Cooperative scheduling method and device based on independent energy storage, equipment and storage medium

Abstract

The invention provides a cooperative scheduling method and device based on independent energy storage, equipment and a storage medium, and belongs to the technical field of energy storage scheduling, and the method comprises the steps: determining an energy storage scheduling comprehensive index corresponding to predicted active power based on a current power stability index of a power grid and a current energy storage life loss index of an independent energy storage power station; obtaining initial charging and discharging power of the independent energy storage power station based on the energy storage scheduling comprehensive 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 power station based on the deviation between the real-time active power and the predicted active power of the power grid to obtain the target charging and discharging power of the independent energy storage power station; and scheduling the independent energy storage power station based on the target charging and discharging power. According to the cooperative scheduling method and device based on independent energy storage, the equipment and the storage medium provided by the invention, the overall stability and reliability of power grid operation can be improved.

Description

Technical Field

[0001] The present application belongs to the field of energy storage scheduling technology, and more specifically, relates to a collaborative scheduling method and apparatus, equipment, and storage medium based on independent energy storage. Background Art

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

[0003] However, the existing dispatching method lacks an effective adjustment mechanism 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 smoothing grid power fluctuations, which in turn affects 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 and device, equipment, and storage medium based on independent energy storage to improve the overall stability and reliability of power grid operation.

[0005] A first aspect of an embodiment of the present application provides a coordinated scheduling method based on independent energy storage, including: 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 energy storage scheduling comprehensive index corresponding to the predicted active power is determined; based on the energy storage scheduling comprehensive index, the initial charging and discharging power of the independent energy storage power station is obtained; the power stability index is an indicator of the degree of power fluctuation of the power grid, and the energy storage life loss index represents the degree of life loss of the independent energy storage power station during the charging and discharging process; the predicted active power is based on the first historical active power of the power grid and the current active power predicted by the 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 charge and discharge power of the independent energy storage power station is adjusted to obtain the target charge and discharge power of the independent energy storage power station; Independent energy storage power stations are dispatched based on target charging and discharging power.

[0006] A second aspect of an embodiment of the present application provides a coordinated scheduling device based on independent energy storage, including: The first charge and discharge power determination module is used to determine the energy storage scheduling comprehensive 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 charge and discharge power of the independent energy storage power station is obtained based on the energy storage scheduling comprehensive index; the power stability index is an indicator that measures the degree of power fluctuation of the power grid, and the energy storage life loss index represents the degree of life loss of the independent energy storage power station during the charging and discharging process; the predicted active power is based on the first historical active power of the power grid and the current active power predicted by the autoregressive integral sliding average model; a second charge and discharge power determination module, configured to adjust the initial charge and discharge power of the independent energy storage power station based on the deviation between the real-time active power of the power grid and the predicted active power, to obtain a target charge and discharge power of the independent energy storage power station; The scheduling module is used to schedule independent energy storage power stations based on target charging and discharging power.

[0007] In a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned collaborative scheduling method based on independent energy storage when executing the computer program.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned collaborative scheduling method based on independent energy storage are implemented.

[0009] The beneficial effects of the collaborative scheduling method, device, equipment, and storage medium based on independent energy storage provided in the embodiments of the present application are as follows: the embodiments of the present application predict the current active power through the historical active power of the power grid, provide direction for energy storage scheduling in advance, enhance the foresight of scheduling, and help to cope with power fluctuations in the power grid. Secondly, the comprehensive scheduling index is determined by combining power stability and energy storage life loss index, and the initial charge and discharge power is obtained. While ensuring the stable operation of the power grid, the life of the energy storage power station is taken into account to reduce operating costs. Furthermore, the initial charge and discharge power is adjusted according to the deviation between real-time and predicted active power, so that the target charge and discharge power is more in line with reality, thereby improving scheduling accuracy. Ultimately, the reasonable scheduling of independent energy storage power stations is achieved, effectively smoothing power fluctuations in the power grid, and improving the stability and reliability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A flowchart of a collaborative scheduling method based on independent energy storage provided in one embodiment of the present application; Figure 2 A structural block diagram of a collaborative scheduling device based on independent energy storage provided in one embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A flowchart of a collaborative scheduling method based on independent energy storage provided in an embodiment of the present application can be executed by an electronic device. The method may include: S101: 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, determine the comprehensive energy storage scheduling index corresponding to the predicted active power; based on the comprehensive energy storage scheduling index, obtain the initial charging and discharging power of the independent energy storage power station; the power stability index is an indicator to measure the degree of power fluctuation of the power grid, and the energy storage life loss index represents the degree of life loss of the independent energy storage power station during the charging and discharging process; the predicted active power is based on the first historical active power of the power grid and the current active power predicted by the autoregressive integral sliding average model.

