A distributed photovoltaic power distribution network shared energy storage optimization method

Abstract

The application discloses a distributed photovoltaic power distribution network shared energy storage optimization method, relates to the technical field of energy storage system optimization, and comprises the following steps: inputting a current electricity market price into a preset electricity price prediction model, acquiring a current rolling period electricity price prediction sequence, constructing a rolling optimization model based on the current rolling period electricity price prediction sequence, a real-time state of charge of an energy storage system battery and an energy storage system operation constraint condition, taking maximization of energy storage system net income as an objective function, taking a battery life attenuation cost as a correction parameter, acquiring an optimal charging and discharging power sequence, and controlling charging and discharging actions of the energy storage system; real-time monitoring of an electricity price prediction deviation index is performed, when the electricity price prediction deviation index is greater than a preset deviation threshold value, an electricity price prediction model is optimized and calibrated, an optimal charging and discharging power sequence is updated, charging and discharging actions are adjusted, and the above steps are repeated when a next rolling period starts, so that energy storage system income is improved, and battery loss is reduced.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system optimization technology, and relates to an optimization method for shared energy storage in distributed photovoltaic distribution networks. Background Technology

[0002] With the large-scale grid connection of new energy sources and the deepening of power market reforms, independent energy storage systems, due to their flexible regulation capabilities, play a crucial role in participating in power market ancillary services and improving grid stability. However, independent energy storage systems suffer from multiple limitations, such as large fluctuations in electricity demand in the power market and low battery charging and discharging efficiency, resulting in low overall profitability and hindering the full realization of their economic value. Therefore, to provide a sustainable economic driving force for the transformation of the power system dominated by new energy sources, it is urgent to optimize the charging and discharging strategies of independent energy storage systems to improve their profitability.

[0003] Existing technologies also include some solutions related to optimizing the charging and discharging strategies of energy storage systems. For example, the charging and discharging method and energy storage system of the energy storage system disclosed in Chinese Patent Publication No. CN119853130B determine the reserved power based on the first photovoltaic power and the first load power, and determine the charging and discharging strategy based on the reserved power and one or more preset targets. This helps to improve charging and discharging efficiency, reduce operating costs, and improve economic benefits.

[0004] Another Chinese patent, CN118554497A, discloses a charging and discharging control method for a battery energy storage system. It predicts the power load for each hour of the day based on user-end data, and optimizes the charging and discharging strategy based on energy storage capacity, remaining power, preset demand, and time-of-use electricity price data, with daily revenue as the optimization target, thereby improving the revenue of the energy storage system.

[0005] Although the above scheme proposes some solutions to optimize the charging and discharging strategy of energy storage system, the existing technology still has the following limitations: (1) The existing technology adopts a global optimization strategy for historical electricity price data, without taking into account the special characteristics and changing patterns of electricity prices in different time periods, which makes it difficult to accurately reflect the peak and valley changes and short-term fluctuation trends of electricity prices in the obtained predicted electricity prices.

[0006] (2) Existing technologies are crude and conservative in managing the charge and power constraints of energy storage systems, and do not analyze the feasibility of strategies, which seriously affects the reliability and stability of charging and discharging strategies. Furthermore, existing technologies lack real-time electricity price prediction and deviation calibration mechanisms, and cannot respond to sudden fluctuations in the electricity market in a timely manner, thereby affecting the formulation of charging and discharging strategies and the optimization of returns.

[0007] (3) Existing technologies do not combine the dynamic quantification of life decay in the analysis of battery penalty costs with the charging and discharging behavior of energy storage systems, resulting in a disconnect between the life cost model and actual aging characteristics, which affects the accuracy of revenue optimization. Summary of the Invention

[0008] In view of this, in order to solve the problems mentioned in the background technology, a method for optimizing shared energy storage in distributed photovoltaic distribution networks is proposed.

[0009] The objective of this invention can be achieved through the following technical solution: This invention provides a method for optimizing shared energy storage in a distributed photovoltaic distribution network, comprising: S1. collecting the electricity market price and the real-time state of charge of the energy storage system battery at the start of the current rolling cycle.

