at the same time reduce the charge and discharge switching times of the energy storage, extending the service life to a certain extent and reducing the life loss of DES.

[0022] 3. The present invention constructs a multi-agent collaborative trading framework for distribution companies - load aggregators - energy storage operators. This framework considers the interaction among DSO, DESO, and LA, defines trading mechanisms and flexible pricing strategies in different markets to promote the coordinated pricing of electricity transactions and collaboratively optimize electricity transactions to achieve win-win cooperation among multiple interest entities; on this basis, a multi-agent collaborative optimal scheduling model considering the SOC balance of distributed energy storage is constructed. This model takes maximizing social benefits as the optimization goal and realizes the fair distribution of interests based on the Nash bargaining theory. Compared with the traditional scheduling method, the present invention can make full use of the resources of each entity, greatly reduce the electricity purchase volume from the upper-layer power grid of the system, and achieve the maximization of social benefits.

[0023] The advantages of the additional aspects of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. Description of the Drawings

[0024] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and do not constitute an improper limitation to the invention.

[0025] Figure 1 It is the overall flowchart of the active distribution network optimization scheduling method considering multi-agent cooperative game according to the embodiment of the invention;

[0026] Figure 2 It is the schematic diagram of the multi-agent trading framework in the embodiment of the invention;

[0027] Figure 3 It is the schematic diagram of 10 typical scenarios of the fan output in the embodiment of the invention;

[0028] Figure 4 It is the schematic diagram of the SOC change curve under Scheme 1 in the embodiment of the invention;

[0029] Figure 5 It is the schematic diagram of the SOC change curve under Scheme 2 in the embodiment of the invention;

[0030] Figure 6 It is the schematic diagram of the analysis result of the peak shaving and valley filling effect in the embodiment of the invention. Detailed Description of the Invention

[0031] It should be noted that the following detailed description is exemplary only for the purpose of describing the specific embodiments, aiming to provide a further explanation of the invention and is not intended to limit the exemplary embodiments of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0032] Embodiment 1

[0033] Considering that in the active distribution network (ADN), the state of charge (SOC) of distributed energy storage (DES) is unbalanced, and coupled with the intertwined interests of multiple stakeholders, it significantly increases the complexity of system coordination and scheduling. Therefore, this embodiment provides an active distribution network optimization scheduling method considering multi-agent cooperative game, which can simultaneously consider the SOC balance of DES and the interests of multiple agents for collaborative optimization scheduling, as Figure 1 shown, and specifically includes the following steps:

[0034] Step S1, considering the interaction between multiple market players in the active distribution network, construct a multi-agent collaborative trading architecture.

[0035] In the multi-agent collaborative trading architecture studied in this embodiment, the market entities include the Distribution System Operator (DSO), the Load Aggregator (LA) distributed in the distribution network, and the Distributed Energy Storage Operator (DESO). The interconnection structure and interaction mechanism among multiple entities in the system are as Figure 2 shown. It should be emphasized that all distributed energy storages connected to the active distribution network are built by the energy storage operator, and it is allowed to serve both the distribution network and the load aggregator simultaneously. In addition, since there is no direct line connection between multiple load aggregators or between the load aggregator and the distributed energy storage, power interaction needs to be carried out through the distribution network lines. To ensure its own interests, the distribution company charges a transmission fee for these two trading forms.

[0036] Among them, the responsibilities of the entities (i.e., each market entity) in the multi-agent trading structure are as follows:

[0037] (1) In the active distribution network, controllable distributed power sources mainly based on gas turbines, conventional loads, and some reactive power compensation devices are equipped, mainly including Static Var Compensator (SVC), Discrete Capacitor (CB), and On-Load Tap Changer (OLTC). As the operator and manager of the active distribution network, the distribution company aims to minimize the total operating cost of the active distribution network.

[0038] (2) Due to its dispersion and the limitation of the power generation scale, it is difficult for distributed power sources on the user side to participate in the market alone. Therefore, the load aggregator is proposed to combine multiple uncontrollable distributed power sources and some adjustable loads together to form a market-competitive entity to participate in electricity trading. The Load Aggregator Alliance (LAA) is responsible for the centralized management and coordination of all LA electricity demands, aiming to minimize the total operating cost of all LAs.

[0039] (3) The distributed energy storage participates in operation in the form of an operator. The operator participates in the competition by coordinating and controlling the charging and discharging power of each distributed energy storage unit in each time period and formulating the discharge price, aiming to minimize the total operating cost of all energy storages.

[0040] The operation of the multi-agent trading model can be described as follows:

[0041] (1) When there is an electricity surplus or deficit within the LAA, to ensure economic benefits, the LAs give priority to electricity trading within the alliance. The LAs negotiate with each other to determine the trading volume and price of electricity, and realize the transfer of electricity through the distribution network. The two trading parties share the transmission fee charged by the distribution company equally.

[0042] (2) When the electricity trading within the alliance cannot meet the electricity demand of the LAs, the LAA will carry out power interaction with the energy storage operator to achieve power balance through the charging and discharging of distributed energy storage. The trading electricity price and volume are determined through negotiation between the two trading parties, and the transmission fee is shared.

[0043] (3) When the above two trading forms still cannot meet the electricity demand of the load aggregator, the LA can conduct electricity transactions with the DSO at a fixed time-of-use electricity price. In addition, in pursuit of higher profits, energy storage operators can also conduct electricity transactions with distribution companies at a fixed time-of-use electricity price.