[0015] In this embodiment, the first historical active power refers to the active power data of the power grid during the second historical period, including information on power variations during grid operation. The active power values ​​in different time periods reflect the fluctuations of power load over time (e.g., daily or weekly cycles). By analyzing the first historical active power data, for example, using a specific prediction algorithm such as a time series analysis algorithm (e.g., an autoregressive integrated moving average model) or a machine learning algorithm, patterns and trends in power variations are identified, and the current active power is predicted to obtain a predicted active power. This prediction result provides a forward-looking basis for energy storage scheduling, facilitating the pre-planning of charging and discharging operations to address future power fluctuations.

[0016] In this embodiment, the power stability index of a power grid is used to measure the degree of power fluctuation in the grid. Excessive power fluctuation can affect the normal operation of various devices in the grid and even cause grid failures. The power stability index of the grid can be determined by calculating the variance of the predicted power fluctuation. A larger value of the power stability index indicates a more stable grid power. The goal is to minimize power fluctuations during grid operation and ensure reliable grid operation.

[0017] The energy storage life loss indicator for a standalone energy storage power station measures the life loss of the energy storage station during the charging and discharging process due to factors such as charge and discharge depth and the number of charge and discharge cycles. This indicator can be determined by establishing a calculation model that correlates these factors. A lower energy storage life loss indicator indicates less energy storage life loss, which helps reduce the operating costs and maintenance requirements of the energy storage power station.

[0018] In this embodiment, the grid power stability index and the energy storage life loss index can be linearly combined according to certain weights to obtain a comprehensive energy storage scheduling index. For example:

[0019] in, represents the comprehensive index of energy storage scheduling, represents the weight corresponding to the grid power stability index, Indicates the value corresponding to the grid power stability index, Represents the weight corresponding to the energy storage life loss index of the independent energy storage power station, Indicates the value corresponding to the energy storage life loss index of an independent energy storage power station.

[0020] The goal is to maintain a stable power grid while minimizing the lifespan loss of the energy storage power station, achieving a balance between the two. The weights can be set based on experience.

[0021] In this embodiment, the initial charge and discharge power of the independent energy storage power station can be obtained by a linear programming algorithm, and the objective function can be constructed with the comprehensive index of energy storage scheduling as the optimization target. , where P is the decision variable, i.e., the candidate charge and discharge power (positive for charging and negative for discharging), and They are the grid power stability index and energy storage life loss index that depend on the charging and discharging power P respectively; at the same time, the state of charge constraint (SOC min ≤SOC(P)≤SOC max ), charge and discharge power constraint (P min ≤P≤P max ) and grid power balance constraint (P 预测 +P≈P 目标), the linear programming algorithm searches for the optimal charging and discharging power for the objective function F(P) within the feasible domain that satisfies these constraints. This optimal solution is the initial charging and discharging power for the preliminary planning of the charging and discharging operations of the energy storage power station, taking into account multiple factors of the power grid and energy storage.

[0022] S102: Based on the deviation between the real-time active power of the power grid and the predicted active power, the initial charge and discharge power is adjusted to obtain the target charge and discharge power.

[0023] In this embodiment, the grid's active power is acquired in real time, compared with the predicted active power, and the deviation between the two is calculated. The predicted active power is based on historical data. However, the actual grid active power can be affected by various unexpected factors (such as the sudden connection of large-scale power users and the impact of sudden weather changes on distributed power sources), resulting in discrepancies from the predicted value. By calculating the deviation, we can understand the degree of deviation between the actual grid operation and the predicted value.

