Shared energy storage income distribution method and system

Abstract

The invention discloses a shared energy storage income distribution method and system, and the method is applied to the shared energy storage income distribution system, and comprises the steps: obtaining the power utilization data of a user and the data of an energy storage power station; the demand income of the target user in the target period is determined according to the user power consumption data; determining a first revenue distribution weight of the target user according to the demand revenue, and determining a second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimizing the Nash bargaining model according to the demand income, the first income distribution weight and the second income distribution weight to obtain a target income distribution model, the target income distribution model comprising a target function; and according to a preset constraint condition, with maximization of the target function as a solving target, solving to obtain a target distribution scheme of the shared energy storage income. According to the invention, the contribution and bargaining capabilities of the user and the energy storage power station are accurately quantified, and reasonable and fair distribution of the shared energy storage income is realized.

Description

Technical Field

[0001] This application relates to the technical fields of power systems and energy management, and particularly to a method and system for sharing energy storage revenue distribution. Background Art

[0002] With the transformation of the energy structure and the continuous growth of power demand, the power system faces multiple challenges such as supply-demand balance, increasing peak-valley differences, and enhanced volatility of renewable energy. In this context, energy storage technology, as a flexible resource regulation means, has gradually become an essential and indispensable part of the power system. The energy storage system can not only smooth the output fluctuations of renewable energy, improve the stability and reliability of the power grid, but also optimize the load curve by cutting peaks and filling valleys, bringing significant economic benefits to users.

[0003] Under the current electricity price mechanism, industrial and commercial users face high demand charge costs. Their demand charges are calculated based on the maximum power consumption, so a short-term power consumption peak will lead to a significant increase in the monthly electricity bill. The shared energy storage model can cut the peak value of the maximum power demand to reduce electricity bill expenditures by centrally deploying energy storage resources and achieving collaborative use among multiple users. It can also improve the equipment utilization rate through load complementarity, realizing the optimal allocation and maximization of resource value. Existing revenue distribution methods based on shared energy storage, such as the cooperative game model based on the Shapley value and the market-based distribution method based on the auction mechanism, are difficult to apply to large-scale user groups due to the complexity of the former, and the latter has extremely high requirements for market information transparency and may cause uneven resource allocation. Therefore, developing a revenue distribution mechanism that is easy to implement, can consider the differences between both parties, and balance the interests of the energy storage provider and the user to ensure the fair and reasonable distribution of the user's cost-saving benefits and the energy storage power station's income has become a technical problem that needs to be further solved. Summary of the Invention

[0004] This application provides a method and system for sharing energy storage revenue distribution. Based on the Nash bargaining model, it reasonably quantifies the utility and bargaining power of users and energy storage power stations, further constructs an objective function, and finally solves the Nash bargaining solution for the revenue distribution of shared energy storage between both parties based on the constraint conditions, accurately quantifying the contributions and bargaining powers of each participating party and realizing the theoretical fairness of revenue distribution.

[0005] In a first aspect, this application provides a method for sharing energy storage revenue distribution, which is applied to a shared energy storage revenue distribution system. The shared energy storage revenue distribution system includes an energy storage power station and target users participating in shared energy storage. The method includes:

[0006] Obtain the user's power consumption data and the energy storage power station data. The user's power consumption data includes a first load curve and a second load curve. The first load curve and the second load curve are respectively used to characterize the power consumption load of the target user before and after participating in shared energy storage within a target period, and the target period is used to characterize the time period for electricity billing;

[0007] Determine the demand revenue of the target user in the target period according to the user's power consumption data;

[0008] Determine the first revenue distribution weight of the target user according to the demand revenue, and determine the second revenue distribution weight of the energy storage power station according to the energy storage power station data;

[0009] Optimize the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, and the target revenue distribution model includes an objective function;

[0010] According to the preset constraint conditions, with the maximization of the objective function as the solution goal, solve to obtain the target distribution plan of the shared energy storage revenue.

[0011] In a second aspect, an embodiment of the present application provides a shared energy storage revenue distribution system, and the system includes an acquisition unit, a processing unit, and an output unit, where:

[0012] The acquisition unit is used to acquire the user's power consumption data and the energy storage power station data. The user's power consumption data includes a first load curve and a second load curve. The first load curve and the second load curve are respectively used to characterize the power consumption load of the target user before and after participating in shared energy storage within a target period, and the target period is used to characterize the time period for electricity billing;

[0013] The processing unit is used to determine the demand revenue of the target user in the target period according to the user's power consumption data; determine the first revenue distribution weight of the target user according to the demand revenue, and determine the second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimize the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, and the target revenue distribution model includes an objective function;

[0014] The output unit is used to solve and obtain the target distribution plan of the shared energy storage revenue according to the preset constraint conditions with the maximization of the objective function as the solution goal.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing some or all of the steps described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program / instructions are stored, and the computer program / instructions are executed by a processor to implement the steps of the method described in the first aspect above.

[0017] It can be seen that in the embodiment of the present application, the shared energy storage revenue distribution system obtains user power consumption data and energy storage power station data; determines the demand revenue of the target user in the target period according to the user power consumption data; determines the first revenue distribution weight of the target user according to the demand revenue, and determines the second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimizes the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, and the target revenue distribution model includes an objective function; according to the preset constraint conditions, with the maximization of the objective function as the solution target, the target distribution plan of the shared energy storage revenue is solved. Based on the Nash bargaining model, the present application reasonably quantifies the utility and bargaining power of users and energy storage power stations, further constructs an objective function, and based on the constraint conditions, solves the Nash bargaining solution for the revenue distribution of shared energy storage between both parties, accurately quantifying the contributions and bargaining power of each participating party and realizing the theoretical fairness of revenue distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1 is a system architecture diagram of a shared energy storage revenue distribution system provided by an embodiment of the present application;

[0020] Figure 2 is a schematic structural diagram of a shared energy storage revenue distribution system provided by an embodiment of the present application;

[0021] Figure 3 is a schematic structural diagram of a server in a shared energy storage revenue distribution system provided by an embodiment of the present application;

[0022] Figure 4 is a flowchart of the steps of a shared energy storage revenue distribution method provided by an embodiment of the present application;

[0023] Figure 5 It is a schematic flowchart of a method for determining demand revenue provided by an embodiment of the present application;

[0024] Figure 6 It is a schematic flowchart of a process for constructing an objective function provided by an embodiment of the present application;

[0025] Figure 7 It is an overall flowchart of a method for sharing energy storage revenue distribution provided by an embodiment of the present application;

[0026] Figure 8 It is a functional unit block diagram of a sharing energy storage revenue distribution system provided by an embodiment of the present application;

[0027] Figure 9 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0029] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include unlisted steps or units, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0030] Referring to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0031] In the embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent the following three situations: A exists alone; A and B exist simultaneously; B exists alone. Here, A and B can be singular or plural.

[0032] In the embodiments of the present application, the symbol " / " can indicate that the front and rear associated objects have an "or" relationship. Additionally, the symbol " / " can also represent a division sign, that is, perform a division operation. For example, A / B can represent A divided by B.

[0033] "At least one (item)" or its similar expressions in the embodiments of the present application refer to any combination of these items, including any combination of single item (item) or plural items (items), referring to one or more, and multiple referring to two or more. For example, at least one (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Here, each of a, b, and c can be an element or a set containing one or more elements.

[0034] "Equal to" in the embodiments of the present application can be used in conjunction with "greater than", applicable to the technical solutions adopted when it is greater than, and can also be used in conjunction with "less than", applicable to the technical solutions adopted when it is less than. When "equal to" is used in conjunction with "greater than", it is not used in conjunction with "less than"; when "equal to" is used in conjunction with "less than", it is not used in conjunction with "greater than".

