Distributed photovoltaic power distribution network shared energy storage optimization method

Abstract

The invention discloses a distributed photovoltaic power distribution network shared energy storage optimization method, and relates to the technical field of energy storage system optimization, and the method comprises the following steps: inputting the current electricity price of an electricity market into a preset electricity price prediction model, and obtaining a current rolling cycle electricity price prediction sequence, constructing a rolling optimization model based on the current rolling cycle electricity price prediction sequence, the energy storage system battery real-time charge state and the energy storage system operation constraint condition, taking the maximum energy storage system net income as an objective function, taking the battery life attenuation cost as a correction parameter, and obtaining an optimal charge and discharge power sequence, and controlling the charging and discharging actions of the energy storage system, monitoring an electricity price prediction deviation index in real time, optimizing and calibrating an electricity price prediction model, updating an optimal charging and discharging power sequence, adjusting the charging and discharging actions when the electricity price prediction deviation index is greater than a preset deviation threshold value, and repeating the steps at the beginning of a next rolling period, so that the benefits of the energy storage system are improved, and the battery loss is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy storage system optimization, and relates to a distributed photovoltaic power distribution network shared energy storage optimization method. BACKGROUND

[0002] With the large-scale grid connection of new energy and the deepening of power market reform, independent energy storage systems play a key role in participating in power market auxiliary services and improving grid stability due to their flexible regulation capability. However, due to multiple limiting factors such as large fluctuations in power demand in the power market, low battery charging and discharging efficiency, and the like, the overall income level of independent energy storage systems is low, and it is difficult to fully realize their economic value. Therefore, in order to provide sustainable economic driving force for the transformation of new energy dominated power systems, it is urgent to optimize the charging and discharging strategy of independent energy storage systems to improve the income of energy storage systems.

[0003] In the prior art, there are also some related solutions related to optimizing the charging and discharging strategy of energy storage systems. For example, the charging and discharging method of the energy storage system and the energy storage system disclosed in Chinese Patent No. CN119853130B determine a reserved power based on a first photovoltaic power and a first load power, and determine a charging and discharging strategy based on the reserved power and one or more preset targets. This helps to improve the charging and discharging efficiency, reduce the operating cost and improve the economic benefit.

[0004] In addition, the charging and discharging control method based on the battery energy storage system disclosed in Chinese Patent No. CN118554497A predicts the future hourly power consumption load of the day according to user end data, and optimizes the charging and discharging strategy based on the energy storage capacity, the remaining power, the preset demand and the time-of-use electricity price data, taking the daily income as the optimization target, thereby improving the income of the energy storage system.

[0005] Although the above-mentioned solutions propose some solutions for optimizing the charging and discharging strategy of energy storage systems, the prior art still has the following limitations. Specifically, (1) the prior art adopts a global optimization strategy for historical electricity price data, without considering the particularity and change rule of electricity price in different time periods, resulting in that the predicted electricity price is difficult to accurately reflect the peak-valley change and short-term fluctuation trend of the electricity price.

[0006] (2) The prior art is extensive and conservative in managing the energy storage system charge and power constraints, and does not analyze the feasibility of the strategy, which seriously affects the reliability and stability of the charging and discharging strategy. Moreover, the prior art lacks real-time electricity price prediction and deviation calibration mechanism, and cannot respond to sudden fluctuations in the power market in time, thereby affecting the formulation of the charging and discharging strategy and the income optimization.

[0007] (3) The prior art does not combine the dynamic quantification of life decay with the charging and discharging behavior of the energy storage system when analyzing the battery penalty cost, resulting in a disconnection between the life cost model and the actual aging characteristics, affecting the accuracy of the benefit optimization. SUMMARY

[0008] In view of this, to solve the problems raised in the background art, a distributed photovoltaic power distribution network shared energy optimization method is proposed.

[0009] The object of the application can be achieved by the following technical solutions: The application provides a distributed photovoltaic power distribution network shared energy optimization method, comprising: S1. Collecting the real-time state of charge of the battery of the energy storage system at the starting time of the current rolling period.

