Shared energy storage dynamic configuration method and system based on income distribution strategy

Abstract

The invention relates to the technical field of energy storage analysis, and discloses a shared energy storage dynamic configuration method and system based on a profit distribution strategy. The method comprises the following steps: acquiring the electricity demand and photovoltaic data of a user, quantitatively analyzing the energy storage use contribution degree, and establishing income distribution parameters; the method comprises the following steps: collecting photovoltaic historical data, decomposing uncertainty into three components, and establishing a differentiated demand prediction model; constructing a source-load matching degree evaluation system, and calculating real-time, time-period and daily matching degrees to obtain a capacity configuration demand matrix; inputting a multi-user collaborative optimization algorithm, and obtaining an optimal capacity distribution scheme through a profit distribution strategy and benefit maximization processing; and establishing a dynamic adjustment mechanism, monitoring the income in real time, redistributing the capacity, and forming a feedback optimization mechanism and an operation configuration report. According to the method, the problems of undefined income distribution of multiple investment subjects and difficulty in photovoltaic uncertainty energy storage configuration are solved, and the operation efficiency and income distribution fairness of the shared energy storage system are improved.

Description

Technical Field

[0001] This application relates to the field of energy storage analysis technology, and in particular to a method and system for dynamic configuration of shared energy storage based on a revenue distribution strategy. Background Technology

[0002] With the rapid development of distributed photovoltaic power generation technology and the continuous decline in the cost of energy storage systems, shared energy storage has begun to be applied as an emerging business model in industrial parks and industrial areas. Existing shared energy storage technologies mainly adopt a centralized energy storage configuration approach, providing services such as peak shaving, load regulation, and emergency backup power to multiple users by constructing large-capacity energy storage facilities. Traditional energy storage configuration methods are typically designed based on historical load data and simple capacity aggregation principles. Revenue distribution is mainly based on a simple allocation according to the user's investment ratio or electricity consumption ratio, and the operation and control of the energy storage system adopts fixed charging and discharging strategies and capacity allocation rules.

[0003] Existing shared energy storage technologies suffer from several shortcomings. Firstly, the profit-sharing mechanism among multiple investors is imperfect, lacking a fair allocation method that considers users' actual contributions and service value, leading to potential conflicts of interest and disputes among investors. Secondly, the energy storage capacity configuration methods are relatively crude, failing to fully consider the uncertainties of photovoltaic power generation and the dynamic changes in user load, resulting in low utilization efficiency and low return on investment for the energy storage system. Thirdly, there is a lack of effective dynamic adjustment mechanisms; once an energy storage system is built and put into operation, its capacity configuration and operating strategies are difficult to flexibly adjust according to actual operating conditions and changes in user demand. Summary of the Invention

[0004] This application provides a shared energy storage dynamic configuration method and system based on a revenue distribution strategy, which is used to solve the problems of unclear revenue distribution among multiple investors and difficulties in configuring photovoltaic energy storage under uncertainty, thereby improving the operating efficiency and revenue distribution fairness of the shared energy storage system.

[0005] Firstly, this application provides a method for dynamic configuration of shared energy storage based on a revenue-sharing strategy, the method comprising:

[0006] Step S1: Obtain electricity demand data and photovoltaic power generation data of each user in the shared energy storage system, evaluate the energy storage usage of each user through quantitative analysis of electricity contribution, and establish a user energy storage usage contribution matrix and basic parameters for revenue distribution.

[0007] Step S2: Collect historical data of distributed photovoltaic power generation and load demand data, decompose the uncertainty of photovoltaic power generation into deterministic components, periodic fluctuation components and random fluctuation components, establish a differentiated energy storage demand prediction model based on the load characteristic data of each user, and obtain the distribution of energy storage demand uncertainty.

[0008] Step S3: Based on the uncertainty distribution of energy storage demand, construct a source-load matching degree evaluation system. Through real-time matching degree, time period matching degree and daily matching degree calculation and processing, combined with the energy storage service contribution of each user, obtain the energy storage capacity configuration demand matrix.

[0009] Step S4: Input the energy storage capacity configuration demand matrix into the multi-user collaborative optimization algorithm, and obtain the optimal energy storage capacity allocation scheme for each user through the capacity allocation mechanism driven by the revenue distribution strategy and the calculation of maximizing energy storage benefits.

[0010] Step S5: Establish a dynamic adjustment mechanism based on the optimal energy storage capacity allocation scheme. Through real-time revenue monitoring and capacity redistribution processing, establish a revenue allocation strategy feedback optimization mechanism to obtain the dynamic operation configuration results and revenue allocation optimization report of the shared energy storage system.

[0011] Secondly, this application provides a shared energy storage dynamic configuration system based on a revenue distribution strategy, the shared energy storage dynamic configuration system based on a revenue distribution strategy includes:

[0012] The evaluation module is used to acquire electricity demand data and photovoltaic power generation data of each user in the shared energy storage system, evaluate the energy storage usage of each user through quantitative analysis of electricity contribution, and establish a user energy storage usage contribution matrix and basic parameters for revenue distribution.

[0013] A module is established to collect historical data and load demand data of distributed photovoltaic power generation. The uncertainty of photovoltaic power generation is decomposed into deterministic components, periodic fluctuation components and random fluctuation components. A differentiated energy storage demand prediction model is established based on the load characteristic data of each user to obtain the distribution of energy storage demand uncertainty.

[0014] The calculation module is used to construct a source-load matching degree evaluation system based on the uncertainty distribution of energy storage demand. Through real-time matching degree, time period matching degree and daily matching degree calculation and processing, combined with the energy storage service contribution of each user, the energy storage capacity configuration demand matrix is ​​obtained.

[0015] The input module is used to input the energy storage capacity configuration demand matrix into the multi-user collaborative optimization algorithm, and obtain the optimal energy storage capacity allocation scheme for each user through the capacity allocation mechanism driven by the revenue distribution strategy and the calculation and processing of maximizing energy storage benefits.

[0016] The allocation module is used to establish a dynamic adjustment mechanism based on the optimal energy storage capacity allocation scheme. Through real-time revenue monitoring and capacity redistribution processing, a revenue allocation strategy feedback optimization mechanism is established to obtain the dynamic operation configuration results and revenue allocation optimization report of the shared energy storage system.

[0017] Thirdly, a shared energy storage dynamic configuration device based on a revenue distribution strategy is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the shared energy storage dynamic configuration device based on the revenue distribution strategy to execute the aforementioned shared energy storage dynamic configuration method based on the revenue distribution strategy.

[0018] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the aforementioned shared energy storage dynamic configuration method based on a revenue distribution strategy.

[0019] The technical solution provided in this application establishes a user energy storage usage contribution matrix and basic parameters for revenue allocation through quantitative analysis of electricity consumption contribution, solving the problem of unclear revenue allocation basis in traditional methods and realizing a fair allocation mechanism based on actual contribution. The three-component decomposition of photovoltaic power generation uncertainty transforms complex uncertainty into quantifiable deterministic components, periodic fluctuation components, and random fluctuation components, providing an accurate data foundation for energy storage demand forecasting. The establishment of a differentiated energy storage demand forecasting model fully considers the differences in load characteristic data among users, avoiding the drawbacks of a one-size-fits-all allocation method and improving the targeting and effectiveness of capacity allocation. The source-load matching degree evaluation system comprehensively quantifies the degree of supply and demand coordination through a three-level evaluation structure of real-time matching degree, time-period matching degree, and daily matching degree, providing a scientific basis for optimized energy storage capacity allocation. The multi-user collaborative optimization algorithm, through a capacity allocation mechanism driven by revenue allocation strategies, achieves overall system benefit optimization while maximizing the interests of each user, solving the coordination problem of conflicting interests among multiple stakeholders. The dynamic adjustment mechanism and the feedback optimization mechanism of the revenue distribution strategy form a complete closed-loop control system, which enables the system to adaptively adjust according to the actual operating conditions, significantly improving the operational flexibility and economic benefits of the shared energy storage system.