[0035] To better understand the solutions of the embodiments of the present application, the electronic devices, related concepts, and backgrounds that the embodiments of the present application may involve will be introduced first below.

[0036] (1) Shared energy storage: An innovative business model that centrally deploys energy storage resources and enables collaborative use among multiple users. Different from the traditional model where a single user independently invests in building energy storage facilities, shared energy storage can utilize the scale effect to reduce the unit capacity investment cost, and can also improve the equipment utilization rate through load complementarity, achieving the optimal allocation of resources and maximizing value.

[0037] (2) Demand Charge: In a two-part tariff system, the basic electricity charge that electricity users pay according to the maximum demand (i.e., the maximum electricity consumption power) of their electrical equipment within a measurement period (such as a month). This billing method makes the electricity charge of users closely related to the maximum electricity consumption power, and a short-term electricity peak may cause a significant increase in the monthly electricity charge of users.

[0038] (3) Demand benefits: Indirect economic benefits obtained by users by optimizing electricity consumption strategies, reducing maximum demand, and thus reducing demand electricity charges. After users install energy storage systems, they use energy storage to discharge during peak electricity consumption, reduce the amount of electricity drawn from the grid, thereby reducing maximum demand and achieving demand benefits. Demand benefits are usually combined with other energy-saving measures such as peak-valley arbitrage and demand response to further improve users' economic benefits.

[0039] (4) Nash Bargaining Model: A cooperative game solution based on a specific fairness axiom, which aims to achieve Pareto optimal resource allocation and allocate the economic benefits obtained by users through load optimization of energy storage systems. The core idea of this model is to find an optimal solution that balances the interests of all parties by maximizing the product of the cooperative benefits of the bargaining parties. The model principles include:

[0040] Utility function construction: Construct a utility function for each participant to measure their satisfaction or benefits under different benefit distribution schemes. For example, in energy storage benefit distribution, the utility function of the target user is related to demand benefits and payment costs, and the utility function of the energy storage power station is related to the fees obtained;

[0041] Nash product maximization: The core is to find a distribution plan that maximizes the product of the utility functions of all participants. This is because product maximization can take into account the interests of all parties. If only the maximum utility of a single participant is pursued, the interests of other parties may be damaged, leading to a breakdown in cooperation. By adjusting the income distribution, the weighted utility product of the target user and the energy storage power station is maximized, achieving a relatively fair and overall optimal distribution;

[0042] Bargaining power: The bargaining power of the parties is reflected by setting weights for the utility function (such as the first and second profit distribution weights). The party with strong bargaining power has a high weight and can get a more favorable share in the profit distribution, which reflects the difference in the voice of the parties in the actual cooperation.

[0043] (5) CPLEX solver: A commercial software tool for solving optimization problems. It can use optimization algorithms such as the simplex method or the interior point method to find the optimal solution or suboptimal solution based on the input user load data, energy storage power station data, and the constructed objective function and constraints, thereby obtaining a cost allocation plan for users to pay to the energy storage power station.

[0044] The existing shared energy storage revenue distribution method cannot simultaneously meet the requirements of being easy to implement and taking into account the differences between the two parties, and a revenue distribution mechanism that takes into account the interests of both energy storage providers and users. It is difficult to ensure a fair and reasonable distribution of user savings and energy storage power station income.

[0045] In view of the above problems, the embodiments of the present application provide a method and system for sharing energy storage revenue distribution. The embodiments of the present application will be introduced in detail below with reference to the accompanying drawings.

[0046] Please refer to Figure 1 , Figure 1 which is a system architecture diagram of a shared energy storage revenue distribution system provided by the embodiments of the present application. As Figure 1 shown, the shared energy storage revenue distribution system 10 includes target users 101 and an energy storage power station 102.

[0047] Among them, the target user 101 is the demand side of revenue distribution and obtains revenue by participating in shared energy storage. By participating in shared energy storage, the target user 101 reduces the maximum demand, saves demand-side electricity charges, and obtains reference demand-side revenue. Adding other demand-side revenues such as peak-valley electricity price difference revenue and government energy-saving subsidies constitutes the total demand-side revenue. The target user 101 can include various types of users, such as industrial users, commercial users, residential users, etc. Different types of users have different electricity consumption characteristics, and their demands and contributions to shared energy storage also vary. For example, industrial users have continuous production processes, large electricity consumption, and large load fluctuations; commercial users' electricity consumption peaks are concentrated during business hours; residential users' electricity consumption is dispersed but the total amount is considerable.

[0048] Among them, the energy storage power station 102 is the supply side providing energy storage services and also a participant in revenue distribution. The energy storage power station 102 has the ability to store and release electric energy. When the power grid is in the low electricity price period, it can absorb electric energy and store it; during the peak electricity consumption period or when the target user has a demand, it releases electric energy to help the target user use electricity smoothly, reduce its dependence on the power grid supply during high electricity price periods, and thus reduce the electricity consumption cost and achieve demand management. For example, users A, B, and C adjust their electricity consumption through the energy storage power station to reduce the maximum demand and obtain demand-side revenue.

[0049] Please refer to Figure 2 , Figure 2 which is a structural schematic diagram of a shared energy storage revenue distribution system provided by the embodiments of the present application. As Figure 2 shown, the shared energy storage revenue distribution system includes a server 210, energy storage users 220, energy storage devices 230, and a power grid 240.

[0050] Among them, the energy storage user 220 includes multiple target users, such as target user A, target user B, target user C, etc., representing the power demand side of the shared energy storage system. Different types of target users (industrial, commercial, residential, etc.) have different electricity consumption characteristics and demands, and are the direct objects of the shared energy storage service. For the power consumption terminal equipment on the side of the energy storage user 220, electricity consumption data is generated through devices such as smart meters and transmitted to the server 210. There is an electricity interaction between the energy storage user 220 and the energy storage device 230, and with the support of the energy storage device 230, its own electricity consumption needs are met, and the electricity consumption cost is reduced.

[0051] Among them, the energy storage device 230 is composed of multiple energy storage power stations, such as energy storage power station 1, energy storage power station 2, etc., and has the functions of electricity storage and release. It charges during the low grid load period and supplies power to the energy storage user 220 during the peak electricity consumption period or when the user needs it, playing the role of peak shaving and valley filling, assisting users to reduce the maximum demand, and realizing demand management to obtain demand benefits. The energy storage device 230 is also used to collect operation data and transmit it to the server 210, and at the same time receive the control instructions of the server 210. In addition, the energy storage device 230 is also a participant in the revenue distribution and participates in the revenue distribution according to its own contribution.

[0052] Among them, the power grid 240 is one of the power sources of the shared energy storage system and provides power when the energy storage device 230 is charging. At the same time, the energy storage device 230 can also discharge to the power grid 240 under certain circumstances (such as when the grid load is tight and the energy storage device has an excessive amount of electricity), realizing two-way electricity interaction with the power grid 240, playing the role of regulating the grid load and improving the grid stability. The electricity price policy (peak-valley electricity price, etc.) of the power grid 240 will also affect the operation strategy and revenue calculation of the shared energy storage system.

[0053] Among them, the server 210 is responsible for collecting, storing, and processing the data of the energy storage user 220 and the energy storage device 230. For example, it receives electricity consumption data such as the electricity consumption load curve and the maximum demand of the energy storage user 220, as well as operation data such as the charge and discharge state and the energy storage capacity of the energy storage device 230, providing data support for the subsequent revenue distribution calculation; and, the server 210 is also used to run the revenue distribution algorithm and model to achieve a fair and reasonable revenue distribution decision.