[0010] S2. Input the electricity market price into the preset electricity price prediction model to obtain the current rolling cycle electricity price prediction sequence.

[0011] S3. Based on the current rolling cycle electricity price prediction sequence, the real-time state of charge of the energy storage system battery, and the operating constraints of the energy storage system, a rolling optimization model is constructed. The optimal charging and discharging power sequence of the energy storage system within the rolling cycle is solved by the rolling optimization model. The rolling optimization model takes maximizing the net revenue of the energy storage system as the objective function and integrates the dynamic correction of revenue calculation based on battery life decay cost.

[0012] S4. Monitor the electricity price prediction deviation index in real time. If it is less than the preset deviation threshold, control the charging and discharging action of the energy storage system according to the optimal charging and discharging power sequence. Otherwise, optimize and calibrate the electricity price prediction model, update the optimal charging and discharging power sequence, adjust the charging and discharging action of the energy storage system, and jump to S1 to repeat the execution when the next rolling cycle begins.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention obtains the initial predicted electricity price by statistically analyzing the historical electricity price data of different time periods, and establishes an electricity price prediction model by adjusting it with trend correction factor and electricity price correction parameter, so that the electricity price prediction sequence generated by the model can accurately reflect the peak and valley changes and short-term fluctuation trend of electricity price, and improve the accuracy of subsequent charging and discharging execution strategies.

[0014] (2) The present invention fully considers the operating constraints of the energy storage system, rationally selects the charging and discharging execution strategy according to the charging and discharging constraint interval, and rationally sets the charging and discharging power of the energy storage system according to the charge constraint boundary value and power constraint threshold, so as to ensure the feasibility of the charging and discharging execution strategy in actual operation and improve the operating reliability and stability of the energy storage system.

[0015] (3) The present invention adopts a rolling optimization strategy, which optimizes and calibrates the electricity price prediction model by obtaining the deviation between the predicted electricity price and the real-time electricity price, updates the optimal charging and discharging power sequence, adjusts the charging and discharging action of the energy storage system, and responds quickly to the drastic fluctuations in electricity price, thereby significantly improving revenue and reducing battery ineffective losses.

[0016] (4) Based on the charging and discharging execution strategy, the present invention dynamically quantifies the battery life decay, effectively reduces the battery life decay rate, improves the accuracy of revenue optimization, and maximizes the revenue of the energy storage system throughout its life cycle. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention.

[0019] Figure 2 This is a flowchart illustrating the logic for determining each candidate charging / discharging time period in step S3 of the present invention.

[0020] Figure 3 This is a tree diagram showing how each charge / discharge execution combination is determined in step S3 of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, the present invention provides a method for optimizing shared energy storage in a distributed photovoltaic distribution network, including: S1. Collecting the electricity market price and the real-time state of charge of the energy storage system battery at the start of the current rolling cycle.

[0023] S2. Input the electricity market price into the preset electricity price prediction model to obtain the current rolling period time-of-use electricity price prediction sequence.

[0024] As a preferred embodiment, the process of obtaining the current rolling cycle time-of-use electricity price prediction sequence includes: dividing the time zone to which the rolling cycle belongs into rolling time periods according to a preset time interval.

[0025] Historical electricity price data is retrieved, and the data is grouped according to the time zone to which each historical day's rolling cycle belongs. The grouping results are then divided into a training set and a validation set according to the time order.

[0026] The average of historical electricity price data for the same rolling period in the training set is calculated to obtain the initial predicted electricity price for each rolling period.

[0027] A state transition probability matrix is ​​constructed based on the historical electricity price fluctuations of adjacent periods in the training set, and the trend correction factor for each rolling period is obtained to correct the initial predicted electricity price.

[0028] The historical electricity price data in the validation set is compared with the corrected initial predicted electricity price in each time period. The average deviation between the historical electricity price data and the initial predicted electricity price in each rolling period of the validation set is calculated and used as the electricity price correction parameter for each rolling period.

[0029] By comparing the electricity market price at the start of the input rolling cycle with the initial predicted electricity price for the first rolling period, the ratio of the two is used as an adjustment coefficient. The price correction parameters for each rolling period are then adjusted to obtain the predicted electricity price for each rolling period, thus generating a preset electricity price prediction model.