[0044] (4) To meet the load demand and improve the operation efficiency, the distribution company can participate in the wholesale market and conduct electricity transactions with the superior power grid.

[0045] Step S2: According to the multi-agent collaborative trading framework, based on the Nash bargaining theory, construct a multi-agent cooperative game model, that is, construct an ADN collaborative optimization scheduling model considering the distributed energy storage SOC equilibrium and multi-agent cooperative game.

[0046] Step S2.1: Based on the multi-agent collaborative trading framework, with the goal of minimizing costs and combined with the constraint conditions, build the operation and revenue model of each agent; among them, as one of the agents, the distributed energy storage operator uses the distributed energy storage allocation strategy based on the distributed energy storage SOC equilibrium to constrain the operation of the distributed energy storage.

[0047] In the multi-agent system, the DSO, DESO, LAA, and each LA are all independent and rational individuals. Each agent will not interact with other agents regardless of its own interests. Therefore, the interest relationships among the agents are both competitive and cooperative, with complex game relationships. Therefore, when each agent participates in cooperative operation, it not only pays attention to the increase of its own interests, but more importantly, pays attention to the fair and reasonable determination of interest distribution. As an important branch of cooperative game theory, the Nash bargaining theory can be used to describe the cooperative interaction among multiple participants, with collective rationality and social optimization as the core. For the bargaining problem, it includes a series of agreements and disagreements. The bargaining disagreement point usually refers to the plan adopted in the most unfavorable scenario. In this embodiment, the operation result when each agent only has electricity interaction with the superior network is selected as the bargaining disagreement point. On this basis, for each agent, with the goal of minimizing costs, establish constraint conditions, and build the operation and revenue models of the distribution system operator DSO, load aggregator LA, and distributed energy storage operator DESO.

[0048] (1) DSO operation and revenue model

[0049] With the goal of minimizing the total operating cost, and with the power balance constraint, node injection power constraint, node voltage and branch current constraint, gateway power constraint, and electricity trading unidirectional constraint as the constraint conditions, construct the operation and revenue model of the distribution system operator DSO. Specifically:

[0050] (1.1) Objective function

[0051] For the DSO, regardless of whether the bargaining is successful or not, its overall goal is to minimize the total operating cost, which can be expressed as:

[0052] (1)

[0053] (2)

[0054] In the formula, the superscripts Ag and Dag respectively represent the situations of reaching an agreement and having a disagreement during the bargaining process; WM and RM respectively represent the wholesale market and the retail market; t is the scheduling time interval; T is the optimization period; / and / are respectively the wholesale market trading cost and the retail market trading cost of the DSO in the case of bargaining failure / success; are respectively the total costs of the DSO in the case of bargaining failure / success; is the network usage fee charged by the DSO to the buyers and sellers.

[0055] Furthermore, the above "cost" is not the single operating cost in the traditional sense, but takes into account a wider range of costs and revenues, including the direct power purchase cost and power sales revenue of the DSO in the wholesale market, the transaction volume in the retail market, and the network usage fee charged during the point-to-point market transaction. Among them, when the transaction volume is positive, it represents an expenditure, while a negative value represents an income. All expressions of "cost" in this embodiment conform to this definition.

[0056] Among them, the wholesale market trading cost is:

[0057] (3)

[0058] (4)

[0059] In the formula, / is the power purchase / sale price of the DSO for power trading with the upper-level power grid at time t; / is the power quantity of the DSO interacting with the upper-level power grid in the case of bargaining failure / success; the operator means .

[0060] The retail market trading cost is:

[0061] (5)

[0062] In the formula, / The electricity price for DSO to purchase / sell electricity from LAA and DESO in the retail market during period t; is the set of nodes where traditional electricity users are located; is the electricity quantity traded by the i-th aggregator (i.e., load aggregator #i) in the retail market under the successful bargaining scenario; / are the charging / discharging powers of DESO in the retail market under the successful bargaining scenario, respectively; is the electricity consumption of the fixed load.

[0063] The network usage fee is:

[0064] (6)

[0065] In the formula, is the set of nodes where all other load aggregators except load aggregator # i are located; is the set of nodes where all load aggregators are located; represents the set of nodes where the energy storage is located; is the power transmission cost coefficient; the operator represents taking the absolute value of the variable ; is the electricity quantity exchanged by load aggregator # i to load aggregator # j ; a positive value indicates purchase, and a negative value indicates sale; / is the charging / discharging power provided by the energy storage for load aggregator # i ;

[0066] (1.2) Constraint conditions

[0067] (1.2.1) Power balance constraint

[0068] According to the network topology of the radial distribution network, the Distflow form of the power flow equation is adopted in this embodiment, which is:

[0069] For node j it can be expressed as:

[0070] (7)

[0071] (8)

[0072] For branch ij it can be expressed as:

[0073] (9)

[0074] (10)

[0075] In the formula, represents the set of the head node of the branch with j as the end node in the power grid; represents the set of the end node of the branch with j as the head node in the power grid; and are t the three-phase active power and reactive power of the first end of the branch ij at time represents t the current of the branch ij at time and are respectively t the active power and reactive power of the node j at time , are t the three-phase voltage amplitudes of the node i, j at time and are ij the three-phase branch resistance and reactance of the branch.

