Revenue distribution method for distributed photovoltaic and power users and related device

Abstract

The invention discloses a revenue distribution method for distributed photovoltaic and power consumers and a related device. The method comprises the following steps: acquiring a target station area with abnormal price fluctuation in a target area within a preset time period; obtaining electricity market price data, photovoltaic station operation data and user electricity consumption data of the target station area in a preset time period; determining the maximum income according to the power market price data, the photovoltaic station operation data and the user power consumption data; establishing a Nash bargaining model according to the maximum income, a first contribution index corresponding to the distributed photovoltaic station and a second contribution index corresponding to the power consumer; and solving the Nash bargaining model to obtain a first income corresponding to the distributed photovoltaic station and a second income corresponding to the power consumer. Through adoption of the method, the Nash bargaining model is constructed and solved based on the maximum income and the contribution index, the evenly distributed income is obtained, and reasonable income distribution of distributed photovoltaic and power users is realized.

Description

Technical Field

[0001] The present application relates to the technical field of resource allocation, and in particular to a method and related apparatus for distributing revenue to distributed photovoltaic and electricity users. Background Art

[0002] Against the backdrop of deepening reforms in the electricity market and the rapid development of distributed energy, the value-sharing concept provides innovative market mechanisms and economic incentives for distributed photovoltaics, users, and energy storage power stations under the same substation (i.e., the same substation). This concept promotes market participants to jointly optimize scheduling and maximize benefits by building a mechanism for resource sharing, coordinated benefit distribution, and risk sharing, thereby improving the flexibility of the power system and resource utilization efficiency.

[0003] While existing value-sharing models can effectively promote the consumption of distributed photovoltaic power and reduce the investment demand for transmission and distribution facilities during peak periods, they lack accurate recognition of incentive benefits and are unable to dynamically adapt to market changes. Therefore, how can we address these issues in existing technologies, such as inaccurate recognition of incentive benefits and their inability to dynamically adapt to market changes, to achieve a reasonable distribution of benefits between distributed photovoltaics and electricity users? Summary of the Invention

[0004] The embodiment of the present application provides a method and related device for distributing profits between distributed photovoltaic and electricity users. By identifying areas with abnormal price fluctuations and calculating the maximum profit based on electricity market data, a Nash bargaining model is constructed and solved in combination with the contribution indexes of distributed photovoltaic stations and electricity users, thereby achieving a reasonable distribution of profits for both parties.

[0005] In a first aspect, an embodiment of the present application provides a method for distributing revenue for distributed photovoltaic and electricity users, which is applied to a server; the method includes:

[0006] Obtain a target substation in a target area that experiences abnormal price fluctuations within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N electricity users; K, M, and N are all positive integers;

[0007] Obtaining electricity market price data, photovoltaic station operation data, and user electricity consumption data for the target area within the preset time period;

[0008] Determine the maximum benefit based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data; the maximum benefit represents the maximum value of the net benefit after the on-grid surplus of the distributed photovoltaic station in the target area is consumed by the electricity users, minus the incremental electricity purchase cost of the electricity users;

[0009] Establishing a Nash bargaining model based on the maximum benefit, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user;

[0010] The Nash bargaining model is solved to obtain a first benefit corresponding to the M distributed photovoltaic stations and a second benefit corresponding to the N power users; the sum of the first benefit and the second benefit is the maximum benefit.

[0011] In a second aspect, an embodiment of the present application provides a profit distribution device for distributed photovoltaic and electricity users, which is applied to a server; the profit distribution device for distributed photovoltaic and electricity users includes: a station detection module, a data acquisition module, a profit calculation module, a Nash bargaining model construction module, and a model solution module, wherein:

[0012] The substation detection module is used to obtain a target substation in a target area that has abnormal price fluctuations within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N electricity users; K, M, and N are all positive integers;

[0013] The data acquisition module is used to obtain the power market price data, photovoltaic station operation data and user power consumption data of the target area within the preset time period;

[0014] The profit calculation module is used to determine the maximum profit based on the electricity market price data, the photovoltaic station operation data and the user electricity consumption data; the maximum profit represents the maximum value of the net profit after the surplus power of the distributed photovoltaic station in the target area is consumed by the power users, minus the incremental power purchase cost of the power users;

[0015] The Nash bargaining model building module is used to establish a Nash bargaining model according to the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station and the second contribution index corresponding to the power user;

[0016] The model solving module is used to solve the Nash bargaining model to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N electricity users; the sum of the first benefits and the second benefits is the maximum benefit.

[0017] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0019] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0020] It can be seen that the embodiments of the present application have the following beneficial effects:

[0021] By implementing the embodiments of the present application, a target substation in a target area in which abnormal price fluctuations occur within a preset time period is obtained; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N power users; K, M, and N are all positive integers; the electricity market price data, photovoltaic station operation data, and user electricity consumption data of the target substation within the preset time period are obtained; the maximum benefit is determined based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data; the maximum benefit represents the maximum value of the net benefit after the on-grid surplus of the distributed photovoltaic station in the target area is absorbed by the power users, after deducting the incremental electricity purchase cost of the power users; a Nash bargaining model is established based on the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the power user; the Nash bargaining model is solved to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N power users; the sum of the first benefit and the second benefit is the maximum benefit. It can be seen that by identifying price-abnormal areas and fusing multi-source data to calculate the maximum benefit of power consumption in the area, and constructing a Nash bargaining model based on the contribution index of distributed photovoltaic stations and power users, and solving the Nash bargaining model, a reasonable distribution of benefits between distributed photovoltaic stations and power users is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0023] Figure 1 This is a flow chart of a method for distributing revenue to distributed photovoltaic and electricity users provided in an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of the structure of a target area provided in an embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of the structure of a target area provided in an embodiment of the present application;

[0026] Figure 4 This is a flowchart of a target area price anomaly detection method provided by an embodiment of the present application;

[0027] Figure 5 This is a structural diagram of a Nash bargaining model provided in an embodiment of the present application;

[0028] Figure 6 This is a system architecture diagram of a revenue distribution system for distributed photovoltaic and power users provided by an embodiment of the present application;

[0029] Figure 7 This is a schematic diagram of the structure of a profit distribution device for distributed photovoltaic and power users provided in an embodiment of the present application;

[0030] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0033] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] The following describes the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0035] First, some of the terms involved in this application are explained:

[0036] Distributed photovoltaic stations: Distributed photovoltaic stations are small- to medium-sized power generation facilities based on photovoltaic power generation technology. These facilities install photovoltaic modules in a dispersed manner near users (such as on building rooftops, industrial plants, and agricultural greenhouses). The generated electricity is primarily consumed locally, with surplus power being fed into the grid. Distributed photovoltaic stations offer flexible construction, proximity to load centers, and reduced transmission losses. They effectively utilize solar energy resources and increase the proportion of renewable energy in electricity supply.

[0037] Electricity users: Electricity users refer to various entities in the power system that use electricity for production, life and other activities, including residential users, commercial users, industrial users, etc.

[0038] Lagrange function: Lagrange function is an important tool for transforming constrained optimization problems into unconstrained optimization problems, and Lagrange multipliers are used to adjust the degree of influence of constraints on the objective function.

[0039] With the widespread application of distributed photovoltaic power generation technology, the coordinated consumption of distributed photovoltaic power and electricity users has become the key to improving energy utilization efficiency. In the power market environment, the electricity prices in each area are affected by factors such as power supply and demand, grid congestion, and policy subsidies, and show dynamic fluctuations. When abnormal price fluctuations occur in the area, the traditional profit distribution method is difficult to deal with effectively, resulting in the obstruction of the consumption of the grid surplus of distributed photovoltaic sites and the lack of enthusiasm of electricity users to participate, which in turn leads to problems such as waste of resources and reduced system operation efficiency. In addition, the existing distribution mechanism does not fully consider the actual contribution differences between distributed photovoltaic sites and electricity users in the consumption process, and cannot achieve fair distribution and incentives of benefits, which restricts the healthy development of the distributed energy market.