[0020] By introducing a revenue allocation strategy as a constraint and component of the objective function, an organic combination of technological optimization and economic incentives is achieved. The algorithm's significant contribution lies in its use of the Lagrange multiplier method to handle complex constraints, ensuring that capacity allocation achieves economic optimality while satisfying physical constraints. Simultaneously, the calculation of maximizing energy storage benefits, through dual optimization of capacity utilization evaluation and revenue efficiency analysis, guarantees that the allocation scheme is both technically rational and economically feasible. The algorithmic characteristics of the revenue allocation strategy feedback optimization mechanism endow the system with self-learning and adaptive capabilities. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an embodiment of the shared energy storage dynamic configuration method based on a revenue distribution strategy in this application.

[0023] Figure 2 This is a schematic diagram of an embodiment of a shared energy storage dynamic configuration system based on a revenue distribution strategy in this application.

[0024] Figure 3 This is a schematic block diagram of the shared energy storage dynamic configuration device based on the revenue distribution strategy in an embodiment of the present invention. Detailed Implementation

[0025] This application provides a method and system for dynamic configuration of shared energy storage based on a revenue-sharing strategy. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0026] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1One embodiment of the shared energy storage dynamic configuration method based on a revenue distribution strategy in this application includes:

[0027] Step S1: Obtain electricity demand data and photovoltaic power generation data of each user in the shared energy storage system, evaluate the energy storage usage of each user through quantitative analysis of electricity contribution, and establish a user energy storage usage contribution matrix and basic parameters for revenue distribution.

[0028] Step S2: Collect historical data of distributed photovoltaic power generation and load demand data, decompose the uncertainty of photovoltaic power generation into deterministic components, periodic fluctuation components and random fluctuation components, establish a differentiated energy storage demand prediction model based on the load characteristic data of each user, and obtain the distribution of energy storage demand uncertainty.

[0029] Step S3: Construct a source-load matching degree evaluation system based on the uncertainty distribution of energy storage demand. Through real-time matching degree, time period matching degree and daily matching degree calculation and processing, combined with the energy storage service contribution of each user, obtain the energy storage capacity configuration demand matrix.

[0030] Step S4: Input the energy storage capacity configuration demand matrix into the multi-user collaborative optimization algorithm, and obtain the optimal energy storage capacity allocation scheme for each user through the capacity allocation mechanism driven by the revenue distribution strategy and the calculation of maximizing energy storage benefits.

[0031] Step S5: Establish a dynamic adjustment mechanism based on the optimal energy storage capacity allocation scheme. Through real-time revenue monitoring and capacity redistribution processing, establish a revenue allocation strategy feedback optimization mechanism to obtain the dynamic operation configuration results and revenue allocation optimization report of the shared energy storage system.

[0032] It is understood that the implementing entity of this application can be a shared energy storage dynamic configuration system based on a revenue distribution strategy, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0033] Specifically, by acquiring electricity demand data and photovoltaic power generation data from each user in the shared energy storage system, a quantitative analysis of electricity contribution is conducted. Specifically, the demand contribution is calculated based on each user's daily electricity consumption, peak-valley electricity ratio, and electricity time distribution. The dependence of each user on the energy storage system's peak-shaving and valley-filling services is quantified by statistically analyzing the proportion of electricity consumption during peak hours and the peak-shaving demand of energy storage. The supply contribution analyzes each user's photovoltaic power generation, grid-connected electricity, and energy storage charging volume, determined by calculating the ratio of the charging volume provided by users to the energy storage system to the total charging volume. The usage contribution is based on statistics of users' energy storage usage frequency, usage duration, and discharge volume, quantified by accumulating the discharge service duration and electricity obtained by users from the energy storage system. Subsequently, the three contribution values ​​are weighted and averaged to obtain a comprehensive energy storage usage contribution, with weighting coefficients determined according to the value importance of different service types. Finally, a revenue distribution ratio coefficient is established based on the total revenue of the energy storage system and the comprehensive energy storage usage contribution of each user, forming a user energy storage usage contribution matrix and basic parameters for revenue distribution.

[0034] Historical data on distributed photovoltaic (PV) power generation and load demand data were collected, and the uncertainty of PV power generation was decomposed and processed. First, the historical PV power generation data was processed using a moving average filter. The moving average filter eliminates short-term fluctuations by taking the arithmetic mean of values ​​within a continuous time window of the time series data. Combined with weather forecast data and solar radiation angle calculations, long-term power generation trends were extracted to obtain the deterministic component of PV power generation. Next, the historical data was input into a discrete Fourier transform (DFT) algorithm for spectral analysis. The DFT converts the time-domain signal into a frequency-domain representation, identifying the dominant frequency components of the 24-hour daily cycle and the seasonal monthly cycle. The periodic power generation pattern was reconstructed using an inverse Fourier transform to obtain the periodic fluctuation component. Then, the residual sequence after removing the deterministic and periodic fluctuation components was fitted with a probability density function using a Gaussian mixture model. The Gaussian mixture model represents the complex probability distribution as a weighted combination of multiple Gaussian distributions. A probability model for random events such as cloud cover and equipment failure was established to obtain the random fluctuation component. Based on the historical electricity consumption patterns of each user in the revenue distribution basic parameters and the load characteristic data in the user energy storage contribution matrix, a personalized energy storage demand function was constructed, including charging period priority weights and discharge demand intensity coefficients. Finally, the three components are coupled with the differentiated energy storage demand prediction model in a time series calculation. The probability density function of energy storage capacity demand for each user at different time periods is generated by Monte Carlo simulation. Monte Carlo simulation simulates the probabilistic behavior of complex systems through a large number of random samples to obtain the uncertainty distribution of energy storage demand.

[0035] A source-load matching evaluation system is constructed based on the uncertainty distribution of energy storage demand. Real-time matching degree is calculated by comparing the instantaneous deviation between photovoltaic power generation and load demand at each moment; a smaller deviation indicates a higher degree of supply-demand matching. Time-period matching degree is calculated by weighting the real-time matching degree over hourly periods, with weights determined based on the importance of each period and user demand density. Daily matching degree involves performing variance analysis on the time-period matching degree over a 24-hour period; variance analysis assesses the dispersion of the data, with smaller variance indicating a more stable intraday matching degree. Then, based on the comprehensive energy storage contribution of each user in the user energy storage contribution matrix, combined with the matching degrees at the three levels, a multi-level weighted fusion algorithm is used for quantification. This algorithm assigns weights to evaluation indicators at different levels according to their importance and performs comprehensive calculations to obtain the energy storage service contribution of each user. Finally, the energy storage service contribution and the uncertainty distribution of energy storage demand are processed through matrix operations, and the energy storage capacity configuration demand matrix is ​​obtained through weighted allocation of capacity demand.

[0036] The energy storage capacity allocation demand matrix is ​​input into a multi-user collaborative optimization algorithm for processing. The algorithm first models the problem, setting the energy storage capacity allocation for each user at different time periods as decision variables. It establishes a set of constraints, including system capacity balance constraints, user capacity upper and lower limits constraints, and time coupling constraints, constructing a multi-objective optimization function aimed at maximizing the total revenue of each user. Then, based on the revenue allocation ratio coefficient in the basic parameters of revenue allocation, a revenue allocation strategy-driven capacity allocation mechanism is constructed. The capacity allocation priority weights are calculated based on users' historical revenue contributions and current demand intensity, forming a capacity allocation priority sequence. Next, the priority sequence is input as a constraint into the optimization model, and iteratively solved using the Lagrange multiplier method. The Lagrange multiplier method is a mathematical method for handling constrained optimization problems; by introducing Lagrange multipliers, the constraints are integrated into the objective function for solving, calculating the optimal energy storage capacity allocation value for each user while satisfying the system's total capacity constraint. Finally, the initial capacity allocation scheme is processed to maximize energy storage benefits. Through capacity utilization evaluation and revenue efficiency analysis, the scheme is optimized and adjusted to obtain the optimal energy storage capacity allocation scheme for each user.