[0076] (1.2.2) Node injection power constraint

[0077] (11)

[0078] (12)

[0079] In the formula, and are respectively t the active power and reactive power of the node j at time and are respectively t the active power and reactive power of the DG connected to the node j at time and are respectively t the active power and reactive power of the load connected to the node j at time is the reactive power of the reactive power compensation device connected to the node j at time

[0080] (1.2.3) Node voltage and branch current constraint

[0081] (13)

[0082] In the formula, Yes t Time node i The voltage amplitude of each phase at And Are the maximum and minimum limits of the voltage amplitude respectively, Is t The current amplitude of the branch at time ij ; Is the critical current amplitude of branch overload.

[0083] (1.2.4) Tie-line power constraint

[0084] To suppress the impact of ADN power fluctuations on the transmission network, it is necessary to consider the power constraint of the transmission network interface switch on the distribution network root bus, which can be expressed as:

[0085] (14)

[0086] In the formula, Is the active power flowing from the transmission network interface into the distribution network at t ; And Are the upper and lower limits of the active power exchange of each relevant port set by the control center. The reactive power constraint is similar, that is, Is the reactive power flowing from the transmission network interface into the distribution network at t ; And Are the upper and lower limits of the reactive power exchange of each relevant port set by the control center.

[0087] (1.2.5) Unidirectional power trading constraint

[0088] To prevent LA from making a profit by buying electricity at a low price and selling it at a high price, its power trading is restricted, and it is not allowed for prosumers to conduct two-way power buying and selling transactions at the same time, which can be expressed as:

[0089] (15)

[0090] In the formula, M is a very large positive integer; Is a 0-1 variable representing the net load status of load aggregator # i In time period t ; Represents that the net load of load aggregator # 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 load aggregators, energy storage or DSO, and selling electricity is prohibited, and vice versa; Is the electricity quantity traded by load aggregator #i in the retail market under the successful bargaining scenario; For load aggregator #​i To the load aggregator# j The electricity quantity exchanged, with a positive value indicating purchase and a negative value indicating sale; / The charging / discharging power provided by the energy storage for the load aggregator# i

[0091] (2) LA operation and revenue model

[0092] Aiming to minimize the total transaction cost, with the constraints of distributed renewable energy output, interruptible load, P2P transaction, and local energy balance, a load aggregator LA operation and revenue model is constructed. Specifically:

[0093] (2.1) Objective function

[0094] For the i th LA (i.e., the load aggregator LA# i ), in the case of negotiation failure and negotiation success, the overall objective is:

[0095] (16)

[0096] (17)

[0097] In the case of bargaining failure, the load aggregator# i only conducts electricity transactions with the DSO in the retail market, which is the transaction cost of the load aggregator# i in this case; in the case of successful bargaining, the load aggregator# i can also conduct electricity transactions with other load aggregators and DESO in the P2P market, which is the total transaction cost of the load aggregator# i , which is the transaction cost for participating in the P2P market; in the case of successful bargaining, the load aggregator# i only conducts electricity transactions with the DSO in the retail market, which is the transaction cost of the load aggregator# i in this case.

[0098] Among them, the retail market transaction cost is:

[0099] (18)

[0100] In the formula, / is the electricity price at which the DSO purchases / sells electricity from the LAA and DESO in the retail market at time t; ​It is the electricity quantity traded by the load aggregator #i in the retail market under the successful bargaining scenario.

[0101] The P2P trading cost is:

[0102] (19)

[0103] In the formula, the P2P market trading cost consists of and two parts. The former refers to the fees paid for electricity trading within the load aggregator alliance and the network usage fees, and the latter refers to the costs and network usage fees paid for electricity trading with the DESO. / is the electricity trading fee paid by the load aggregator # i to the load aggregator # j / DESO# s . A positive value indicates buying, and a negative value indicates selling; is the adjustment parameter.

[0104] (2.2) Constraint conditions

[0105] (2.2.1) Distributed renewable energy output constraint

[0106] In the case of failed bargaining, the load aggregator will increase the active power output of renewable energy as much as possible to meet its own electricity demand, and at the same time sell the surplus electricity to obtain profits, which can be expressed as:

[0107] (20)

[0108] In the formula, is the upper limit of the active power that can be output by renewable energy in the load aggregator # i ; is the planned active power output of renewable energy in the load aggregator # i at the t moment in the case of failed bargaining.

[0109] In the case of successful bargaining, the load aggregator can cooperate with the DSO to provide reactive power to regulate the system power flow, which can be expressed as:

[0110] (21)

[0111] In the formula, and are the planned active and reactive power outputs of renewable energy in the load aggregator # i in the case of successful bargaining; is the rated power of renewable energy in the load aggregator # i .

[0112] Furthermore, the renewable energy considered in this embodiment includes two types: distributed photovoltaic power generation devices and distributed wind turbines. Since their output characteristics are similar and their operating modes can be described by the above mathematical formulas, they are not distinguished here.

[0113] (2.2.2) Interruptible Load Constraint

[0114] (22)

[0115] In the formula, and are the upper and lower limits of the interruptible power of the interruptible load, represents the interruptible load; represents the number of interruptions of the interruptible load (IL) agreed in the contract, represents the maximum number of interruptions of the interruptible load (IL) agreed in the contract; represents the continuous response time of the interruptible load, and are the upper and lower limits of the continuous interruption time.