[0024] The initial charge and discharge power is adjusted according to the size 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 Indicates the predicted upper limit of active power, P min,Y Indicates the lower limit of the predicted active power.

[0025] If the real-time active power is higher than P max,Y , indicating that the actual power demand of the power grid is greater than expected. If the state of charge of the independent energy storage station allows, the discharge power should be appropriately increased. On the contrary, if the real-time active power is lower than P min,Y , then if the charge state of the independent energy storage power station allows, the charging power should be appropriately increased.

[0026] If the real-time active power is P max,Y -P min,Y It indicates that the current predicted active power is consistent with the actual one, and there is no need to adjust the initial charge and discharge power. The current initial charge and discharge power can be used as the target charge and discharge power.

[0027] Through this real-time deviation-based adjustment, this embodiment makes the charging and discharging operations of the energy storage power station more aligned with the actual needs of the power grid, improves the stability and reliability of the power grid operation, and obtains a target charging and discharging power that is more in line with actual conditions.

[0028] S103: Dispatching the independent energy storage power station based on the target charging and discharging power.

[0029] In this embodiment, the independent energy storage station can store or release electricity according to the target charge and discharge power. In this way, the independent energy storage station works in conjunction with the power grid to achieve peak load shifting and valley filling, ensuring the stable operation of the power grid.

[0030] For example, when the grid has excess power (high real-time active power), the independent energy storage power station charges according to the target charging power and stores excess electricity; when the grid has insufficient power (low real-time active power), the independent energy storage power station injects electricity into the grid according to the target discharge power to maintain grid power balance.

[0031] From the above, it can be concluded that this embodiment predicts the active power in the current preset time period through the historical active power of the power grid, provides direction for energy storage scheduling in advance, enhances the forward-looking nature of scheduling, and helps to cope with power fluctuations in the power grid. Secondly, the comprehensive scheduling index is determined by integrating power stability and energy storage life loss indicators, and the initial charge and discharge power is obtained. While ensuring the stable operation of the power grid, the life of the energy storage power station is taken into account, reducing operating costs. Furthermore, the initial charge and discharge power is adjusted based on the deviation between the real-time and predicted active power, so that the target charge and discharge power is more in line with reality, thereby improving scheduling accuracy. Ultimately, the reasonable scheduling of independent energy storage power stations is achieved, effectively smoothing out power fluctuations in the power grid, and improving the stability and reliability of power grid operation.

[0032] In one embodiment of the present application, the first historical active power of the power grid is the active power corresponding to the first historical period; Among them, the autoregressive integrated 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 is 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, and the second historical period is earlier than the first historical period; In response to the stationarity being greater than the stationarity threshold, taking the order of the differential processing corresponding to the stationarity as the differential order; Determining an autoregressive order based on a first correlation degree between the active power at the plurality of lag moments and the active power at the target moment within the second historical period; the first correlation degree indicates consistency between a changing trend of the active power at the plurality of lag moments and a changing trend of the active power at the target moment; Determining the moving average order based on a second degree of correlation between the historical predicted active power errors at multiple lag moments and the predicted active power errors at the target moment within the second historical period; the second degree of correlation indicates consistency between a changing trend of the historical predicted active power errors at multiple lag moments within the second historical period and a changing trend of the predicted active power errors at the target moment; wherein the historical predicted active power error refers to a difference between the predicted active power at the target moment obtained by the historical prediction model and the actual active power at the target moment within the second historical period; An autoregressive integrated moving average model is constructed based on the autoregressive order, difference order and moving average order.

[0033] In this embodiment, an autoregressive integrated moving average model is constructed for short-term power forecasting of a power grid. The parameters of the autoregressive integrated moving average model include the autoregressive order, the differencing order, and the moving average order. The second historical active power is the fundamental data used to determine the model parameters and is chronologically earlier than the first historical active power. The first historical active power is the historical data used to input the model to generate forecast results.