[0054] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the server in a shared energy storage revenue distribution system provided by an embodiment of the present application. As Figure 3As shown, the server 210 includes a processor 211 and a memory 212, and the processor 211 is communicatively connected to the memory 212. Among them, one or more programs are stored in the memory 212, and the one or more programs are configured to be executed by the processor 211. The functions of the one or more programs are to obtain user power consumption data and energy storage power station data; determine the demand revenue of the target user in the target period according to the user power consumption data; determine the first revenue distribution weight of the target user according to the demand revenue, and determine the second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimize the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, where the target revenue distribution model includes an objective function; and solve to obtain a target distribution plan for the shared energy storage revenue with the maximization of the objective function as the solution target according to the preset constraint conditions.

[0055] The following introduces the shared energy storage revenue distribution method provided by the embodiments of the present application.

[0056] Please refer to Figure 4 , Figure 4 which is a flowchart of the steps of a shared energy storage revenue distribution method provided by the embodiments of the present application, and is applied to the Figure 3 server 210 in Figure 4 As shown, the method includes the following steps:

[0057] Step S401, obtain user power consumption data and energy storage power station data, where the user power consumption data includes a first load curve and a second load curve, and the first load curve and the second load curve are respectively used to characterize the power consumption load of the target user before and after participating in the shared energy storage in the target period, and the target period is used to characterize the time period for electricity billing.

[0058] Among them, the first load curve records the change of the power consumption load of the target user in the target period (such as one month) before participating in the shared energy storage, reflecting its original power consumption mode; the second load curve presents the power consumption load after participating in the shared energy storage, and is used for comparative analysis of the impact of energy storage on user power consumption.

[0059] Among them, shared energy storage is a new energy storage application model. It refers to multiple electricity-consuming terminals (such as industrial users, commercial users, residential users and other target users) jointly using the energy storage resources of an energy storage power station. Functionally, the energy storage power station stores electric energy during the low-grid-tariff period and releases electric energy to target users during the peak electricity consumption period or when users have demands. In this way, by means of the energy storage power station, the target users can adjust their electricity consumption, reduce their maximum demand, cut down the demand charge expenditure, and also save electricity costs by taking advantage of the peak-valley electricity price difference. Furthermore, by participating in shared energy storage, the target users reduce their maximum demand and obtain demand benefits. In terms of the relationship between participating parties, the target users and the energy storage power station are the core participants in the shared energy storage model. The target users provide electricity consumption data, enjoy energy storage services and obtain benefits; the energy storage power station provides energy storage services and participates in the income distribution according to its own contributions. Through data interaction and collaborative operation, under the shared energy storage income distribution system, resource sharing and mutual benefit are achieved, and the utilization efficiency of electric power resources and the overall economic benefits are improved.

[0060] Among them, the user electricity consumption data also includes the demand electricity price. The demand electricity price is related to the user's electricity consumption characteristics and the category to which the user belongs, and different user categories correspond to different demand electricity price standards. Under the two-part tariff system, users can choose the capacity electricity price or the demand electricity price to calculate the basic electricity charge according to their own situations. The demand electricity price is the basis for calculating the basic electricity charge based on the maximum demand within the measurement period, that is, the maximum electricity consumption power, and is a key parameter for calculating the demand benefit, directly affecting the user's electricity charge expenditure and income calculation.

[0061] Among them, the energy storage power station data mainly includes basic parameter data, operation status data, economic cost data, control and dispatching data. Specifically, the basic parameter data includes energy storage capacity, rated power, charge and discharge power, battery type and life, etc.; the operation status data includes real-time charge and discharge power, remaining battery capacity, charge and discharge times, equipment operation temperature, etc.; the economic cost data includes construction cost, operation cost, income data, etc.; the control and dispatching data includes charge and discharge strategy parameters, dispatching instruction execution records, etc.

[0062] It can be understood that by comparing the first load curve and the second load curve, the impact of shared energy storage on the user's electricity load can be visually seen. If the peak value of the second load curve is significantly lower than that of the first load curve, it indicates that the shared energy storage has played a role in peak shaving and valley filling, reducing the user's electricity cost, and at the same time contributing to the stable operation of the power grid, which helps to evaluate the service effect of the energy storage power station and provides a reference for subsequent income distribution.

[0063] Step S402, determining the demand benefit of the target user in the target period according to the user electricity consumption data.

[0064] Demand benefits refer to the indirect economic benefits that users gain by optimizing electricity consumption strategies, reducing maximum demand, and thus reducing demand electricity charges. After users install energy storage systems, they use energy storage to discharge during peak hours to reduce the amount of electricity drawn from the grid, thereby reducing maximum demand and achieving demand benefits.

[0065] The demand benefit of the target user in the target period is expressed as:

[0066] R u =B u +∈ u

[0067] Among them, R u is the demand revenue of target user u in the target period, ∈ u Other demand benefits obtained by users through load optimization through energy storage power stations.

[0068] Among them, the sources of other demand benefits obtained by users through load optimization of energy storage power stations are diverse, mainly covering electricity market incentives, equipment and operation optimization, energy management strategy optimization and other aspects. These benefits together constitute the comprehensive benefits of users participating in shared energy storage. Among them, the benefits related to electricity market incentives include demand response rewards and ancillary service benefits, such as users using energy storage to provide frequency regulation, voltage regulation and other ancillary services for the power grid to obtain benefits; equipment and operation optimization benefits include reduced equipment maintenance costs and improved production efficiency benefits; energy management strategy optimization benefits include peak-valley electricity price arbitrage benefits and renewable energy consumption benefits. If the user has renewable energy power generation equipment, shared energy storage can store excess electricity to avoid power abandonment.

[0069] Step S403: determining a first profit distribution weight of the target user according to the demand profit, and determining a second profit distribution weight of the energy storage power station according to the energy storage power station data.

[0070] Among them, in the Nash bargaining model, the target user's first profit distribution weight reflects the target user's contribution to the overall profit and its influence in the negotiation. The higher the weight, the greater the proportion of the target user's demand profit in the total profit. During the profit distribution negotiation, its influence on the distribution result is stronger, and it is more capable of obtaining a more favorable distribution plan; the second profit distribution weight of the energy storage power station measures the relative importance of the energy storage power station in the entire energy storage system and its voice in the profit distribution negotiation. The energy storage power station with high discharge power has a significant second profit distribution weight, indicating that it plays a greater role in power supply. In the Nash bargaining process, it can strive for more profits based on its own power advantage.

[0071] In a possible embodiment, the first revenue allocation weight of the target user is represented by the proportion of the demand revenue of a single target user in the total demand revenue of multiple target users;

[0072] The second revenue allocation weight of the energy storage power station is represented by the proportion of the discharge power of a single energy storage power station in the total discharge power of multiple energy storage power stations, and the energy storage power station data includes the discharge power.

[0073] Among them, the first revenue allocation weight is expressed as:

[0074]

[0075] Among them, w u is the first revenue allocation weight of target user u, R u is the demand revenue of target user u in the target period, and N u is the number of target users.

[0076] Among them, the second revenue allocation weight is expressed as:

[0077]

[0078] Among them, w s is the second revenue allocation weight of energy storage power station s, D u,t,s is the discharge power of energy storage power station s to supply power to target user u at time t, T is the time set, and N s is the number of energy storage power stations.

