Cooperative scheduling methods, devices, equipment, and storage media based on independent energy storage

Abstract

This application provides a collaborative scheduling method, device, equipment, and storage medium based on independent energy storage, belonging to the field of energy storage scheduling technology. The method includes: determining a comprehensive energy storage scheduling index corresponding to the predicted active power based on the current power stability index of the power grid and the current energy storage lifetime loss index of the independent energy storage station; obtaining the initial charging and discharging power of the independent energy storage station based on the comprehensive energy storage scheduling index; the predicted active power is the active power predicted based on the first historical active power of the power grid; adjusting the initial charging and discharging power of the independent energy storage station based on the deviation between the real-time active power of the power grid and the predicted active power to obtain the target charging and discharging power of the independent energy storage station; and scheduling the independent energy storage station based on the target charging and discharging power. The collaborative scheduling method, device, equipment, and storage medium based on independent energy storage provided in this application can improve the overall stability and reliability of power grid operation.

Description

Technical Field

[0001] This application belongs to the field of energy storage dispatching technology, and more specifically, it relates to a collaborative dispatching method, device, equipment, and storage medium based on independent energy storage. Background Technology

[0002] With the continuous expansion of the power grid and the large-scale integration of renewable energy, the problem of power grid fluctuations is becoming increasingly prominent, posing a severe challenge to the safe and stable operation of the power grid. As an important means of regulating power grid output and improving grid flexibility, the rationality of the dispatch strategy for independent energy storage power stations is crucial.

[0003] However, existing dispatching methods lack effective adjustment mechanisms when faced with deviations between actual grid power and forecasts, resulting in insufficient dispatching accuracy and an inability to fully leverage the role of independent energy storage power stations in mitigating grid power fluctuations, thereby affecting the overall stability and reliability of grid operation. Summary of the Invention

[0004] The purpose of this application is to provide a collaborative scheduling method, device, equipment, and storage medium based on independent energy storage, so as to improve the overall stability and reliability of power grid operation.

[0005] A first aspect of this application provides a cooperative scheduling method based on independent energy storage, comprising:

[0006] Based on the current power stability index of the power grid and the current energy storage lifetime loss index of the independent energy storage power station, the comprehensive energy storage dispatch index corresponding to the predicted active power is determined; the initial charging and discharging power of the independent energy storage power station is obtained based on the comprehensive energy storage dispatch index; the power stability index is an indicator that measures the degree of power fluctuation in the power grid, and the energy storage lifetime loss index represents the degree of lifetime loss of the independent energy storage power station during the charging and discharging process; the predicted active power is the current active power predicted based on the first historical active power of the power grid and through an autoregressive integral moving average model.

[0007] Based on the deviation between the real-time active power of the power grid and the predicted active power, the initial charging and discharging power of the independent energy storage power station is adjusted to obtain the target charging and discharging power of the independent energy storage power station.

[0008] Independent energy storage power stations are scheduled based on target charging and discharging power.

[0009] A second aspect of this application provides a cooperative scheduling device based on independent energy storage, comprising:

[0010] The first charging and discharging power determination module is used to determine the comprehensive energy storage dispatch index corresponding to the predicted active power based on the current power stability index of the power grid and the current energy storage lifetime loss index of the independent energy storage station; the initial charging and discharging power of the independent energy storage station is obtained based on the comprehensive energy storage dispatch index; the power stability index is an indicator that measures the degree of power fluctuation in the power grid, and the energy storage lifetime loss index represents the degree of lifetime loss of the independent energy storage station during the charging and discharging process; the predicted active power is the current active power predicted based on the first historical active power of the power grid and through an autoregressive integral moving average model.

[0011] The second charging and discharging power determination module is used to adjust the initial charging and discharging power of the independent energy storage station based on the deviation between the real-time active power of the power grid and the predicted active power, so as to obtain the target charging and discharging power of the independent energy storage station.

[0012] The scheduling module is used to schedule independent energy storage power stations based on target charging and discharging power.

[0013] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described cooperative scheduling method based on independent energy storage.

[0014] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described cooperative scheduling method based on independent energy storage.

[0015] The beneficial effects of the collaborative scheduling method, device, equipment, and storage medium based on independent energy storage provided in this application are as follows: Firstly, this application predicts the current active power based on the historical active power of the power grid, providing direction for energy storage scheduling in advance, enhancing the foresight of scheduling, and helping to cope with power grid fluctuations. Secondly, it determines the comprehensive scheduling index by integrating power stability and energy storage lifespan loss indicators, and obtains the initial charging and discharging power, ensuring stable grid operation while considering the lifespan of the energy storage station and reducing operating costs. Thirdly, it adjusts the initial charging and discharging power based on the deviation between real-time and predicted active power, making the target charging and discharging power more realistic and improving scheduling accuracy. Ultimately, it achieves reasonable scheduling of independent energy storage stations, effectively smoothing power grid fluctuations and improving the stability and reliability of grid operation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a collaborative scheduling method based on independent energy storage provided in an embodiment of this application;

[0018] Figure 2 A structural block diagram of a collaborative scheduling device based on independent energy storage provided in an embodiment of this application;

[0019] Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0022] Please refer to Figure 1 , Figure 1 A flowchart illustrating a collaborative scheduling method based on independent energy storage provided in an embodiment of this application is available. This method can be executed by an electronic device and may include:

[0023] S101: Based on the current power stability index of the power grid and the current energy storage lifetime loss index of the independent energy storage power station, determine the comprehensive energy storage dispatch index corresponding to the predicted active power; obtain the initial charging and discharging power of the independent energy storage power station based on the comprehensive energy storage dispatch index; the power stability index is an indicator that measures the degree of power fluctuation in the power grid, and the energy storage lifetime loss index represents the degree of lifetime loss of the independent energy storage power station during the charging and discharging process; the predicted active power is the current active power predicted based on the first historical active power of the power grid and through an autoregressive integral moving average model.