[0037] A dynamic adjustment mechanism is established based on the optimal energy storage capacity allocation scheme. A real-time revenue monitoring mechanism is established to collect data on energy storage service revenue, electricity sales revenue, and cost expenditures from each user. Revenue calculations are performed and compared with the expected revenue values ​​in the basic revenue allocation parameters to obtain the revenue deviation value for each user. When the revenue deviation value exceeds a preset threshold, a capacity reallocation mechanism is triggered. This dynamically adjusts the energy storage capacity allocation scheme by reallocating energy storage capacity quotas and adjusting charging and discharging priorities. Simultaneously, the revenue deviation value and capacity utilization efficiency data are input into the revenue allocation strategy feedback optimization mechanism. By adjusting the allocation ratio coefficients and weight parameters in the basic revenue allocation parameters, the optimized revenue allocation strategy parameters are obtained. Finally, comprehensive analysis data including detailed revenue allocation for each user, capacity usage statistics, and system operating efficiency indicators is generated, forming a dynamic operation configuration result and revenue allocation optimization report for the shared energy storage system.

[0038] In one specific embodiment, step S1 includes:

[0039] Statistics are compiled on each user's daily electricity consumption, peak-valley electricity consumption ratio, and electricity consumption time distribution to calculate each user's contribution to the energy storage system's peak shaving and valley filling services.

[0040] Analyze the photovoltaic power generation, grid-connected power, and energy storage charging volume of each user, and calculate the contribution of each user to the supply of energy storage system charging services;

[0041] Based on statistics of users’ energy storage usage frequency, usage duration and discharge amount, calculate each user’s contribution to the energy storage system’s discharge service.

[0042] The demand contribution, supply contribution, and usage contribution are weighted and averaged to obtain the comprehensive energy storage usage contribution of each user.

[0043] Based on the total revenue of the energy storage system and the comprehensive energy storage contribution of each user, a revenue distribution ratio coefficient is established to obtain the user energy storage contribution matrix and basic parameters for revenue distribution.

[0044] Specifically, the dependence of users on peak-shaving and valley-filling services of energy storage systems is quantified by statistically analyzing each user's daily electricity consumption, peak-valley electricity consumption ratio, and electricity consumption time distribution. Daily electricity consumption statistics include the user's total electricity consumption over 24 hours, calculated by collecting electricity consumption data every 15 minutes from smart meters and summing the data. The peak-valley electricity consumption ratio is calculated by dividing the user's electricity consumption period into peak and valley periods. Peak periods typically refer to the high-price period from 8:00 AM to 8:00 PM, while valley periods refer to the low-price period at night. The ratio is determined by calculating the ratio of a user's electricity consumption during peak periods to that during valley periods. Electricity consumption time distribution statistics analyze the distribution patterns of users' electricity intensity at different times, assessing the intensity of their demand for energy storage peak-shaving services by analyzing the temporal concentration and dispersion of user electricity consumption curves. The demand contribution is calculated by dividing a user's electricity consumption during peak periods by the sum of all users' peak-time electricity consumption; a higher value indicates a higher demand contribution from the user to energy storage peak-shaving and valley-filling services.

[0045] Supply contribution analysis assesses each user's contribution to the energy storage system's charging service by analyzing their photovoltaic (PV) power generation, grid-connected power, and energy storage charging volume. PV power generation is recorded by the power metering devices of the user's distributed PV system, including DC power generation and AC power generation converted by the inverter. Grid-connected power refers to the electricity directly transmitted from the user's PV system to the grid, measured by a bidirectional electricity meter. Energy storage charging volume is calculated by charging the shared energy storage device from the user's PV system, with each user's charging contribution recorded by the energy storage system's charging controller. Supply contribution is calculated by dividing the amount of electricity charged by a single user to the energy storage system by the total amount charged by all users, reflecting the user's proportion of the energy storage system's energy reserve supply.

[0046] Contribution to energy storage usage is quantified based on user frequency, duration, and discharge volume statistics. Frequency is calculated by tracking the number of times a user discharges from the system within a given time period, obtained through timestamps of each discharge operation recorded by the energy storage management system. Duration is calculated by summing the total time a user spends using the system, determined by adding the durations of each discharge operation. Discharge volume is calculated by tracking the total electricity received from the system, recorded by the system's metering devices and accumulated. Contribution to energy storage usage is calculated by dividing a single user's discharge volume by the total discharge volume received by all users; a higher value indicates a greater contribution to the system's discharge service.

[0047] The weighted average calculation integrates demand contribution, supply contribution, and usage contribution to obtain the overall energy storage usage contribution for each user. The weighting coefficients in the weighted average are determined based on the value importance of different service types in the shared energy storage system. The demand contribution weight reflects the economic value of peak shaving and valley filling services, the supply contribution weight reflects the importance of charging services to the energy source of the energy storage system, and the usage contribution weight represents the direct value of discharging services to the user. The overall energy storage usage contribution is obtained by multiplying each of the three contributions by its corresponding weighting coefficient and then summing the results. The calculation result reflects the user's overall participation and value contribution in the shared energy storage system.

[0048] The revenue distribution ratio coefficient is calculated based on the total revenue of the energy storage system and the comprehensive energy storage usage contribution of each user. The total revenue of the energy storage system includes the sum of multiple revenue sources such as peak shaving and valley filling service revenue, electricity ancillary service revenue, and capacity leasing revenue. The revenue distribution ratio coefficient is calculated by dividing the comprehensive energy storage usage contribution of a single user by the sum of the comprehensive energy storage usage contributions of all users, ensuring that the revenue distribution ratio is proportional to the actual contribution of each user. The user energy storage usage contribution matrix organizes the contribution data of all users in a row and column format, with rows representing different users and columns representing different contribution types. The matrix elements contain specific contribution values. The basic parameters for revenue distribution include the revenue distribution ratio coefficient, weight coefficient, and adjustment factor for each user, forming the basic dataset for subsequent revenue distribution strategies.

[0049] In one specific embodiment, step S2 includes:

[0050] The historical data of distributed photovoltaic power generation is processed by moving average filtering. Based on weather forecast data and solar radiation angle calculation, the long-term power generation trend and solar radiation intensity benchmark value are extracted to obtain the deterministic component of photovoltaic power generation.

[0051] Historical data of distributed photovoltaic power generation is input into a discrete Fourier transform algorithm for spectrum analysis to identify the dominant frequency components of the 24-hour daily cycle and the seasonal monthly cycle. The periodic power generation pattern is reconstructed through inverse Fourier transform to obtain the periodic fluctuation components of photovoltaic power generation.

[0052] Based on the residual sequence after subtracting deterministic and periodic fluctuation components from historical data of distributed photovoltaic power generation, a probability density function is fitted using a Gaussian mixture model to establish a probability model of random events such as cloud cover and equipment failure, thereby obtaining the random fluctuation components of photovoltaic power generation.

[0053] Based on the historical electricity consumption patterns of each user in the basic parameters of revenue distribution and the load characteristic data in the user energy storage contribution matrix, a personalized energy storage demand function is constructed, which includes the priority weight of charging periods and the discharge demand intensity coefficient, and a differentiated energy storage demand prediction model is established for each user.

[0054] The deterministic component, periodic fluctuation component, and random fluctuation component of photovoltaic power generation are coupled with a differentiated energy storage demand prediction model for time series calculation. The probability density function of energy storage capacity demand for each user at different time periods is generated by Monte Carlo simulation method, and the uncertainty distribution of energy storage demand is obtained.

[0055] Specifically, moving average filtering extracts deterministic components by smoothing historical distributed photovoltaic (PV) power generation data. The moving average filtering algorithm selects data points within a fixed time window for arithmetic mean calculation, typically set to 7 or 30 days. The average value is calculated progressively at each time point using a sliding window. The data processing involves arranging historical PV power generation data in a time series, selecting a window length, calculating the arithmetic mean of the data within the window, using the result as the filtered output value for that time point, and then moving the window to the next time point and repeating the calculation. Weather forecast data includes parameters such as cloud cover, temperature, humidity, and wind speed. Predicted weather parameters for the next 72 hours are obtained using numerical weather prediction models from meteorological departments. Solar radiation angle calculation determines the solar altitude angle and azimuth angle based on geographical location (latitude and longitude coordinates, date, and time). The solar altitude angle is calculated using trigonometric functions of the solar declination angle, geographical latitude, and solar hour angle. Long-term power generation trends are identified by analyzing the changing patterns of the data after moving average filtering, thus recognizing seasonal variation patterns and annual increasing trends. The benchmark value for solar radiation intensity is determined by statistically analyzing the average solar radiation intensity data for the same period in history, combined with cloud cover predictions from weather forecasts.