[0034] The second historical active power of the power grid exhibits trend or seasonal variations, which can affect the accuracy of the prediction model. Therefore, its stability needs to be determined. The stability of this embodiment refers to the stability of the mean value of the second historical active power of the power grid at different historical moments. By performing differential processing (such as primary differential processing, secondary differential processing, etc.) on the second historical active power, the trend and seasonal components in the data are eliminated. After each differential processing, the data stability is evaluated. When the stability is greater than a preset stability threshold, it indicates that the sequence after differential processing has reached a relatively stable state, and the order of this differential processing is determined as the differential order. For example, after performing a primary differential on the historical active power, if its mean value is found to be stable at different moments and the stability is greater than the threshold, the differential order is 1. This is done because stable data better meets the requirements of the autoregressive integrated moving average model and enables the model to better capture the inherent laws of the data.

[0035] The autoregressive order is determined based on second historical active power data in a second historical period.

[0036] Analyze the first degree of correlation between the active power at multiple delayed moments (the power values ​​at multiple moments earlier than the target moment in the second historical active power) and the active power at the target moment (the power value at a specific moment in the second historical active power that serves as an analysis benchmark), where the first degree of correlation indicates the consistency between the changing trend of the active power in the second historical period and the changing trend of the active power in the current period.

[0037] Active power at multiple delayed moments refers to the grid active power data recorded at multiple earlier moments in the second historical period, relative to a specific analysis time point (the subsequent "target moment"). For example, if "time t" in the historical period is used as the target moment, the delayed moments could be t-1, t-2, t-3, and so on (where "t-1" represents a time unit earlier than time t, and the time unit can be set to minutes, hours, etc. depending on the actual scenario).

[0038] The target moment active power refers to the grid active power data at a specific moment in the second historical period, serving as the analysis target. The target moment active power serves as a reference for the active power at the subsequent moment and is used to measure the correlation between the power at the subsequent moment and the power at the current moment. For example, when analyzing historical data, each moment in the historical period can be selected as the target moment and its correlation with the power at multiple subsequent moments can be calculated.

[0039] For example, if the rising / falling trend of active power at lags 1, 2, and 3 in the second historical period is highly consistent with the power change trend at the target moment, that is, the power change at the target moment is significantly affected by the trends of the first three lag moments, then the autoregressive order is set to 3.

[0040] The moving average order is determined based on the historical prediction error data within the second historical period. The second correlation between the historical prediction active power errors at multiple lag moments (the error values ​​at multiple moments prior to the target moment within the historical prediction error corresponding to the second historical active power) and the target moment prediction active power error (the error value at a specific moment within the historical prediction error corresponding to the second historical active power that serves as the analysis benchmark) is analyzed. This correlation indicates the consistency between the changing trends of the errors at the lag moments and the errors at the target moment. The historical prediction active power error is defined as the difference between the predicted active power at the target moment, obtained using the historical prediction model, and the actual active power at that moment (i.e., the actual value of the second historical active power at the target moment) within the second historical period.

[0041] For example, if the increase or decrease trend of the forecast error at lags 1 and 2 is highly consistent with the error change trend at the target moment, that is, the error change at the target moment is significantly affected by the error trend at the first two lag moments, 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 interference.

[0042] An autoregressive integrated moving average model is constructed based on the determined autoregressive order, differencing order, and moving average order. These three parameters together define the model structure, enabling it to accurately model grid power by combining the correlation and stationarity of grid power trends with the correlation of error term trends.

[0043] During the prediction phase, the grid's first historical active power is fed into the constructed model. The model learns from this historical data based on parameter settings, uncovering power variation patterns and ultimately outputting the current predicted active power. This prediction provides a reliable, forward-looking basis for subsequent energy storage coordinated scheduling, supporting the precise formulation of scheduling strategies.

[0044] As can be seen from the above, this embodiment achieves short-term power forecasting for the power grid by constructing an autoregressive integrated moving average model. Based on the second historical active power, the differential order (to ensure data stationarity), autoregressive order (to capture power trend correlations), and moving average order (to fit historical error correlations) are determined. The first historical active power is then used as input to generate forecast results. This complete logic ensures a deep alignment of model parameters with historical data characteristics, significantly improving forecast accuracy and laying a reliable data foundation for energy storage coordinated scheduling, thereby enhancing grid operational stability.