[0024] In this embodiment, the first historical active power refers to the active power data of the power grid during the second historical period, which includes power change information during the grid operation. The active power values ​​in different time periods reflect the fluctuations in electricity load over time (such as daily or weekly cycles). By analyzing the first historical active power data, such as using time series analysis algorithms (e.g., autoregressive integral moving average models) or specific prediction algorithms like machine learning algorithms, the patterns and trends of power changes are identified, and the current active power is predicted to obtain the predicted active power. This prediction result can provide a forward-looking basis for energy storage scheduling, facilitating advance planning of charging and discharging operations to cope with future power fluctuations.

[0025] In this embodiment, the power stability index of the power grid is used to measure the degree of power fluctuation in the power grid. Excessive power fluctuation can affect the normal operation of various devices in the power grid and even cause power grid failures. The power stability index of the power grid can be determined by calculating the variance of the predicted power fluctuation. The larger the value of the power stability index, the more stable the power grid power. The purpose is to minimize the power fluctuation of the power grid during operation and ensure the reliable operation of the power grid.

[0026] The energy storage lifespan loss index for independent energy storage power stations refers to the lifespan reduction caused by factors such as depth of charge / discharge and number of charge / discharge cycles during the charging and discharging process. This index can be determined by establishing a calculation model related to these factors. A lower energy storage lifespan loss index indicates less energy loss, which helps reduce the operating costs and maintenance requirements of the energy storage power station.

[0027] This embodiment can linearly combine the grid power stability index and the energy storage lifetime loss index according to certain weights to obtain a comprehensive energy storage dispatch index. For example:

[0028]

[0029] in, This represents a comprehensive indicator for energy storage dispatch. This indicates the weights corresponding to the power grid stability index. This represents the value corresponding to the power grid stability index. This indicates the weight corresponding to the energy storage lifespan loss index of an independent energy storage power station. This indicates the value corresponding to the energy storage life loss index of an independent energy storage power station.

[0030] The goal is to balance the need for stable grid power output with minimizing the lifespan loss of energy storage power stations. The weighting can be determined empirically.

[0031] This embodiment can obtain the initial charging and discharging power of an independent energy storage power station using a linear programming algorithm, and construct an objective function with the comprehensive energy storage scheduling index as the optimization objective. , where P is the decision variable, i.e., the candidate charge / discharge power (charge is positive, discharge is negative). and These are the grid power stability index and the energy storage lifetime loss index, which depend on the charging and discharging power P, respectively; and the state of charge (SOC) constraint of the energy storage power station is also considered. min ≤SOC(P)≤SOC max ), charge and discharge power constraint (P) min ≤P≤P max ) and grid power balance constraints (P 预测 +P≈P 目标 The linear programming algorithm searches for the optimal charging and discharging power of the objective function F(P) within the feasible region that satisfies these constraints. The optimal solution is the initial charging and discharging power of the preliminary planning of the charging and discharging operation of the energy storage power station, taking into account various factors of the power grid and energy storage.

[0032] S102: Based on the deviation between the real-time active power of the power grid and the predicted active power, the initial charging and discharging power is adjusted to obtain the target charging and discharging power.

[0033] In this embodiment, the active power of the power grid is acquired in real time and compared with the predicted active power to calculate the deviation between the two. The predicted active power is a prediction result based on historical data, while the actual active power of the power grid in operation is affected by various unforeseen factors (such as sudden large-scale power equipment access, the impact of weather changes on distributed power sources, etc.), resulting in differences from the predicted value. By calculating the deviation, the degree of deviation between the actual operation of the power grid and the predicted situation can be understood.

[0034] The initial charge / discharge power is adjusted based on the magnitude and direction of the deviation. In practical applications, the predicted active power can be a range, such as P. max,Y -P min,Y , where P max,Y P represents the upper limit of the predicted active power. min,Y This indicates the lower limit of the predicted active power.

[0035] If the real-time active power is higher than P max,Y This indicates that the actual power demand of the power grid is greater than expected. If the independent energy storage power station's state of charge allows, the discharge power should be appropriately increased; conversely, if the real-time active power is lower than P... min,Y If the state of charge of the independent energy storage power station allows, the charging power should be increased appropriately.

[0036] If the real-time active power is at P max,Y -P min,YThe value between these values ​​indicates that the current predicted active power matches the actual value, and there is no need to adjust the initial charge / discharge power. The current initial charge / discharge power can be used as the target charge / discharge power.

[0037] This embodiment uses real-time deviation-based adjustments to make the charging and discharging operations of the energy storage power station more aligned with the actual needs of the power grid, thereby improving the stability and reliability of the power grid operation and obtaining a target charging and discharging power that is more in line with the actual situation.

[0038] S103: Dispatch of independent energy storage power stations based on target charging and discharging power.

[0039] In this embodiment, the independent energy storage power station can store or release electrical energy according to the target charging and discharging power. In this way, the independent energy storage power station works in conjunction with the power grid to achieve peak shaving and valley filling, ensuring the stable operation of the power grid.

[0040] For example, when the grid has excess power (high real-time active power), the independent energy storage station charges according to the target charging power to store the excess energy; when the grid has insufficient power (low real-time active power), the independent energy storage station injects energy into the grid according to the target discharging power to maintain the grid power balance.

[0041] As can be seen from the above, this embodiment predicts the active power within the current preset time period by using the historical active power of the power grid, providing direction for energy storage dispatch in advance, enhancing the foresight of dispatch, and helping to cope with power fluctuations in the power grid. Secondly, it determines the comprehensive dispatch index by combining power stability and energy storage lifespan loss indicators, and obtains the initial charging and discharging power, ensuring stable operation of the power grid while taking into account the lifespan of the energy storage station and reducing operating costs. Furthermore, it adjusts the initial charging and discharging power based on the deviation between real-time and predicted active power, making the target charging and discharging power more realistic and improving dispatch accuracy. Ultimately, it achieves reasonable dispatch of independent energy storage stations, effectively smoothing power grid power fluctuations and improving the stability and reliability of power grid operation.