[0056] The Discrete Fourier Transform (DFT) algorithm processes historical photovoltaic (PV) power generation data for spectral analysis to identify periodic fluctuation patterns. The DFT converts the time-domain signal into a frequency-domain representation, analyzing the signal's spectral characteristics by calculating the amplitude and phase of different frequency components. The data processing first inputs the PV power generation time-series data into the DFT function, yielding complex frequency coefficients, each corresponding to a specific frequency's sine and cosine components. 24-hour daily cycle identification is achieved by finding the frequency peaks in the spectrum corresponding to the 24-hour cycle, which correspond to the daily sunrise and sunset light variation patterns. Seasonal monthly cycle identification is achieved by analyzing frequency components in the spectrum corresponding to approximately 30-day cycles, reflecting the impact of monthly climate changes on PV power generation. The inverse Fourier transform reconstructs the periodic power generation pattern by selecting the coefficients of the dominant frequency components, converting these frequency components back to the time-domain signal, and reconstructing the PV power generation pattern containing the main periodic characteristics. The selection of dominant frequency components is based on an amplitude threshold, retaining frequency components whose amplitude exceeds a specific proportion of the total energy.

[0057] Gaussian mixture models (GMMs) are used to model stochastic fluctuations in residual sequences. The residual sequence is derived by subtracting deterministic and periodic fluctuation components from the original photovoltaic (PV) power generation data, encompassing power generation fluctuations caused by random factors such as cloud cover and equipment failure. The GMM represents a complex probability distribution as a linear combination of multiple Gaussian distributions, each corresponding to a specific type of random event. The data processing involves initializing model parameters, setting the number, mean, variance, and initial weights of the Gaussian components, and then iteratively optimizing the parameters using an expectation-maximization (EM) algorithm. The EEM algorithm includes an expectation step and a maximization step. The expectation step calculates the posterior probability of each data point belonging to each Gaussian component, and the maximization step updates the parameters of each Gaussian component based on the posterior probability. Cloud cover events are identified by analyzing negative abrupt changes in the residual sequence, corresponding to a rapid decline in PV power generation. Equipment failure events are identified by detecting abnormally low or zero values ​​in the residual sequence, reflecting the fault status of PV modules or inverters.

[0058] Personalized energy storage demand functions are constructed based on revenue distribution parameters and data from the user energy storage contribution matrix. Historical electricity consumption patterns include users' daily electricity consumption curves, peak-valley electricity consumption habits, and load variation patterns, derived by analyzing the statistical characteristics of users' past electricity consumption data. Load characteristic data includes users' load power factors, harmonic content, and volatility indicators, reflecting the technical characteristics of users' electrical equipment.

[0059] Load characteristic data includes the user load's power factor, harmonic content, and volatility index, reflecting the technical characteristics of the user's electrical equipment. Specifically, the power factor is the ratio of active power consumed by the user load to apparent power, ranging from 0 to 1. A power factor closer to 1 indicates higher energy efficiency of the electrical equipment; users with low power factors require reactive power compensation support from energy storage systems. Harmonic content refers to the proportion of non-sinusoidal current generated by the user's electrical equipment to the fundamental current, quantified by measuring the total distortion rate of each harmonic current. Nonlinear loads such as rectifiers and frequency converters generate higher harmonic content, and users with high harmonic content require higher power quality regulation capabilities from energy storage systems. Volatility index refers to the degree of drastic change in user load power over time, measured by calculating the standard deviation, coefficient of variation, and peak-to-valley difference of load power. Impulsive and intermittent loads have larger volatility indices, and users with high volatility require stronger rapid response and capacity regulation capabilities from energy storage systems.

[0060] The priority weight of charging periods is determined based on the matching degree between users' electricity demand and photovoltaic power generation periods. When users' peak electricity consumption coincides with peak photovoltaic power generation, the priority weight of energy storage charging is higher. The discharge demand intensity coefficient is calculated by analyzing users' electricity gaps and electricity price sensitivity at different times. Periods with large electricity gaps and high electricity price sensitivity correspond to higher discharge demand intensity coefficients. The differentiated energy storage demand forecasting model establishes an independent demand forecasting function for each user, and the function parameters are adjusted based on the user's personalized characteristics.

[0061] The time-series coupled calculation combines the three components of photovoltaic power generation with a differentiated energy storage demand forecasting model. The coupled calculation process recombines the deterministic, periodic, and random fluctuation components according to the time series to obtain a complete photovoltaic power generation time series. The differentiated energy storage demand forecasting model outputs the predicted energy storage demand values ​​for each user at different time periods, including the temporal distribution of charging and discharging demand. The Monte Carlo simulation method simulates system behavior under uncertainty conditions through extensive random sampling. The simulation process includes generating random number sequences, sampling according to probability distributions, performing system model calculations, and analyzing statistical results. The energy storage capacity demand probability density function describes the probability distribution characteristics of the energy storage capacity required by each user at a specific time period, and is obtained by statistically analyzing the frequency distribution of user energy storage demand from a large number of simulation results. The energy storage demand uncertainty distribution integrates the energy storage capacity demand probability density functions of all users to form a comprehensive probability distribution describing the demand uncertainty of the entire shared energy storage system.

[0062] In one specific embodiment, step S3 includes:

[0063] The instantaneous deviation between photovoltaic power generation and load demand is calculated at each moment based on the uncertainty distribution of energy storage demand to obtain the real-time matching degree; the real-time matching degree is weighted and averaged by hourly time period to obtain the time period matching degree; the time period matching degree is subjected to variance analysis within a 24-hour period to obtain the daily matching degree.

[0064] Based on the comprehensive energy storage contribution of each user in the user energy storage contribution matrix, combined with real-time matching degree, time period matching degree and daily matching degree, the energy storage service contribution of each user is quantified through a multi-level weighted fusion algorithm.

[0065] The energy storage service contribution of each user and the uncertainty distribution of energy storage demand are processed by matrix operation, and the energy storage capacity configuration demand matrix is ​​obtained by weighted allocation calculation of capacity demand.

[0066] Specifically, photovoltaic (PV) power generation data is derived from the real-time power output of PV systems associated with each user in the energy storage demand uncertainty distribution, including the sum of AC power converted from the DC power of each PV module by the inverter. Load demand power is obtained by summing the instantaneous power consumption of all users in the energy storage demand uncertainty distribution, including the user's base load power and the supplementary power obtained through energy storage system discharge. The instantaneous deviation value is calculated by subtracting the load demand power from the PV power generation at each moment; a positive value indicates excess PV power generation requiring energy storage charging, while a negative value indicates that load demand exceeds power generation requiring energy storage discharge. The real-time matching degree is calculated by dividing the absolute value of the instantaneous deviation value by the load demand power; the closer the value is to zero, the higher the degree of supply-demand matching, and the larger the value, the more severe the supply-demand imbalance. The time-period matching degree calculation involves weighted averaging of the real-time matching degree within each hour. The weighting coefficient is determined based on the frequency and importance of user energy storage service usage within that time period, with higher usage frequency periods receiving greater weight. The weighted average calculation process multiplies the real-time matching degree of each 15-minute time point by its corresponding weight, sums the results, and then divides by the total weighted sum to obtain the hourly time-period matching degree. The daily matching degree is calculated by performing variance analysis on the matching degree of each time period over 24 hours. The variance analysis calculates the sum of squares of the deviations between the matching degree of each time period and the daily average matching degree, divided by the number of time periods. The smaller the variance value, the more stable the matching degree within the day.