[0040] In the electricity market trading system, the revenue of distributed photovoltaic stations is closely related to the amount of surplus power consumed and the market electricity price, while the electricity purchase cost of electricity users is affected by the real-time electricity price and the amount of power consumed. Due to the lack of an accurate identification mechanism for abnormal price fluctuations and a profit distribution model based on contribution quantification, it is difficult to achieve coordinated optimization between distributed photovoltaics and electricity users. Therefore, there is an urgent need for a method that can dynamically identify areas with abnormal price fluctuations, accurately calculate the maximum system profit, and fairly distribute it based on the contribution index of each entity. This method can improve the efficiency of distributed energy consumption, ensure the reasonable profits of various electricity market entities, and promote the stable operation and sustainable development of the electricity market.

[0041] See Figure 1 , Figure 1 This is a flow chart of a method for distributing revenue to distributed photovoltaic and electricity users provided in an embodiment of the present application. The method is applied to a server and includes but is not limited to the following steps:

[0042] S101. Acquire target substations in a target area where abnormal price fluctuations occur within a preset time period; the target substations include: M distributed photovoltaic stations and N electricity users.

[0043] In the embodiment of the present application, the target area refers to a specific geographical area in the power system determined according to the grid planning, the scope of power market transactions or the administrative division. The area includes K substations, where K is a positive integer. The power supply and demand conditions and electricity price fluctuations in the substations directly reflect the operating characteristics of the power system in the target area. The delineation of the target area is usually based on the integrity of the grid structure, the convenience of power dispatching and the uniformity of market transactions. Its scope can vary. It can be a local power supply area of ​​a city or an area covered by multiple adjacent substations.

[0044] See Figure 2 , Figure 2 This is a schematic diagram of the structure of a target area provided in an embodiment of the present application. As shown in the figure, the target area can be divided into K substations, namely Substation 1, Substation 2, and Substation K. These substations together constitute the basic components of the target area. When the revenue distribution of distributed photovoltaic stations and power users is subsequently involved, each substation can be used as the object of independent or collaborative analysis to accurately characterize the energy consumption, revenue distribution and other characteristics within different regions, providing basic structural support for the overall optimization and refined management of the distributed energy system in the target area.

[0045] In the embodiment of the present application, the preset time period is a specific time interval pre-set according to the electricity market trading rules, electricity price fluctuation patterns and distributed photovoltaic power generation characteristics. Its setting can be considered from the perspective of the electricity market, with reference to the trading cycles of the day-ahead market and the real-time market. For example, taking one day as a unit, the peak load period (such as 18:00-22:00 on weekdays) or the new energy peak generation period (such as the daytime sunlight period) is selected as the time window to capture typical scenarios with large fluctuations in electricity prices.

[0046] In the embodiment of the present application, in the power market environment, electricity prices are affected by various factors such as changes in supply and demand on the power generation side, grid congestion, and intermittent power generation from new energy sources, and may fluctuate violently. When abnormal price fluctuations occur in the substation (such as a sudden drop in real-time electricity prices, zero electricity prices, or a significant deviation from the market-clearing electricity prices of the day before), the traditional profit distribution method will be difficult to deal with effectively. On the one hand, price anomalies may cause the distributed photovoltaic site to be blocked from absorbing the surplus power on the grid, resulting in energy waste; on the other hand, unreasonable electricity price signals will affect the enthusiasm of power users to participate in photovoltaic absorption and reduce resource allocation efficiency. By analyzing the substations with abnormal price fluctuations, potential problems and risks in the operation of the power system can be discovered in a timely manner, and a basis can be provided for the subsequent optimization of the coordinated absorption strategy of distributed photovoltaics and power users, and the formulation of fair and reasonable profit distribution plans, thereby ensuring the reasonable benefits of various power market entities (distributed photovoltaic sites, power users) and improving the operational stability and energy utilization efficiency of the power system.

[0047] In a specific embodiment, real-time electricity price data and related market price signals are obtained for K substations within a target area within a preset time period, and price fluctuations in each substation are individually detected. When the price fluctuation characteristics of a substation meet a preset abnormality condition, the substation is determined to have experienced abnormal price fluctuations and is designated as the target substation. The preset abnormality condition refers to a pre-set condition for determining whether an substation has experienced abnormal price fluctuations.

[0048] The target area includes M distributed photovoltaic stations and N electricity users, where M and N are both positive integers. Distributed photovoltaic stations are small power generation units that mainly consume on-site, and electricity users are various electricity users involved in photovoltaic consumption.

[0049] See Figure 3 , Figure 3 This is a schematic diagram of the structure of a target substation provided in an embodiment of the present application. As shown in the figure, the target substation includes two types of entities: M distributed photovoltaic sites (distributed photovoltaic site 1 to distributed photovoltaic site M) and N power users (power user 1 to power user N). These distributed photovoltaic sites can connect photovoltaic power generation to the target substation, and power users are the main power consumption entities in the substation. Together, the two constitute the basic units of energy production and consumption in the target substation.

[0050] It can be seen that by detecting abnormal fluctuations in the electricity price data of the target area during the preset time period, the target area with price anomalies can be accurately located, providing an accurate target for the subsequent profit distribution model.

[0051] Alternatively, see Figure 4 , Figure 4 This is a flow chart of price anomaly detection in a target area provided by an embodiment of the present application; wherein the server is connected to the electricity market; the electricity market is used to obtain electricity price information; the above step of obtaining target areas in the target area that have experienced abnormal price fluctuations within a preset time period may include the following steps:

[0052] A101. Obtain, from the electricity market, a rolling clearing electricity price for each of the K substations in the target area within a preset future time period to obtain K rolling clearing electricity price sequences;

[0053] A102. Obtaining the day-ahead market-clearing electricity price corresponding to the target area from the electricity market;

[0054] A103. Determine the substation where abnormal price fluctuations occur among the K substations based on the K rolling clearing electricity price sequences and the day-ahead market clearing electricity price, and obtain the target substation.

[0055] In this embodiment of the present application, the server can access the electricity market and establish a communication connection with the electricity market to obtain real-time price signals from the electricity market. The electricity market is a systematic platform that includes electricity trading, price formation, and resource allocation. Its main function is to provide electricity price information services to electricity market participants. This information includes but is not limited to real-time electricity prices for each substation, market clearing prices, and other electricity market transaction-related data.

[0056] In a specific embodiment, the rolling clearing electricity price is a price signal that the power market predicts and publishes for each substation in the target area within a preset future time period (e.g., the next 4 hours, or every 15 minutes within a day) based on factors such as short-term load forecasts and power output plans. The server can obtain the rolling clearing electricity price for each of the K substations in the target area from the power market by sending a data query request to the power market. The server receives and parses the electricity price data for each time period returned by the power market, thereby forming K rolling clearing electricity price sequences that reflect the future electricity price fluctuation trends of each substation.

[0057] The day-ahead market-clearing price is the market-clearing price for each time period, determined through centralized bidding one day before the actual power supply date. This price reflects market participants' expectations of the next day's power supply and demand and serves as a benchmark for determining whether electricity prices in a specific area are abnormal. The server can obtain the day-ahead market-clearing price for the target area from the power market.

[0058] Then, the price fluctuation of each of the K substations is analyzed based on the K rolling clearing electricity price series and the day-ahead market clearing electricity price. The substation with abnormal price fluctuations among the K substations can be determined and the target substation can be obtained.

[0059] It can be seen that by accessing the power market through the server to obtain the rolling clearing electricity price series and the day-ahead market clearing electricity price, it is possible to quantitatively identify abnormal fluctuations in electricity prices in the substation based on the real transaction price signals of the power market, effectively improving the market adaptability and implementation efficiency of distributed photovoltaic consumption and profit distribution strategies.

[0060] Optionally, the above step of determining the substation where abnormal price fluctuations occur among the K substations based on the K rolling clearing electricity price sequences and the day-ahead market clearing electricity price to obtain the target substation may include the following steps:

[0061] B101. Determine the absolute value of the difference between each rolling-clearing electricity price in the target rolling-clearing electricity price sequence and the day-ahead market-clearing electricity price to obtain a target difference sequence; the target rolling-clearing electricity price sequence is any one of the K rolling-clearing electricity price sequences;

[0062] B102. If the absolute value of at least one difference in the target difference sequence exceeds a preset difference threshold, or if at least one rolling clearing electricity price in the target rolling clearing electricity price sequence is 0, the substation corresponding to the target rolling clearing electricity price sequence is determined as the target substation.