[0045] In one embodiment of the present application, a method for determining a current power stability index of a power grid includes: Based on the first historical period, obtaining multiple historical predicted active powers; Calculate the fluctuation variance of multiple historically predicted active powers and determine the current power stability index of the power grid based on the inverse of the fluctuation variance; Among them, the methods for determining the current energy storage life loss indicators of independent energy storage power stations include: The energy storage life loss index of the independent energy storage power station is determined based on the current charge and discharge depth and the cumulative charge and discharge times of the independent energy storage power station.

[0046] In this embodiment, multiple historical predicted active powers are obtained based on the first historical time period. These historical predicted active powers are the prediction results of the power grid in the first historical time period, reflecting the changes in the predicted power in this time period. By calculating the fluctuation variance of multiple historical predicted active powers, the degree of dispersion of the predicted power around its mean is measured. The larger the fluctuation variance, the more drastic the fluctuation of the predicted power and the worse the stability of the power grid; conversely, the smaller the fluctuation variance, the smoother the power fluctuation and the better the stability. The current power stability index of the power grid is determined based on the inverse of the above-mentioned fluctuation variance. Due to the inverse relationship, the power stability index is positively correlated with the power stability of the power grid, that is, the smaller the fluctuation variance, the larger its inverse, which means that the better the stability of the power grid, thereby intuitively and effectively reflecting the stability of the power grid.

[0047] In this embodiment, the energy storage life loss indicator for an independent energy storage power station is determined based on the charge / discharge depth and charge / discharge cycles up to that point. The charge / discharge depth refers to the ratio of the amount of charge discharged or charged during the charge / discharge process to the battery's rated capacity. Deep charge / discharge accelerates battery aging. For example, the greater the charge / discharge depth, the more intense the chemical reactions within the battery, the faster the electrode material wears out, and thus shortens the service life of the energy storage station.

[0048] At the same time, the current number of charge and discharge cycles will also affect the energy storage life. Frequent charge and discharge operations will aggravate the loss of 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 gradually declines and the life is shortened.

[0049] Comprehensively considering the current charge and discharge depth and charge and discharge times to determine the energy storage life loss index can fully reflect the life loss of independent energy storage power stations during operation. By establishing a calculation model related to these two factors, the charge and discharge depth and charge and discharge times are quantified into index values. For example, a weighted formula (such as life loss = Depth of charge and discharge+ Cumulative charge and discharge times, including 、 The indicator comprehensively reflects the life loss status of independent energy storage power stations during operation and provides a key basis for calculating comprehensive energy storage scheduling indicators.

[0050] As can be seen from the above, this embodiment determines the grid power stability index by calculating the inverse of the variance of multiple historically predicted active power fluctuations within the first historical period. This can intuitively quantify the degree of grid power stability and provide a clear stability reference for scheduling. The energy storage life loss index is determined based on the current charge and discharge depth and number of times, comprehensively reflecting the life loss of the energy storage power station.

[0051] In one embodiment of the present application, based on the power stability index of the power grid and the energy storage life loss index of the independent energy storage power station, a comprehensive energy storage scheduling index corresponding to the predicted active power is determined, including: Determine the weight corresponding to the power stability index and the weight corresponding to the energy storage life loss index; Based on the power stability index of the power grid and the energy storage life loss index of the independent energy storage power station, as well as their respective corresponding weights, a weighted fusion is performed to obtain a comprehensive energy storage scheduling index corresponding to the predicted active power.

[0052] In this embodiment, the weights corresponding to the power stability indicator and the energy storage life loss indicator can be set based on experience. This empirical setting should 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 operation phase, where power stability is more demanding, the power stability indicator can be given a higher weight based on experience. If the energy storage equipment is nearing the end of its service life or the replacement cost is high, the weight of the energy storage life loss indicator can be increased based on experience to prioritize the protection of the energy storage equipment.

[0053] In this embodiment, the sum of the weight of the power stability index and the weight of the energy storage life loss index is equal to 1, ensuring the rationality and standardization of the weight distribution.