[0030] The electricity market price data at the start of the current rolling cycle is input into the preset electricity price prediction model to obtain the time-period electricity price prediction sequence for the current rolling cycle.

[0031] It should be noted that the process of obtaining the trend correction factor for each rolling period includes: analyzing the state changes of electricity price decline and rise in adjacent rolling periods in the training set, counting the frequency of the same state transition in the same rolling period in each historical day, constructing a state transition matrix, and normalizing it to obtain a state transition probability matrix, wherein the rows of the matrix correspond to each rolling period, the columns of the matrix correspond to the state changes of each rolling period relative to the previous rolling period, and the elements of the matrix correspond to the probability of state transition.

[0032] Using the preset downward state characterization coefficient and upward state characterization coefficient as weighting factors, the state transition probability of each rolling period is weighted and summed to obtain the trend indicator. If the trend indicator of a certain rolling period is negative, the trend correction direction of the rolling period is downward, and vice versa. Similarly, the trend correction direction of each rolling period can be obtained. The preset upward state characterization coefficient and downward state characterization coefficient can be set to 0.05 and -0.05, respectively.

[0033] If the trend correction direction for a certain rolling period is downward, then the absolute average deviation of the historical electricity price decline state in that rolling period is calculated and used as the downward trend correction amplitude for that rolling period. Conversely, the absolute average deviation of the historical electricity price increase state in that rolling period is calculated and used as the upward trend correction amplitude. Similarly, the downward and upward trend correction amplitudes for each rolling period are obtained.

[0034] By combining the trend correction direction and corresponding trend correction magnitude of each rolling period, the trend correction factor for each rolling period is obtained.

[0035] As a preferred embodiment, the formula for the electricity price prediction model is: ,in , The first Within the time zone of the rolling cycle, the first Forecast electricity price and initial forecast electricity price for each rolling period. For the first Within the time zone of the rolling cycle, the first Electricity price adjustment parameters for each rolling period, For the first Within the time zone of the rolling cycle, the first Trend correction factor for each rolling period, Here is the adjustment coefficient, where The time zone number to which each rolling cycle belongs. , The number of time zones to which the rolling cycle belongs. For each rolling period, , This represents the number of rolling periods.

[0036] This invention provides an initial predicted electricity price by statistically analyzing historical electricity price data from different time periods. The predicted electricity price is then adjusted using trend correction factors and electricity price correction parameters to establish an electricity price prediction model. This model generates an electricity price prediction sequence that accurately reflects the peak and valley changes and short-term fluctuation trends of electricity prices, thereby improving the accuracy of subsequent charging and discharging execution strategies.

[0037] S3. Based on the current rolling cycle time-of-use electricity price prediction sequence, the real-time state of charge of the energy storage system battery, and the operating constraints of the energy storage system, a rolling optimization model is constructed. The optimal charging and discharging power sequence of the energy storage system within the rolling cycle is solved by the rolling optimization model. The rolling optimization model takes maximizing the net revenue of the energy storage system as the objective function and integrates the dynamic correction of revenue calculation based on battery life decay cost.

[0038] Please see Figure 2As shown, as a preferred embodiment, the process of solving the optimal charge and discharge power sequence of the energy storage system within the rolling cycle using the rolling optimization model includes determining each candidate charge and discharge period based on the current rolling cycle time-of-use electricity price prediction sequence. The specific method is as follows: based on the current rolling cycle time-of-use electricity price prediction sequence, calculate the relative change rate of the predicted electricity price of each rolling period and its adjacent period, and use this to identify each valley electricity price rolling period and each peak electricity price rolling period in the current rolling cycle.

[0039] The rolling period of off-peak electricity price when the price is lower than the preset off-peak electricity price threshold is marked as the candidate period for charging, and the rolling period of peak electricity price when the price is higher than the preset peak electricity price threshold is marked as the candidate period for discharging, thereby constructing the candidate period set for charging and the candidate period set for discharging.