[0079] It can be seen that in this embodiment, by determining the first revenue allocation weight based on the proportion of the demand revenue of a single target user in the total demand revenue of multiple target users, and determining the second revenue allocation weight based on the proportion of the discharge power of a single energy storage power station in the total discharge power of multiple energy storage power stations, it is possible to fairly and reasonably allocate revenues based on the demand revenue contribution of target users and the discharge power contribution of energy storage power stations, improve the enthusiasm of all participating parties, optimize resource allocation, and enhance the overall economic efficiency and operation efficiency of the system.

[0080] Step S404, optimize the Nash bargaining model according to the demand revenue, the first revenue allocation weight, and the second revenue allocation weight to obtain a target revenue allocation model, and the target revenue allocation model includes an objective function.

[0081] Among them, the initial function included in the Nash bargaining model is expressed as:

[0082]

[0083] Among them, U u is the utility function after target user u cooperates with the energy storage power station, Us is the utility function after the cooperation between the energy storage power station s and the target user, D u is the negotiation breakdown point of the target user u, D s is the negotiation breakdown point of the energy storage power station s.

[0084] Among them, in the Nash bargaining model, the negotiation breakdown point represents the minimum benefit of each participating party when the negotiation fails, that is, the utility value of each party in the non-cooperative state. Since the demand benefit cannot be obtained without energy storage sharing, D u , D s are both set to 0.

[0085] In a possible embodiment, the objective function included in the target revenue distribution model is expressed as:

[0086]

[0087] Among them, R u is the demand revenue of the target user u, π u,s is the fee paid by the target user u to the energy storage power station s, N u is the number of target users, N s is the number of energy storage power stations, w u is the first revenue distribution weight of the target user u, w s is the second revenue distribution weight of the energy storage power station s.

[0088] It can be seen that in this embodiment, by combining the demand revenue and the revenue distribution weight to optimize the Nash bargaining model, a target revenue distribution model is constructed. The model uses the first and second utility functions to comprehensively consider the user's net revenue and the energy storage power station's revenue, ensuring the accurate measurement of the interests of all parties; introducing the revenue distribution weight can perform differential distribution based on the contributions of all parties, improving the fairness and rationality of the distribution plan; the optimized objective function can effectively guide the sharing of energy storage revenue distribution, promote the cooperation between users and energy storage power stations, and promote the stable and efficient operation of the shared energy storage system.

[0089] Step S405, according to the preset constraint conditions, with the maximization of the objective function as the solution target, solve to obtain the target distribution plan of the shared energy storage revenue.

[0090] Among them, the target distribution plan of the shared energy storage revenue is mainly obtained by solving the objective function through the CPLEX solver based on the preset solution algorithm, and the solution algorithms include the simplex method and the interior point method.

[0091] Specifically, the simplex method is one of the most classical algorithms in linear programming. It starts from a vertex (basic feasible solution) of the feasible region and iteratively moves along the edges of the feasible region to adjacent vertices. Each move improves the value of the objective function until the optimal solution is found. Its principle is based on the fundamental property of linear programming problems, that is, the optimal solution must be obtained at the vertices of the feasible region. For example, in a resource allocation problem, if the goal is to maximize profit and the resource constraints and profit function are both linear, the simplex method can be used to solve it; the interior point method is different from the simplex method in that it searches for the optimal solution along the boundary of the feasible region. The interior point method starts from a point inside the feasible region and gradually approaches the optimal solution by continuously adjusting the position of the point. It often has better computational efficiency when dealing with large-scale linear programming problems, especially suitable for cases with more variables and constraints.

[0092] In a possible embodiment, the constraint condition of the objective function is expressed as:

[0093]

[0094] where, used to constrain that the cost paid to the energy storage power station is not greater than the demand-side revenue, π u,s ≥0 is used to constrain that the cost paid to the energy storage power station is non-negative.

[0095] where, This constraint condition ensures that the total cost paid by the target user to the energy storage power station does not exceed its obtained demand-side revenue. From the perspective of economic rationality, the purpose of users participating in shared energy storage is to reduce electricity costs or obtain certain economic benefits by cooperating with the energy storage power station. If the cost paid to the energy storage power station exceeds the demand-side revenue, then the user will suffer economic losses, which obviously does not conform to the original intention of the user to participate in the cooperation and will also cause the user to lose the enthusiasm for participating in shared energy storage, which is not conducive to the sustainable development of the shared energy storage model.

[0096] where, π u,s ≥0 this constraint condition ensures that the cost paid by the target user to the energy storage power station is non-negative. A negative cost is unreasonable in actual economic transactions. It ensures the basic logic and economic order of the transaction. If a negative payment is allowed, it means that the energy storage power station not only provides energy storage services for users but also pays additional fees to users, which violates the business model and economic principle of shared energy storage and will also undermine the stability and fairness of the entire revenue distribution system.

[0097] In a possible embodiment, according to the preset constraint conditions, with the maximization of the objective function as the solution goal, the target allocation plan of the shared energy storage revenue is obtained, including:

[0098] According to the above constraints, aiming to maximize the objective function, the objective function is solved through the preset solution algorithm of the solver to obtain the target cost paid by the target user to the energy storage power station;

[0099] Determine the target cost as the target revenue of the energy storage power station participating in shared energy storage;

[0100] Determine the target net revenue of the target user participating in shared energy storage according to the target cost and the demand revenue.

[0101] Among them, aiming to maximize the objective function is to achieve the maximization of the product of the utility functions of the participating parties based on the Nash bargaining model under the condition of meeting the constraints. Furthermore, through the maximization of the product, the interests of all parties can be taken into account. If only the utility of a single participating party is maximized, it may damage the interests of other parties and lead to the breakdown of cooperation. By adjusting the revenue distribution, the weighted utility product of the target user and the energy storage power station is maximized to achieve a relatively fair and overall optimal distribution.

[0102] It can be seen that in the embodiment of the present application, the shared energy storage revenue distribution system obtains user power consumption data and energy storage power station data; determines the demand revenue of the target user in the target period according to the user power consumption data; determines the first revenue distribution weight of the target user according to the demand revenue, and determines the second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimizes the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, and the target revenue distribution model includes an objective function; according to the preset constraints, with the maximization of the objective function as the solution target, the target distribution plan of the shared energy storage revenue is solved. Based on the Nash bargaining model, the utility and bargaining power of users and energy storage power stations are reasonably quantified, the contributions and bargaining power of each participating party are accurately quantified, and the theoretical fairness of revenue distribution is realized.

[0103] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of a method for determining demand revenue provided by an embodiment of the present application. Among them, in terms of determining the demand revenue of the target user in the target period according to the user power consumption data, the above method may further include the following steps:

[0104] Step S501, determine the first maximum demand of the target user in the target period before participating in shared energy storage according to the first load curve.

[0105] Among them, the maximum demand characterizes the maximum power consumption. In scenarios such as electricity billing, it refers to the maximum average power value calculated by averaging at regular time intervals (such as every 15 minutes) for a user within an electricity bill settlement cycle (such as one month). It is not simply the maximum power at a certain moment, but the maximum value of the average power considering a certain time period.

[0106] In a possible embodiment, the determining of the first maximum demand of the target user within the target period before participating in shared energy storage according to the first load curve includes:

[0107] Determining multiple first average powers of the target user within multiple first periods before participating in shared energy storage according to the first load curve, where the target period includes the multiple first periods, and the multiple first periods correspond one-to-one with the multiple first average powers;

[0108] Determining the first average power with the largest value among the multiple first average powers as the first maximum demand.

[0109] Step S502, determining the second maximum demand of the target user within the target period after participating in shared energy storage according to the second load curve.

[0110] Among them, the first maximum demand is greater than the second maximum demand.