[0067] A multi-level weighted fusion algorithm comprehensively quantifies the user energy storage contribution matrix and three levels of matching degree data. The comprehensive energy storage contribution data in the user energy storage contribution matrix includes quantified values ​​for each user's contribution in terms of demand, supply, and usage, reflecting the user's overall participation in the energy storage system. The multi-level weighted fusion algorithm first assigns different weight coefficients to real-time matching degree, time-period matching degree, and daily matching degree. The real-time matching degree weight reflects the importance of immediate supply and demand balance, the time-period matching degree weight reflects phased operational stability, and the daily matching degree weight represents long-term operational reliability. The weight coefficients are dynamically adjusted based on the shared energy storage system's operating strategy and user demand characteristics; the real-time matching degree weight is higher during peak periods, while the daily matching degree weight is larger during stable operating periods. The fusion algorithm multiplies each user's comprehensive energy storage contribution degree with the weighted matching degree index. The product result reflects the user's service contribution level to the energy storage system under specific matching degree conditions. The contribution level of energy storage services is obtained by normalizing the product results of each user. The normalization process divides the product results of each user by the sum of the product results of all users to ensure that the sum of the energy storage service contribution levels of all users is equal to 1.

[0068] Matrix operations process mathematically combines the energy storage service contribution of each user with the energy storage demand uncertainty distribution. The energy storage demand uncertainty distribution data structure is a multi-dimensional matrix, where rows represent different users, columns represent different time periods, and matrix elements represent the probability density value of energy storage capacity demand for that user in a specific time period. The energy storage service contribution data structure is a vector, with vector elements corresponding to the contribution values ​​of each user. The matrix operation process performs a broadcast multiplication operation between the energy storage service contribution vector and the energy storage demand uncertainty distribution matrix, that is, multiplying each user's contribution value by the corresponding demand probability density value. The weighted allocation calculation of capacity demand is performed by summing the matrix operation results by rows and columns. The row summation yields the overall energy storage capacity demand weight for each user, and the column summation yields the overall energy storage capacity allocation demand for each time period. The energy storage capacity allocation demand matrix is ​​formed by reorganizing the weighted allocation calculation results. Rows represent user identifiers, columns represent time period identifiers, and matrix element values ​​represent the energy storage capacity allocation demand of a specific user in a specific time period.

[0069] In one specific embodiment, step S4 includes:

[0070] The energy storage capacity configuration demand matrix is ​​input into a multi-user collaborative optimization algorithm for problem modeling. The decision variables and constraints for energy storage capacity allocation of each user are set, and a multi-objective optimization function with the goal of maximizing the benefits of each user is established to obtain a multi-user resource competition optimization model.

[0071] Based on the revenue distribution ratio coefficient of each user in the basic parameters of revenue distribution, a capacity allocation mechanism driven by revenue distribution strategy is constructed. The capacity allocation priority weight is calculated according to the user's historical revenue contribution and current demand intensity to obtain the capacity allocation priority sequence.

[0072] The capacity allocation priority sequence is input as a constraint into the multi-user resource competition optimization model. The Lagrange multiplier method is used for iterative solution to calculate the optimal energy storage capacity allocation value for each user under the constraint of the total system capacity, thus obtaining the initial capacity allocation scheme.

[0073] The initial capacity allocation scheme is processed to maximize energy storage benefits. The scheme is then optimized and adjusted through capacity utilization evaluation and revenue efficiency analysis to obtain the optimal energy storage capacity allocation scheme for each user.

[0074] Specifically, the energy storage capacity allocation demand matrix is ​​input into a multi-user collaborative optimization algorithm for data structure transformation and variable definition. The data in the energy storage capacity allocation demand matrix is ​​organized by user and time period. The algorithm first parses the matrix structure to identify the number of users and time periods, and then defines energy storage capacity allocation decision variables for each user in each time period. The decision variables represent the energy storage capacity allocation obtained by a specific user in a specific time period, and the variable values ​​are set to non-negative real numbers that do not exceed the user's maximum capacity demand. Constraints include total system capacity constraints, user capacity upper and lower limit constraints, and time coupling constraints. The total system capacity constraint ensures that the sum of capacity allocations for all users in the same time period does not exceed the total capacity of the energy storage system. The user capacity upper and lower limit constraints restrict the capacity allocation for each user to a reasonable range, and the time coupling constraints ensure that the capacity allocation for users in consecutive time periods has a smooth transition characteristic. The multi-objective optimization function is constructed by comprehensively combining the revenue functions of each user. Each user's revenue function includes an energy storage service revenue item, a usage cost item, and a capacity utilization efficiency item. The revenue function parameters are determined based on the user's historical revenue data and cost statistics.

[0075] The capacity allocation mechanism driven by the revenue distribution strategy is constructed based on the weighted calculation of the revenue distribution ratio coefficient of each user in the basic parameters of revenue distribution. The revenue distribution ratio coefficient reflects the user's share of revenue distribution in the shared energy storage system, and its value comes from the user's comprehensive energy storage usage contribution calculated in step S1. The user's historical revenue contribution is calculated by statistically analyzing the economic value generated by the user's past participation in energy storage services, including electricity cost savings from peak shaving and valley filling services, revenue from selling electricity to the grid, and compensation revenue from participating in ancillary services. The current demand intensity is calculated based on the real-time capacity demand data of users in the energy storage capacity configuration demand matrix. Users with higher demand intensity receive higher priority in capacity allocation. The capacity allocation priority weight is calculated by weighting the user's historical revenue contribution and current demand intensity. The weight calculation process standardizes the historical revenue contribution and multiplies it by the historical contribution weight coefficient, and standardizes the current demand intensity and multiplies it by the demand intensity weight coefficient. The two are added together to obtain the comprehensive priority weight. The capacity allocation priority sequence is obtained by sorting the priority weights of all users. Users with higher weight values ​​are ranked at the front of the sequence and have the right to priority allocation of energy storage capacity.

[0076] The Lagrange multiplier method iteratively solves the problem by inputting the capacity allocation priority sequence as a constraint into a multi-user resource competition optimization model for mathematical calculations. The Lagrange multiplier method is a classic algorithm for handling constrained optimization problems. It integrates the constraints into the objective function by introducing Lagrange multipliers, forming a Lagrange function. The Lagrange function contains a linear combination of the original objective function and the constraints, with the Lagrange multipliers acting as weighting coefficients for the constraints. The iterative solution process first initializes the initial values ​​of the decision variables and the Lagrange multipliers. Then, it calculates the partial derivatives of the Lagrange function with respect to the decision variables and multipliers, setting these partial derivatives to zero to obtain a system of first-order optimality condition equations. Newton's method or gradient descent is used to solve the system of equations, iteratively updating the decision variables and multiplier values ​​until convergence. The total system capacity constraint is satisfied during the iteration process by adjusting the Lagrange multiplier values. When the total capacity allocation in a certain period exceeds the system capacity limit, the corresponding multiplier value is increased to reduce the capacity allocation in that period. The initial capacity allocation scheme is determined by the decision variable values ​​after iterative convergence, and the scheme includes the specific capacity allocation values ​​for each user in each time period.

[0077] The energy storage benefit maximization calculation process optimizes and adjusts the initial capacity allocation scheme. Capacity utilization evaluation quantifies the ratio of actual usage to allocated capacity for each user. Utilization data is derived from the actual operation records of the energy storage system, including user charging and discharging frequency, duration, and power consumption statistics. Revenue efficiency analysis assesses the economic rationality of the allocation scheme by calculating the ratio of actual user revenue to capacity allocation costs. Actual revenue includes direct and indirect economic benefits from energy storage services, while capacity allocation costs include depreciation of energy storage equipment, operation and maintenance costs, and opportunity costs. Scheme optimization and adjustment reallocates capacity based on the analysis results of capacity utilization and revenue efficiency. Users with low utilization and poor revenue efficiency have their capacity allocation reduced, while users with high utilization and good revenue efficiency have their capacity allocation increased. The adjustment process employs marginal utility analysis to calculate the marginal impact of capacity adjustments on the total system revenue, selecting the adjustment scheme with the highest marginal revenue. The optimal energy storage capacity allocation scheme is formed after multiple rounds of optimization and adjustment, where the capacity allocation for each user satisfies both the revenue allocation strategy requirements and the overall system benefit maximization objective.