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

[0011] S3. Based on the current rolling period electricity price prediction sequence, the real-time state of charge of the battery of the energy storage system and the operation constraint condition of the energy storage system, a rolling optimization model is constructed, and the optimal charging and discharging power sequence of the energy storage system in the rolling period is solved by the rolling optimization model, wherein the rolling optimization model takes maximizing the net benefit of the energy storage system as the objective function, and dynamically corrects the benefit calculation by fusing the battery life decay cost.

[0012] S4. Real-time monitoring of the electricity price prediction deviation index, if it is less than the preset deviation threshold, then according to the optimal charging and discharging power sequence, the charging and discharging action of the energy storage system is controlled, otherwise the electricity price prediction model is optimized and calibrated, the optimal charging and discharging power sequence is updated, the charging and discharging action of the energy storage system is adjusted, and when the next rolling period starts, jump to S1 to repeat the execution.

[0013] Compared with the prior art, the application has the following advantages: (1) The application statistically analyzes the historical electricity price data of different time periods to obtain the initial predicted electricity price, and adjusts it by a trend correction factor and an electricity price correction parameter to establish an electricity price prediction model, so that the electricity price prediction sequence generated by the model can accurately reflect the peak-valley change and short-term fluctuation trend of the electricity price, improving the accuracy of the subsequent charging and discharging execution strategy.

[0014] (2) The application fully considers the operation constraint condition of the energy storage system, reasonably selects the charging and discharging execution strategy according to the charging and discharging constraint interval length, and reasonably sets the charging and discharging power of the energy storage system according to the charge constraint boundary value and the power constraint threshold, to ensure the feasibility of the charging and discharging execution strategy in actual operation, and improve the operation reliability and stability of the energy storage system.

[0015] (3) The application adopts a rolling optimization strategy, optimizes and calibrates the electricity price prediction model by obtaining the deviation of 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 quickly responds to the sharp fluctuation of the electricity price, thereby significantly improving the income and reducing the invalid loss of the battery.

[0016] (4) According to the charging and discharging execution strategy, the application dynamically quantifies the life attenuation of the battery, effectively reduces the life attenuation speed of the battery, improves the accuracy of the income optimization, and realizes the maximization of the income of the energy storage system in the life cycle. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 The method of the application is implemented by the flow chart.

[0019] Figure 2 The logic flow chart for determining each charging and discharging candidate period in S3 of the application.

[0020] Figure 3 The tree chart for determining each charging and discharging execution combination in S3 of the application. DETAILED DESCRIPTION

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

[0022] Please refer to Figure 1 As shown in the drawings, the application provides a distributed photovoltaic power distribution network shared energy storage optimization method, which comprises: S1. Collecting the electricity market price at the starting time of the current rolling period and the real-time state of charge of the battery of the energy storage system.

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

[0024] As a preferred, the process of obtaining the time period electricity price prediction sequence of the current rolling period comprises: performing rolling time period division on the time zone to which the rolling period belongs according to a preset time interval.

[0025] The historical electricity price data is called, grouped according to the time zone of each day rolling period in history, and the grouped results are divided into a training set and a validation set according to time sequence.

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

[0027] A state transition probability matrix is constructed according to the adjacent period historical electricity price fluctuation state in the training set to obtain a trend correction factor for each rolling period, which is used 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 period, and the average deviation of the historical electricity price data in each rolling period in the validation set from the initial predicted electricity price is calculated as the electricity price correction parameter of each rolling period.

[0029] The ratio of the electricity market price at the start time of the input rolling period to the initial predicted electricity price of the first rolling period is taken as the adjustment coefficient, and the electricity price correction parameter of each rolling period is adjusted to obtain the predicted electricity price of each rolling period, and a preset electricity price prediction model is generated.

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

[0031] It should be noted that the process of obtaining the trend correction factor of each rolling period includes: analyzing the state change of the price of adjacent rolling periods in the training set, counting the frequency of the same state transition in the same rolling period of each 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 change of each rolling period relative to the previous rolling period, and the elements of the matrix correspond to the probability of state transition.