[0063] Among them, the preset difference threshold is a benchmark value determined based on statistical analysis of historical electricity market operation data. It is usually taken as the percentile of historical electricity price fluctuations (such as the 95% percentile) or the value recommended by industry standards, which is used to measure whether the degree of electricity price deviation exceeds the normal range.

[0064] In a specific embodiment, the absolute value of the difference between each rolling clearing electricity price in the target rolling clearing electricity price sequence and the day-ahead market clearing electricity price can be determined to obtain a target difference sequence, wherein the target rolling clearing electricity price sequence is any one of the K rolling clearing electricity price sequences.

[0065] If the absolute value of at least one difference in the target difference sequence exceeds the preset difference threshold, or at least one rolling clearing electricity price in the target rolling clearing electricity price sequence is 0, in these two cases, it indicates that one of the substations may have abnormal conditions such as supply and demand imbalance and grid congestion, and the substation corresponding to the target rolling clearing electricity price sequence is determined as the target substation.

[0066] It can be seen that by quantitatively analyzing the deviation of electricity prices and extreme price signals, an accurate diagnosis of the operating status of the power market in the substation area is achieved, so as to identify the substations with abnormal price fluctuations, and provide accurate targets for the subsequent optimization of distributed photovoltaic revenue distribution strategies for abnormal substations.

[0067] S102: Obtain electricity market price data, photovoltaic station operation data, and user electricity consumption data for the target area within the preset time period.

[0068] In the embodiment of the present application, the target substation is a specific power supply area determined by the abnormal price fluctuation identification process, which includes M distributed photovoltaic stations and N electricity users.

[0069] In a specific embodiment, the electricity market price data, photovoltaic station operation data and user electricity consumption data of the target area within a preset time period can be obtained. For example, the server can access the electricity market and obtain the above data through the electricity market.

[0070] It should be noted that electricity market price data is a collection of price signals that reflect the electricity supply and demand relationship and market transaction status in the target area. It includes but is not limited to medium- and long-term contract prices, day-ahead electricity prices, real-time electricity prices, mechanism electricity prices, and weighted average transaction prices. Among them, medium- and long-term contract prices are electricity transaction prices agreed upon between photovoltaic stations and electricity users over a longer time scale (such as monthly or annual); day-ahead electricity prices are the clearing prices for each time period determined by the electricity market through centralized bidding the day before power supply is due; and real-time electricity prices are the prices dynamically adjusted during the real-time power supply period.

[0071] PV station operation data is used to characterize the power generation and trading status of distributed PV stations. It includes, but is not limited to, medium- and long-term contracted electricity, day-ahead electricity, and actual grid-connected electricity. Medium- and long-term contracted electricity refers to the power supply agreed upon by the PV station in the medium- and long-term contract; day-ahead electricity refers to the electricity declared and traded by the PV station in the day-ahead market; and actual grid-connected electricity refers to the electricity actually delivered to the grid by the PV station during a preset time period, collected in real time by smart meters installed at the PV station.

[0072] User electricity consumption data mainly includes the increased power generation of power users within a preset time period, that is, the incremental part of the user's conventional electricity consumption after absorbing distributed photovoltaic power. This data can be collected through the user-side smart meter.

[0073] It can be seen that through the systematic acquisition of target area power market price data, photovoltaic station operation data and user electricity consumption data, a full-chain data system covering market price signals, power generation entity operation status and user electricity consumption behavior has been constructed, providing multi-dimensional data support for the distributed photovoltaic and power user profit distribution model.

[0074] S103: Determine the maximum profit based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data.

[0075] In the embodiment of the present application, the maximum profit represents the maximum value of the net profit after the surplus power of the distributed photovoltaic station in the target area is absorbed by the power users, minus the incremental power purchase cost of the power users.

[0076] In a specific embodiment, the maximum benefit can be determined based on electricity market price data, photovoltaic station operation data and user electricity consumption data. Specifically, the grid-connected surplus absorption revenue of each distributed photovoltaic station is calculated, and then the incremental electricity purchase cost of each electricity user is calculated. Finally, the net benefit of the target substation is calculated by the difference between the revenue and the cost.

[0077] Because the revenue of photovoltaic stations in the power market is directly related to the amount of surplus power consumed and the market electricity price, while the user's electricity purchase cost is affected by the real-time electricity price and the amount of power consumed, it is necessary to accurately calculate the revenue and cost through multi-source data fusion. By maximizing the difference between revenue and cost, it is possible to dynamically optimize the coordinated consumption strategy of distributed photovoltaics and power users in the context of power market price fluctuations, guiding photovoltaic stations to increase the rate of surplus power consumption and users to actively adjust their electricity consumption behavior, thereby maximizing the efficiency of resource allocation within the target substation. Therefore, the maximum value of this difference is the maximum net revenue achievable in the target substation, that is, the maximum revenue.

[0078] It can be seen that by integrating electricity market price data, photovoltaic station operation data and user electricity consumption data, the net income of distributed photovoltaic absorption in the target area can be accurately quantified.

[0079] Optionally, the electricity market price data includes: medium- and long-term contract prices, day-ahead electricity prices, real-time electricity prices, mechanism electricity prices, and weighted average transaction electricity prices; the photovoltaic site operation data includes: medium- and long-term contract electricity volume, day-ahead electricity volume, and actual grid-connected electricity volume; the user electricity consumption data includes: increased electricity generation; and the above step of determining the maximum profit based on the electricity market price data, the photovoltaic site operation data, and the user electricity consumption data may include the following steps:

[0080] The maximum profit is determined based on the electricity market price data, the photovoltaic station operation data, the user electricity consumption data, and a preset first formula; wherein the preset first formula is as follows:

[0081]

[0082] In the above formula, R max Represents the maximum benefit; R m,t represents the consumption income of the on-grid surplus of distributed photovoltaic station m in period t after being consumed by users, the distributed photovoltaic station m is the mth distributed photovoltaic station among the M distributed photovoltaic stations, and the period t is the preset time period; ΔC n,t represents the incremental electricity purchase cost of power user n in the period t, where power user n represents the nth power user among the N power users;

[0083] The absorbed income is determined according to a preset second formula, which is as follows:

[0084]

[0085] In the above formula, R m,t represents the absorbed income; Q m,mid,t P represents the medium- and long-term contracted electricity volume of the distributed photovoltaic station m during the period t; m,mid,t represents the medium- and long-term contract price of the distributed photovoltaic station m during the period t; Q m,dd,t represents the day-ahead power consumption of the distributed photovoltaic station m during the period t; P da,t represents the day-ahead electricity price of the target area during the period t; β m W represents the proportion of the actual grid-connected power of the distributed photovoltaic station m that participates in the real-time price linkage of the power market; m,t represents the actual grid-connected power of the distributed photovoltaic station m during the period t; P rt,t represents the real-time electricity price of the target area during the period t; m,mc represents the mechanism electricity price of the distributed photovoltaic station m; represents the weighted average transaction price of electricity in the target area;

[0086] The incremental electricity purchase cost is determined according to a preset third formula, which is as follows:

[0087] ΔC n,t =P n,rt,t Q n,m,t

[0088] In the above formula, ΔC n,t represents the incremental electricity purchase cost; P n,rt,t represents the real-time electricity price of the power user n in the target area during the time period t; Qn,m,t It represents the increased power generation of the distributed photovoltaic station m used by the power user n during the period t.