[0040] It should be noted that the process of obtaining the relative change rate of the predicted electricity price between adjacent periods includes: calculating the difference between the predicted electricity price of each rolling period and the previous rolling period, and using the ratio of this difference to the predicted electricity price of each rolling period as the relative change rate of the preceding predicted electricity price. Similarly, the relative change rate of the subsequent predicted electricity price of each rolling period relative to the following rolling period is obtained.

[0041] It should be noted that if the relative rate of change of the predicted electricity price for a certain rolling period and its adjacent periods are both positive, then the rolling period is the peak electricity price rolling period; otherwise, the rolling period is the off-peak electricity price rolling period.

[0042] It should be noted that the aforementioned preset off-peak electricity price threshold and preset peak electricity price threshold are respectively the electricity price benchmark values ​​for the energy storage system to perform charging and discharging operations. If the predicted electricity price for a certain rolling period is less than the preset off-peak electricity price threshold, it means that the electricity cost is lower at this time, which is the right time for the energy storage system to perform charging operations. If the predicted electricity price is greater than the preset peak electricity price threshold, it means that the electricity cost is higher at this time, and the energy storage system can sell electricity to the grid to obtain economic benefits. The average electricity price during the historical charging and discharging operations of the energy storage system can be used as a reference.

[0043] As a preferred embodiment, the process of solving the optimal charge and discharge power sequence of the energy storage system within the rolling cycle through the rolling optimization model includes determining whether each candidate charge and discharge period can be executed based on the real-time state of charge of the energy storage system battery. The specific method is as follows: real-time monitoring of the battery charge at the start time of the candidate charge and discharge period and recording it as the battery's initial charge.

[0044] If the initial battery charge during a candidate charging period is within a preset charging action range, and the initial battery charge during a candidate discharging period is within a preset discharging action range, then the corresponding charging and discharging actions can be executed. This allows for the selection of feasible charging and discharging periods from the set of candidate charging and discharging periods.

[0045] It should be noted that the aforementioned preset charging and discharging action ranges are the battery charge ranges defined by the energy storage system to determine whether charging and discharging actions can be performed. The preset charging action range and preset discharging action range can be exemplarily set to [0.2, 0.4] and (0.7, 0.9], respectively. The reason is that when the battery charge is between 0.2 and 0.4, the battery's internal resistance is relatively small, and the efficiency of converting electrical energy into chemical energy during charging is relatively high, which is conducive to improving charging speed and reducing energy loss. At the same time, a certain charge margin is reserved to cope with emergencies. When the battery charge is less than 0.2, the battery's internal resistance increases, the charging efficiency decreases, and heat generation increases. When the battery charge is greater than 0.4, the charging current will decrease, thereby reducing the charging power, prolonging the charging time, and damaging the battery life.

[0046] When the battery charge is between 0.7 and 0.9, its discharge performance is relatively stable, with small fluctuations in output voltage and current, providing continuous and stable power while retaining a certain charge margin to cope with emergencies. When the battery charge is less than 0.7, the battery output voltage will drop rapidly as discharge progresses, affecting the normal operation of the system. When the battery charge is greater than 0.9, the battery's internal resistance will increase, and the discharge efficiency will decrease.

[0047] Please see Figure 3 As shown, as a preferred embodiment, the process of solving the optimal charge and discharge power sequence of the energy storage system within the rolling cycle through the rolling optimization model includes determining each charge and discharge execution combination according to the operating constraints of the energy storage system. The specific method is as follows: the feasible charging time period and the feasible discharging time period are arranged in ascending order according to the time sequence to construct the feasible charge and discharge sequence.

[0048] The preceding preset number of elements in the feasible charge-discharge sequence are taken as the root element. Based on the charge-discharge alternation rule and the preset charge-discharge constraint interval, the tree diagram statistical method is used to exhaustively enumerate all branches from the subsequent elements, and the end element of all branches is recorded as the leaf element.

[0049] For each branch from the root element to the leaf element in the statistical tree diagram, obtain each charge-discharge execution combination, wherein the feasible charging and discharging time period in the charge-discharge execution combination is recorded as the charge-discharge execution time period.