[0032] The preset decline state representation coefficient and the rise state representation coefficient are taken as weight factors to obtain a change trend index by weighted sum of the state transition probability of each rolling period. If the change trend index of a certain rolling period is negative, the trend correction direction of the rolling period is the decline direction, otherwise the trend correction direction of the rolling period is the rise direction. Similarly, the trend correction direction of each rolling period can be obtained, wherein the preset rise state representation coefficient and the decline state representation coefficient can be exemplarily set to 0.05 and -0.05.

[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, the process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period by the rolling optimization model includes that, the process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period by the rolling optimization model includes determining each charging and discharging candidate period according to the current rolling period time-of-use price prediction sequence, and the specific method is as follows: based on the current rolling period time-of-use price prediction sequence, the relative change rate of the predicted price of each rolling period and its adjacent period is calculated, and each valley price rolling period and each peak price rolling period in the current rolling period is identified according to the relative change rate.

[0039] The valley price rolling period with the price lower than the preset valley price threshold is marked as a charging candidate period, and the peak price rolling period with the price higher than the preset peak price threshold is marked as a discharging candidate period, so as to construct the charging candidate period set and the discharging candidate period set.

[0040] It should be noted that the process of obtaining the relative change rate of the predicted price of the adjacent period includes: calculating the difference value of the predicted price of each rolling period and the previous rolling period, and taking the difference value and the predicted price of each rolling period as the relative change rate of the previous sequence predicted price, and the relative change rate of the next sequence predicted price of each rolling period relative to the next rolling period is obtained in the same way.

[0041] It should be noted that if the relative change rates of the predicted price of a rolling period and its adjacent period are both positive, the rolling period is a peak price rolling period, otherwise the rolling period is a valley price rolling period.

[0042] It should be noted that the preset valley price threshold and the preset peak price threshold are respectively the price reference value of the charging action of the energy storage system and the price reference value of the discharging action, and if the predicted price of a rolling period is less than the preset valley price threshold, it means that the power cost is low at this time, which is the time for the energy storage system to charge, and if the predicted price is greater than the preset peak price threshold, it means that the power cost is high at this time, and the energy storage system can sell electricity to the grid to obtain economic benefits. The average price of the historical charging and discharging action of the energy storage system can be referred to.

[0043] As a preferred, the process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period by the rolling optimization model includes that, the process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period by the rolling optimization model includes determining each charging and discharging candidate period according to the current rolling period time-of-use price prediction sequence, and the specific method is as follows: real-time monitoring of the battery charge at the start time of the charging and discharging candidate period, and recording it as the battery starting charge.

[0044] If the battery initial state of charge of the charging candidate time period is located in the preset charging action interval and the battery initial state of charge of the discharging candidate time period is located in the preset discharging action interval, corresponding charging and discharging actions can be performed to screen each charging feasible time period and each discharging feasible time period from the charging candidate time period set and the discharging candidate time period set.

[0045] It should be noted that the above-mentioned preset charging and discharging action intervals are respectively the battery state of charge intervals defined by the energy storage system for judging whether the charging and discharging actions can be performed, wherein the preset charging action interval and the preset discharging action interval can be exemplarily set as [0.2, 0.4] and (0.7, 0.9]. The basis is that when the battery state of charge is located between 0.2 and 0.4, the internal resistance of the battery is relatively small, the efficiency of converting electrical energy into chemical energy during charging is relatively high, which is beneficial to improve the charging speed and reduce energy loss, and a certain state of charge space is reserved to cope with unexpected situations. When the battery state of charge is less than 0.2, the internal resistance of the battery increases, the charging efficiency decreases, and the heat generation increases. When the battery state of charge is greater than 0.4, the charging current will decrease, thereby reducing the charging power and prolonging the charging time, and damaging the battery life.

[0046] When the battery state of charge is located between 0.7 and 0.9, the discharging performance is relatively stable, the output voltage and current fluctuate less, and the battery can provide continuous and stable electrical energy. At the same time, a certain state of charge space is reserved to cope with unexpected situations. When the battery state of charge is less than 0.7, the output voltage of the battery will rapidly decrease with the discharging, which affects the normal operation of the system. When the battery state of charge is greater than 0.9, the internal resistance of the battery will increase, and the discharging efficiency will decrease.