[0042] In one embodiment of this application, the first historical active power of the power grid is the active power corresponding to the first historical time period;

[0043] The autoregressive integral moving average model is obtained in the following way:

[0044] Determine the stationarity of the second historical active power of the power grid after differential processing; stationarity refers to the stability of the average value of the second historical active power of the power grid at different historical moments; the second historical active power is the active power corresponding to the second historical period, which is earlier than the first historical period;

[0045] In response to a stationarity greater than a stationarity threshold, the order of the difference processing corresponding to that stationarity is taken as the difference order;

[0046] The autoregression order is determined based on the degree of first correlation between the active power at multiple lag times and the active power at the target time within the second historical period; the degree of first correlation indicates the consistency between the changing trend of active power at multiple lag times and the changing trend of active power at the target time.

[0047] The moving average order is determined based on the second degree of correlation between the historical predicted active power error at multiple lag times and the predicted active power error at the target time within the second historical period. The second degree of correlation indicates the consistency between the changing trend of the historical predicted active power error at multiple lag times and the changing trend of the predicted active power error at the target time within the second historical period. The historical predicted active power error refers to the difference between the predicted active power at the target time obtained through the historical prediction model and the actual active power at the target time within the second historical period.

[0048] An autoregressive integral moving average model is constructed based on the autoregressive order, the difference order, and the moving average order.

[0049] In this embodiment, an autoregressive integral moving average model is constructed for short-term power forecasting of the power grid. The parameters of the autoregressive integral moving average model include the autoregressive order, the difference order, and the moving average order. The second historical active power is the basic data used to determine the model parameters, and it is earlier in time than the first historical active power. The first historical active power is the historical data used to input the model to generate the prediction results.

[0050] The second historical active power of the power grid exhibits trend or seasonal variations, which can affect the accuracy of prediction models. Therefore, it is necessary to determine its stationarity. In this embodiment, stationarity refers to the stability of the average second historical active power of the power grid at different historical times. By performing differential processing on the second historical active power (such as first-order differential, second-order differential, etc.), trend and seasonal components in the data are eliminated. After each differential processing, the stationarity of the data is evaluated. When the stationarity is greater than a preset stationarity threshold, it indicates that the sequence after differential processing has reached a relatively stable state, and the order of that differential processing is determined as the differential order. For example, after performing first-order differential processing on the historical active power, if the mean tends to stabilize at different times and the stationarity is greater than the threshold, then the differential order is 1. This is done because stable data better meets the requirements of the autoregressive integral moving average model, enabling the model to better capture the inherent patterns of the data.

[0051] The autoregressive order is determined based on the second historical active power data within the second historical period.

[0052] The first degree of correlation is analyzed between the active power at multiple lag times (power values ​​at multiple times earlier than the target time in the second historical active power) and the active power at the target time (power value at a specific time in the second historical active power used as the analysis benchmark). The first degree of correlation indicates the consistency between the trend of active power change in the second historical period and the trend of active power change in the current period.

[0053] Active power at multiple lag times refers to the active power data of the power grid recorded at multiple earlier times relative to a specific analysis point (i.e., the subsequent "target time") within the second historical period. For example, if "time t" in the historical period is taken as the target time, the lag times can be time t-1, time t-2, time t-3, etc. (where "t-1" represents a time one unit earlier than time t, and the unit of time can be set to minutes, hours, etc., depending on the actual scenario).

[0054] Target-time active power refers to the active power data of the power grid at a specific moment within the second historical time period, which is the object of analysis. Target-time active power serves as a reference benchmark for lagging-time active power, used to measure the correlation between lagging-time power and the power at that moment. For example, when analyzing historical data, each moment within a historical time period can be selected sequentially as the target moment, and its correlation with the power at multiple previous lagging moments can be calculated.

[0055] For example, if the trend of active power increase / decrease at lags 1, 2, and 3 in the second historical period is highly consistent with the trend of power change at the target time, that is, the power change at the target time is significantly affected by the trend of the first three lags, then the autoregression order is set to 3.

[0056] The moving average order is determined based on historical prediction error data within the second historical period. The analysis examines the second degree of correlation between the historical predicted active power errors at multiple lag times (error values ​​at multiple earlier times relative to the target time within the historical prediction errors corresponding to the second historical active power) and the predicted active power errors at the target time (error values ​​at a specific time serving as the analysis benchmark within the historical prediction errors corresponding to the second historical active power). This degree of correlation indicates the consistency between the changing trends of the lag time errors and the changing trends of the target time errors. Specifically, the historical predicted active power error is defined as the difference between the target time predicted active power obtained through the historical prediction model within the second historical period and the actual active power at that time (i.e., the actual target time value within the second historical active power).

[0057] For example, if the trends of prediction error increase and decrease at lags of 1 and 2 are highly consistent with the trend of error change at the target time, meaning that the change in error at the target time is significantly affected by the error trends at the first two lags, then the moving average order is set to 2. This order can effectively capture the historical correlation of the error term and improve the model's ability to handle random disturbances.

[0058] Based on the determined autoregressive order, differencing order, and moving average order, an autoregressive integral moving average model is constructed. These three parameters together define the model's structure, enabling it to accurately model grid power by incorporating the correlation of grid power variation trends, stationarity handling, and the correlation of error term variation trends.

[0059] During the forecasting phase, the first historical active power of the power grid is input into the constructed model. The model learns from the first historical data based on parameter settings, identifies power change patterns, and ultimately outputs the current predicted active power. This forecasting result provides a reliable forward-looking basis for subsequent energy storage coordinated scheduling, supporting the accurate formulation of scheduling strategies.

[0060] As can be seen from the above, this embodiment achieves short-term power prediction of the power grid by constructing an autoregressive integral moving average model: the differential order (ensuring data stability), autoregressive order (capturing power trend correlation), and moving average order (fitting error historical correlation) are determined based on the second historical active power, and then the prediction result is generated using the first historical active power as input. The entire logic enables the model parameters to be deeply matched with the characteristics of historical data, significantly improving the prediction accuracy, laying a reliable data foundation for energy storage collaborative scheduling, and thus improving the stability of power grid operation.