[0089] In the embodiment of the present application, the medium- and long-term contract price refers to the electricity transaction price agreed upon by the two parties to the transaction (photovoltaic station and electricity market) in the electricity market through a medium- and long-term contract (usually a longer time scale such as month, quarter, year, etc.), which is used to determine the settlement basis of the medium- and long-term contract electricity quantity. The day-ahead electricity price refers to the electricity price for each time period of the next day announced by the electricity market in the day-ahead stage (usually the day before the transaction day), which is used for the transaction settlement of the day-ahead electricity quantity and reflects the expected price level of electricity supply and demand for the next day. The real-time electricity price refers to the electricity price determined by the electricity market during the actual operation period (such as real-time transaction or settlement) based on factors such as real-time supply and demand balance and grid operation status, which reflects the real-time price fluctuations of the electricity market in real time. The mechanism electricity price refers to the price set for a specific electricity market mechanism, which is used for electricity settlement in specific scenarios (such as electricity subsidy scenarios). The weighted average transaction electricity price refers to the comprehensive electricity price obtained by weighted average calculation of different transaction prices with the transaction volume of each type of electricity transaction as the weight, which is used to characterize the overall electricity transaction price level of the target area within a preset time period.

[0090] In the embodiments of the present application, the medium- and long-term contracted electricity refers to the amount of electricity that a distributed photovoltaic station must complete on-grid during a specific period of time as agreed upon in a medium- and long-term contract, and is subject to the medium- and long-term contract price. The day-ahead electricity refers to the amount of electricity that a photovoltaic station declares and confirms on-grid the next day during the day-ahead phase, and is settled at the day-ahead electricity price. This is a further refinement and adjustment of the medium- and long-term contracted electricity. The actual on-grid electricity refers to the amount of electricity that a photovoltaic station actually transmits to the electricity market through the power grid during the actual operating period, reflecting the actual power generation and on-grid capacity of the station, and part of the electricity is involved in the real-time electricity price linkage.

[0091] In the embodiment of the present application, the increased power generation refers to the power consumption generated by the additional consumption of the distributed photovoltaic station's surplus power generated by the power user in addition to the normal power demand, that is, the part of the excess power generated by the photovoltaic station used by the user, which is the basis for calculating the corresponding incremental power purchase cost.

[0092] In a specific embodiment, the maximum revenue can be determined based on electricity market price data, photovoltaic station operation data, user electricity consumption data, and a preset first formula. The maximum revenue is calculated by calculating the corresponding absorbed revenue of the distributed station and the incremental electricity purchase cost corresponding to the electricity user, wherein the absorbed revenue can be calculated using the preset second formula, and the incremental electricity purchase cost can be calculated using the preset third formula. Specifically, the medium- and long-term contract prices are matched with the corresponding contracted electricity volume to determine the basic revenue of the photovoltaic station in the medium- and long-term market. The day-ahead electricity price is combined with the day-ahead declared electricity volume of the photovoltaic station to calculate the day-ahead market transaction revenue. The real-time electricity price is used to calculate the electricity revenue of the photovoltaic station that exceeds the day-ahead plan, as well as the incremental electricity purchase cost of the photovoltaic electricity consumed by the electricity user.

[0093] The medium- and long-term contracted electricity and the day-ahead electricity consumption correspond to different market transaction volumes. The server compares the difference between the actual online electricity consumption and the contracted electricity consumption to determine the grid-connected surplus capacity of the PV station. For example, if the actual online electricity consumption of a PV station exceeds the sum of the medium- and long-term contracted electricity consumption and the day-ahead electricity consumption, the excess is calculated based on the real-time electricity price. If the amount is less than the contracted electricity consumption, deviation processing is performed in accordance with market rules to ensure the accuracy of the profit calculation.

[0094] The additional electricity purchase cost incurred by the user's electricity consumption data must be calculated in conjunction with the real-time electricity price. Specifically, the additional electricity generated by the user's consumption of distributed photovoltaic power multiplied by the real-time electricity price for the corresponding period is the additional electricity purchase cost incurred by the user for consuming photovoltaic power. This cost must be deducted from the photovoltaic station's consumption revenue to form the net profit.

[0095] The maximum benefit comprehensively considers the price and electricity matching relationship of medium- and long-term contracts, day-ahead market and real-time market, and then calculates the incremental electricity purchase cost of each electricity user based on the product of the photovoltaic power consumed by the user and the real-time electricity price. Finally, the net benefit of the target substation can be calculated by the difference between the benefit and the cost, and the maximum value of the net benefit can be determined to obtain the maximum benefit.

[0096] It can be seen that by integrating electricity market price data, photovoltaic station operation data and user electricity consumption data, a maximum benefit calculation model was constructed, which achieved accurate quantification of the net benefit of distributed photovoltaic absorption in the target area.

[0097] S104: Establish a Nash bargaining model according to the maximum benefit, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user.

[0098] In the embodiment of the present application, the maximum benefit refers to the maximum value of the net benefit of the target substation calculated by integrating electricity market price data, photovoltaic station operation data and user electricity consumption data, which represents the optimal economic total that can be achieved by the coordinated consumption of distributed energy in the area. The first contribution index and the second contribution index are used to quantify the contribution of distributed photovoltaic stations and electricity users in the coordinated consumption process, and are weight indicators for measuring the total benefit formed by each party.

[0099] In a specific embodiment, the contribution indexes of distributed photovoltaic stations and electricity users are first determined. The calculation of the first contribution index requires comprehensive consideration of factors such as the photovoltaic station's power generation, grid-connected power, and consumption stability. For example, the actual grid-connected power and the proportion of excess contracted power are used as core parameters, and a weighted calculation is performed in combination with the market environment to highlight the station's contribution to power supply. The second contribution index requires evaluating indicators such as the power user's power response capability, increased power generation, and power flexibility. For example, the proportion of users who actively adjust their loads to consume photovoltaic surplus power and the frequency of their participation in demand response are used to reflect the role of users in promoting photovoltaic consumption.

[0100] After determining the maximum revenue, the first contribution index corresponding to the distributed PV station, and the second contribution index corresponding to the electricity user, a Nash bargaining model is constructed based on these factors. The Nash bargaining model aims to maximize the utility of all parties, using the revenue of the distributed PV station and the electricity user as decision variables and constructing an objective function based on the contribution index. Furthermore, constraints are set based on the maximum revenue to ensure that the total revenue allocated does not exceed the maximum achievable net revenue for the region.

[0101] Specifically, the objective function uses a logarithmic function to relate each entity's revenue to its contribution index, ensuring that revenue distribution reflects differences in contribution while also adhering to the principle of game equilibrium. Constraints ensure that the distribution plan is economically feasible. By constructing a Nash bargaining model, a fair distribution of revenue between distributed photovoltaic stations and electricity users is achieved while maximizing total revenue.

[0102] It can be seen that by combining the maximum benefit and the subject contribution index to establish a Nash bargaining model, a scientific and quantitative solution is provided for the profit distribution of distributed photovoltaics and electricity users.

[0103] Optionally, the above step of establishing a Nash bargaining model based on the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the power user may include the following steps:

[0104] A401. Determine a first incentive variable and a second incentive variable;

[0105] A402. Determine an objective constraint function based on the maximum benefit, the first incentive variable, and the second incentive variable; wherein the objective constraint function is as follows:

[0106]

[0107] In the above formula, Represents the first excitation variable and the second excitation variable The objective constraint function constructed; R represents the maximum benefit; π n,m,t represents the incentive income allocated by the distributed photovoltaic power station m to the power user n during the period t; ΔR m,t C represents the market revenue of the distributed photovoltaic station m after the surplus power of the distributed photovoltaic station m in the period t is consumed by the users; n,t C represents the total electricity purchase cost of the power user n after consuming distributed electricity during the period t; n,t,0 represents the total electricity purchase cost of the power user n before consuming distributed electricity in the period t;

[0108] A403, determining a target constraint condition according to the target constraint function;

[0109] A404. Determine an objective function based on the first incentive variable, the second incentive variable, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user; wherein the objective function is as follows:

[0110]

[0111] In the above formula, Represents the first excitation variable and the second excitation variable The objective function constructed; ω m,t represents the first contribution index of the distributed photovoltaic station m in the period t; ω n,t represents the second contribution index of the power user n in the time period t;

[0112] A405. Establish the Nash bargaining model according to the target constraint and the target function. The Nash bargaining model is as follows:

[0113]

[0114] In the above formula, represents maximizing the objective function; represents the target constraint.