[0116] (2.2.3) P2P Transaction Constraint

[0117] During the P2P transaction process, the electricity price and electricity quantity of both trading parties should satisfy:

[0118] (23)

[0119] (24)

[0120] In the formula, / is the electricity trading fee paid by the load aggregator # i to the load aggregator # j / DESO# s Positive values indicate buying, and negative values indicate selling; is the electricity quantity exchanged by the load aggregator # i to the load aggregator # j Positive values indicate purchase, and negative values indicate sale.

[0121] (2.2.4) Local Energy Balance Constraint

[0122] For each load aggregator, the power flow should be balanced at any time. Therefore, the total trading electricity quantity in each trading market should be equal to the local net load, which can be expressed as:

[0123] (25)

[0124] (26)

[0125] In the formula, is the net load prediction of the load aggregator # i during the time period t under the successful bargaining scenario.

[0126] (3) DESO Operation and Revenue Model

[0127] Taking the maximization of revenue as the goal and the constructed operation constraints of distributed energy storage as the constraints, the operation and revenue model of the distributed energy storage operator DESO is constructed. Specifically:

[0128] (3.1) Objective Function

[0129] In both the successful and failed bargaining cases, the goal of DESO is to maximize revenue, which is equivalent to minimizing cost, specifically expressed as:

[0130] (27)

[0131] (28)

[0132] In the formula, / is the cost of DESO's electricity transaction with the DSO in the retail market t during the time period in the failed / successful bargaining scenario; is the cost of DESO's transaction with prosumers in the P2P market under the successful bargaining scenario; / is the degradation cost of shared energy storage charging and discharging in the failed / successful bargaining scenario.

[0133] Among them, the retail market cost is:

[0134] (29)

[0135] The P2P transaction cost is:

[0136] (30)

[0137] The shared energy storage degradation cost is:

[0138] (31)

[0139] (32)

[0140] In the formula, is the DES life loss cost coefficient, which is a comprehensive index used to describe the performance degradation and loss of DES during the charging and discharging processes.

[0141] (3.2) Constraints

[0142] In this embodiment, considering the problem of unbalanced state of charge (SOC) of distributed energy storage (DES), a distributed energy storage allocation strategy based on DES SOC balance is proposed, and the operation constraints of distributed energy storage are constructed according to this strategy.

[0143] Specifically, the proposed distributed energy storage allocation strategy based on DES SOC balance is as follows:

[0144] First, several distributed energy storage units in the distributed energy storage system are divided into two groups that respectively perform charge / discharge tasks, and the priority charge / discharge states of the two groups are determined according to the actual SOC of the distributed energy storage units in the two groups.

[0145] Specifically, in the actual scheduling process, in order to suppress the output fluctuations of renewable energy, the energy storage system needs to frequently switch the charge / discharge state, which to a certain extent increases the charge / discharge frequency of the energy storage, accelerates the aging of the battery, and at the same time the capacity of each energy storage unit is not fully utilized. Based on the above considerations, the distributed energy storage units in the energy storage system are divided into two groups to respectively perform charge / discharge tasks, so as to reduce unnecessary charge / discharge state switching, thereby reducing the charge / discharge frequency of the battery and reducing the life loss.

[0146] Furthermore, for a distributed energy storage system composed of 2N distributed energy storages, at the initial moment, the N distributed energy storages with lower SOC are set as the priority charging group, and the N distributed energy storages with higher SOC are set as the priority discharging group. It should be noted that there are certain moments when the total power instruction exceeds the rated power of a single group, cross-group coordination is required to provide short-term power support to jointly complete the total power instruction during this period. In addition, when the SOC balance within the group is poor or the SOC of any single unit reaches a predetermined threshold during operation, the role exchange between the groups will be triggered; the threshold can be adjusted adaptively according to the system operation. In the normal operation scenario, the goal of setting the threshold is to minimize the loss of DES life; in extreme cases, the SOC limit will be appropriately relaxed to ensure the stable operation of the system.

[0147] Secondly, a two-layer power distribution strategy is executed, including a power upper-layer distribution strategy and a power lower-layer distribution strategy, which are as follows:

[0148] The power upper-layer distribution strategy, that is, based on the priority charge / discharge states of the two groups, according to the actual charge / discharge demand of the distributed energy storage system, the charge / discharge demand responses and response sequences of the two groups are determined. Among them, after determining the priority charge / discharge states of the two battery groups, the total charge / discharge instruction of the system needs to be allocated to the two battery groups. First, the response sequence of the two battery groups needs to be determined: If is a charging instruction, that is if < 0, the priority charging group responds first; otherwise if > 0, the priority discharging group responds first until the charging and discharging power meets the total instruction requirements or reaches the maximum power of the battery pack. After the total power instruction is distributed by the upper layer, assuming that the maximum charging and discharging power of both battery packs is , the charging and discharging power borne by each of the two battery packs is shown in Table 1.

[0149] Table 1: Charging and discharging power borne by two battery packs

[0150]

[0151] The power lower-layer distribution strategy, that is, according to the charging / discharging demand response and response order of the two groups, the power is distributed to each distributed energy storage unit in each group respectively; among them, the power distribution process is: if the charging / discharging demand of the current group is the maximum value, the power is distributed according to the maximum charging / discharging power principle; otherwise, the power is distributed according to the SOC balance principle.