[0111] In a possible embodiment, the determining of the second maximum demand of the target user within the target period after participating in shared energy storage according to the second load curve includes:

[0112] Determining multiple second average powers of the target user within the multiple first periods after participating in shared energy storage according to the second load curve, where the multiple reference periods correspond one-to-one with the multiple second average powers;

[0113] Determining the second average power with the largest value among the multiple second average powers as the first maximum demand.

[0114] Step S503, obtaining the demand-based electricity price corresponding to the target user.

[0115] Among them, the demand-based electricity price is usually formulated by the power company according to relevant policies and market conditions, and there may be differences in the demand-based electricity prices for different regions and different user types. For example, the demand-based electricity price for industrial users may be different from that of commercial users and residential users, and there may also be special regulations for the demand-based electricity price of high-energy-consuming enterprises.

[0116] Step S504, determining the reference demand-based revenue according to the first maximum demand, the second maximum demand, and the demand-based electricity price.

[0117] Among them, the reference demand benefit is determined according to the first maximum demand, the second maximum demand, and the demand electricity price, and the reference demand benefit is expressed as:

[0118] B u = ((L max,before - L max,after ) · P demand,u )

[0119] Among them, B u is the reference demand benefit of the target user u, L max,before is the first maximum demand of the target user u, L max,after is the second maximum demand of the target user u, and P demand,u represents the demand electricity price corresponding to the category of the target user u.

[0120] Step S505: Obtain other demand benefits that the target user can obtain by participating in shared energy storage in addition to the reference demand benefit.

[0121] Among them, the sources of other demand benefits obtained by users through optimizing the load of the energy storage power station are diverse, mainly covering aspects such as electricity market incentives, equipment and operation optimization, and energy management strategy optimization. These benefits together constitute the comprehensive benefits of users participating in shared energy storage. Among them, the benefits related to electricity market incentives include demand response rewards and ancillary service benefits. For example, users can obtain benefits by using energy storage to provide ancillary services such as frequency regulation and voltage regulation for the power grid; the benefits of equipment and operation optimization include the reduction of equipment maintenance costs and the improvement of production efficiency; the benefits of energy management strategy optimization include peak-valley electricity price arbitrage benefits and renewable energy consumption benefits. If users have renewable energy generation equipment, shared energy storage can store excess electric energy to avoid waste of electricity.

[0122] Step S506: Determine the demand benefit of the target user in the target period according to the reference demand benefit and the other demand benefits.

[0123] Among them, the demand benefit of the target user in the target period is determined according to the reference demand benefit and the other demand benefits, and the demand benefit of the target user in the target period is expressed as:

[0124] R u = B u + ∈ u

[0125] Among them, R u is the demand benefit of the target user u in the target period, and ∈ u represents other demand benefits obtained by the user through optimizing the load of the energy storage power station.

[0126] It can be seen that in this embodiment, by determining the demand benefit of the target user in the target period based on the user's electricity consumption data, the shared energy storage benefit can be accurately evaluated, the demand reduction effect can be quantified, the reference demand benefit can be accurately calculated, and other benefits can be comprehensively considered; it provides a decision-making basis for users and energy storage power stations, helps users optimize their electricity consumption strategies, and guides the operation of energy storage power stations; it supports the stable operation and optimized management of the power grid, reduces the peak load of the power grid, and optimizes the power grid resource allocation.

[0127] Please refer to Figure 6 , Figure 6 which is a schematic flowchart of a process for constructing an objective function provided by an embodiment of the present application. Among them, in terms of optimizing the Nash bargaining model according to the demand benefit, the first benefit distribution weight, and the second benefit distribution weight to obtain an objective benefit distribution model, the above method may further include the following steps:

[0128] Step S601, obtain the initial function included in the Nash bargaining model.

[0129] Among them, the initial function included in the Nash bargaining model is expressed as:

[0130]

[0131] Among them, U u is the utility function after the target user u cooperates with the energy storage power station, U s is the utility function after the energy storage power station s cooperates with the target user, D u is the negotiation breakdown point of the target user u, and D s is the negotiation breakdown point of the energy storage power station s.

[0132] Among them, in the Nash bargaining model, the negotiation breakdown point represents the minimum benefit of each participating party when the negotiation fails, that is, the utility value of each party in the non-cooperative state. Since the demand benefit cannot be obtained without energy storage sharing, D u , D s are both set to 0.

[0133] Step S602, determine the first utility function in the initial function according to the demand benefit and the fee paid by the target user to the energy storage power station.

[0134] Among them, the first utility function is used to calculate the net benefit that the target user can obtain by participating in the shared energy storage benefit. By considering the demand benefit and the fee paid by the target user to the energy storage power station, the actual benefit situation of the user after participating in the shared energy storage project is comprehensively evaluated.

[0135] Among them, the first utility function of the target user u in the initial function included in the Nash bargaining model can be expressed as:

[0136]

[0137] Among them, π u,s is the fee paid by the target user u to the energy storage power station s, and N s is the number of energy storage power stations.

[0138] Step S603: Determine the second utility function in the initial function according to the fee paid by the target user to the energy storage power station.

[0139] Among them, the second utility function is used to calculate and determine the income that the energy storage power station can obtain by participating in the shared energy storage income. It only measures the income situation of the energy storage power station based on the fee paid by the target user to the energy storage power station, and clarifies the economic return of the energy storage power station in the shared energy storage project.

[0140] Among them, the second utility function of the energy storage power station s in the initial function included in the Nash bargaining model can be expressed as:

[0141]

[0142] Among them, π u,s is the fee paid by the target user u to the energy storage power station s, and N u is the number of target users.

[0143] Step S604: Set weights for the first utility function based on the first income distribution weight, and set weights for the second utility function based on the second income distribution weight to obtain the objective function.

[0144] In a possible embodiment, the objective function included in the objective income distribution model is expressed as:

[0145]

[0146] Among them, is the first utility function, is the second utility function, R u is the demand income of the target user u, π u,s is the fee paid by the target user u to the energy storage power station s, N u is the number of target users, N s is the number of energy storage power stations, w u is the first income distribution weight of the target user u, w s is the second income distribution weight of the energy storage power station s.

[0147] Among them, for the convenience of calculation, the objective function included in the objective income distribution model can also be expressed in logarithmic form, specifically expressed as:

[0148]

[0149] It can be seen that in this embodiment, by combining the demand benefit, the first and second benefit distribution weights to optimize the Nash bargaining model, the first utility function for calculating the net benefit of the target user and the second utility function for calculating the benefit of the energy storage power station are determined based on the demand benefit of the target user, the payment cost, etc. Then, weights are assigned to the two utility functions to obtain the target benefit distribution model. This model can make the benefit distribution between the target user and the energy storage power station more reasonable and fair, improve the enthusiasm of all parties to participate in shared energy storage, optimize resource allocation, and enhance the economic efficiency and stability of the system.

[0150] Please refer to Figure 7 , Figure 7 which is the overall flowchart of a shared energy storage benefit distribution method provided by an embodiment of the present application. As Figure 7 shown, the method includes:

[0151] Step S710, obtain user power consumption data.

[0152] Among them, the user power consumption data includes a first load curve and a second load curve. The first load curve and the second load curve are respectively used to characterize the power consumption load of the target user before and after participating in shared energy storage within the target period, and the target period is used to characterize the time period for electricity billing. Among them, the first load curve records the change of the power consumption load of the target user within the target period (such as one month) before participating in shared energy storage, reflecting its original power consumption mode; the second load curve presents the power consumption load after participating in shared energy storage, and is used for comparative analysis of the impact of energy storage on user power consumption.