[0061] In one embodiment of this application, the method for determining the current power stability index of the power grid includes:

[0062] Based on the first historical time period, obtain multiple historical predicted active power;

[0063] Calculate the fluctuation variance of multiple historical predicted active power, and determine the current power stability index of the power grid based on the reciprocal of the fluctuation variance;

[0064] The methods for determining the current energy storage life loss indicators of independent energy storage power stations include:

[0065] The energy storage life loss index of an independent energy storage power station is determined based on the current depth of charge and discharge and the cumulative number of charge and discharge cycles.

[0066] In this embodiment, multiple historical predicted active power values ​​are obtained within a first historical time period. These historical predicted active power values ​​represent the predicted power of the grid during the first historical time period, reflecting the changes in predicted power during that period. By calculating the variance of the fluctuations of these multiple historical predicted active power values, the degree of dispersion of the predicted power around its mean is measured. The larger the variance, the more drastic the fluctuations in predicted power, and the worse the grid power stability; conversely, the smaller the variance, the smoother the power fluctuations, and the better the stability. The current power stability index of the grid is determined based on the reciprocal of the aforementioned variance. Due to the reciprocal relationship, the power stability index is positively correlated with the grid power stability; that is, the smaller the variance and the larger its reciprocal, the better the grid power stability, thus intuitively and effectively reflecting the degree of grid power stability.

[0067] In this embodiment, the energy storage lifespan degradation index of an independent energy storage power station is determined based on the current depth of charge / discharge and number of charge / discharge cycles. The depth of charge / discharge refers to the ratio of the amount of electricity released or charged during the charging / discharging process to the battery's rated capacity. Deep charge / discharge cycles accelerate battery aging; for example, the greater the depth of charge / discharge each time, the more intense the internal chemical reactions within the battery, leading to faster wear and tear on the electrode materials, thus shortening the lifespan of the energy storage power station.

[0068] Meanwhile, the number of charge and discharge cycles up to now also affects the lifespan of the energy storage. Frequent charge and discharge operations will exacerbate the wear and tear on the energy storage power station equipment. Each charge and discharge cycle will cause certain changes in the internal structure of the battery. After multiple cycles, the battery performance will gradually decline and the lifespan will be shortened.

[0069] Determining energy storage lifespan loss indicators by comprehensively considering the current depth of charge / discharge and the number of charge / discharge cycles can fully reflect the lifespan loss of independent energy storage power stations during operation. By establishing a calculation model related to these two factors, the depth of charge / discharge and the number of charge / discharge cycles are quantified into indicator values. For example, this can be achieved through a weighted formula (such as lifespan loss = ...). Depth of charge and discharge + The cumulative number of charge and discharge cycles, of which , The weighted coefficients are used to comprehensively assess the degree of lifespan loss. This indicator comprehensively reflects the lifespan loss status of independent energy storage power stations during operation, providing a key basis for calculating comprehensive energy storage dispatch indicators.

[0070] As can be seen from the above, this embodiment determines the grid power stability index by calculating the reciprocal of the variance of multiple historical predicted active power fluctuations within the first historical period. This provides a clear quantitative measure of grid power stability and a definite stability reference for dispatching. Furthermore, the energy storage lifespan loss index is determined based on the current charging and discharging depth and number of cycles, comprehensively reflecting the lifespan loss of the energy storage power station.

[0071] In one embodiment of this application, a comprehensive energy storage dispatch index corresponding to the predicted active power is determined based on the power stability index of the power grid and the energy storage lifetime loss index of independent energy storage power stations, including:

[0072] Determine the weights corresponding to the power stability index and the energy storage lifetime loss index.

[0073] The power stability index of the power grid and the energy storage life loss index of independent energy storage power stations are weighted and fused together with their respective weights to obtain the comprehensive energy storage dispatch index corresponding to the predicted active power.

[0074] In this embodiment, the weights corresponding to the power stability index and the energy storage lifespan loss index can be set based on experience. The empirical setting needs to be combined with the grid operation requirements and energy storage operation goals in the actual application scenario. For example, if the grid is in a peak load period or a critical operating phase, the power stability requirements are higher, and the power stability index can be given a higher weight through empirical adjustment; if the energy storage equipment is nearing its service life or the replacement cost is high, the weight of the energy storage lifespan loss index can be increased through experience to prioritize the protection of the energy storage equipment.

[0075] In this embodiment, the sum of the weights of the power stability index and the energy storage life loss index is equal to 1, ensuring the rationality and standardization of the weight allocation.

[0076] In this embodiment, after determining the power stability index and its weight, as well as the energy storage lifetime loss index and its weight, a comprehensive energy storage dispatch index is obtained through weighted fusion. Specifically, the calculation involves multiplying the power stability index by its corresponding weight and adding the energy storage lifetime loss index multiplied by its corresponding weight. This weighted fusion method comprehensively considers both grid power stability and independent energy storage lifetime loss—two key factors. In energy storage dispatch decisions, the comprehensive index serves as a holistic evaluation standard, enabling the implementation of an optimal dispatch strategy that minimizes energy storage lifetime loss while ensuring stable grid operation, thus achieving efficient and economical power system operation.

[0077] As can be seen from the above, this embodiment determines the weights of power stability index and energy storage lifetime loss index based on experience, and then obtains a comprehensive energy storage dispatch index through weighted fusion. This can flexibly adapt to dispatch requirements under different scenarios, effectively balance grid power stability and energy storage lifetime loss, improve grid operation reliability and energy storage utilization efficiency, and reduce overall operating costs.

[0078] In one embodiment of this application, the target charge / discharge power includes charging power and discharging power; the energy storage device of the independent energy storage power station includes multiple sets of batteries connected in parallel;

[0079] Dispatching independent energy storage power stations based on target charging and discharging power includes:

[0080] In response to the predicted active power being less than the first preset power, multiple parallel batteries are charged based on the charging power.

[0081] In response to the predicted active power being greater than the second preset power, the number of batteries to be discharged is determined based on the discharge power. Based on the number, the battery group to be discharged is determined from multiple parallel battery groups. The battery group to be discharged is then used to discharge the power grid.

[0082] The first preset power is less than the second preset power.