[0078] In one specific embodiment, the energy storage capacity allocation demand matrix is ​​input into a multi-user collaborative optimization algorithm for problem modeling. Decision variables and constraints for energy storage capacity allocation are set for each user, and a multi-objective optimization function is established with the goal of maximizing the revenue of each user. This results in a multi-user resource competition optimization model, including:

[0079] Based on the capacity demand value of each user in the energy storage capacity configuration demand matrix, the energy storage capacity allocation amount of each user in different time periods is set as the decision variable, and the value range and boundary constraints of the decision variable are defined to obtain the set of energy storage capacity allocation decision variables.

[0080] Based on the total capacity limit of the shared energy storage system and the maximum capacity demand of each user, system capacity balance constraints, user capacity upper and lower limit constraints, and time coupling constraints are established to obtain a set of energy storage capacity allocation constraints.

[0081] By weighting and combining the energy storage service revenue function, usage cost function, and capacity utilization efficiency function of each user, a multi-objective optimization function is constructed with the goal of maximizing the total revenue of each user, resulting in a multi-user resource competition optimization model.

[0082] Specifically, variables are defined and ranges are set based on the capacity demand data of each user in the energy storage capacity configuration demand matrix. The data structure of the energy storage capacity configuration demand matrix includes user identifiers, time period identifiers, and corresponding capacity demand values. The algorithm first parses the matrix dimensions to determine the total number of users and the total number of time periods, and then creates independent decision variables for each user in each time period. The decision variable represents the amount of capacity allocated to a specific user from the shared energy storage system in a specific time period. Variable naming uses a double subscript form to distinguish combinations of different users and time periods. The value range is defined based on two levels: physical constraints and economic constraints. Physical constraints ensure that the lower limit of the decision variable is not less than zero and the upper limit does not exceed the user's maximum capacity demand in that time period. Economic constraints further limit the actual value range of the variable based on the user's payment ability and expected returns. Boundary constraints prevent unreasonable allocation results during the optimization process by setting strict upper and lower bounds for the variables. The upper bound is determined based on the user's historical maximum capacity usage record, and the lower bound is set based on the user's minimum capacity guarantee demand. The set of decision variables is formed by organizing the decision variables of all users in all time periods into a vector form. The order of the vector elements is determined by user priority and time period order, forming the basic data structure for algorithm optimization.

[0083] The energy storage capacity allocation constraint set establishes mathematical constraint expressions based on the physical limitations and operating rules of the shared energy storage system. System capacity balance constraints ensure that the total capacity allocation for all users within any given time period does not exceed the total capacity limit of the energy storage system. This constraint expression is achieved by summing the decision variables of all users within that time period and comparing them with the total system capacity using an inequality comparison. The total system capacity data is derived from the technical specifications and installed capacity statistics of the energy storage equipment, including the rated capacity of battery packs, the power limits of inverters, and the parallel capacity of energy storage units. User capacity upper and lower limit constraints set the maximum and minimum capacity allocation boundaries for each user. The upper limit constraint is determined based on the user's maximum capacity demand and payment ability, while the lower limit constraint is set based on the user's basic capacity guarantee requirements. Time coupling constraints address the continuity and smoothness requirements of capacity allocation between adjacent time periods. These constraints avoid drastic fluctuations by limiting the magnitude of changes in user capacity allocation between consecutive time periods. The magnitude limit is determined based on the charging and discharging rate of the energy storage system and the user load variation characteristics. The constraint set combines multiple constraint expressions into a constraint matrix. Rows in the matrix represent different constraint conditions, columns represent decision variables, and matrix elements represent the coefficients of the variables in their corresponding constraints.

[0084] The multi-objective optimization function is constructed by mathematically modeling and comprehensively combining the economic objectives of each user. The energy storage service revenue function calculates the economic benefits users gain from using energy storage services, including electricity cost savings from peak shaving and valley filling services, revenue from energy storage discharge services, and compensation revenue from participating in ancillary power services. The parameters of the revenue function are determined based on electricity price data, service rates, and user electricity consumption patterns. The cost function calculates the fees users need to pay for using energy storage services, including capacity leasing fees, frequency-related service fees, and premium fees for increased capacity allocation priority. The parameters of the cost function are determined based on the operating cost allocation of the energy storage system and the user's service level. The capacity utilization efficiency function evaluates the actual usage effect of the allocated capacity by users, measuring allocation efficiency by calculating the ratio of actual user-used capacity to allocated capacity. The efficiency function parameters are determined based on historical user usage data and the accuracy of capacity demand forecasting. The weighted combination process linearly combines the three functions by setting weight coefficients. These weight coefficients reflect the importance of different objectives in the overall user benefit, and the weight values ​​are personalized according to user type, business characteristics, and risk preferences. The multi-objective optimization function obtains the overall objective function at the system level by summing the weighted combination functions of all users. The objective function expression reflects the coordinated unity of maximizing the benefits of each user, while taking into account the balance of interests among users and the optimization of the overall system benefits.

[0085] In one specific embodiment, step S5 includes:

[0086] A real-time revenue monitoring mechanism is established based on the optimal energy storage capacity allocation scheme for each user. The revenue is calculated and processed by collecting data on energy storage service revenue, electricity sales revenue and cost expenditure of each user, and compared with the expected revenue value in the basic parameters of revenue allocation to obtain the revenue deviation value of each user.

[0087] Capacity redistribution is determined based on the revenue deviation of each user. When the revenue deviation exceeds a preset threshold of 15%, a capacity adjustment mechanism is triggered. Dynamic capacity redistribution is carried out by reallocating energy storage capacity quotas and adjusting charging and discharging priorities to obtain an adjusted energy storage capacity allocation scheme.

[0088] The revenue deviation value and capacity utilization efficiency data of each user are input into the revenue distribution strategy feedback optimization mechanism for strategy update processing. By adjusting the distribution ratio coefficient and weight parameter in the basic parameters of revenue distribution, the optimized revenue distribution strategy parameters are obtained.

[0089] Based on the adjusted energy storage capacity allocation scheme and optimized revenue distribution strategy parameters, comprehensive analysis data is generated, including revenue distribution details for each user, capacity usage statistics, and system operation efficiency indicators, resulting in dynamic operation configuration results and revenue distribution optimization reports for the shared energy storage system.

[0090] Specifically, data collection and processing are performed based on the optimal energy storage capacity allocation scheme for each user. Energy storage service revenue data collection monitors the economic benefits generated by each user through peak shaving, load shifting, and demand management services using energy storage systems. Revenue data includes avoided peak demand costs, electricity cost savings from optimized usage periods, and subsidies received from demand response participation. Electricity sales revenue data collects statistics on the revenue each user receives from feeding electricity back into the grid through the energy storage system, including grid connection tariffs, peak shaving and frequency regulation service fees, and ancillary service compensation. Electricity sales revenue data is obtained through settlement records from electricity metering devices and the electricity trading platform. Cost expenditure data records the costs incurred by each user using energy storage services, including capacity leasing fees, charging and discharging operation fees, equipment depreciation amortization, and operation and maintenance service fees. Cost data is derived through financial accounting and cost allocation calculations. Revenue calculation processing adds each user's energy storage service revenue and electricity sales revenue to obtain the total revenue, then subtracts the cost expenditure to obtain the net revenue. The comparative analysis calculates the difference between each user's actual net income and the expected income value in the income allocation baseline parameters. The expected income value is determined based on the user's overall energy storage contribution and historical income level. The income deviation value is obtained by calculating the difference between the actual income and the expected income and then dividing it by the expected income to obtain the relative deviation ratio. A positive deviation indicates that the actual income exceeds expectations, while a negative deviation indicates that the actual income is lower than expected.