[0153] Among them, the user power consumption data also includes the demand electricity price. The demand electricity price is related to the user's power consumption characteristics and the category to which it belongs, and different user categories correspond to different demand electricity price standards. Under the two-part electricity price system, users can choose the capacity electricity price or the demand electricity price to calculate the basic electricity bill according to their own situations. The demand electricity price is the basis for calculating the basic electricity bill according to the maximum demand within the measurement period, that is, the maximum power consumption, and is a key parameter for calculating the demand benefit, directly affecting the user's electricity bill expenditure and benefit calculation.

[0154] Step S720, obtain energy storage power station data.

[0155] Among them, the energy storage power station data mainly includes basic parameter data, operation status data, economic cost data, control and scheduling data. Specifically, the basic parameter data includes energy storage capacity, rated power, charge and discharge power, battery type and life, etc.; the operation status data includes real-time charge and discharge power, remaining battery capacity, charge and discharge times, equipment operation temperature, etc.; the economic cost data includes construction cost, operation cost, revenue data, etc.; the control and scheduling data includes charge and discharge strategy parameters, scheduling instruction execution records, etc.

[0156] Step S730: Input data into the solver.

[0157] Among them, the solver can, based on the input user load data, energy storage power station data, as well as the constructed objective function and constraint conditions, use optimization algorithms such as the simplex method or the interior point method to find the optimal solution or sub-optimal solution, thereby obtaining the cost allocation plan for the user to pay the energy storage power station.

[0158] Among them, the target allocation plan for sharing energy storage benefits obtained by solving is mainly obtained by the CPLEX solver through solving the objective function based on the preset solution algorithm, and the solution algorithms include the simplex method and the interior point method.

[0159] Specifically, the simplex method is one of the most classic algorithms in linear programming. It starts from a vertex (basic feasible solution) of the feasible region and moves along the edges of the feasible region iteratively to adjacent vertices. Each move improves the value of the objective function until the optimal solution is found. Its principle is based on the basic properties of linear programming problems, that is, the optimal solution must be obtained at the vertices of the feasible region. For example, in a resource allocation problem, if the goal is to maximize profit and the resource constraints and profit functions are linear, the simplex method can be used to solve it; different from the simplex method that searches for the optimal solution along the boundary of the feasible region, the interior point method starts from a point inside the feasible region and gradually approaches the optimal solution by continuously adjusting the position of the point. It often has better computational efficiency when dealing with large-scale linear programming problems, especially suitable for cases with more variables and constraint conditions.

[0160] Step S740: Calculate the demand charge benefit.

[0161] Among them, the demand charge benefit refers to the indirect economic benefit obtained by the user through optimizing the electricity consumption strategy, reducing the maximum demand, and then reducing the demand charge expenditure. After the user installs the energy storage system, it discharges the energy storage during the peak electricity consumption period, reduces the power taken from the power grid, thereby reducing the maximum demand and realizing the demand charge benefit.

[0162] In a possible embodiment, determining the demand charge benefit of the target user in the target period according to the user electricity consumption data includes:

[0163] Determine the first maximum demand of the target user in the target period before participating in shared energy storage according to the first load curve, and the maximum demand represents the maximum power consumption;

[0164] Determine the second maximum demand of the target user in the target period after participating in shared energy storage according to the second load curve, and the first maximum demand is greater than the second maximum demand;

[0165] Obtain the demand charge price corresponding to the target user;

[0166] determining a reference demand revenue according to the first maximum demand, the second maximum demand, and the demand electricity price;

[0167] Obtaining other demand benefits that the target user can obtain by participating in shared energy storage in addition to the reference demand benefit;

[0168] The demand benefit of the target user in the target period is determined according to the reference demand benefit and the other demand benefits.

[0169] The demand benefit of the target user in the target period is expressed as:

[0170] R u =B u +∈ u

[0171] Among them, R u is the demand revenue of target user u in the target period, ∈ u Other demand benefits obtained by users through load optimization through energy storage power stations.

[0172] Among them, the sources of other demand benefits obtained by users through load optimization of energy storage power stations are diverse, mainly covering electricity market incentives, equipment and operation optimization, energy management strategy optimization and other aspects. These benefits together constitute the comprehensive benefits of users participating in shared energy storage. Among them, the benefits related to electricity market incentives include demand response rewards and ancillary service benefits, such as users using energy storage to provide frequency regulation, voltage regulation and other ancillary services for the power grid to obtain benefits; equipment and operation optimization benefits include reduced equipment maintenance costs and improved production efficiency benefits; energy management strategy optimization benefits include peak-valley electricity price arbitrage benefits and renewable energy consumption benefits. If the user has renewable energy power generation equipment, shared energy storage can store excess electricity to avoid power abandonment.

[0173] Step S741, calculating the profit distribution weight.

[0174] Among them, in the Nash bargaining model, the target user's first profit distribution weight reflects the target user's contribution to the overall profit and its influence in the negotiation. The higher the weight, the greater the proportion of the target user's demand profit in the total profit. During the profit distribution negotiation, its influence on the distribution result is stronger, and it is more capable of obtaining a more favorable distribution plan; the second profit distribution weight of the energy storage power station measures the relative importance of the energy storage power station in the entire energy storage system and its voice in the profit distribution negotiation. The energy storage power station with high discharge power has a significant second profit distribution weight, indicating that it plays a greater role in power supply. In the Nash bargaining process, it can strive for more profits based on its own power advantage.

[0175] In a possible embodiment, the first revenue allocation weight of the target user is represented by the proportion of the demand revenue of a single target user in the total demand revenue of multiple target users;

[0176] The second revenue allocation weight of the energy storage power station is represented by the proportion of the discharge power of a single energy storage power station in the total discharge power of multiple energy storage power stations, and the energy storage power station data includes the discharge power.

[0177] Among them, the first revenue allocation weight is expressed as:

[0178]

[0179] Among them, w u is the first revenue allocation weight of target user u, R u is the demand revenue of target user u in the target period, and N u is the number of target users.

[0180] Among them, the second revenue allocation weight is expressed as:

[0181]

[0182] Among them, w s is the second revenue allocation weight of energy storage power station s, D u,t,s is the discharge power of energy storage power station s for supplying power to target user u at time t, T is the time set, and N s is the number of energy storage power stations.

[0183] Step S742, set the utility function.

[0184] Among them, the first utility function is used to calculate the net revenue that the target user can obtain by participating in the shared energy storage revenue. By considering the demand revenue and the fees paid by the target user to the energy storage power station, the actual revenue situation of the user after participating in the shared energy storage project is comprehensively evaluated; the second utility function is used to calculate and determine the revenue that the energy storage power station can obtain by participating in the shared energy storage revenue. It measures the revenue situation of the energy storage power station only based on the fees paid by the target user to the energy storage power station, and clarifies the economic return of the energy storage power station in the shared energy storage project.

[0185] Among them, the first utility function of target user u in the initial function included in the Nash bargaining model can be expressed as:

[0186]

[0187] Among them, π u,s is the fee paid by target user u to energy storage power station s, and N s is the number of energy storage power stations.

[0188] Among them, the second utility function of the energy storage power station s in the initial function included in the Nash bargaining model can be expressed as:

[0189]

[0190] Among them, π u,s is the fee paid by the target user u to the energy storage power station s, and N u is the number of target users.

[0191] Step S743, set the constraint conditions.

[0192] In a possible embodiment, the constraint condition of the objective function is expressed as:

[0193]

[0194] Among them, is used to constrain that the fee paid to the energy storage power station is not greater than the demand-side revenue, and π u,s ≥0 is used to constrain that the fee paid to the energy storage power station is non-negative.

[0195] Step S750, construct a target revenue allocation model for shared energy storage revenue.