[0083] In this embodiment, when the predicted active power is less than the first preset power, from the perspective of grid operation, it indicates that the grid is currently or within the predicted period in a state of relative power surplus. The first preset power is a threshold pre-set based on multiple factors such as grid load characteristics, power generation, and the charging capacity of the energy storage station. It serves as a standard for judging whether the grid has excess power, providing a basis for the charging decisions of the energy storage station.

[0084] If the predicted active power is less than a first preset power, multiple sets of parallel-connected batteries are charged based on a given charging power. These parallel-connected batteries form the energy storage devices of an independent energy storage power station. This parallel structure can improve the total capacity and output capability of the energy storage system. Charging based on the target charging power can effectively store excess electrical energy in the grid, avoiding energy waste, and providing energy reserves for when the grid power is insufficient. This not only helps maintain the balance of grid power but also fully utilizes the storage function of the energy storage power station, optimizing the grid's energy distribution.

[0085] When the predicted active power exceeds the second preset power, it means that the active power provided by the power grid in the current or predicted period cannot meet the load demand. The second preset power is also a threshold set by comprehensively considering factors such as the power grid load demand and the discharge capacity of energy storage power stations, and is used to determine whether the power grid is in a state of insufficient power.

[0086] The required number of batteries is determined based on the target discharge power. Since multiple battery banks are connected in parallel, the number of batteries participating in the discharge will affect the total discharge power output. By calculating the relationship between the target discharge power and the discharge power of a single battery bank, the number of batteries required to meet the target discharge power can be determined. For example, if the rated discharge power of a single battery bank is P0, and the target discharge power is P... d The number of discharge batteries required ,in This indicates rounding up. This method of determining the number of batteries to discharge based on actual power demand allows for precise control of discharge power, ensuring that the power output of the energy storage station matches the grid's needs and avoiding adverse effects on the grid and energy storage equipment caused by over-discharge or under-discharge. Once the required number of batteries is determined, the grid is discharged based on this number of batteries, promptly replenishing the grid's power, alleviating grid power shortages, and maintaining stable grid operation.

[0087] When the predicted active power is greater than the first preset power and less than or equal to the second preset power, it indicates that the grid power is in a relatively balanced state, with neither significant excess nor significant deficiency. At this time, there is no need to initiate large-scale charging and discharging operations; the independent energy storage power station maintains its current state, only monitoring grid power fluctuations in real time. If the fluctuations are within acceptable limits, multiple parallel-connected batteries maintain their existing state of charge without additional charging or discharging scheduling, thus reducing unnecessary energy storage lifespan loss.

[0088] As can be seen from the above, this embodiment intelligently regulates the charging and discharging of independent energy storage power stations by comparing predicted active power with preset power. Charging can store excess electrical energy, while discharging can promptly replenish the power gap in the power grid. Furthermore, the number of batteries discharged is determined based on the discharge power, accurately matching the power grid demand, effectively maintaining the power balance of the power grid, optimizing the utilization of energy storage resources, and ensuring the stable operation of the power grid.

[0089] In one embodiment of this application, the battery pack to be discharged comprises at least two battery packs, and the process of discharging the power grid based on the battery pack to be discharged further includes:

[0090] Obtain the remaining power data for each group of batteries to be discharged;

[0091] The degree of difference in charge between each group of batteries to be discharged is calculated based on the remaining charge data.

[0092] In response to a difference in charge level exceeding a threshold, the system performs equalization management on the batteries participating in discharging into the grid.

[0093] In this embodiment, the battery pack to be discharged consists of at least two battery packs. During the discharge process to the grid based on these batteries, it is necessary to acquire the remaining power data of each battery pack in real time. By using various power monitoring devices (such as voltage sensors or high-precision power metering chips), this embodiment accurately reflects the energy consumption of each battery pack during the discharge process by precisely measuring the current remaining power of each battery pack.

[0094] Based on the remaining capacity data of each battery group, the capacity difference between the battery groups to be discharged is calculated. The capacity difference is an indicator that quantifies the uniformity of capacity distribution among the battery groups. For example, it can be obtained by calculating the ratio of the standard deviation of the remaining capacity to the average remaining capacity. This indicator directly reflects the dispersion of capacity among the battery groups: a small capacity difference indicates that the remaining capacity of each battery group is relatively balanced; a large difference indicates a significant imbalance in capacity between the battery groups.

[0095] When the calculated power imbalance exceeds the preset threshold, it indicates that the power imbalance between battery banks has reached a level requiring intervention. At this point, balancing management of the batteries participating in grid discharge is necessary. This is because even batteries of the same specifications can experience power imbalances during discharge due to differences in manufacturing processes and usage environments. Long-term imbalances can lead to over-discharge of some batteries, accelerating aging, shortening the lifespan of the entire energy storage system, and affecting the reliability and stability of the energy storage power station.

[0096] The purpose of equalization management is to adjust the remaining charge of each battery group to achieve a more balanced distribution. Specific methods include using switched-capacitor equalization circuits, which transfer charge between battery groups with different charge levels via capacitors; or using transformer-based equalization circuits, which utilize electromagnetic induction to transfer electrical energy between battery groups. This transfers some energy from higher-charge battery groups to lower-charge battery groups, reducing charge differences, ensuring consistency in the discharge process of each battery group, and improving the overall performance and lifespan of the energy storage system.

[0097] This embodiment allows for balanced management of all batteries after the grid discharge is completed, preventing imbalances in battery charge levels. This ensures more even charging and discharging of multiple parallel battery banks during subsequent use, extending the lifespan of the entire energy storage system, improving the operational stability and reliability of the energy storage power station, and better serving grid dispatch.

[0098] When the energy difference is less than or equal to the difference threshold, it indicates that the remaining energy distribution of the batteries to be discharged is relatively balanced, and no balancing management is required. At this point, the current discharge state should be maintained, allowing each battery group to discharge to the grid normally according to the original plan. Simultaneously, the remaining energy changes of each battery group should be continuously monitored to ensure that the difference remains within the threshold range. During the discharge process, the power output should not be interrupted or adjusted to ensure stable grid power supply and avoid unnecessary intervention that could affect the efficiency of the energy storage system and the continuity of grid operation.