[0050] It should be noted that the above-mentioned charge-discharge alternation rule means that after a charging operation is completed, a discharging operation must be performed before charging can be performed again, and vice versa. This alternation mode can prevent the battery from being in a single charging or discharging state for a long time, reducing the damage to the battery caused by overcharging and discharging.

[0051] It should be noted that the above-mentioned preset charge and discharge constraint interval is the minimum time interval between charging and discharging operations set to ensure battery safety, stable operation, and to meet the overall system requirements. This interval can be obtained from the product specifications provided by the battery manufacturer.

[0052] As a preferred embodiment, the process of solving the optimal charge and discharge power sequence of the energy storage system within the rolling cycle through the rolling optimization model includes determining the charge and discharge power of each charge and discharge execution period based on the operating constraints of the energy storage system. The specific method is as follows: based on the initial battery charge and preset charge constraint boundary value of the charge and discharge execution period, calculate the absolute change in charge of each charge and discharge execution period, and combine it with the fixed duration of the execution period to obtain the corresponding charge and discharge power of the charge and discharge execution period.

[0053] If the charging or discharging power during a certain execution period is greater than the preset power constraint threshold, then the preset power constraint threshold will be used as the charging or discharging power during that execution period.

[0054] Based on the charging power of each charging execution period and the discharging power of each discharging execution period, a charging and discharging power sequence for each charging and discharging execution combination is constructed.

[0055] It should be noted that the aforementioned preset charge constraint boundary values ​​include the upper limit of the battery's state of charge when the energy storage system performs a charging operation and the lower limit of the battery's state of charge when performing a discharging operation. In order to avoid overcharging and over-discharging of the battery, ensure that the battery is within a safe charge range, reduce the loss of electrode materials and the occurrence of chemical side reactions, thereby extending the battery's cycle life, these values ​​can be obtained from the product specifications provided by the battery manufacturer.

[0056] It should be noted that the aforementioned preset power constraint threshold refers to the maximum allowable charging and discharging power of the power grid. This can prevent the energy storage system from causing sudden power surges to the power grid due to excessive charging and discharging power, which could affect the voltage and frequency stability of the power grid. This information can be obtained from the technical requirements document provided by the power grid dispatching department.

[0057] The embodiments of the present invention fully consider the operating constraints of the energy storage system, rationally select the charging and discharging execution strategy according to the charging and discharging constraint interval, and rationally set the charging and discharging power of the energy storage system according to the charge constraint boundary value and power constraint threshold, so as to ensure the feasibility of the charging and discharging execution strategy in actual operation and improve the operational reliability and stability of the energy storage system.

[0058] As a preferred option, the optimal charge-discharge power sequence of the energy storage system within the rolling cycle is obtained by solving the rolling optimization model. The specific method is as follows: Substitute the data of a certain charge-discharge execution combination into the net profit calculation formula. Calculate the net profit of this portfolio, where , The first Forecast electricity price and charging power for each charging period. , The first The predicted electricity price and discharge power for each discharge execution period, The execution period is fixed in duration. The cost of battery life degradation for this combination, Number each charging execution period. , The number of charging execution periods. These are the numbers for each discharge execution period. , The number of discharge execution periods is used to calculate the net profit of each charge-discharge execution combination.

[0059] Select the charge / discharge execution combination corresponding to the maximum net profit, and use its charge / discharge power sequence as the optimal charge / discharge power sequence for the energy storage system in the current rolling cycle.

[0060] As a preferred embodiment, the process of obtaining the battery life degradation cost includes: calculating the charge change rate of each charge-discharge execution period in the charge-discharge execution combination, and performing linear weighted fusion to obtain the battery degradation index.

[0061] The system calls upon a preset battery life degradation table stored in the web cloud, determines the battery capacity degradation level of the combination based on the battery degradation index, matches the corresponding battery capacity degradation rate, and calculates the battery life degradation cost of the combination by combining preset unit capacity cost parameters.

[0062] It should be noted that the process of obtaining the above-mentioned rate of change of charge includes: calculating the absolute difference of charge between the start and end times of each charge / discharge execution period, and then performing a ratio calculation between the absolute difference and the charge at the start time to obtain the rate of change of charge for each charge / discharge execution period.