[0091] The capacity reallocation process is based on threshold comparisons and trigger mechanisms using the revenue deviation values ​​of each user. The revenue deviation value is compared to a preset threshold; when the absolute value of a user's revenue deviation exceeds 15% of the preset threshold, it is considered an abnormal deviation requiring adjustment. After the capacity adjustment mechanism is triggered, the causes of the deviation are first analyzed, including changes in user demand, fluctuations in market electricity prices, equipment operating conditions, and the influence of other user behaviors. Energy storage capacity quota reallocation is adjusted based on the deviation analysis results. Users with negative revenue deviations have their capacity quotas increased to improve their revenue levels, while users with positive and excessively high revenue deviations have their capacity quotas appropriately reduced to balance the interests of all parties. Charging and discharging priority adjustment reorders users based on their revenue deviation and the urgency of their capacity needs, giving higher priority to users with larger deviations to quickly correct their deviations. Dynamic capacity reallocation involves re-running the multi-user collaborative optimization algorithm, using the adjusted capacity quotas and priorities as new constraints to calculate a new capacity allocation scheme. The adjusted energy storage capacity allocation scheme includes new capacity allocation values ​​for each user at each time period, taking into account both the need to correct revenue deviations and maintaining overall system efficiency.

[0092] The revenue distribution strategy feedback optimization mechanism uses the revenue deviation value and capacity utilization efficiency data of each user as input to update strategy parameters. Capacity utilization efficiency data is calculated by statistically analyzing the actual usage of allocated capacity for each user, including indicators such as user charging and discharging frequency, capacity utilization rate, energy conversion efficiency, and service response time. The strategy update process analyzes the correlation between revenue deviation and utilization efficiency data to identify key factors and influencing mechanisms leading to deviations. The allocation ratio coefficient in the basic revenue distribution parameters is adjusted based on the user's actual contribution and revenue deviation; users with high contribution but large revenue deviation have their allocation ratio coefficient increased, while users with low contribution but high utilization efficiency also have their allocation ratio appropriately increased. Weight parameter adjustments involve the redistribution of weights across three dimensions: demand contribution, supply contribution, and usage contribution, adjusting the weight values ​​according to the degree of influence of each dimension on revenue deviation. The optimized revenue distribution strategy parameters undergo parameter validation and sensitivity analysis to ensure the rationality and stability of the adjustments, keeping the parameter adjustment range within a reasonable range to avoid system oscillations.

[0093] The comprehensive analysis of data generates reports based on the adjusted energy storage capacity allocation scheme and optimized revenue distribution strategy parameters. Detailed revenue distribution for each user includes actual revenue, expected revenue, revenue deviation, adjusted allocation ratio, and expected revenue improvement. This detailed data provides a basis for users to evaluate participation effectiveness and adjust investment strategies. Capacity usage statistics summarize operational data for each user, including allocated capacity, actual usage, utilization efficiency, number of charge / discharge cycles, and service duration. These statistics reflect the activity level and usage patterns of users participating in shared energy storage. System operational efficiency indicators include comprehensive evaluation indicators such as overall capacity utilization, energy conversion efficiency, load balance, revenue distribution fairness, and user satisfaction. These indicator values ​​are derived through weighted calculation and statistical analysis. Dynamic operation configuration results describe the adjustment of energy storage capacity allocation, trends in user participation patterns, and the degree of improvement in overall system performance.

[0094] The above describes the shared energy storage dynamic configuration method based on a revenue sharing strategy in the embodiments of this application. The following describes the shared energy storage dynamic configuration system based on a revenue sharing strategy in the embodiments of this application. Please refer to [link / reference]. Figure 2 One embodiment of the shared energy storage dynamic configuration system based on a revenue distribution strategy in this application includes:

[0095] The evaluation module is used to acquire electricity demand data and photovoltaic power generation data of each user in the shared energy storage system, evaluate the energy storage usage of each user through quantitative analysis of electricity contribution, and establish a user energy storage usage contribution matrix and basic parameters for revenue distribution.

[0096] A module is established to collect historical data and load demand data of distributed photovoltaic power generation. The uncertainty of photovoltaic power generation is decomposed into deterministic components, periodic fluctuation components and random fluctuation components. A differentiated energy storage demand prediction model is established based on the load characteristic data of each user to obtain the distribution of energy storage demand uncertainty.

[0097] The calculation module is used to construct a source-load matching degree evaluation system based on the uncertainty distribution of energy storage demand. Through real-time matching degree, time period matching degree and daily matching degree calculation and processing, combined with the energy storage service contribution of each user, the energy storage capacity configuration demand matrix is ​​obtained.

[0098] The input module is used to input the energy storage capacity configuration demand matrix into the multi-user collaborative optimization algorithm, and obtain the optimal energy storage capacity allocation scheme for each user through the capacity allocation mechanism driven by the revenue distribution strategy and the calculation and processing of maximizing energy storage benefits.

[0099] The allocation module is used to establish a dynamic adjustment mechanism based on the optimal energy storage capacity allocation scheme. Through real-time revenue monitoring and capacity redistribution processing, a revenue allocation strategy feedback optimization mechanism is established to obtain the dynamic operation configuration results and revenue allocation optimization report of the shared energy storage system.

[0100] above Figure 2 The shared energy storage dynamic configuration system based on the revenue distribution strategy in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The shared energy storage dynamic configuration device based on the revenue distribution strategy in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0101] Reference Figure 3 This invention also provides a shared energy storage dynamic configuration device based on a revenue distribution strategy. This shared energy storage dynamic configuration device can be a server, and its internal structure can be as follows: Figure 3 As shown, the shared energy storage dynamic configuration device based on a revenue-sharing strategy includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed as a computer, provides computing and control capabilities. The memory of the shared energy storage dynamic configuration device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the shared energy storage dynamic configuration device based on a revenue-sharing strategy stores the data corresponding to this embodiment. The network interface of the shared energy storage dynamic configuration device based on a revenue-sharing strategy is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0102] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the shared energy storage dynamic configuration device based on the revenue distribution strategy applied thereto.

[0103] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the shared energy storage dynamic configuration method based on the revenue distribution strategy.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a shared energy storage dynamic configuration device (which may be a personal computer, server, or network device, etc.) based on a revenue distribution strategy to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic configuration of shared energy storage based on a revenue distribution strategy, characterized in that, The method includes: Step S1: Obtain electricity demand data and photovoltaic power generation data of each user in the shared energy storage system, evaluate the energy storage usage of each user through quantitative analysis of electricity contribution, and establish a user energy storage usage contribution matrix and basic parameters for revenue distribution. Step S2: Collect historical data of distributed photovoltaic power generation and load demand data, decompose the uncertainty of photovoltaic power generation into deterministic components, periodic fluctuation components and random fluctuation components, establish a differentiated energy storage demand prediction model based on the load characteristic data of each user, and obtain the distribution of energy storage demand uncertainty. Step S3: Based on the uncertainty distribution of energy storage demand, construct a source-load matching degree evaluation system. Through real-time matching degree, time period matching degree and daily matching degree calculation and processing, combined with the energy storage service contribution of each user, obtain the energy storage capacity configuration demand matrix. Step S4: Input the energy storage capacity configuration demand matrix into the multi-user collaborative optimization algorithm, and obtain the optimal energy storage capacity allocation scheme for each user through the capacity allocation mechanism driven by the revenue distribution strategy and the calculation of maximizing energy storage benefits. Step S5: Establish a dynamic adjustment mechanism based on the optimal energy storage capacity allocation scheme. Through real-time revenue monitoring and capacity redistribution processing, establish a revenue allocation strategy feedback optimization mechanism to obtain the dynamic operation configuration results and revenue allocation optimization report of the shared energy storage system.

2. The shared energy storage dynamic configuration method based on a revenue distribution strategy according to claim 1, characterized in that, Step S1 includes: Statistics are compiled on each user's daily electricity consumption, peak-valley electricity consumption ratio, and electricity consumption time distribution to calculate each user's contribution to the energy storage system's peak shaving and valley filling services. Analyze the photovoltaic power generation, grid-connected power, and energy storage charging volume of each user, and calculate the contribution of each user to the supply of energy storage system charging services; Based on statistics of users’ energy storage usage frequency, usage duration and discharge amount, calculate each user’s contribution to the energy storage system’s discharge service. The demand contribution, supply contribution, and usage contribution are weighted and averaged to obtain the comprehensive energy storage usage contribution of each user. Based on the total revenue of the energy storage system and the comprehensive energy storage contribution of each user, a revenue distribution ratio coefficient is established to obtain the user's energy storage contribution matrix and basic parameters for revenue distribution.