[0099] As can be seen from the above, this embodiment monitors the difference in battery charge during the battery discharge process and performs equalization management when the difference exceeds the threshold. This can promptly detect and resolve the problem of uneven battery charge, prevent some batteries from over-discharging and aging, extend the overall lifespan of the energy storage system, ensure the stable operation of the energy storage power station, improve the reliability of power supply, and optimize the collaborative scheduling effect of independent energy storage.

[0100] In one embodiment of this application, the energy storage device of the independent energy storage power station further includes a backup battery pack; the method further includes:

[0101] In response to a real-time deviation between the grid frequency and the preset frequency being greater than the preset frequency deviation, the frequency-regulated charging and discharging power is determined based on the real-time deviation. The frequency-regulated charging and discharging power includes the charging power when the grid charges the backup battery pack or the power when the backup battery pack discharges to the grid.

[0102] Independent energy storage power stations are scheduled based on frequency-regulated charging and discharging power.

[0103] In this embodiment, during power grid operation, its actual operating frequency may deviate from the preset frequency due to various factors (such as sudden load changes, power generation equipment failures, etc.). The deviation between the power grid frequency and the preset frequency is monitored in real time; this deviation reflects the gap between the current power grid frequency and the ideal stable operating frequency. The preset frequency deviation is a manually set threshold used to determine whether the power grid frequency deviation reaches a level requiring intervention.

[0104] When the real-time deviation exceeds the preset frequency deviation, it indicates that the power grid frequency has fluctuated significantly and needs adjustment. In this case, the frequency adjustment charging and discharging power can be determined based on the magnitude and direction of the real-time deviation.

[0105] Specifically, if the grid frequency is higher than the preset frequency, it means that the grid has excess power, and to stabilize the frequency, excess energy needs to be consumed. In this case, the power of the grid charging the backup battery bank is determined. By storing the excess energy in the backup battery bank, the power in the grid is reduced, thereby causing the frequency to drop to the normal range. The charging power is usually proportional to the real-time frequency deviation; that is, the larger the deviation, the greater the charging power, so as to adjust the frequency more quickly.

[0106] If the grid frequency is lower than the preset frequency, it indicates insufficient grid power, requiring additional electrical energy. In this case, the power output of the backup battery bank is determined, allowing it to release energy, increasing the power in the grid, and thus pushing the frequency back to normal. Similarly, the discharge power is also related to the real-time frequency deviation; the greater the deviation, the greater the discharge power, in order to quickly correct the frequency deviation.

[0107] In this embodiment, the backup battery bank does not participate in charge / discharge scheduling based on predicted active power, but is specifically used to respond to sudden anomalies in grid frequency. When there is a large deviation in grid frequency, it can respond quickly by providing or absorbing power to the grid through charge / discharge operations, helping the grid to quickly return to a stable frequency operating state. This enhances the flexibility and timeliness of grid frequency regulation and improves the stability and reliability of grid operation.

[0108] As can be seen from the above, this embodiment adds a backup battery bank to an independent energy storage power station, determining the charging and discharging power based on the real-time deviation of the grid frequency. When the frequency is abnormal, the backup battery bank can quickly charge and discharge, promptly adjusting the grid power and stabilizing the frequency. This enhances the grid's ability to cope with frequency fluctuations, improves power supply stability, reduces damage to electrical equipment caused by frequency issues, and ensures the safe and efficient operation of the grid.

[0109] In one embodiment of this disclosure, determining the frequency adjustment charging and discharging power based on real-time deviation includes:

[0110] The adjustment coefficient is determined based on the ratio of the absolute value of the real-time deviation of the power grid frequency to the preset frequency deviation, and the adjustment coefficient is positively correlated with the ratio.

[0111] The safe power threshold is determined based on the current state of charge of the backup battery pack. The safe power threshold decreases as the state of charge increases, and decreases as the state of charge decreases.

[0112] The frequency-regulated charging and discharging power is obtained by multiplying the adjustment coefficient by the rated charging and discharging power of the backup battery pack and then applying a limit to the safe power threshold.

[0113] In this embodiment, when determining the frequency adjustment charging and discharging power based on the real-time deviation, the actual operating frequency of the power grid is first collected in real time by the power grid frequency monitoring module, and the absolute value of its real-time deviation from the preset frequency is calculated. The ratio of this absolute value to the preset frequency deviation is used as the core parameter to construct a mapping relationship for the adjustment coefficient. The larger the ratio, the larger the adjustment coefficient, ensuring that the adjustment force is stronger when the frequency deviation is more significant. For example, when the real-time deviation is twice the preset deviation, the adjustment coefficient can be set to 1.5 to accelerate the frequency recovery speed.

[0114] Simultaneously, the current state of charge (SOC) of the backup battery bank is obtained through the energy storage state monitoring unit, and a safe power threshold is dynamically generated. When the SOC is higher than 80%, the charging power threshold decreases linearly with the increase of SOC to avoid the risk of overcharging; when the SOC is lower than 20%, the discharging power threshold decreases linearly with the decrease of SOC to prevent over-discharging. The product of the adjustment coefficient and the rated charging and discharging power of the backup battery bank is compared with the safe power threshold, and the smaller value is taken as the final frequency adjustment charging and discharging power. If the calculated value exceeds the safe threshold, a limiting is triggered to ensure rapid response to frequency deviations while protecting the backup battery bank from damage, achieving dual protection of grid frequency stability and energy storage equipment safety.

[0115] As can be seen from the above, this embodiment enhances response timeliness by dynamically matching the frequency deviation level through adjustment coefficients; and avoids overcharging and over-discharging of backup batteries by setting a safe power threshold and limiting it in conjunction with the state of charge. This not only quickly smooths out grid frequency fluctuations and ensures grid stability, but also extends the lifespan of energy storage equipment, achieving synergistic optimization of grid security and energy storage protection.

[0116] Corresponding to the cooperative scheduling method based on independent energy storage in the above embodiment, Figure 2 This is a structural block diagram of a cooperative scheduling device based on independent energy storage, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The collaborative scheduling device 20 based on independent energy storage includes: a first charging and discharging power determination module 21, a second charging and discharging power determination module 22, and a scheduling module 23.