[0115] In a specific embodiment, a first incentive variable and a second incentive variable may be determined first, wherein the first incentive variable represents the total incentive income of distributed photovoltaic stations, and the second incentive variable represents the total incentive income of power users.

[0116] Then, the target constraint function can be determined based on the maximum benefit, the first incentive variable and the second incentive variable. The target constraint condition can be determined based on the target constraint function. The target constraint condition is based on the maximum benefit to ensure that the total incentive benefit allocated does not exceed the maximum net benefit. That is, by calculating the total market benefit after the photovoltaic site's grid-connected surplus is absorbed, and the total cost change of users caused by absorbing photovoltaic power, the two are added together to form the total benefit source. Then, the actual allocated incentive benefit is calculated, and the economic feasibility of the allocation plan is ensured by the difference between the two.

[0117] After determining the target constraints, the objective function can be determined based on the first incentive variable, the second incentive variable, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the electricity user. The actual benefits of each entity (photovoltaic station benefits, user benefits) are weightedly combined with the contribution index to maximize the benefit utility of both parties while ensuring that the distribution result is in line with the contribution ratio.

[0118] Finally, the objective function is combined with the objective constraints to construct a Nash bargaining model. This model optimizes by maximizing the objective function while simultaneously satisfying the constraint that total revenue does not exceed an upper limit. By solving this model, the optimal revenue distribution plan between distributed photovoltaic stations and electricity users can be obtained, achieving a balance of interests for both parties.

[0119] It can be seen that by constructing a Nash bargaining model based on the contribution index, the problem of electricity market revenue distribution is transformed into a mathematical optimization problem, avoiding the subjectivity and inefficiency of traditional distribution methods. The Nash bargaining model not only ensures the feasibility of distribution under the total revenue constraint, but also realizes the fairness principle of distribution according to contribution through the contribution index, effectively motivating both parties to enhance the enthusiasm for collaborative consumption, and providing a scientific and efficient revenue distribution solution for the distributed energy market.

[0120] See Figure 5 , Figure 5 This is a structural diagram of a Nash bargaining model provided in an embodiment of the present application. As shown in the figure, the Nash bargaining model is used to solve the problem of profit distribution between distributed photovoltaics and electricity users. It includes: input parameters, objective functions, objective constraints, Lagrangian functions and profit output cores.

[0121] The input parameters, including maximum revenue, contribution index, and incentive variables, serve as the fundamental data for model calculations. Maximum revenue represents the upper limit of the total revenue achievable through the coordinated consumption of distributed photovoltaic power plants and electricity users within the target area, determined by factors such as system energy supply and demand and market transactions. The contribution index measures the contribution of photovoltaic power plants and electricity users, reflecting the weight of each entity in energy consumption. The incentive variable is the revenue allocation parameter to be optimized, linking the revenue of photovoltaic power plants and electricity users.

[0122] The objective function and objective constraints form the model optimization framework. The objective function integrates the incentive benefits and contribution indexes of PV and users in the form of a logarithmic weighted sum. The goal is to maximize the benefits of both parties, reflecting the game logic of distribution based on contribution. The objective constraints limit the total incentive benefits to no more than the maximum benefit, ensuring that the distribution is within the economically feasible region and avoiding over-distribution.

[0123] The Lagrange function is a solution tool. By introducing the Lagrange multiplier, the constrained objective function is transformed into an unconstrained optimization problem. It linearly combines the objective function with the constraints so that the optimization process can simultaneously meet the maximum utility and total amount constraints, providing a mathematical basis for deriving the benefit distribution equation.

[0124] Based on the Lagrangian function, the model calculates partial derivatives and simultaneous equations to ultimately output photovoltaic revenue (the primary revenue of M distributed photovoltaic stations) and user revenue (the secondary revenue of N electricity users). This allocation is strictly linked to the input parameters, satisfying the maximum revenue constraint while ensuring fairness through the contribution index. This enables the scientific application of the Nash bargaining model to the allocation of distributed energy revenue, providing a quantitative tool for coordinating the interests of energy entities in the electricity market environment.

[0125] S105. Solve the Nash bargaining model to obtain first benefits corresponding to the M distributed photovoltaic stations and second benefits corresponding to the N electricity users; the sum of the first benefits and the second benefits is the maximum benefit.

[0126] In the embodiment of the present application, the first benefit represents the total benefit ultimately allocated to M distributed photovoltaic stations, and the second benefit represents the total benefit ultimately allocated to N electricity users. The sum of the first benefit and the second benefit is the maximum benefit.

[0127] In a specific embodiment, the Nash bargaining model includes an objective function aimed at maximizing the utility of both parties' benefits, as well as constraints ensuring that the total distributed benefits do not exceed the maximum benefit. To achieve this, the Lagrange multiplier method can be used. By introducing the Lagrange multiplier λ, the constrained optimization problem is transformed into an unconstrained optimization problem. The Lagrange function L is constructed, and the objective function and constraint function are linearly combined. This allows the optimization process to simultaneously consider the maximization objective and the conditional constraints, thereby completing the solution of the Nash bargaining model and obtaining the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N electricity users.

[0128] It can be seen that the solution of the Nash bargaining model based on mathematical optimization theory has achieved the quantitative determination of the profit distribution plan between distributed photovoltaic stations and power users, avoiding the distribution deviation caused by subjective experience, and ensuring that the profit distribution is consistent with the contribution level of both parties and meets the maximum profit constraint of the system.

[0129] See Figure 6 , Figure 6 This is a system architecture diagram of a revenue distribution system for distributed photovoltaic and electricity users, provided in an embodiment of the present application. This revenue distribution system for distributed photovoltaic and electricity users is applied to a server and includes a substation identification module, a data processing module, and a model calculation module. The electricity market, as the external environment, provides market rules and transaction information for revenue distribution. The target area includes K substations, which serve as specific analysis units and include M distributed photovoltaic stations and N electricity users, forming the main participants in revenue distribution.

[0130] The substation identification module is responsible for identifying K substations within the target area, identifying the target substation with price anomalies from these K substations, and then obtaining the M distributed photovoltaic stations and N electricity users in the target substation as the main subjects for revenue distribution. The data processing module can obtain electricity market price data, photovoltaic station operation data, and user electricity consumption data corresponding to the target substation, and perform operations such as cleaning, integration, and conversion to extract valid data related to revenue distribution.

[0131] Based on the processed data, the model calculation module invokes and solves the Nash bargaining model to balance the interests of distributed photovoltaic stations and electricity users. Through model calculations, the module derives the primary benefits of M distributed photovoltaic stations, reflecting the benefits of participating in energy supply and consumption collaboration, and the secondary benefits of N electricity users, reflecting the returns from participating in distributed energy consumption. Ultimately, the module feeds the resulting benefits back to relevant sectors of the electricity market, ensuring a reasonable distribution of benefits between distributed photovoltaics and electricity users within the market environment. This promotes the effective consumption of distributed energy, the healthy operation of the electricity market, and the efficient and sustainable development of the energy system.

[0132] Optionally, the above step of solving the Nash bargaining model to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N power users may include the following steps:

[0133] A501. Construct a target solution formula based on the target function and the target constraint function in the Nash bargaining model; wherein the target solution formula is as follows:

[0134]

[0135] In the above formula, represents the Lagrange function; λ represents the Lagrange multiplier;

[0136] A502. Solve the target solution formula according to the target constraint condition to obtain the first benefit and the second benefit.

[0137] In a specific embodiment, the objective function and the objective constraint function in the Nash bargaining model are linearly combined to construct a target solution formula, namely the Lagrangian function L. This function integrates the contribution index, incentive benefits and maximum benefit constraints of distributed photovoltaic stations and power users, and uses the Lagrangian multiplier λ as a link to organically combine the goal of maximizing the benefit utility of both parties with the constraint that the total benefit does not exceed the upper limit.

[0138] After constructing the target solution formula, the target solution formula is solved according to the target constraint conditions to calculate the optimal values ​​of the first incentive variable and the second incentive variable, that is, the first income corresponding to the distributed photovoltaic station and the second income corresponding to the power user. The sum of the first income and the second income is equal to the maximum income of the target substation, which ensures the rationality and scientific nature of the income distribution plan in terms of the total economic volume.