3. The shared energy storage dynamic configuration method based on a revenue distribution strategy according to claim 2, characterized in that, Step S2 includes: The historical data of distributed photovoltaic power generation is processed by moving average filtering. Based on weather forecast data and solar radiation angle calculation, the long-term power generation trend and solar radiation intensity benchmark value are extracted to obtain the deterministic component of photovoltaic power generation. The historical data of distributed photovoltaic power generation is input into the discrete Fourier transform algorithm for spectrum analysis processing to identify the dominant frequency components of the 24-hour daily cycle and the seasonal monthly cycle. The periodic power generation pattern is reconstructed through the inverse Fourier transform to obtain the periodic fluctuation components of photovoltaic power generation. Based on the residual sequence after subtracting the deterministic component and the periodic fluctuation component from the historical data of distributed photovoltaic power generation, a probability density function is fitted using a Gaussian mixture model to establish a probability model of random events such as cloud cover and equipment failure, thereby obtaining the random fluctuation component of photovoltaic power generation. Based on the historical electricity consumption patterns of each user in the revenue distribution basic parameters and the load characteristic data in the user energy storage use contribution matrix, a personalized energy storage demand function containing charging period priority weights and discharge demand intensity coefficients is constructed, and a differentiated energy storage demand prediction model for each user is established. The deterministic component of photovoltaic power generation, the periodic fluctuation component of photovoltaic power generation, and the random fluctuation component of photovoltaic power generation are coupled with the differentiated energy storage demand prediction model in a time series calculation. The probability density function of energy storage capacity demand for each user at different time periods is generated by Monte Carlo simulation method, and the uncertainty distribution of energy storage demand is obtained.

4. The shared energy storage dynamic configuration method based on a revenue distribution strategy according to claim 1, characterized in that, Step S3 includes: Based on the uncertainty distribution of energy storage demand, the instantaneous deviation between photovoltaic power generation and load demand power at each moment is calculated to obtain the real-time matching degree; the real-time matching degree is weighted and averaged by hourly time period to obtain the time period matching degree; the time period matching degree is subjected to variance analysis within a 24-hour period to obtain the daily matching degree. Based on the comprehensive energy storage contribution of each user in the user energy storage contribution matrix, combined with the real-time matching degree, the time period matching degree and the daily matching degree, a multi-level weighted fusion algorithm is used to quantify the energy storage service contribution of each user. The energy storage service contribution of each user is processed by matrix operation with the uncertainty distribution of energy storage demand, and the energy storage capacity configuration demand matrix is ​​obtained by weighted allocation calculation of capacity demand.

5. The shared energy storage dynamic configuration method based on a revenue distribution strategy according to claim 1, characterized in that, Step S4 includes: The energy storage capacity configuration demand matrix is ​​input into a multi-user collaborative optimization algorithm for problem modeling. The energy storage capacity allocation decision variables and constraints of each user are set, and a multi-objective optimization function with the goal of maximizing the benefits of each user is established to obtain a multi-user resource competition optimization model. Based on the revenue distribution ratio coefficient of each user in the revenue distribution basic parameters, a capacity allocation mechanism driven by revenue distribution strategy is constructed. The capacity allocation priority weight is calculated according to the user's historical revenue contribution and current demand intensity to obtain the capacity allocation priority sequence. The capacity allocation priority sequence is input as a constraint into the multi-user resource competition optimization model. The Lagrange multiplier method is used for iterative solution. Under the constraint of total system capacity, the optimal energy storage capacity allocation value of each user is calculated to obtain the initial capacity allocation scheme. The initial capacity allocation scheme is processed by energy storage benefit maximization calculation. The scheme is optimized and adjusted through capacity utilization evaluation and revenue efficiency analysis to obtain the optimal energy storage capacity allocation scheme for each user.

6. The shared energy storage dynamic configuration method based on a revenue distribution strategy according to claim 5, characterized in that, The process involves inputting the energy storage capacity configuration demand matrix into a multi-user collaborative optimization algorithm for problem modeling. This includes setting decision variables and constraints for energy storage capacity allocation for each user, establishing a multi-objective optimization function aimed at maximizing the revenue of each user, and obtaining a multi-user resource competition optimization model, including: Based on the capacity demand value of each user in the energy storage capacity configuration demand matrix, the energy storage capacity allocation amount of each user in different time periods is set as a decision variable, and the value range and boundary constraints of the decision variable are defined to obtain the set of energy storage capacity allocation decision variables. Based on the total capacity limit of the shared energy storage system and the maximum capacity demand of each user, system capacity balance constraints, user capacity upper and lower limit constraints, and time coupling constraints are established to obtain a set of energy storage capacity allocation constraints. The energy storage service revenue function, usage cost function, and capacity utilization efficiency function of each user are weighted and combined to construct a multi-objective optimization function with the goal of maximizing the total revenue of each user, thus obtaining the multi-user resource competition optimization model.

7. The shared energy storage dynamic configuration method based on a revenue distribution strategy according to claim 1, characterized in that, Step S5 includes: A real-time revenue monitoring mechanism is established based on the optimal energy storage capacity allocation scheme for each user. The revenue is calculated by collecting data on energy storage service revenue, electricity sales revenue, and cost expenditure of each user, and then compared with the expected revenue value in the revenue allocation basic parameters to obtain the revenue deviation value of each user. Based on the revenue deviation value of each user, capacity redistribution is determined. When the revenue deviation value exceeds a preset threshold of 15%, a capacity adjustment mechanism is triggered. Dynamic capacity redistribution is performed by reallocating energy storage capacity quotas and adjusting charging and discharging priorities to obtain an adjusted energy storage capacity allocation scheme. The revenue deviation value and capacity utilization efficiency data of each user are input into the revenue distribution strategy feedback optimization mechanism for strategy update processing. By adjusting the distribution ratio coefficient and weight parameter in the basic parameters of revenue distribution, the optimized revenue distribution strategy parameters are obtained. Based on the adjusted energy storage capacity allocation scheme and the optimized revenue distribution strategy parameters, comprehensive analysis data is generated, including revenue distribution details for each user, capacity usage statistics, and system operation efficiency indicators, to obtain the dynamic operation configuration results and revenue distribution optimization report of the shared energy storage system.

8. A shared energy storage dynamic configuration system based on a revenue distribution strategy, characterized in that, A method for dynamically configuring shared energy storage based on a revenue-sharing strategy as described in any one of claims 1-7, wherein the dynamically configured shared energy storage system based on a revenue-sharing strategy comprises: The evaluation module is used to acquire electricity demand data and photovoltaic power generation data of each user in the shared energy storage system, evaluate the energy storage usage of each user through quantitative analysis of electricity contribution, and establish a user energy storage usage contribution matrix and basic parameters for revenue distribution. A module is established to collect historical data and load demand data of distributed photovoltaic power generation. The uncertainty of photovoltaic power generation is decomposed into deterministic components, periodic fluctuation components and random fluctuation components. A differentiated energy storage demand prediction model is established based on the load characteristic data of each user to obtain the distribution of energy storage demand uncertainty. The calculation module is used to construct a source-load matching degree evaluation system based on the uncertainty distribution of energy storage demand. Through real-time matching degree, time period matching degree and daily matching degree calculation and processing, combined with the energy storage service contribution of each user, the energy storage capacity configuration demand matrix is ​​obtained. The input module is used to input the energy storage capacity configuration demand matrix into the multi-user collaborative optimization algorithm, and obtain the optimal energy storage capacity allocation scheme for each user through the capacity allocation mechanism driven by the revenue distribution strategy and the calculation and processing of maximizing energy storage benefits. The allocation module is used to establish a dynamic adjustment mechanism based on the optimal energy storage capacity allocation scheme. Through real-time revenue monitoring and capacity redistribution processing, a revenue allocation strategy feedback optimization mechanism is established to obtain the dynamic operation configuration results and revenue allocation optimization report of the shared energy storage system.

9. A shared energy storage dynamic configuration device based on a revenue distribution strategy, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the shared energy storage dynamic configuration method based on the revenue distribution strategy according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the shared energy storage dynamic configuration method based on the revenue distribution strategy as described in any one of claims 1 to 7.

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