[0196] Among them, the initial function included in the Nash bargaining model is expressed as:

[0197]

[0198] Among them, U u is the utility function after the target user u cooperates with the energy storage power station, U s is the utility function after the energy storage power station s cooperates with the target user, D u is the breakdown point of negotiation for the target user u, and D s is the breakdown point of negotiation for the energy storage power station s.

[0199] Among them, in the Nash bargaining model, the breakdown point of negotiation represents the minimum revenue of each participating party when the negotiation fails, that is, the utility value of each party in the non-cooperative state. Since the demand-side revenue cannot be obtained without energy storage sharing, D u , D s are both set to 0.

[0200] In a possible embodiment, the optimization of the Nash bargaining model according to the demand-side revenue, the first revenue allocation weight, and the second revenue allocation weight to obtain the target revenue allocation model includes:

[0201] Obtain the initial function included in the Nash bargaining model;

[0202] Determine the first utility function in the initial function according to the demand revenue and the fee paid by the target user to the energy storage power station. The first utility function is used to calculate and determine the net revenue that the target user can obtain by participating in the shared energy storage revenue; and,

[0203] Determine the second utility function in the initial function according to the fee paid by the target user to the energy storage power station. The second utility function is used to calculate and determine the revenue that the energy storage power station can obtain by participating in the shared energy storage revenue;

[0204] Set a weight for the first utility function based on the first revenue distribution weight, and set a weight for the second utility function based on the second revenue distribution weight to obtain the objective function.

[0205] In a possible embodiment, the objective function included in the target revenue distribution model is expressed as:

[0206]

[0207] Wherein, is the first utility function, is the second utility function, R u is the demand revenue of the target user u, π u,s is the fee paid by the target user u to the energy storage power station s, N u is the number of target users, N s is the number of energy storage power stations, w u is the first revenue distribution weight of the target user u, w s is the second revenue distribution weight of the energy storage power station s.

[0208] Step S760, model solution and output.

[0209] Specifically, according to the constraint conditions, with the maximization of the objective function as the solution target, the target distribution plan of the shared energy storage revenue is solved.

[0210] Among them, taking the maximization of the objective function as the goal is to maximize the product of the utility functions of the participating parties based on the Nash bargaining model under the condition of meeting the constraint conditions. Furthermore, through the maximization of the product, the interests of all parties can be taken into account. If only the utility of a single participating party is maximized, it may damage the interests of other parties and lead to the breakdown of cooperation. By adjusting the revenue distribution, the weighted utility product of the target user and the energy storage power station is maximized to achieve a relatively fair and overall optimal distribution.

[0211] It can be seen that in this embodiment, based on the Nash bargaining model, the utilities and bargaining powers of the user and the energy storage power station are reasonably quantified, and then the objective function is constructed. Finally, based on the constraint conditions, the Nash bargaining solution for the sharing of energy storage benefits is solved, accurately quantifying the contributions and bargaining powers of all parties involved and achieving the theoretical fairness of benefit distribution.

[0212] Please refer to Figure 8 , Figure 8 which is a functional unit block diagram of a shared energy storage benefit distribution system provided by an embodiment of the present application. As Figure 8 shown, the shared energy storage benefit distribution system includes the following units:

[0213] An acquisition unit 810, configured to acquire user power consumption data and energy storage power station data. The user power consumption data includes a first load curve and a second load curve, and the first load curve and the second load curve are respectively used to characterize the power consumption loads of the target user before and after participating in the shared energy storage within a target period, and the target period is used to characterize the time period for electricity billing;

[0214] A processing unit 820, configured to determine the demand benefit of the target user in the target period according to the user power consumption data; determine a first benefit distribution weight of the target user according to the demand benefit, and determine a second benefit distribution weight of the energy storage power station according to the energy storage power station data; optimize the Nash bargaining model according to the demand benefit, the first benefit distribution weight, and the second benefit distribution weight to obtain a target benefit distribution model, and the target benefit distribution model includes an objective function;

[0215] An output unit 830, configured to solve for a target distribution plan for the shared energy storage benefit with the maximization of the objective function as the solution objective according to preset constraint conditions.

[0216] In one embodiment, the optimizing the Nash bargaining model according to the demand benefit, the first benefit distribution weight, and the second benefit distribution weight to obtain a target benefit distribution model includes: acquiring an initial function included in the Nash bargaining model; determining a first utility function in the initial function according to the demand benefit and the fee paid by the target user to the energy storage power station, and the first utility function is used to calculate and determine the net benefit that the target user can obtain by participating in the shared energy storage benefit; and determining a second utility function in the initial function according to the fee paid by the target user to the energy storage power station, and the second utility function is used to calculate and determine the benefit that the energy storage power station can obtain by participating in the shared energy storage benefit; setting a weight for the first utility function based on the first benefit distribution weight, and setting a weight for the second utility function based on the second benefit distribution weight to obtain the objective function.

[0217] In one embodiment, the objective function included in the target revenue allocation model is expressed as:

[0218]

[0219] Wherein, is the first utility function, is the second utility function, R u is the demand revenue of the target user u, π u,s is the fee paid by the target user u to the energy storage power station s, N u is the number of target users, N s is the number of energy storage power stations, w u is the first revenue allocation weight of the target user u, w s is the second revenue allocation weight of the energy storage power station s.

[0220] In one embodiment, the constraint condition of the objective function is expressed as:

[0221]

[0222] Wherein, is used to constrain that the fee paid to the energy storage power station is not greater than the demand revenue, π u,s ≥0 is used to constrain that the fee paid to the energy storage power station is non - negative.

[0223] In one embodiment, according to the preset constraint conditions, with the maximization of the objective function as the solution goal, the target allocation scheme of the shared energy storage revenue is obtained, including: according to the constraint conditions, with the maximization of the objective function as the goal, the objective function is solved through the preset solution algorithm of the solver to obtain the target fee paid by the target user to the energy storage power station; determining the target fee as the target revenue of the energy storage power station participating in the shared energy storage; and determining the target net revenue of the target user participating in the shared energy storage according to the target fee and the demand revenue.

[0224] In one embodiment, the first revenue allocation weight of the target user is represented by the proportion of the demand revenue of a single target user in the total demand revenue of multiple target users; the second revenue allocation weight of the energy storage power station is represented by the proportion of the discharge power of a single energy storage power station in the total discharge power of multiple energy storage power stations, and the energy storage power station data includes the discharge power.

[0225] In one embodiment, determining the demand charge revenue of the target user in the target period based on the user power consumption data includes: determining the first maximum demand of the target user before participating in the shared energy storage in the target period according to the first load curve, where the maximum demand represents the maximum power consumption; determining the second maximum demand of the target user after participating in the shared energy storage in the target period according to the second load curve, and the first maximum demand is greater than the second maximum demand; obtaining the demand charge rate corresponding to the target user; determining the reference demand charge revenue according to the first maximum demand, the second maximum demand, and the demand charge rate; obtaining other demand charge revenues that the target user can obtain in addition to the reference demand charge revenue by participating in the shared energy storage; and determining the demand charge revenue of the target user in the target period according to the reference demand charge revenue and the other demand charge revenues.

[0226] It can be seen that in this embodiment, the shared energy storage revenue distribution system obtains user power consumption data and energy storage power station data; determines the demand charge revenue of the target user in the target period according to the user power consumption data; determines the first revenue distribution weight of the target user according to the demand charge revenue, and determines the second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimizes the Nash bargaining model according to the demand charge revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, where the target revenue distribution model includes an objective function; and solves for the target distribution plan of the shared energy storage revenue with the maximization of the objective function as the solution objective according to the preset constraint conditions. Based on the Nash bargaining model, this application reasonably quantifies the utility and bargaining power of the user and the energy storage power station, further constructs an objective function, and solves the Nash bargaining solution for the revenue distribution of the shared energy storage between the two parties based on the constraint conditions, accurately quantifying the contributions and bargaining powers of each participating party and realizing the theoretical fairness of revenue distribution.