[0152] Specifically, after the upper layer obtains the charging and discharging instructions that the two battery packs should bear, it is necessary to further determine the power distribution inside them. When the charging and discharging instruction of the battery pack is , the lower layer distributes according to the maximum charging and discharging power principle, that is, the power instruction borne by each distributed energy storage is the maximum charging and discharging power ; when the charging and discharging instruction of the battery pack is not , the lower layer distributes according to the SOC balance principle, allowing the distributed energy storage with a higher SOC to charge less and discharge more, and the distributed energy storage with a lower SOC to charge more and discharge less, so as to achieve the relative balance of SOC among each distributed energy storage.

[0153] The above strategy can be described in detail by the following formula, which is

[0154] First, use the Sigmoid function to describe the charging and discharging function of the distributed energy storage, that is

[0155] (33)

[0156] In the formula is t the SOC value of the energy storage unit at time - 1 i , and respectively represent the charging function and discharging function of the distributed energy storage m and n are parameters affecting the function slope and center point.

[0157] Secondly, to ensure the reasonable distribution of power within the group and determine the output power of each distributed energy storage by considering its rated power and SOC value, it can be expressed as:

[0158] (34)

[0159] In the formula, and are the charging and discharging powers borne by the energy storage i at t moment respectively; is the rated charging and discharging power of the energy storage i ; N is the number of distributed energy storages.

[0160] Furthermore, the SOC equalization distribution strategy based on the charging and discharging function is obtained as shown in the following formula:

[0161] (35)

[0162] In the formula, is the charging and discharging power that the battery pack k needs to bear at t moment.

[0163] Furthermore, considering that the distributed energy storage should respond to the charging and discharging instructions on the premise of meeting the operation constraints, according to the above strategy, the operation constraints of the distributed energy storage DES are constructed as:

[0164] (3.2.1) Charging / discharging constraint

[0165] At any moment, the charging and discharging power of the energy storage should not exceed its maximum charging and discharging power, which is:

[0166] (36)

[0167] (37)

[0168] In the formula, and are the charging / discharging power of the DES at t moment; and are the maximum charging / discharging power of the DES. Considering that the energy storage system can only be in the charging or discharging state at a certain moment and there is no phenomenon of simultaneous charging and discharging, 0 / 1 variables and are introduced. , indicating that the energy storage system is in the charging state; , indicating that the energy storage is in the discharging state, and its constraint is expressed as follows:

[0169] (38)

[0170] (3.2.2) Power Constraint

[0171] To ensure that the energy storage system has the same regulation performance in the new scheduling period, the power should satisfy:

[0172] (39)

[0173] Wherein, is the remaining power of the DES at time t; and are the charging / discharging efficiency of the DES; ∆t is the scheduling time interval.

[0174] (3.2.3) SOC Constraint

[0175] When the energy storage is charging, it absorbs active power and the SOC increases; when discharging, it emits active power and the SOC decreases. The calculation formula for the SOC value of the energy storage unit at time t is:

[0176] (40)

[0177] In the formula, is the SOC value at the initial time, and are the charging and discharging powers respectively during the period from t to t-1, and at most only one of them can be non-zero; and are the charging and discharging efficiencies respectively, both of which are less than 1; is the duration of charging and discharging (scheduling period); is the rated capacity of the energy storage.

[0178] In addition, to improve the SOC balance degree, when the charging and discharging instruction k of the battery pack is non-zero, the SOC standard deviation of the battery cells in the battery pack k should gradually decrease; when is 0, the SOC standard deviation of the battery cells in the battery pack k should remain unchanged. This constraint can be expressed as:

[0179] (41)

[0180] Step S2.2. According to the Nash bargaining theory, a multi-agent cooperative game model is constructed by integrating the operation and revenue models of multiple agents.

[0181] Based on the Nash bargaining theory, the above problem can be modeled as:

[0182] (42)

[0183] Step S3: Based on the actual energy demand on the user side, solve the multi-agent cooperative game model to obtain the optimal energy scheduling strategy in the active distribution network, and perform energy scheduling according to the scheduling strategy.

[0184] Specifically, in the original problem, both the energy trading volume and the energy trading price are taken as optimization variables, and there is a product term between them. Essentially, it is a mixed-integer non-convex optimization problem, which is difficult to solve directly. To reduce the complexity of problem-solving, the model is transformed into two linear sub-problems that are easy to solve: maximizing social benefits (denoted as sub-problem 1) and cooperative revenue distribution (denoted as sub-problem 2), and the optimal solution of the original problem is obtained through sequential optimization, where:

[0185] For sub-problem 1: maximizing social benefits, it can be expressed as:

[0186] (43)

[0187] In the formula, and are the wholesale market transaction cost and the retail market transaction cost of the DSO in the successful bargaining scenario respectively, is the charge and discharge degradation cost of the shared energy storage of the DESO in the successful bargaining scenario.