[0047] Please refer to Figure 3 As a preferred, the process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period by the rolling optimization model includes determining each charging and discharging execution combination according to the energy storage system operation constraint condition, and the specific method is as follows: arranging the charging feasible time period and the discharging feasible time period in ascending order according to time sequence to construct a charging and discharging feasible sequence.

[0048] Taking the first preset number of elements in the charging and discharging feasible sequence as root elements in turn, based on the charging and discharging alternation rule and the preset charging and discharging constraint interval length, all branches are exhaustively enumerated from the subsequent elements by using the tree diagram statistical method, and the end elements of all branches are recorded as leaf elements.

[0049] Each branch from the root element to the leaf element in the tree diagram is counted to obtain each charging and discharging execution combination, wherein the charging and discharging feasible time period in the charging and discharging execution combination is recorded as a charging and discharging execution time period.

[0050] It should be noted that the above charging and discharging alternation rule refers to that after a charging operation is completed, a discharging operation must be performed, and then charging can be performed again, and vice versa. This alternation mode can avoid the battery being in a single charging or discharging state for a long time, and reduce damage to the battery caused by excessive charging and discharging.

[0051] It should be noted that the preset charging and discharging interval time length is a minimum time interval between charging operation and discharging action set to ensure safe and stable operation of the battery and meet the comprehensive demand of the system, and can be obtained from a product specification provided by a battery manufacturer.

[0052] As a preferred, the process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period by the rolling optimization model comprises determining the charging and discharging power of each charging and discharging execution period according to the energy storage system operation constraint condition, and the specific method is as follows: the absolute change amount of the charge of each charging and discharging execution period is calculated according to the battery initial state of charge and the preset state of charge constraint boundary value of the charging and discharging execution period, and the corresponding charging and discharging power of the charging and discharging execution period is obtained in combination with the fixed time length of the execution period.

[0053] If the charging or discharging power of an execution period is greater than the preset power constraint threshold, the preset power constraint threshold is taken as the charging or discharging power of the execution period.

[0054] According to the charging power of each charging execution period and the discharging power of each discharging execution period, the charging and discharging power sequence of each charging and discharging execution combination is constructed.

[0055] It should be noted that the preset state of charge constraint boundary value includes the upper limit value of the state of charge of the battery when the energy storage system performs the charging action and the lower limit value of the state of charge of the battery when the energy storage system performs the discharging action. In order to avoid overcharging and discharging of the battery, ensure that the battery is within a safe power range, reduce the loss of electrode materials and the occurrence of chemical side reactions, and thus prolong the cycle life of the battery, the product specification provided by the battery manufacturer can be obtained.

[0056] It should be noted that the preset power constraint threshold refers to the maximum allowed charging and discharging power of the power grid operation, which can avoid sudden power impact on the power grid caused by excessive charging and discharging power of the energy storage system, and affect the voltage and frequency stability of the power grid. The technical requirement book provided by the power grid dispatching department can be obtained.

[0057] The embodiment of the application fully considers the operation constraint condition of the energy storage system, reasonably selects the charging and discharging execution strategy according to the charging and discharging constraint interval time length, and reasonably sets the charging and discharging power of the energy storage system according to the state of charge constraint boundary value and the power constraint threshold, so as to ensure the feasibility of the charging and discharging execution strategy in actual operation, and improve the operation reliability and stability of the energy storage system.

[0058] As a preferred, the optimal charging and discharging power sequence of the energy storage system in the rolling period is solved by rolling optimization model, and the specific acquisition method is as follows: a certain charging and discharging execution combination data is substituted into the net profit calculation formula The net profit of the combination is calculated, wherein , are the predicted electricity price and charging power of the first charging execution period, respectively, , are the predicted electricity price and discharging power of the first discharging execution period, respectively, is the fixed length of the execution period, is the battery life attenuation cost of the combination, is the number of each charging execution period, , is the number of charging execution periods, is the number of each discharging execution period, , is the number of discharging execution periods, and the net profit of each charging and discharging execution combination is obtained in the same way.

[0059] The charging and discharging execution combination corresponding to the maximum net profit is screened, and the charging and discharging power sequence thereof is taken as the optimal charging and discharging power sequence of the energy storage system in the current rolling period.