[0139] It can be seen that the Lagrange multiplier method effectively balances the objective function and the constraints, which not only maximizes the benefit utility, but also ensures that the total distributed benefit does not exceed the maximum net benefit achievable by the system.

[0140] Optionally, the above step of solving the target solution formula according to the target constraint condition to obtain the first benefit and the second benefit may include the following steps:

[0141] B501. Perform a first solution operation on the target solution equation to obtain a first conditional equation. The first solution operation includes taking a partial derivative of the target solution equation with respect to the first excitation variable and setting the partial derivative equal to 0. The first conditional equation is as follows:

[0142]

[0143] B502. Perform a second solution operation on the target solution equation to obtain a second conditional equation; the second solution operation includes taking a partial derivative of the target solution equation with respect to the second excitation variable and setting it equal to 0; wherein the second conditional equation is as follows:

[0144]

[0145] B503. Determine a value corresponding to the Lagrange multiplier according to the first conditional equation, the second conditional equation, and the target constraint;

[0146] B504. Determine the first benefit according to the first conditional equation and the Lagrange multiplier;

[0147] B505. Determine the second benefit based on the second conditional equation and the Lagrange multiplier.

[0148] In a specific embodiment, a first conditional equation can be obtained by performing a first solving operation on the target solution formula, wherein the first solving operation includes taking the partial derivative of the target solution formula with respect to the first excitation variable and setting it equal to 0. A second conditional equation can be obtained by performing a second solving operation on the target solution formula, wherein the second solving operation includes taking the partial derivative of the target solution formula with respect to the second excitation variable and setting it equal to 0. It should be noted that the essence of the first solving operation and the second solving operation is to find the extreme value points of the function, and the first conditional equation obtained is to establish the relationship between the incentive income of the photovoltaic station and the contribution index, the market income and the Lagrange multiplier, and the second conditional equation is to establish the relationship between the incentive income of the power user and the contribution index, the market income and the Lagrange multiplier.

[0149] Then, the value corresponding to the Lagrange multiplier can be determined based on the first condition equation, the second condition equation, and the target constraint. Specifically, the first equation can be determined based on the first condition equation, that is, summing the distributed photovoltaic stations from 1 to M, where the first equation is as follows:

[0150]

[0151] In the above formula, π is Represents the maximum incentive benefit available.

[0152] The second equation can be determined based on the second conditional equation, that is, summing the power users from 1 to N, where the second equation is as follows:

[0153]

[0154] In the above formula, π is Represents the maximum incentive benefit available.

[0155] Determine the third equation, where the third equation is as follows:

[0156]

[0157] In the above formula, W represents the sum of the contribution indexes of distributed photovoltaic stations and electricity users, which is used to quantify the total contribution weight of all market players in the target area.

[0158] Since in the objective constraint function, The fourth equation can be determined according to the first equation, the second equation, and the third equation, wherein the fourth equation is as follows:

[0159] 2π=R+W / λ

[0160] According to the target constraint, when π=R, the optimal solution can be obtained. Therefore, the value of λ can be obtained, that is, λ=W / R.

[0161] After determining the corresponding Lagrange multiplier value, substituting it into the first conditional equation can calculate the primary benefit of the distributed photovoltaic station. Substituting it into the second conditional equation can calculate the secondary benefit of the electricity user. Because the solution process always adheres to the target constraints, the sum of the calculated primary and secondary benefits must equal the maximum benefit of the target substation, thus achieving precise benefit distribution based on mathematical optimization.

[0162] In summary, by implementing the embodiments of the present application, a target substation in the target area where abnormal price fluctuations occur within a preset time period is obtained; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N power users; K, M, and N are all positive integers; the electricity market price data, photovoltaic station operation data, and user electricity consumption data of the target substation within the preset time period are obtained; the maximum benefit is determined based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data; the maximum benefit represents the maximum value of the net benefit after the on-grid surplus of the distributed photovoltaic station in the target substation is absorbed by the power users, after deducting the incremental electricity purchase cost of the power users; a Nash bargaining model is established based on the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the power user; the Nash bargaining model is solved to obtain the first benefit corresponding to the M distributed photovoltaic stations and the second benefit corresponding to the N power users; the sum of the first benefit and the second benefit is the maximum benefit. It can be seen that by identifying price-abnormal areas and fusing multi-source data to calculate the maximum benefit of power consumption in the area, and constructing a Nash bargaining model based on the contribution index of distributed photovoltaic stations and power users, and solving the Nash bargaining model, a reasonable distribution of benefits between distributed photovoltaic stations and power users is achieved.

[0163] See Figure 7 , Figure 7 This is a schematic diagram of a profit distribution device for distributed photovoltaic and power users provided in an embodiment of the present application, which is applied to a server; the profit distribution device 700 for distributed photovoltaic and power users includes: a station detection module 701, a data acquisition module 702, a profit calculation module 703, a Nash bargaining model construction module 704, and a model solution module 705, wherein:

[0164] The substation detection module 701 is used to obtain a target substation in a target area where abnormal price fluctuations occur within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N electricity users; K, M, and N are all positive integers;

[0165] The data acquisition module 702 is used to obtain the power market price data, photovoltaic station operation data and user power consumption data of the target area within the preset time period;

[0166] The profit calculation module 703 is used to determine the maximum profit based on the electricity market price data, the photovoltaic station operation data and the user electricity consumption data; the maximum profit represents the maximum value of the net profit after the surplus power of the distributed photovoltaic station in the target area is consumed by the power users, minus the incremental power purchase cost of the power users;

[0167] The Nash bargaining model building module 704 is used to establish a Nash bargaining model according to the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station and the second contribution index corresponding to the power user;

[0168] The model solving module 705 is used to solve the Nash bargaining model to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N electricity users; the sum of the first benefits and the second benefits is the maximum benefit.

[0169] Optionally, the server is connected to the power market; the power market is used to obtain electricity price information; in terms of obtaining the target substation in the target area where abnormal price fluctuations occur within a preset time period, the substation detection module 701 is further specifically used to:

[0170] Obtaining, from the electricity market, a rolling clearing electricity price for each of the K substations in the target area within a preset future time period, to obtain K rolling clearing electricity price sequences;

[0171] Obtaining the day-ahead market clearing electricity price corresponding to the target area from the electricity market;

[0172] The target electricity area is obtained by determining an electricity area having abnormal price fluctuations among the K electricity areas according to the K rolling clearing electricity price sequences and the day-ahead market clearing electricity price.

[0173] Optionally, in determining the substation where abnormal price fluctuations occur among the K substations based on the K rolling clearing electricity price sequences and the day-ahead market clearing electricity price, and obtaining the target substation, the substation detection module 701 is further specifically configured to:

[0174] Determine the absolute value of the difference between each rolling clearing electricity price in the target rolling clearing electricity price sequence and the day-ahead market clearing electricity price to obtain a target difference sequence; the target rolling clearing electricity price sequence is any one of the K rolling clearing electricity price sequences;

[0175] If the absolute value of at least one difference in the target difference sequence exceeds a preset difference threshold, or if at least one rolling clearing electricity price in the target rolling clearing electricity price sequence is 0, the substation corresponding to the target rolling clearing electricity price sequence is determined as the target substation.