[0188] By solving the above sub-problem 1, the variable values can be optimized as follows: t The three-phase active power ij at the head node of branch and the reactive power at time ij , the square of the current amplitude of branch i , the square of the voltage amplitude of node , the purchased (positive value) or sold (negative value) electricity #i by the DSO in the wholesale market in the successful bargaining scenario, the electricity traded by the load aggregator / in the retail market in the successful bargaining scenario, the charge / discharge power i / j exchanged by the load aggregator # to the load aggregator # i , the charge / discharge power / provided by the energy storage for the load aggregator # i , the planned active power output and reactive power output of renewable energy in the load aggregator #

[0189] For sub - problem 2: maximizing social benefits, since the time - of - use electricity price for the DSO's transaction with the upper - level power grid considered in this embodiment is a fixed one, only the load aggregator coalition and the energy storage operator participate in the bargaining in sub - problem 2. The participants negotiate according to their respective bargaining powers, and finally determine an energy trading price that satisfies all parties involved in the bargaining. Specifically:

[0190] First, construct a first - stage cooperative revenue distribution model based on the bargaining powers of the load aggregator coalition and the energy storage operator, which is:

[0191] (44)

[0192] Furthermore, based on the obtained revenue of the load aggregator coalition , redistribute the revenue among the load aggregators. Construct a second - stage cooperative revenue distribution model according to the bargaining powers of the load aggregators, which is:

[0193] (45)

[0194] Finally, take the logarithm of the formula of the above - mentioned second - stage cooperative revenue distribution model, and then take its negative value, transforming the product maximum problem into a sum minimum problem, which is:

[0195] (46)

[0196] (47)

[0197] Substitute the energy trading volume obtained from sub - problem 1 into the above equation. By solving the equation, obtain the cooperative benefit distribution, and thus obtain the energy trading price, that is, at this time, obtain the optimal scheduling strategy of energy in the active distribution network. Then, corresponding energy scheduling can be carried out according to this scheduling strategy.

[0198] Furthermore, through the case study of the IEEE 33 - node system, further verify the effectiveness of the method proposed in this embodiment.

[0199] Specifically, first, set up the case. Take the improved IEEE 33 - node distribution system as an example to verify the effectiveness of the proposed method. This model divides the 24 - hour scheduling period into 24 time intervals, sets the node voltage operation limit values to 0.95 p.u. and 1.05 p.u., and sets the operating costs of wind energy and photovoltaic to 0 to promote the consumption of renewable energy.

[0200] The example system includes 4 load aggregators (nodes 13, 16, 24, and 29) and 6 distributed energy storages (nodes 3, 10, 15, 21, 30, 31). For ease of description, the 4 load aggregators are named Load Aggregator #1 - Load Aggregator #4 in ascending order of node numbers. Load Aggregator #1 and Load Aggregator #3 mainly rely on photovoltaic power generation, while Load Aggregator #2 and Load Aggregator #4 mainly rely on wind power generation. The scheduling time interval is 1 h, the optimization period is 24 h, and the power trading between each market participant and the DSO adopts a fixed time-of-use electricity price. The electricity purchase and sale price of the DSO from / to the upper-level power grid is set to 0.8 times the electricity trading price with other market participants. The modeling tool for the example simulation is Yalmip, the operating environment is MATLAB 2020a, the solver called is Cplex, and the computer hardware configuration is Intel(R) Core(TM) i5-11400 2.60GHz, 16G RAM.

[0201] To verify the effectiveness of the method proposed in this embodiment, 3 scenarios are set for comparative analysis:

[0202] Case1: Without considering the cooperation among multiple parties, the LLA and DESO only trade with the DSO.

[0203] Case2: Without considering the cooperation among multiple parties, but adopting a distributed energy storage scheduling strategy based on SOC balance.

[0204] Case3: The proposed method.

[0205] Secondly, scenario generation and reduction. Generally, the probability density distribution function of the prediction error of the wind-solar output satisfies the normal distribution, but the actual probability distribution is unknown. If the normal distribution is directly adopted, a large error will be generated. Therefore, in this embodiment, based on historical data, the kernel density estimation method is used to obtain the actual probability density distribution function of the prediction error of the renewable energy output at each scheduling moment, and random scenario generation and reduction are carried out. Finally, 10 typical wind output scenarios are obtained as follows Figure 3 shown.

[0206] After that, analyze the operation results of the distributed energy storage. The daily change curves of the distributed energy storage SOC with and without adopting the proposed distributed energy storage SOC balance scheduling strategy are respectively as Figure 4 and Figure 5As shown. From the comparison of the figures, it can be seen that without the use of an equilibrium strategy for scheduling, the SOC equilibrium degree in the energy storage room is poor, and the utilization rate of the energy storage capacity is also low. When the SOC equilibrium strategy is used to schedule distributed energy storage, although the initial SOCs of each energy storage unit are different, over time, the gap gradually narrows, and the SOC values of each energy storage in the group slowly converge and tend to be consistent. When the state of charge of the energy storage in the group reaches the upper and lower limits, the two groups will exchange "roles", which can effectively improve the utilization rate of the energy storage capacity.

[0207] Further analyze the economy. To verify the effectiveness of the method proposed in this embodiment in improving the economic benefits of each market entity, optimize and solve Schemes 1, 2, and 3 respectively. The comparison of the total costs of each entity under the three schemes is shown in Table 2 below. A positive cost indicates an expenditure, and a negative cost indicates a revenue.

[0208] Table 2: Comparison of the costs of each entity in three cases

[0209]

[0210] When considering the cooperative game and successful bargaining scenarios among multiple entities, the overall economic efficiency of the system is significantly improved. For the distribution system operator, the total dispatching cost is greatly reduced. This is because when multiple entities cooperate, they can jointly optimize the power flow of the distribution system, promote the maximization of local energy utilization, increase the consumption of renewable energy, and thus reduce the cost of purchasing electricity from the superior grid by the distribution network. Among them, the total costs of the DSO in Schemes 2 and 3 are equal because the DSO trades at a fixed electricity price in each market. When the energy trading volume is determined, its revenue is also determined, that is, the revenue of the DSO in Scheme 3 is not redistributed.