[0060] As a preferred, the acquisition process of the battery life attenuation cost includes: calculating the state of charge change rate of each charging and discharging execution period in the charging and discharging execution combination, and linearly weighting and fusing to obtain a battery attenuation index.

[0061] The preset battery life attenuation table stored in the WEB cloud is called, the battery capacity attenuation grade of the combination is determined according to the battery attenuation index, the corresponding grade battery capacity attenuation rate is matched, and the preset unit capacity cost parameter is combined to calculate the battery life attenuation cost of the combination.

[0062] It should be noted that the acquisition process of the above state of charge change rate includes: calculating the absolute state of charge difference between the start time and the end time of each charging and discharging execution period, and performing ratio operation with the state of charge at the start time to obtain the state of charge change rate of each charging and discharging execution period.

[0063] It should be noted that the acquisition process of the above battery attenuation index includes: taking the preset charging and discharging influence coefficient as the weight, linearly weighting and fusing the charge change rate of each charging and discharging execution period to obtain the battery attenuation index, wherein the preset charging and discharging influence coefficient can be exemplarily set to 0.6, 0.4, which can be determined by performing multiple charging and discharging experiments on the battery, statistically analyzing the experimental data, and determining the contribution degree of the charging process and the discharging process to the battery capacity attenuation and the internal resistance increase respectively. The experimental results show that in the common charging and discharging application scene, the influence of the charging process on the battery life attenuation is relatively larger, and the influence of the discharging process is relatively smaller.

[0064] It should be noted that the preset battery life attenuation table contains battery attenuation indexes corresponding to different battery capacity attenuation levels and adapted battery capacity attenuation rates.

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

[0066] According to the charging and discharging execution strategy, the embodiments of the present application dynamically quantify the life attenuation of the battery, effectively reduce the life attenuation speed of the battery, improve the accuracy of the benefit optimization, and maximize the benefit of the energy storage system in the life cycle.

[0067] S4. Real-time monitoring of the price prediction deviation index, if it is less than the preset deviation threshold, the optimal charging and discharging power sequence is used to control the charging and discharging action of the energy storage system, otherwise the price prediction model is optimized and calibrated, the optimal charging and discharging power sequence is updated, and the charging and discharging action of the energy storage system is adjusted, and when the next rolling period starts, jump to S1 to repeat the execution.

[0068] As a preferred, the process of optimizing and calibrating the price prediction model, updating the optimal charging and discharging power sequence, and adjusting the charging and discharging action of the energy storage system includes: calling the actual electricity market price data, and calculating the price prediction cumulative deviation of the previous rolling period before each charging and discharging execution period.

[0069] The integral ratio operation is performed on the price prediction deviation value and the predicted price of the previous rolling period to obtain the price prediction deviation index.

[0070] When the price prediction deviation index is greater than or equal to the preset price deviation threshold, the ratio of the actual price and the predicted price of the previous rolling period is taken as the optimization coefficient, and the predicted price of each subsequent rolling period is optimized to obtain the optimized price prediction sequence.

[0071] The optimized price prediction sequence is input into the rolling optimization model, the optimal charging and discharging power sequence is recalculated, and the charging and discharging control instructions of the subsequent rolling period of the energy storage system are adjusted.

[0072] It should be noted that the above process of obtaining the electricity price prediction deviation index includes: predicting the electricity price according to each rolling period between the charge-discharge execution period and the previous charge-discharge execution period, calculating the absolute difference between the predicted electricity price and the actual electricity price, and taking the cumulative sum as the electricity price prediction cumulative deviation.

[0073] The integral operation is performed on the electricity price prediction cumulative deviation, and the integral sum operation is performed on each rolling period predicted electricity price, and the ratio of the two is taken as the electricity price prediction deviation index.

[0074] The embodiment of the present application adopts a rolling optimization strategy, 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 charge-discharge power sequence, adjusts the charge-discharge action of the energy storage system, and quickly responds to the sharp fluctuation of the electricity price, thereby significantly improving the income and reducing the invalid loss of the battery.