[0176] Optionally, the electricity market price data includes: medium- and long-term contract prices, day-ahead electricity prices, real-time electricity prices, mechanism electricity prices, and weighted average transaction electricity prices; the photovoltaic site operation data includes: medium- and long-term contract electricity volume, day-ahead electricity volume, and actual grid-connected electricity volume; the user electricity consumption data includes: increased electricity generation;

[0177] In determining the maximum benefit based on the power market price data, the photovoltaic station operation data, and the user power consumption data, the benefit calculation module 703 is further specifically configured to:

[0178] The maximum profit is determined based on the electricity market price data, the photovoltaic station operation data, the user electricity consumption data, and a preset first formula; wherein the preset first formula is as follows:

[0179]

[0180] In the above formula, R max Represents the maximum benefit; R m,t represents the consumption income of the on-grid surplus of distributed photovoltaic station m in period t after being consumed by users, the distributed photovoltaic station m is the mth distributed photovoltaic station among the M distributed photovoltaic stations, and the period t is the preset time period; ΔC n,t represents the incremental electricity purchase cost of power user n in the period t, where power user n represents the nth power user among the N power users;

[0181] The absorbed income is determined according to a preset second formula, which is as follows:

[0182]

[0183] In the above formula, R m,t represents the absorbed income; Q m,mid,t P represents the medium- and long-term contracted electricity volume of the distributed photovoltaic station m during the period t; m,mid,t represents the medium- and long-term contract price of the distributed photovoltaic station m during the period t; Q m,dd,t represents the day-ahead power consumption of the distributed photovoltaic station m during the period t; P da,t represents the day-ahead electricity price of the target area during the period t; β m represents the proportion of the actual grid-connected power of the distributed photovoltaic station m that participates in the real-time price linkage of the power market; Q m,t represents the actual grid-connected power of the distributed photovoltaic station m during the period t; P rt,t represents the real-time electricity price of the target area during the period t; m,mcrepresents the mechanism electricity price of the distributed photovoltaic station m; represents the weighted average transaction price of electricity in the target area;

[0184] The incremental electricity purchase cost is determined according to a preset third formula, which is as follows:

[0185] ΔC n,t =P n,rt,t Q n,m,t

[0186] In the above formula, ΔC n,t represents the incremental electricity purchase cost; P n,rt,t represents the real-time electricity price of the power user n in the target area during the time period t; Q n,m,t It represents the increased power generation of the distributed photovoltaic station m used by the power user n during the period t.

[0187] Optionally, in terms of establishing a Nash bargaining model based on the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the power user, the Nash bargaining model construction module 704 is further specifically configured to:

[0188] determining a first stimulus variable and a second stimulus variable;

[0189] An objective constraint function is determined according to the maximum benefit, the first incentive variable, and the second incentive variable; wherein the objective constraint function is as follows:

[0190]

[0191] In the above formula, Represents the first excitation variable and the second excitation variable The objective constraint function constructed; R represents the maximum benefit; π n,m,t represents the incentive income allocated by the distributed photovoltaic power station m to the power user n during the period t; ΔR m,t C represents the market revenue of the distributed photovoltaic station m after the surplus power of the distributed photovoltaic station m in the period t is consumed by the users; n,t C represents the total electricity purchase cost of the power user n after consuming distributed electricity during the period t; n,t,0 represents the total electricity purchase cost of the power user n before consuming distributed electricity in the period t;

[0192] Determining target constraint conditions according to the target constraint function;

[0193] An objective function is determined according to the first incentive variable, the second incentive variable, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user; wherein the objective function is as follows:

[0194]

[0195] In the above formula, Represents the first excitation variable and the second excitation variable The objective function constructed; ω m,t represents the first contribution index of the distributed photovoltaic station m in the period t; ω n,t represents the second contribution index of the power user n in the time period t;

[0196] The Nash bargaining model is established according to the target constraint and the target function; wherein the Nash bargaining model is as follows:

[0197]

[0198] In the above formula, represents maximizing the objective function; represents the target constraint.

[0199] Optionally, in solving the Nash bargaining model to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N power users, the model solving module 705 is further specifically configured to:

[0200] A target solution formula is constructed according to the target function and the target constraint function in the Nash bargaining model; wherein the target solution formula is as follows:

[0201]

[0202] In the above formula, represents the Lagrange function; λ represents the Lagrange multiplier;

[0203] The target solution formula is solved according to the target constraint condition to obtain the first benefit and the second benefit.

[0204] Optionally, in solving the target solution formula according to the target constraint condition to obtain the first benefit and the second benefit, the model solving module 705 is further specifically configured to:

[0205] A first solving operation is performed on the target solution equation to obtain a first conditional equation; the first solving operation includes taking a partial derivative of the target solution equation with respect to the first excitation variable and setting the partial derivative equal to 0; wherein the first conditional equation is as follows:

[0206]

[0207] A second solution operation is performed on the target solution equation to obtain a second conditional equation; the second solution operation includes taking a partial derivative of the target solution equation with respect to the second excitation variable and setting the partial derivative equal to 0; wherein the second conditional equation is as follows:

[0208]

[0209] Determining a value corresponding to the Lagrange multiplier according to the first condition equation, the second condition equation, and the target constraint;

[0210] determining the first benefit according to the first conditional equation and the Lagrange multiplier;

[0211] The second benefit is determined according to the second conditional equation and the Lagrange multiplier.

[0212] The profit distribution device 700 for distributed photovoltaic and power users described in this application can obtain a target substation in a target area where abnormal price fluctuations occur within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic sites and N power users; K, M, and N are all positive integers; obtain electricity market price data, photovoltaic site operation data, and user electricity consumption data of the target substation within the preset time period; determine the maximum profit based on the electricity market price data, the photovoltaic site operation data, and the user electricity consumption data; the maximum profit represents the maximum net profit after the profit of the distributed photovoltaic site on the grid in the target substation is absorbed by the power users, after deducting the incremental power purchase cost of the power users; establish a Nash bargaining model based on the maximum profit, the first contribution index corresponding to the distributed photovoltaic site, and the second contribution index corresponding to the power users; solve the Nash bargaining model to obtain the first profit corresponding to the M distributed photovoltaic sites and the second profit corresponding to the N power users; the sum of the first profit and the second profit is the maximum profit. It can be seen that by identifying price-abnormal areas and fusing multi-source data to calculate the maximum benefit of power consumption in the area, and constructing a Nash bargaining model based on the contribution index of distributed photovoltaic stations and power users, and solving the Nash bargaining model, a reasonable distribution of benefits between distributed photovoltaic stations and power users is achieved.

[0213] See Figure 8 , Figure 8 : is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, memory, and communication interface may be interconnected via a bus. The one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for performing the following steps:

[0214] Obtain a target substation in a target area that experiences abnormal price fluctuations within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N electricity users; K, M, and N are all positive integers;

[0215] Obtaining electricity market price data, photovoltaic station operation data, and user electricity consumption data for the target area within the preset time period;

[0216] Determine the maximum benefit based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data; the maximum benefit represents the maximum value of the net benefit after the on-grid surplus of the distributed photovoltaic station in the target area is consumed by the electricity users, minus the incremental electricity purchase cost of the electricity users;

[0217] Establishing a Nash bargaining model based on the maximum benefit, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user;

[0218] The Nash bargaining model is solved to obtain a first benefit corresponding to the M distributed photovoltaic stations and a second benefit corresponding to the N power users; the sum of the first benefit and the second benefit is the maximum benefit.

[0219] The electronic device described in the present application can obtain a target substation in a target area where abnormal price fluctuations occur within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N power users; K, M, and N are all positive integers; obtain electricity market price data, photovoltaic station operation data, and user electricity consumption data of the target substation within the preset time period; determine the maximum benefit based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data; the maximum benefit represents the maximum value of the net benefit after the distributed photovoltaic station's on-grid surplus in the target substation is absorbed by the power user, after deducting the power user's incremental electricity purchase cost; establish a Nash bargaining model based on the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the power user; solve the Nash bargaining model to obtain the first benefit corresponding to the M distributed photovoltaic stations and the second benefit corresponding to the N power users; the sum of the first benefit and the second benefit is the maximum benefit. It can be seen that by identifying price-abnormal areas and fusing multi-source data to calculate the maximum benefit of power consumption in the area, and constructing a Nash bargaining model based on the contribution index of distributed photovoltaic stations and power users, and solving the Nash bargaining model, a reasonable distribution of benefits between distributed photovoltaic stations and power users is achieved.

[0220] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0221] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0222] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0223] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also be present in a terminal device or a management device as discrete components.