[0117] The first charging and discharging power determination module 21 is used to determine the comprehensive energy storage dispatch index corresponding to the predicted active power based on the current power stability index of the power grid and the current energy storage lifetime loss index of the independent energy storage station; and to obtain the initial charging and discharging power of the independent energy storage station based on the comprehensive energy storage dispatch index. The power stability index is an indicator that measures the degree of power fluctuation in the power grid, and the energy storage lifetime loss index represents the degree of lifetime loss of the independent energy storage station during the charging and discharging process. The predicted active power is the current active power predicted based on the first historical active power of the power grid and through an autoregressive integral moving average model.

[0118] The second charging and discharging power determination module 22 is used to adjust the initial charging and discharging power of the independent energy storage station based on the deviation between the real-time active power of the power grid and the predicted active power, so as to obtain the target charging and discharging power of the independent energy storage station.

[0119] The scheduling module 23 is used to schedule independent energy storage power stations based on the target charging and discharging power.

[0120] In one embodiment of this application, the first historical active power of the power grid is the active power corresponding to the first historical time period;

[0121] The independent energy storage coordinated dispatch device 20 also includes: a model building module; specifically used for:

[0122] Determine the stationarity of the second historical active power of the power grid after differential processing; stationarity refers to the stability of the average value of the second historical active power of the power grid at different historical moments; the second historical active power is the active power corresponding to the second historical period, which is earlier than the first historical period;

[0123] In response to a stationarity greater than a stationarity threshold, the order of the difference processing corresponding to that stationarity is taken as the difference order;

[0124] The autoregression order is determined based on the degree of first correlation between the active power at multiple lag times and the active power at the target time within the second historical period; the degree of first correlation indicates the consistency between the changing trend of active power at multiple lag times and the changing trend of active power at the target time.

[0125] The moving average order is determined based on the second degree of correlation between the historical predicted active power error at multiple lag times and the predicted active power error at the target time within the second historical period. The second degree of correlation indicates the consistency between the changing trend of the historical predicted active power error at multiple lag times and the changing trend of the predicted active power error at the target time within the second historical period. The historical predicted active power error refers to the difference between the predicted active power at the target time obtained through the historical prediction model and the actual active power at the target time within the second historical period.

[0126] An autoregressive integral moving average model is constructed based on the autoregressive order, the difference order, and the moving average order.

[0127] In one embodiment of this application, the first charge / discharge power determination module 21 is specifically used for:

[0128] Based on the first historical time period, obtain multiple historical predicted active power;

[0129] Calculate the fluctuation variance of multiple historical predicted active power, and determine the current power stability index of the power grid based on the reciprocal of the fluctuation variance;

[0130] The energy storage life loss index of an independent energy storage power station is determined based on the current depth of charge and discharge and the cumulative number of charge and discharge cycles.

[0131] In one embodiment of this application, the first charge / discharge power determination module 21 is further configured to:

[0132] Determine the weights corresponding to the power stability index and the energy storage lifetime loss index.

[0133] The power stability index of the power grid and the energy storage life loss index of independent energy storage power stations are weighted and fused together with their respective weights to obtain the comprehensive energy storage dispatch index corresponding to the predicted active power.

[0134] In one embodiment of this application, the target charge / discharge power includes charging power and discharging power; the energy storage device of the independent energy storage power station includes multiple sets of batteries connected in parallel;

[0135] The scheduling module 23 is specifically used for:

[0136] In response to the predicted active power being less than the first preset power, multiple parallel batteries are charged based on the charging power.

[0137] In response to the predicted active power being greater than the second preset power, the number of batteries to be discharged is determined based on the discharge power. Based on the number, the battery group to be discharged is determined from multiple parallel battery groups. The battery group to be discharged is then used to discharge the power grid.

[0138] The first preset power is less than the second preset power.

[0139] In one embodiment of this application, the scheduling module 23 is further configured to:

[0140] Obtain the remaining power data for each group of batteries to be discharged;

[0141] The degree of difference in charge between each group of batteries to be discharged is calculated based on the remaining charge data.

[0142] In response to a difference in charge level exceeding a threshold, the system performs equalization management on the batteries participating in discharging into the grid.

[0143] In one embodiment of this application, the energy storage device of the independent energy storage power station further includes a backup battery pack; the scheduling module 23 is specifically used for:

[0144] In response to a real-time deviation between the grid frequency and the preset frequency being greater than the preset frequency deviation, the frequency-regulated charging and discharging power is determined based on the real-time deviation. The frequency-regulated charging and discharging power includes the charging power when the grid charges the backup battery pack or the power when the backup battery pack discharges to the grid.

[0145] Independent energy storage power stations are scheduled based on frequency-regulated charging and discharging power.

[0146] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the first charge / discharge power determination module 21, the second charge / discharge power determination module 22, and the scheduling module 23 are shown.

[0147] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0148] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0149] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information such as historical predicted active power.

[0150] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the cooperative scheduling method based on independent energy storage provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0151] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0152] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0153] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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 implementations should not be considered beyond the scope of this application.

[0154] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces or units, or they may be electrical, mechanical, or other forms of connection.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0157] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0158] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered 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.