[0063] It should be noted that the process of obtaining the above-mentioned battery degradation index includes: using preset charging and discharging influence coefficients as weights, the rate of change of charge during each charging and discharging period is linearly weighted and fused to obtain the battery degradation index. The preset charging and discharging influence coefficients can be set to 0.6 and 0.4, respectively. These coefficients can be determined by conducting multiple charging and discharging experiments on the battery, statistically analyzing the experimental data, and determining the contribution of the charging process and the discharging process to the battery capacity degradation and internal resistance increase, respectively. The experimental results show that in common charging and discharging application scenarios, the charging process has a relatively greater impact on battery life degradation, while the discharging process has a relatively smaller impact.

[0064] It should be noted that the above preset battery life degradation table includes battery degradation indicators and corresponding battery capacity degradation rates for different battery capacity degradation levels.

[0065] It should be noted that the preset unit capacity cost parameters can be obtained from the product specifications provided by the battery manufacturer.

[0066] According to the charging and discharging execution strategy, the embodiments of the present invention dynamically quantify the battery life degradation, effectively reduce the battery life degradation rate, improve the accuracy of revenue optimization, and maximize the revenue of the energy storage system throughout its life cycle.

[0067] S4. Monitor the electricity price prediction deviation index in real time. If it is less than the preset deviation threshold, control the charging and discharging action of the energy storage system according to the optimal charging and discharging power sequence. Otherwise, optimize and calibrate the electricity price prediction model, update the optimal charging and discharging power sequence, adjust the charging and discharging action of the energy storage system, and jump to S1 to repeat the execution when the next rolling cycle begins.

[0068] As a preferred embodiment, the process of optimizing and calibrating the electricity price prediction model, updating the optimal charging and discharging power sequence, and adjusting the charging and discharging actions of the energy storage system includes: retrieving actual electricity price data from the electricity market, and calculating the cumulative deviation of the electricity price prediction for the preceding rolling period before executing actions in each charging and discharging period.

[0069] The electricity price prediction deviation value and the predicted electricity price of the previous rolling period are integrated and compared to obtain the electricity price prediction deviation index.

[0070] When the electricity price prediction deviation index is greater than or equal to the preset electricity price deviation threshold, the ratio of the actual electricity price to the predicted electricity price in the preceding rolling period is used as the optimization coefficient to optimize the predicted electricity price in subsequent rolling periods and obtain an optimized electricity price prediction sequence.

[0071] The optimized electricity price prediction sequence is input into the rolling optimization model to resolve the optimal charging and discharging power sequence, and the charging and discharging control commands of the energy storage system in subsequent rolling periods are adjusted accordingly.

[0072] It should be noted that the process of obtaining the above-mentioned electricity price prediction deviation index includes: based on the predicted electricity price for each rolling period between the charging and discharging execution period and the previous charging and discharging execution period, calculating the absolute difference between the predicted electricity price for each rolling period and the actual electricity price, and summing them up as the cumulative deviation of the electricity price prediction.

[0073] The cumulative deviation of electricity price forecasts is integrated, and the predicted electricity prices for each rolling period are summed. The ratio of the two is used as the electricity price forecast deviation index.

[0074] The embodiments of the present invention employ a rolling optimization strategy, which optimizes and calibrates the electricity price prediction model by obtaining the deviation between the predicted electricity price and the real-time electricity price, updates the optimal charging and discharging power sequence, adjusts the charging and discharging actions of the energy storage system, and quickly responds to drastic fluctuations in electricity prices, thereby significantly improving revenue and reducing battery ineffective losses.