[0227] Please refer to Figure 9 , Figure 9 which is a structural block diagram of an electronic device provided by an embodiment of the present application. As Figure 9 shown, the electronic device 9 may include one or more of the following components: a memory, a processor, a communication bus, a communication interface, and one or more programs. The one or more programs are stored in the memory and are configured to be executed by the processor. The one or more programs include instructions for executing any step in the following method embodiments. Specifically, the processor is used to execute any step in the following method embodiments, and when performing data transmissions such as sending, the communication interface can be optionally called to complete the corresponding operation.

[0228] The processor may include one or more processing cores. The processor connects various parts within the entire electronic device 9 through various interfaces and circuits, and executes various functions of the electronic device 9 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by invoking the data stored in the memory. Optionally, the processor may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the display content; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately through a communication chip.

[0229] The memory may include random access memory (RAM) and may also include read-only memory (ROM). The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created during the use of the electronic device 9.

[0230] It can be understood that the electronic device 9 may include more or fewer structural elements than those in the above structural block diagram. For example, it includes a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, a sensor, etc., which are not limited herein.

[0231] In addition, the embodiments of the present application further provide a computer storage medium, which stores a computer program that can be loaded and executed by a processor and is such as the above-mentioned shared energy storage revenue distribution method. The computer-readable storage medium may include, for example, various media that can store program codes, such as a USB flash drive, a mobile hard disk, read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disc.

[0232] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0233] In several embodiments provided by this application, it should be understood that the disclosed methods, devices, and systems can be implemented in other ways. For example, the device embodiments described above are only illustrative; for example, the division of the units is only a logical function division, and there can be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in electrical, mechanical, or other forms.

[0234] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0235] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can be physically included separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0236] The integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units are stored in a storage medium and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute some steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, magnetic disks, optical disks, volatile memories, or non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM), etc., which are various media that can store program code.

[0237] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0238] The embodiments of the present application have been introduced in detail above. Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

[0239] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can easily conceive of changes or substitutions without departing from the spirit and scope of the present application, and can make various modifications and alterations, including combinations of the above different functions and implementation steps, including software and hardware implementation manners, all within the protection scope of the present application.

Claims

1. A method for sharing energy storage revenue distribution, characterized in that, Applied to a shared energy storage revenue distribution system, the shared energy storage revenue distribution system includes an energy storage power station and target users participating in the shared energy storage; the method includes: Obtain user power consumption data and energy storage power station data. The user power consumption data includes a first load curve and a second load curve, and the first load curve and the second load curve are respectively used to characterize the power consumption loads of the target user before and after participating in the shared energy storage within a target period, and the target period is used to characterize the time period for electricity billing; Determine the demand revenue of the target user in the target period according to the user power consumption data; Determine the first revenue distribution weight of the target user according to the demand revenue, and determine the second revenue distribution weight of the energy storage power station according to the energy storage power station data; Optimize the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, and the target revenue distribution model includes an objective function; According to the preset constraint conditions, with the maximization of the objective function as the solution target, solve to obtain the target distribution plan of the shared energy storage revenue.

2. The method according to claim 1, characterized in that, The optimizing the Nash bargaining model according to the demand revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model includes: Obtain the initial function included in the Nash bargaining model; Determine the first utility function in the initial function according to the demand revenue and the fee paid by the target user to the energy storage power station. The first utility function is used to calculate and determine the net revenue that the target user can obtain by participating in the shared energy storage revenue; and Determine the second utility function in the initial function according to the fee paid by the target user to the energy storage power station. The second utility function is used to calculate and determine the revenue that the energy storage power station can obtain by participating in the shared energy storage revenue; Set a weight for the first utility function based on the first revenue distribution weight, and set a weight for the second utility function based on the second revenue distribution weight to obtain the objective function.

3. The method according to claim 2, wherein The objective function included in the target revenue distribution model is expressed as: Among them, is the first utility function, is the second utility function, R u is the demand revenue of the target user u, π u,s is the fee paid by the target user u to the energy storage power station s, N u is the number of target users, N s is the number of energy storage power stations, w u is the first revenue allocation weight of the target user u, w s is the second revenue allocation weight of the energy storage power station s.

4. The method according to claim 3, characterized in that The constraint conditions of the objective function are expressed as: Among them, is used to constrain that the cost paid to the energy storage power station is not greater than the demand revenue, π u,s ≥ 0 is used to constrain that the cost paid to the energy storage power station is a non - negative number.

5. The method according to any one of claims 1 to 4, characterized in that, The solving to obtain the target distribution plan of the shared energy storage revenue according to the preset constraint conditions with the maximization of the objective function as the solution target includes: According to the constraint conditions, with the maximization of the objective function as the target, solve the objective function through the preset solving algorithm of the solver to obtain the target fee paid by the target user to the energy storage power station; Determine that the target fee is the target revenue of the energy storage power station participating in the shared energy storage; Determine the target net revenue of the target user participating in the shared energy storage according to the target fee and the demand revenue.

6. The method according to claim 5, characterized in that, The first revenue distribution weight of the target user is represented by the proportion of the demand revenue of a single target user in the total demand revenue of multiple target users; The second revenue distribution weight of the energy storage power station is represented by the proportion of the discharge power of a single energy storage power station in the total discharge power of multiple energy storage power stations, and the energy storage power station data includes the discharge power.

7. The method according to claim 6, characterized in that, Determining the demand charge revenue of the target user in the target period according to the user power consumption data includes: Determining a first maximum demand of the target user before participating in shared energy storage in the target period according to the first load curve, where the maximum demand represents the maximum power consumption; Determining a second maximum demand of the target user after participating in shared energy storage in the target period according to the second load curve, and the first maximum demand is greater than the second maximum demand; Obtaining the demand charge price corresponding to the target user; Determining a reference demand charge revenue according to the first maximum demand, the second maximum demand, and the demand charge price; Obtaining other demand charge revenues that the target user can obtain in addition to the reference demand charge revenue by participating in shared energy storage; Determining the demand charge revenue of the target user in the target period according to the reference demand charge revenue and the other demand charge revenues.

8. A shared energy storage revenue distribution system, characterized in that, The system includes an acquisition unit, a processing unit, and an output unit, where: The acquisition unit is configured to acquire user power consumption data and energy storage power station data. The user power consumption data includes a first load curve and a second load curve, and the first load curve and the second load curve are respectively used to characterize the power consumption load of the target user before and after participating in shared energy storage in the target period, and the target period is used to characterize the time period for electricity billing; The processing unit is configured to determine the demand charge revenue of the target user in the target period according to the user power consumption data; determine a first revenue distribution weight of the target user according to the demand charge revenue, and determine a second revenue distribution weight of the energy storage power station according to the energy storage power station data; optimize the Nash bargaining model according to the demand charge revenue, the first revenue distribution weight, and the second revenue distribution weight to obtain a target revenue distribution model, and the target revenue distribution model includes an objective function; The output unit is configured to solve for a target allocation plan for the shared energy storage revenue with the maximization of the objective function as the solution objective according to preset constraint conditions.

9. An electronic device, characterized in that, It includes a processor and a memory. The memory is configured to store one or more programs and is configured to be executed by the processor. The programs include instructions for executing the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, the steps of the method according to any one of claims 1-7 are implemented.

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