Claims

1. A cooperative scheduling method based on independent energy storage, characterized in that, include: Based on the current power stability index of the power grid and the current energy storage life loss index of independent energy storage power stations, the comprehensive energy storage dispatch index corresponding to the predicted active power is determined. The initial charging and discharging power of the independent energy storage power station is obtained based on the comprehensive energy storage dispatch index; the power stability index is an indicator that measures the degree of power fluctuation in the power grid; the energy storage lifetime loss index represents the degree of lifetime loss of the independent energy storage power station during the charging and discharging process; the predicted active power is the current active power predicted based on the first historical active power of the power grid and through an autoregressive integral moving average model. Based on the deviation between the real-time active power of the power grid and the predicted active power, the initial charging and discharging power of the independent energy storage power station is adjusted to obtain the target charging and discharging power of the independent energy storage power station. The independent energy storage power station is scheduled based on the target charging and discharging power. The first historical active power of the power grid is the active power corresponding to the first historical time period; The autoregressive integral moving average model is obtained in the following way: Determine the stability of the second historical active power of the power grid after differential processing; the stability refers to the stability of the average value of the second historical active power of the power grid at different historical moments; the second historical active power is the active power corresponding to the second historical period, which is earlier than the first historical period; In response to the stationarity being greater than the stationarity threshold, the order of the difference processing corresponding to the stationarity is taken as the difference order; The autoregression order is determined based on the first degree of correlation between the active power at multiple lag times and the active power at the target time within the second historical period; the first degree of correlation indicates the consistency between the changing trend of active power at multiple lag times and the changing trend of active power at the target time. The moving average order is determined based on the second degree of correlation between the historical predicted active power error at multiple lag times and the predicted active power error at the target time within the second historical period. The second degree of correlation indicates the consistency between the changing trend of the historical predicted active power error at multiple lag times and the changing trend of the predicted active power error at the target time within the second historical period. The historical predicted active power error refers to the difference between the predicted active power at the target time obtained by the historical prediction model and the actual active power at the target time within the second historical period. An autoregressive integral moving average model is constructed based on the autoregressive order, the difference order, and the moving average order.

2. The collaborative scheduling method based on independent energy storage as described in claim 1, characterized in that, The method for determining the current power stability index of the power grid includes: Based on the first historical time period, obtain multiple historical predicted active power; Calculate the fluctuation variance of multiple historical predicted active power, and determine the current power stability index of the power grid based on the reciprocal of the fluctuation variance; The method for determining the current energy storage life loss index of the independent energy storage power station includes: The energy storage life loss index of the independent energy storage power station is determined based on the current charging and discharging depth and the cumulative number of charging and discharging cycles.

3. The collaborative scheduling method based on independent energy storage as described in claim 2, characterized in that, The comprehensive energy storage dispatch index, based on the power stability index of the power grid and the energy storage life loss index of independent energy storage power stations, is determined, including: Determine the weights corresponding to the power stability index and the energy storage lifetime loss index. The power stability index of the power grid and the energy storage life loss index of the independent energy storage power station are weighted and fused together with their respective weights to obtain the comprehensive energy storage dispatch index corresponding to the predicted active power.

4. The collaborative scheduling method based on independent energy storage as described in claim 1, characterized in that, The target charge and discharge power includes charging power and discharging power; the energy storage equipment of the independent energy storage power station includes multiple sets of parallel-connected batteries; Dispatching independent energy storage power stations based on the target charging and discharging power includes: In response to the predicted active power being less than a first preset power, the multiple sets of parallel-connected batteries are charged based on the charging power; In response to the predicted active power being greater than the second preset power, the number of batteries to be discharged is determined based on the discharge power, and based on the number, a battery group to be discharged is determined from the multiple parallel battery groups, and the power grid is discharged based on the battery group to be discharged. The first preset power is less than the second preset power.

5. The collaborative scheduling method based on independent energy storage as described in claim 4, characterized in that, The battery pack to be discharged comprises at least two battery packs. The process of discharging the power grid based on the battery pack to be discharged also includes: Obtain the remaining power data for each group of batteries to be discharged; The degree of difference in charge between each group of batteries to be discharged is calculated based on the remaining charge data. In response to the power difference exceeding a difference threshold, equalization management is performed on the batteries participating in discharging into the grid.

6. The collaborative scheduling method based on independent energy storage as described in claim 4, characterized in that, The energy storage equipment of the independent energy storage power station also includes a backup battery pack; the method further includes: In response to a real-time deviation between the grid frequency and a preset frequency that is greater than the preset frequency deviation, the frequency-regulated charging and discharging power is determined based on the real-time deviation. The frequency-regulated charging and discharging power includes the charging power when the grid charges the backup battery pack or the power when the backup battery pack discharges to the grid. Independent energy storage power stations are scheduled based on frequency-regulated charging and discharging power.

7. A collaborative dispatching device based on independent energy storage, characterized in that, include: The first charging and discharging power determination module is used to determine the comprehensive energy storage dispatch index corresponding to the predicted active power based on the current power stability index of the power grid and the current energy storage life loss index of the independent energy storage power station. The initial charging and discharging power of the independent energy storage power station is obtained based on the comprehensive energy storage dispatch index; the power stability index is an indicator that measures the degree of power fluctuation in the power grid; the energy storage lifetime loss index represents the degree of lifetime loss of the independent energy storage power station during the charging and discharging process; the predicted active power is the current active power predicted based on the first historical active power of the power grid and through an autoregressive integral moving average model. The second charging and discharging power determination module is used to adjust the initial charging and discharging power of the independent energy storage station based on the deviation between the real-time active power of the power grid and the predicted active power, so as to obtain the target charging and discharging power of the independent energy storage station. The scheduling module is used to schedule independent energy storage power stations based on the target charging and discharging power. The first historical active power of the power grid is the active power corresponding to the first historical time period; It also includes: a model building module; specifically used for: Determine the stability of the second historical active power of the power grid after differential processing; the stability refers to the stability of the average value of the second historical active power of the power grid at different historical moments; the second historical active power is the active power corresponding to the second historical period, which is earlier than the first historical period; In response to the stationarity being greater than the stationarity threshold, the order of the difference processing corresponding to the stationarity is taken as the difference order; The autoregression order is determined based on the first degree of correlation between the active power at multiple lag times and the active power at the target time within the second historical period; the first degree of correlation indicates the consistency between the changing trend of active power at multiple lag times and the changing trend of active power at the target time. The moving average order is determined based on the second degree of correlation between the historical predicted active power error at multiple lag times and the predicted active power error at the target time within the second historical period. The second degree of correlation indicates the consistency between the changing trend of the historical predicted active power error at multiple lag times and the changing trend of the predicted active power error at the target time within the second historical period. The historical predicted active power error refers to the difference between the predicted active power at the target time obtained by the historical prediction model and the actual active power at the target time within the second historical period. An autoregressive integral moving average model is constructed based on the autoregressive order, the difference order, and the moving average order.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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