[0224] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0225] The modules / units included in the devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0226] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A method for distributing income to distributed photovoltaic and electricity users, characterized in that: Applied to a server; the method comprises: Obtain a target substation in a target area that experiences abnormal price fluctuations within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N electricity users; K, M, and N are all positive integers; Obtaining electricity market price data, photovoltaic station operation data, and user electricity consumption data for the target area within the preset time period; Determine the maximum benefit based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data; the maximum benefit represents the maximum value of the net benefit after the on-grid surplus of the distributed photovoltaic station in the target area is consumed by the electricity users, minus the incremental electricity purchase cost of the electricity users; Establishing a Nash bargaining model based on the maximum benefit, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user; The Nash bargaining model is solved to obtain a first benefit corresponding to the M distributed photovoltaic stations and a second benefit corresponding to the N power users; the sum of the first benefit and the second benefit is the maximum benefit.

2. The method according to claim 1, wherein The server is connected to the electricity market; The electricity market is used to obtain electricity price information; The step of obtaining target areas in the target area that have experienced abnormal price fluctuations within a preset time period includes: Obtaining, from the electricity market, a rolling clearing electricity price for each of the K substations in the target area within a preset future time period, to obtain K rolling clearing electricity price sequences; Obtaining the day-ahead market clearing electricity price corresponding to the target area from the electricity market; The target electricity area is obtained by determining an electricity area having abnormal price fluctuations among the K electricity areas according to the K rolling clearing electricity price sequences and the day-ahead market clearing electricity price.

3. The method according to claim 2, wherein The step of determining a substation having abnormal price fluctuations among the K substations based on the K rolling clearing electricity price sequences and the day-ahead market clearing electricity price to obtain the target substation includes: Determine the absolute value of the difference between each rolling clearing electricity price in the target rolling clearing electricity price sequence and the day-ahead market clearing electricity price to obtain a target difference sequence; the target rolling clearing electricity price sequence is any one of the K rolling clearing electricity price sequences; If the absolute value of at least one difference in the target difference sequence exceeds a preset difference threshold, or if at least one rolling clearing electricity price in the target rolling clearing electricity price sequence is 0, the substation corresponding to the target rolling clearing electricity price sequence is determined as the target substation.

4. The method according to any one of claims 1 to 3, wherein The electricity market price data includes: medium- and long-term contract prices, day-ahead electricity prices, real-time electricity prices, mechanism electricity prices, and weighted average transaction electricity prices; the photovoltaic site operation data includes: medium- and long-term contract electricity volume, day-ahead electricity volume, and actual grid-connected electricity volume; the user electricity consumption data includes: increased electricity generation; The determining of the maximum benefit based on the electricity market price data, the photovoltaic station operation data, and the user electricity consumption data includes: The maximum profit is determined based on the electricity market price data, the photovoltaic station operation data, the user electricity consumption data, and a preset first formula; wherein the preset first formula is as follows: In the above formula, R max Represents the maximum benefit; R m,t represents the consumption income of the on-grid surplus of distributed photovoltaic station m in period t after being consumed by users, the distributed photovoltaic station m is the mth distributed photovoltaic station among the M distributed photovoltaic stations, and the period t is the preset time period; ΔC n,t represents the incremental electricity purchase cost of power user n in the period t, where power user n represents the nth power user among the N power users; The absorbed income is determined according to a preset second formula, which is as follows: In the above formula, R m,t represents the absorbed income; Q m,mod,t P represents the medium- and long-term contracted electricity volume of the distributed photovoltaic station m during the period t; m,mid,t represents the medium- and long-term contract price of the distributed photovoltaic station m during the period t; Q m,dd,t represents the day-ahead power consumption of the distributed photovoltaic station m during the period t; P da,t represents the day-ahead electricity price of the target area during the period t; β m represents the proportion of the actual grid-connected power of the distributed photovoltaic station m that participates in the real-time price linkage of the power market; Q m,t represents the actual grid-connected power of the distributed photovoltaic station m during the period t; P rt,t represents the real-time electricity price of the target area during the period t; m,mc represents the mechanism electricity price of the distributed photovoltaic station m; represents the weighted average transaction price of electricity in the target area; The incremental electricity purchase cost is determined according to a preset third formula, which is as follows: ΔC n,t =P n,rt,t ·Q n,m,t In the above formula, ΔC n,t represents the incremental electricity purchase cost; P n,rt,t represents the real-time electricity price of the power user n in the target area during the time period t; Q n,m,t It represents the increased power generation of the distributed photovoltaic station m used by the power user n during the period t.

5. The method according to claim 4, wherein The Nash bargaining model is established according to the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station, and the second contribution index corresponding to the power user, including: determining a first stimulus variable and a second stimulus variable; An objective constraint function is determined according to the maximum benefit, the first incentive variable, and the second incentive variable; wherein the objective constraint function is as follows: In the above formula, Represents the first excitation variable and the second excitation variable The objective constraint function constructed; R represents the maximum benefit; π n,m,t represents the incentive income allocated by the distributed photovoltaic power station m to the power user n during the period t; ΔR m,t C represents the market revenue of the distributed photovoltaic station m after the surplus power of the distributed photovoltaic station m in the period t is consumed by the users; n,t C represents the total electricity purchase cost of the power user n after consuming distributed electricity during the period t; n,t,0 represents the total electricity purchase cost of the power user n before consuming distributed electricity in the period t; Determining target constraint conditions according to the target constraint function; An objective function is determined according to the first incentive variable, the second incentive variable, a first contribution index corresponding to the distributed photovoltaic station, and a second contribution index corresponding to the power user; wherein the objective function is as follows: In the above formula, Represents the first excitation variable and the second excitation variable The objective function constructed; ω m,t represents the first contribution index of the distributed photovoltaic station m in the period t; ω n,t represents the second contribution index of the power user n in the time period t; The Nash bargaining model is established according to the target constraint and the target function; wherein the Nash bargaining model is as follows: In the above formula, represents maximizing the objective function; represents the target constraint.

6. The method according to claim 5, wherein Solving the Nash bargaining model to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N power users includes: A target solution formula is constructed according to the target function and the target constraint function in the Nash bargaining model; wherein the target solution formula is as follows: In the above formula, represents the Lagrange function; λ represents the Lagrange multiplier; The target solution formula is solved according to the target constraint condition to obtain the first benefit and the second benefit.

7. The method according to claim 6, wherein Solving the target solution formula according to the target constraint condition to obtain the first benefit and the second benefit includes: A first solving operation is performed on the target solution equation to obtain a first conditional equation; the first solving operation includes taking a partial derivative of the target solution equation with respect to the first excitation variable and setting the partial derivative equal to 0; wherein the first conditional equation is as follows: A second solution operation is performed on the target solution equation to obtain a second conditional equation; the second solution operation includes taking a partial derivative of the target solution equation with respect to the second excitation variable and setting the partial derivative equal to 0; wherein the second conditional equation is as follows: Determining a value corresponding to the Lagrange multiplier according to the first condition equation, the second condition equation, and the target constraint; determining the first benefit according to the first conditional equation and the Lagrange multiplier; The second benefit is determined according to the second conditional equation and the Lagrange multiplier.

8. A profit distribution device for distributed photovoltaic and electricity users, characterized in that: Applied to the server; the profit distribution device for distributed photovoltaic and power users includes: a station detection module, a data acquisition module, a profit calculation module, a Nash bargaining model construction module, and a model solution module, wherein, The substation detection module is used to obtain a target substation in a target area that has abnormal price fluctuations within a preset time period; the target area includes K substations; the target substation is one of the K substations; the target substation includes: M distributed photovoltaic stations and N electricity users; K, M, and N are all positive integers; The data acquisition module is used to obtain the power market price data, photovoltaic station operation data and user power consumption data of the target area within the preset time period; The profit calculation module is used to determine the maximum profit based on the electricity market price data, the photovoltaic station operation data and the user electricity consumption data; the maximum profit represents the maximum value of the net profit after the surplus power of the distributed photovoltaic station in the target area is consumed by the power users, minus the incremental power purchase cost of the power users; The Nash bargaining model building module is used to establish a Nash bargaining model according to the maximum benefit, the first contribution index corresponding to the distributed photovoltaic station and the second contribution index corresponding to the power user; The model solving module is used to solve the Nash bargaining model to obtain the first benefits corresponding to the M distributed photovoltaic stations and the second benefits corresponding to the N electricity users; the sum of the first benefits and the second benefits is the maximum benefit.

9. An electronic device, characterized in that: include: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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