[0075] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0076] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0077] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0078] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0080] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing shared energy storage in a distributed photovoltaic distribution network, characterized in that, include: S1. Collect the electricity market price and the real-time state of charge of the energy storage system batteries at the start of the current rolling cycle; S2. Input the electricity market price into the preset electricity price prediction model to obtain the current rolling period time-of-use electricity price prediction sequence; S3. Based on the current rolling cycle time-of-use electricity price prediction sequence, the real-time state of charge of the energy storage system battery, and the operating constraints of the energy storage system, a rolling optimization model is constructed. The optimal charging and discharging power sequence of the energy storage system within the rolling cycle is solved through the rolling optimization model. The rolling optimization model takes maximizing the net revenue of the energy storage system as the objective function and integrates the dynamic correction of revenue calculation based on battery life decay cost. S4. Monitor the electricity price prediction deviation index in real time. If it is less than the preset deviation threshold, control the charging and discharging actions of the energy storage system according to the optimal charging and discharging power sequence. Otherwise, optimize and calibrate the electricity price prediction model, update the optimal charging and discharging power sequence, and adjust the charging and discharging actions of the energy storage system. When the next rolling cycle begins, jump to S1 to repeat the execution. The process of obtaining the current rolling period time-of-use electricity price forecast sequence includes: The rolling period is divided into rolling time slots according to a preset time interval; Historical electricity price data is retrieved, and the data is grouped according to the time zone to which each historical day's rolling cycle belongs. The grouping results are then divided into training set and validation set according to the time order. The average of historical electricity price data for the same rolling period in the training set is calculated to obtain the initial predicted electricity price for each rolling period; A state transition probability matrix is ​​constructed based on the historical electricity price fluctuations of adjacent time periods in the training set, and the trend correction factor for each rolling time period is obtained to correct the initial predicted electricity price. The historical electricity price data in the validation set is compared with the corrected initial predicted electricity price in each time period. The average deviation between the historical electricity price data and the initial predicted electricity price in each rolling period of the validation set is calculated and used as the electricity price correction parameter for each rolling period. By comparing the electricity market price at the start of the input rolling cycle with the initial predicted electricity price for the first rolling period, the ratio of the two is used as the adjustment coefficient, and the price correction parameters for each rolling period are adjusted to obtain the predicted electricity price for each rolling period, thus generating a preset electricity price prediction model. The electricity market price data at the start of the current rolling cycle is input into the preset electricity price prediction model to obtain the time-period electricity price prediction sequence for the current rolling cycle.

2. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 1, characterized in that, The electricity price prediction model is as follows: ,in , The first Within the time zone of the rolling cycle, the first Forecast electricity price and initial forecast electricity price for each rolling period. For the first Within the time zone of the rolling cycle, the first Electricity price adjustment parameters for each rolling period, For the first Within the time zone of the rolling cycle, the first Trend correction factor for each rolling period, Here is the adjustment coefficient, where The time zone number to which each rolling cycle belongs. , The number of time zones to which the rolling cycle belongs. For each rolling period, , This represents the number of rolling periods.

3. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 1, characterized in that, The process of solving the optimal charging and discharging power sequence of the energy storage system within the rolling cycle using the rolling optimization model includes determining each candidate charging and discharging period based on the current rolling cycle time-of-use electricity price prediction sequence. The specific method is as follows: Based on the current rolling cycle time-of-use electricity price forecast sequence, calculate the relative change rate of the forecast electricity price of each rolling period and its adjacent periods, and use this to identify the rolling periods of each valley electricity price and each rolling period of each peak electricity price in the current rolling cycle; The rolling period of off-peak electricity price when the price is lower than the preset off-peak electricity price threshold is marked as the candidate period for charging, and the rolling period of peak electricity price when the price is higher than the preset peak electricity price threshold is marked as the candidate period for discharging, thereby constructing the candidate period set for charging and the candidate period set for discharging.

4. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 3, characterized in that, The process of solving the optimal charge and discharge power sequence of the energy storage system within the rolling cycle using the rolling optimization model includes determining whether a charge and discharge action can be performed in each candidate charging and discharging period based on the real-time state of charge of the energy storage system's batteries. The specific method is as follows: Real-time monitoring of the battery charge at the start of the candidate charging / discharging period, and recording it as the battery's initial charge. If the initial battery charge during a candidate charging period is within a preset charging action range, and the initial battery charge during a candidate discharging period is within a preset discharging action range, then the corresponding charging and discharging actions can be executed. This allows for the selection of feasible charging and discharging periods from the candidate charging and discharging periods.

5. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 4, characterized in that, The process of solving the optimal charge and discharge power sequence of the energy storage system within a rolling cycle using a rolling optimization model includes determining each charge and discharge execution combination based on the operating constraints of the energy storage system. The specific method is as follows: The feasible charging and discharging periods are arranged in ascending order according to time sequence to construct a feasible charging and discharging sequence. The elements of the preceding preset number in the feasible charging and discharging sequence are taken as the root elements in turn. Based on the charging and discharging alternation rule and the preset charging and discharging constraint interval, the tree diagram statistical method is used to exhaustively enumerate all branches from the subsequent elements, and the end element of all branches is recorded as the leaf element. For each branch from the root element to the leaf element in the tree diagram, obtain each charge / discharge execution combination, and record the feasible charging / discharge time period in the charge / discharge execution combination as the charge / discharge execution time period.

6. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 5, characterized in that, The process of solving the optimal charge and discharge power sequence of the energy storage system within the rolling cycle using the rolling optimization model includes determining the charge and discharge power for each charge and discharge execution period based on the operating constraints of the energy storage system. The specific method is as follows: Based on the initial battery charge and preset charge constraint boundary value during the charge and discharge execution period, the absolute change in charge during each charge and discharge execution period is calculated, and combined with the fixed duration of the execution period, the corresponding charge and discharge power during the charge and discharge execution period is obtained. If the charging or discharging power during a certain execution period is greater than the preset power constraint threshold, then the preset power constraint threshold will be used as the charging or discharging power during that execution period. Based on the charging power of each charging execution period and the discharging power of each discharging execution period, a charging and discharging power sequence for each charging and discharging execution combination is constructed.

7. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 6, characterized in that, The method for obtaining the optimal charge and discharge power sequence of the energy storage system within the rolling cycle through the rolling optimization model is as follows: Substitute a specific charge / discharge performance combination data into the net profit calculation formula. Calculate the net profit of this portfolio, where , The first Forecast electricity price and charging power for each charging period. , The first The predicted electricity price and discharge power for each discharge execution period, The execution period is fixed in duration. The cost of battery life degradation for this combination, Number each charging execution period. , The number of charging execution periods. These are the numbers for each discharge execution period. , Similarly, the net profit of each charge-discharge execution combination is obtained by calculating the number of discharge execution periods. Select the charge / discharge execution combination corresponding to the maximum net profit, and use its charge / discharge power sequence as the optimal charge / discharge power sequence for the energy storage system in the current rolling cycle.

8. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 7, characterized in that, The process of obtaining the battery life degradation cost includes: Calculate the rate of change of charge for each charging and discharging execution period in the charge and discharge execution combination, and then perform linear weighted fusion to obtain the battery degradation index. The system calls upon a preset battery life degradation table stored in the web cloud, determines the battery capacity degradation level of the combination based on the battery degradation index, matches the corresponding battery capacity degradation rate, and calculates the battery life degradation cost of the combination by combining preset unit capacity cost parameters.

9. The method for optimizing shared energy storage in a distributed photovoltaic distribution network according to claim 1, characterized in that, The process of optimizing and calibrating the electricity price prediction model, updating the optimal charging and discharging power sequence, and adjusting the charging and discharging actions of the energy storage system includes: Retrieve actual electricity price data from the electricity market and calculate the price prediction deviation value of the preceding rolling period before executing actions during each charging and discharging period; The electricity price forecast deviation value and the forecast electricity price of the previous rolling period are integrated and the ratio is calculated to obtain the electricity price forecast deviation index. When the electricity price prediction deviation index is greater than or equal to the preset electricity price deviation threshold, the ratio of the actual electricity price to the predicted electricity price in the previous rolling period is used as the optimization coefficient to optimize the predicted electricity price in subsequent rolling periods and obtain an optimized electricity price prediction sequence. The optimized electricity price forecast sequence is input into the rolling optimization model to resolve the optimal charging and discharging power sequence, and the charging and discharging control commands of the energy storage system in subsequent rolling periods are adjusted accordingly.

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