[0054] In this embodiment, after determining the power stability index and its weight, as well as the energy storage life loss index and its weight, a comprehensive energy storage scheduling index is obtained through weighted fusion. The specific calculation is: multiply the power stability index by its corresponding weight, and add the energy storage life loss index multiplied by its corresponding weight. This weighted fusion method can fully take into account the two key factors of power grid power stability and independent energy storage life loss. When making energy storage scheduling decisions, the comprehensive index can be used as a comprehensive evaluation standard to achieve the optimal scheduling strategy that minimizes energy storage life loss while ensuring the stable operation of the power grid, thereby achieving efficient and economical operation of the power system.

[0055] As can be seen from the above, this embodiment determines the weights of the power stability index and the energy storage life loss index based on experience, and then obtains the comprehensive energy storage scheduling index through weighted fusion. This can flexibly adapt to the scheduling needs in different scenarios, effectively balance the power stability of the power grid and the energy storage life loss, improve the reliability of the power grid operation and the efficiency of energy storage utilization, and reduce the overall operating costs.

[0056] In one embodiment of the present application, 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 groups of parallel-connected batteries; Dispatching independent energy storage power stations based on target charge and discharge power, including: In response to the predicted active power being less than the first preset power, charging the plurality of parallel-connected storage batteries based on the charging power; In response to the predicted active power being greater than a second preset power, determining the number of storage batteries to be discharged based on the discharge power, determining a battery group to be discharged from a plurality of parallel-connected battery groups based on the number, and discharging the battery group to be discharged into the grid; The first preset power is less than the second preset power.

[0057] In this embodiment, when the predicted active power is less than the first preset power, this indicates, from the perspective of the grid's operating status, that the grid is currently or will be in a relatively excess power state during the predicted period. The first preset power is a pre-set threshold based on multiple factors, including the grid's load characteristics, power generation conditions, and the energy storage station's charging capacity. This serves as a criterion for determining grid power excess and provides a basis for charging decisions by the energy storage station.

[0058] If the predicted active power is less than the first preset power, multiple parallel battery groups are charged based on the given charging power. Multiple parallel battery groups form the energy storage equipment of an independent energy storage station. This parallel structure can increase the total capacity and output capability of the energy storage system. Charging operations based on the target charging power can effectively store excess electrical energy in the grid, avoiding energy waste and providing energy reserves for subsequent grid power shortages. This not only helps maintain grid power balance but also fully utilizes the storage capacity of the energy storage station to optimize energy distribution on the grid.

[0059] If the predicted active power exceeds the second preset power, it means the grid's active power supply during the current or predicted time period cannot meet the load demand. The second preset power is also a threshold set based on factors such as the grid's load demand and the energy storage plant's discharge capacity, and is used to determine whether the grid is experiencing power shortages.

[0060] Determine the number of batteries required for discharge based on the target discharge power. Since multiple groups of batteries are connected in parallel, the different number of batteries involved 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 group of batteries, the number of batteries required to meet the target discharge power can be determined. For example, if the rated discharge power of a single group of batteries is P0 and the target discharge power is P d , then the number of batteries required to discharge ,in Indicates rounding up. This method of determining the number of batteries to be discharged 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 the adverse effects of over- or under-discharge on the grid and energy storage equipment. After determining the required number of batteries to be discharged, these batteries are used to discharge the grid, replenishing power in a timely manner, alleviating power shortages, and maintaining stable grid operation.

[0061] When the predicted active power is greater than the first preset power and less than or equal to the second preset power, the grid power is relatively balanced, with neither significant excess nor significant deficit. At this point, no large-scale charging or discharging operations are required; the independent energy storage station maintains its current state, monitoring grid power fluctuations in real time. If fluctuations are within the permitted range, the multiple parallel battery groups maintain their current state of charge, with no additional charging or discharging scheduled, thus minimizing unnecessary loss of energy storage life.

[0062] 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 energy, while discharging can promptly replenish grid power gaps. The battery discharge quantity is determined based on the discharge power, accurately matching grid demand, effectively maintaining grid power balance, optimizing energy storage resource utilization, and ensuring stable grid operation.