[0211] Compared with Scheme 1, the costs of the DESO and the load aggregator in Scheme 3 are reduced to varying degrees. For the DESO and the load aggregator, they can participate in the retail market and the P2P market at the same time. The electricity price in the retail market is fixed, and the selling electricity price of the DSO is higher than the purchasing electricity price. Therefore, in the trading process, they can more flexibly choose the trading partners that are beneficial to themselves to increase their income.

[0212] Table 3: Benefit distribution of Scheme 3

[0213]

[0214] Further analyze the dispatching results. To test the effectiveness of the proposed method in optimizing the power flow of the distribution system and improving the system operation state, Figure 6 The comparison of the peak shaving and valley filling effects of the system under three schemes (i.e., Method1~3) is shown.

[0215] From Figure 6It can be seen that, compared with Methods 1 and 2, the load curve of the method proposed in this embodiment is smoother. This is because on the one hand, distributed energy storage can trade with the DSO and cooperate with the DSO to optimize the power flow; on the other hand, it can store the excess power of the load aggregator and release electrical energy to the load aggregator in a power shortage state. At the same time, using its energy time-shifting characteristic, distributed energy storage transports power among load aggregators, making its total net load curve smoother, thus alleviating the power supply pressure caused by the large peak-to-valley difference of the distribution network load.

[0216] Embodiment 2

[0217] This embodiment provides an active distribution network optimal scheduling system considering multi-agent cooperative game, including:

[0218] An optimal scheduling model construction module, which is used to consider the interaction among multiple market entities in the active distribution network, construct a multi-agent collaborative trading architecture; based on the multi-agent collaborative trading architecture, with the goal of minimizing cost and combining constraint conditions, build the operation and revenue models of each entity; among them, as one of the entities, the distributed energy storage operator uses a distributed energy storage allocation strategy based on the SOC balance of distributed energy storage to constrain the operation of distributed energy storage; according to the Nash bargaining theory, comprehensively considering the operation and revenue models of multiple agents, construct a multi-agent cooperative game model;

[0219] A model solving and scheduling module, which is used to solve the multi-agent cooperative game model based on the actual energy demand on the user side, obtain the optimal scheduling strategy of energy in the active distribution network, and perform energy scheduling according to the scheduling strategy.

[0220] Embodiment 3

[0221] This embodiment provides an electronic device, including: a memory for storing executable instructions; a processor, when executing the executable instructions stored in the memory, implements the above method provided in this embodiment.

[0222] Embodiment 4

[0223] This embodiment also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, will cause the processor to execute the above method provided in this embodiment.

[0224] Embodiment 5

[0225] This embodiment provides a computer program product, which includes executable instructions, and the executable instructions are a kind of computer instructions; the executable instructions are stored in a computer-readable storage medium. When the processor of an electronic device reads the executable instructions from the computer-readable storage medium and the processor executes the executable instructions, the electronic device is caused to execute the above method provided in this embodiment.

[0226] The steps involved in the second to fifth embodiments above correspond to those in the first method embodiment. For specific implementation manners, reference may be made to the relevant description part of the first embodiment. The term "computer-readable storage medium" should be understood to include a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0227] Those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device for execution by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0228] The above are only the preferred embodiments of the present invention. Although the specific implementation manners of the present invention have been described in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. An active distribution network optimal scheduling method considering multi-agent cooperative game, characterized in that Including: Considering the interaction among multiple market players in the active distribution network, a multi-agent collaborative trading architecture is constructed. Based on the multi-agent collaborative trading architecture, with the goal of minimizing costs and combining constraint conditions, an operation and revenue model for each agent is built. Among them, as one of the agents, the distributed energy storage operator uses a distributed energy storage allocation strategy based on the balance of the state of charge (SOC) of distributed energy storage to constrain the operation of distributed energy storage. According to the Nash bargaining theory, a multi-agent cooperative game model is constructed by integrating the operation and revenue models of multiple agents. Based on the actual energy demand on the user side, the multi-agent cooperative game model is solved to obtain the optimal energy scheduling strategy in the active distribution network, and energy scheduling is carried out according to the scheduling strategy. The distributed energy storage allocation strategy based on the balance of the SOC of distributed energy storage is as follows: Several distributed energy storage units in the distributed energy storage system are divided into two groups that respectively perform charge / discharge tasks, and the priority charge / discharge states of the two groups are determined according to the actual SOC conditions of the distributed energy storage units in the two groups. Based on the priority charge / discharge states of the two groups, according to the actual charge / discharge demand of the distributed energy storage system, the charge / discharge demand responses and response sequences of the two groups are determined. According to the charge / discharge demand responses and response sequences of the two groups, power is allocated to each distributed energy storage unit in each group respectively. Among them, the power allocation process is as follows: When the charge / discharge demand of the current group is the maximum value, power is allocated according to the maximum charge / discharge power principle; otherwise, power is allocated according to the SOC balance principle. When the energy storage is charging, it absorbs active power and the SOC increases; when discharging, it emits active power and the SOC decreases. The calculation formula for the SOC value of the energy storage unit at time t is: ， Wherein, is the SOC value at the initial moment, and are the charge and discharge powers respectively during the time period from t to t-1, and at most one of them can be non-zero at the same time; and are the charge and discharge efficiencies respectively, both of which are less than 1; is the duration of charge and discharge; is the rated capacity of the energy storage; To improve the SOC balance degree, when the charge and discharge command of battery pack k is not 0, the standard deviation of SOC of battery cells in battery pack k should gradually decrease; when is 0, the standard deviation of SOC of battery cells in battery pack k should remain unchanged. This constraint is expressed as: ， Using the Sigmoid function to describe the charge / discharge function of distributed energy storage, that is: ， Wherein, is the SOC value of the energy storage unit i at time t-1, and respectively represent the charging function and discharging function of the distributed energy storage, and m and n are parameters affecting the function slope and center point; Secondly, to ensure the reasonable allocation of power within the group and considering the rated power and SOC value of each distributed energy storage to determine its output power, it can be expressed as: ， Wherein, and are respectively the charging and discharging powers borne by the energy storage i at time t; is the rated charging and discharging power of the energy storage i; N is the number of distributed energy storages; Furthermore, the SOC balance allocation strategy based on the charge / discharge function is obtained as shown in the following formula: ， Wherein, is the charge and discharge power that the battery pack k needs to bear at time t.