[0075] The above formulas are all dimensionless numerical calculations, and the formulas are obtained by collecting a large amount of data to simulate the recent real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0076] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product.

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

[0078] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0080] Finally, the above only is the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A distributed photovoltaic power distribution network shared energy storage optimization method, characterized in that, The method comprises the following steps: S1. Collect the real-time state of charge of the energy storage system battery at the starting time of the current rolling cycle and the electricity market price at the starting time of the current rolling cycle; S2. Input the electricity market price into a preset electricity price prediction model to obtain a time-of-use electricity price prediction sequence of the current rolling cycle; S3. Construct a rolling optimization model based on the time-of-use electricity price prediction sequence of the current rolling cycle, the real-time state of charge of the energy storage system battery, and the operating constraints of the energy storage system, and solve the optimal charging and discharging power sequence of the energy storage system in the rolling cycle by the rolling optimization model, wherein the rolling optimization model takes maximizing the net income of the energy storage system as the objective function, and dynamically corrects the income calculation by fusing the battery life attenuation cost; S4. Real-time monitor the electricity price prediction deviation index, 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, and adjust the charging and discharging action of the energy storage system, and when the next rolling cycle starts, jump to S1 and repeat the execution.

2. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 1, characterized in that, The process of obtaining the time-of-use electricity price prediction sequence of the current rolling cycle comprises: Divide the time zone to which the rolling cycle belongs into rolling periods according to a preset time interval; Retrieve historical electricity price data, group the data according to the time zone of the rolling cycle of each day in history, and divide the grouping results into a training set and a validation set according to time sequence; Calculate the mean of the historical electricity price data of the same rolling period in the training set to obtain the initial predicted electricity price of each rolling period; Construct a state transition probability matrix according to the historical electricity price fluctuation state of adjacent periods in the training set to obtain the trend correction factor of each rolling period, and correct the initial predicted electricity price by the trend correction factor; Compare the historical electricity price data in the validation set with the corrected initial predicted electricity price period by period, calculate the average deviation of the historical electricity price data and the initial predicted electricity price of each rolling period in the validation set, and take the average deviation as the electricity price correction parameter of each rolling period; Compare the electricity market price at the starting time of the input rolling cycle with the initial predicted electricity price of the first rolling period, take the ratio of the two as the adjustment coefficient, and adjust the electricity price correction parameter of each rolling period to obtain the predicted electricity price of each rolling period, and generate a preset electricity price prediction model; Input the electricity market price data at the starting time of the current rolling cycle into the preset electricity price prediction model to obtain the time-of-use electricity price prediction sequence of the current rolling cycle.

3. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 2, characterized in that, The electricity price prediction model comprises: wherein , are the predicted electricity price and the initial predicted electricity price of the th rolling period in the th rolling period in the time zone to which the th rolling period belongs, th rolling period in the time zone to which the th rolling period belongs, th rolling period in the time zone to which the th rolling period belongs, th rolling period in the time zone to which the th rolling period belongs, is the number of the time zone to which each rolling period belongs, , is the number of the time zone to which the rolling period belongs, is the number of each rolling period, , is the number of the rolling period.

4. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 1, characterized in that, The process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling cycle by the rolling optimization model comprises determining each charging and discharging candidate period according to the time-of-use electricity price prediction sequence of the current rolling cycle, and the specific method is as follows: Based on the time-of-use electricity price prediction sequence of the current rolling cycle, calculate the relative change rate of the predicted electricity price of each rolling period and its adjacent period, and identify each valley electricity price rolling period and each peak electricity price rolling period in the current rolling cycle according to the relative change rate; Mark the valley electricity price rolling period with the electricity price lower than a preset valley electricity price threshold as a charging candidate period, mark the peak electricity price rolling period with the electricity price higher than a preset peak electricity price threshold as a discharging candidate period, and construct a charging candidate period set and a discharging candidate period set according to the above method.

5. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 4, characterized in that, The process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period through the rolling optimization model comprises judging whether each charging and discharging candidate period can perform charging and discharging actions according to the real-time state of charge of the battery of the energy storage system, and the specific method is as follows: The battery charge at the starting time of the charging and discharging candidate period is monitored in real time, and is recorded as the battery starting charge; If the battery starting charge of the charging candidate period is in the preset charging action interval, and the battery starting charge of the discharging candidate period is in the preset discharging action interval, the corresponding charging and discharging actions can be performed, so as to screen each charging feasible period and each discharging feasible period from the charging candidate period set and the discharging candidate period set.

6. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 5, characterized in that, The process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period through the rolling optimization model comprises determining each charging and discharging execution combination according to the operating constraints of the energy storage system, and the specific method is as follows: The charging feasible periods and the discharging feasible periods are arranged in ascending order according to the time sequence to construct a charging and discharging feasible sequence; The first preset number of elements in the charging and discharging feasible sequence are sequentially taken as root elements, and based on the charging and discharging alternation rule and the preset charging and discharging constraint interval time length, all branches are exhaustively enumerated from the subsequent elements by using a tree diagram statistical method, and the end elements of all branches are recorded as leaf elements; Each branch from the root element to the leaf element in the tree diagram is counted to obtain each charging and discharging execution combination, wherein the charging and discharging feasible periods in the charging and discharging execution combination are recorded as charging and discharging execution periods.

7. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 6, characterized in that, The process of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period through the rolling optimization model comprises determining the charging and discharging power of each charging and discharging execution period according to the operating constraints of the energy storage system, and the specific method is as follows: According to the battery starting charge of the charging and discharging execution period and the preset charge constraint boundary value, the absolute charge change of each charging and discharging execution period is calculated, and combined with the fixed time length of the execution period, the corresponding charging and discharging power of the charging and discharging execution period is obtained; If the charging or discharging power of an execution period is greater than the preset power constraint threshold, the preset power constraint threshold is taken as the charging or discharging power of the execution period; According to the charging power of each charging execution period and the discharging power of each discharging execution period, a charging and discharging power sequence of each charging and discharging execution combination is constructed.

8. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 7, characterized in that, The specific acquisition method of solving the optimal charging and discharging power sequence of the energy storage system in the rolling period through the rolling optimization model is as follows: Substitute the certain charging and discharging execution combination data into the net profit calculation formula Calculate the net profit of the combination, wherein , are the predicted electricity price and charging power of the first charging execution period, respectively, , are the predicted electricity price and discharging power of the first discharging execution period, respectively, is the fixed length of the execution period, is the battery life attenuation cost of the combination, is the number of charging execution periods, , is the number of charging execution periods, is the number of discharging execution periods, , is the number of discharging execution periods, and the net profit of each charging and discharging execution combination is obtained in the same way. The charging and discharging execution combination corresponding to the maximum net profit is screened, and the charging and discharging power sequence thereof is taken as the optimal charging and discharging power sequence of the energy storage system in the current rolling period.

9. The distributed photovoltaic power distribution network shared energy storage optimization method according to claim 8, characterized in that, The process of obtaining the battery life attenuation cost comprises: The charge change rate of each charging and discharging execution period in the charging and discharging execution combination is calculated, and the battery attenuation index is obtained by linearly fusing the charge change rate; The preset battery life attenuation table stored in the WEB cloud is called, the battery capacity attenuation grade of the combination is determined according to the battery attenuation index, the corresponding grade battery capacity attenuation rate is matched, and the battery life attenuation cost of the combination is calculated combined with the preset unit capacity cost parameter.

10. The distributed photovoltaic power distribution network shared energy storage optimization method 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 comprises: The actual electricity market price data is called, and before the action is performed in each charging and discharging execution period, the price prediction deviation value of the previous rolling period is calculated; The price prediction deviation value and the predicted price of the previous rolling period are subjected to integral ratio operation to obtain a price prediction deviation index; When the price prediction deviation index is greater than or equal to a preset price deviation threshold, the ratio of the actual price to the predicted price of the previous rolling period is taken as an optimization coefficient, and the predicted price of each subsequent rolling period is optimized to obtain an optimized price prediction sequence; The optimized price prediction sequence is input into a rolling optimization model, the optimal charging and discharging power sequence is recalculated, and the charging and discharging control instructions of the subsequent rolling period of the energy storage system are adjusted.

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