[0063] In one embodiment of the present application, the battery pack to be discharged is at least two battery packs, and the process of discharging the power grid based on the battery pack to be discharged further includes: Obtaining the remaining power data corresponding to each group of batteries to be discharged; Calculating the difference in power between the groups of batteries to be discharged based on the remaining power data; In response to the electric quantity difference being greater than the difference threshold, balancing management is performed on the electric storage batteries that participate in discharging to the power grid.

[0064] In this embodiment, the battery groups to be discharged are at least two groups of batteries. During the process of discharging these batteries to the power grid, it is necessary to obtain real-time remaining power data corresponding to each group of batteries to be discharged. By using various power monitoring devices (such as voltage sensors or high-precision power metering chips), in this embodiment of the application, the current remaining power of each battery group is accurately measured to accurately reflect the power consumption of each battery group during the discharge process.

[0065] Based on the remaining charge data obtained for each battery group, the charge variance between the battery groups to be discharged is calculated. This variance is a metric that quantifies the uniformity of charge distribution across battery groups. For example, it can be calculated by calculating the ratio of the standard deviation of the remaining charge to the average remaining charge. This metric intuitively reflects the degree of dispersion of charge between battery groups: a small variance indicates relatively balanced remaining charge across battery groups; a large variance indicates significant imbalance in charge between battery groups.

[0066] When the calculated power disparity exceeds the preset disparity threshold, it indicates that the power imbalance between battery groups has reached a level that requires intervention. At this point, balancing management must be implemented for the batteries involved in discharging to the grid. This is because even batteries with identical specifications can experience power disparity during discharge due to factors such as manufacturing process differences and operating environments. Long-term imbalance can lead to over-discharge of some batteries, accelerating aging, shortening the service life of the entire energy storage system, and affecting the reliability and stability of the energy storage power station.

[0067] The goal of balancing management is to adjust the remaining charge of each battery group so that it approaches equilibrium. Specific methods include using a switched-capacitor balancing circuit, which transfers charge between battery groups with different charge levels, or a transformer-based balancing circuit, which utilizes the principle of electromagnetic induction to transfer energy between battery groups. This transfers some of the energy from the higher-charged battery group to the lower-charged battery group, reducing charge differences and ensuring consistent discharge between battery groups, thereby improving the overall performance and service life of the energy storage system.

[0068] After discharging to the grid, this embodiment can perform balancing management on all batteries to avoid imbalances in charge between batteries. This ensures that multiple parallel battery groups can charge and discharge more evenly during subsequent use, extending the service life of the entire energy storage system, improving the operational stability and reliability of the energy storage power station, and better serving grid dispatch.

[0069] When the power variance is less than or equal to the variance threshold, the remaining power of each battery group to be discharged is relatively evenly distributed, and no balancing management is required. The current discharge state is maintained, allowing each battery group to discharge normally into the grid according to the original plan. The remaining power of each battery group is continuously monitored to ensure that the variance remains within the threshold. Power output is not interrupted or adjusted during the discharge process, ensuring stable grid power supply and avoiding unnecessary intervention that could affect energy storage system efficiency and grid continuity.

[0070] As can be seen from the above, this embodiment monitors the power difference during battery discharge and performs balancing management when the power difference is greater than a threshold. This can promptly detect and resolve battery power imbalance problems, prevent excessive discharge and aging of some batteries, extend the overall life of the energy storage system, ensure the stable operation of the energy storage power station, improve the reliability of power supply, and optimize the coordinated scheduling effect of independent energy storage.

[0071] In one embodiment of the present application, the energy storage device of the independent energy storage power station further includes a backup battery pack; and the method further includes: In response to a real-time deviation between the grid frequency and the preset frequency being greater than the preset frequency deviation, determining a frequency-regulated charging and discharging power based on the real-time deviation, where the frequency-regulated charging and discharging power includes a charging power when the grid charges the backup battery pack or a power when the backup battery pack discharges the power to the grid; Independent energy storage power stations are dispatched based on frequency-adjusted charging and discharging power.