2. The active distribution network optimal scheduling method considering multi-agent cooperative game according to claim 1, characterized in that The multiple agents include the distribution system operator (DSO), the load aggregator (LA), and the distributed energy storage operator (DESO). For each agent, an operation and revenue model of the distribution system operator (DSO), the load aggregator (LA), and the distributed energy storage operator (DESO) is built with the goal of minimizing costs and establishing constraint conditions respectively. Among them, with the goal of minimizing the total operating cost and taking power balance constraints, node injection power constraints, node voltage and branch current constraints, gateway power constraints, and power trading unidirectional constraints as constraint conditions, an operation and revenue model of the distribution system operator (DSO) is constructed. Among them, the total operating cost of the distribution system operator includes wholesale market trading costs, retail market trading costs, and network usage fees.

3. The active distribution network optimal scheduling method considering multi-agent cooperative game according to claim 2, characterized in that With the goal of minimizing the total trading cost and taking distributed renewable energy output constraints, interruptible load constraints, P2P trading constraints, and local energy balance constraints as constraint conditions, an operation and revenue model of the load aggregator (LA) is constructed. Among them, the total trading cost includes retail market trading costs and P2P trading costs.

4. The active distribution network optimal scheduling method considering multi-agent cooperative game according to claim 2, characterized in that According to the distributed energy storage allocation strategy based on the balance of distributed energy storage SOC, the operation constraints of distributed energy storage are constructed, which include: charge / discharge constraints, power quantity constraints and SOC constraints; With the goal of maximizing revenue and taking the constructed operation constraints of distributed energy storage as the constraint conditions, the operation and revenue model of the distributed energy storage operator DESO is constructed; among them, maximizing revenue is equivalent to minimizing cost, and the cost includes retail market cost, P2P transaction cost, and shared energy storage degradation cost.

5. The active distribution network optimal scheduling method considering multi-agent cooperative game according to claim 1, wherein In the process of solving the multi-agent cooperative game model, the model is transformed into two linear sub-problems, and the optimal solution is obtained through sequential optimization, including: Based on the multi-agent cooperative game model, two sub-problems of maximizing social benefits and cooperative revenue distribution are split; Solve the sub-problem of maximizing social benefits to obtain the energy trading volume; For the sub-problem of cooperative revenue distribution, first construct the first-stage cooperative revenue distribution model according to the bargaining power of the load aggregator alliance and the energy storage operator, then re-distribute the revenue among the load aggregators according to the obtained revenue of the load aggregator alliance, construct the second-stage cooperative revenue distribution model according to the bargaining power of each load aggregator, transform the second-stage cooperative revenue distribution model into a minimum value problem, substitute the energy trading volume, and solve to obtain the cooperative benefit distribution and energy trading price.

6. An active distribution network optimal scheduling system considering multi-agent cooperative game, which adopts the active distribution network optimal scheduling method considering multi-agent cooperative game as described in any one of claims 1-5, is characterized in that, Including: An optimized scheduling model construction module, which is used to consider the interaction between multiple market players in the active distribution network and construct a multi-agent collaborative trading architecture; Based on the multi-agent collaborative trading architecture, with the goal of minimizing cost and combining the constraint conditions, the operation and revenue model of each agent is built; among them, the distributed energy storage operator is one of the agents, and the operation of the distributed energy storage is constrained by using the distributed energy storage allocation strategy based on the balance of distributed energy storage SOC; according to the Nash bargaining theory, the multi-agent cooperative game model is constructed by integrating the operation and revenue models of multiple agents; A model solving and scheduling module, which is used to solve the multi-agent cooperative game model based on the actual energy demand on the user side, obtain the optimized scheduling strategy of energy in the active distribution network, and perform energy scheduling according to the scheduling strategy.

7. An electronic device, characterized in that, Including: A memory for storing executable instructions; A processor, when executing the executable instructions stored in the memory, realizes the active distribution network optimized scheduling method considering multi-agent cooperative game according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, Stored with executable instructions, which are used to cause the processor to execute the executable instructions to realize the active distribution network optimized scheduling method considering multi-agent cooperative game according to any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes executable instructions, and the executable instructions are stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the active distribution network optimized scheduling method considering multi-agent cooperative game according to any one of claims 1-5 is realized.

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