[0072] In this embodiment, during grid operation, the actual operating frequency of the grid may deviate from the preset frequency due to various factors (such as sudden load changes and power generation equipment failures). The deviation between the grid frequency and the preset frequency is monitored in real time. This deviation reflects the difference between the current grid frequency and the ideal stable operating frequency. The preset frequency deviation is a manually set threshold used to determine whether the grid frequency deviation has reached a level requiring intervention.

[0073] When the real-time deviation is greater than the preset frequency deviation, it indicates that the grid frequency has fluctuated significantly and needs to be adjusted. In this case, the frequency adjustment charging and discharging power can be determined based on the size and direction of the real-time deviation.

[0074] Specifically, if the grid frequency exceeds a preset frequency, it indicates excess grid power, necessitating the consumption of excess energy to stabilize the frequency. At this point, the grid determines the power it uses to charge the backup battery bank. By storing the excess energy in the backup battery bank, grid power is reduced, thereby reducing the frequency to a normal range. The amount of charging power is typically proportional to the real-time frequency deviation; greater deviations increase the charging power, enabling faster frequency regulation.

[0075] If the grid frequency falls below the preset frequency, it indicates insufficient grid power and requires additional energy. At this point, the system determines the power the backup battery pack should discharge to the grid. This releases energy, increasing grid power and ultimately bringing the frequency back to normal. Similarly, the discharge power is correlated with the real-time frequency deviation; greater deviations increase the discharge power, ensuring rapid correction.

[0076] In this embodiment, the backup battery pack does not participate in charge and discharge scheduling based on predicted active power. Instead, it is specifically used to respond to sudden abnormalities in grid frequency. When the grid frequency deviates significantly, it can quickly respond by providing or absorbing power to the grid through charge and discharge operations, helping the grid quickly restore 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.

[0077] As can be seen from the above, this embodiment adds a backup battery pack to an independent energy storage power station, determining charge and discharge power based on real-time grid frequency deviations. When the frequency is abnormal, the backup battery pack can rapidly charge and discharge, promptly adjusting 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 fluctuations, and ensures safe and efficient grid operation.

[0078] In one embodiment of the present disclosure, determining the frequency based on the real-time deviation and adjusting the charge and discharge power includes: The adjustment coefficient is determined based on the ratio of the absolute value of the real-time frequency deviation of the power grid to the preset frequency deviation, and the adjustment coefficient is positively correlated with the ratio; Determine a safe power threshold based on the current state of charge of the backup battery pack, wherein the safe power threshold decreases as the state of charge increases and decreases as the state of charge decreases; The frequency regulation charging and discharging power is obtained by multiplying the regulation coefficient by the rated charging and discharging power of the backup battery pack and performing amplitude limiting processing in combination with the safety power threshold.

[0079] In this embodiment, when frequency-adjusting charging and discharging power based on real-time deviation is determined, the grid frequency monitoring module first collects the actual grid operating frequency in real time and calculates the absolute value of its real-time deviation from the preset frequency. The ratio of this absolute value to the preset frequency deviation is used as the core parameter to construct an adjustment coefficient mapping relationship. A larger ratio indicates a larger adjustment coefficient, ensuring stronger adjustment 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 frequency recovery.

[0080] At the same time, the energy storage status monitoring unit obtains the current state of charge (SOC) of the backup battery pack and dynamically generates a safety power threshold. When the SOC is above 80%, the charging power threshold decreases linearly with increasing SOC to avoid the risk of overcharging. When the SOC is below 20%, the discharge power threshold decreases linearly with decreasing SOC to prevent excessive discharge. The product of the adjustment coefficient and the rated charge and discharge power of the backup battery pack is compared with the safety power threshold, and the smaller value is taken as the final frequency adjustment charge and discharge power. If the calculated value exceeds the safety threshold, the limiter is triggered, ensuring rapid response to frequency deviations while protecting the backup battery pack from damage, achieving dual protection of grid frequency stability and energy storage equipment safety.

[0081] As can be seen from the above, this embodiment enhances responsiveness by dynamically adjusting the frequency deviation by adjusting the coefficient. It also sets a safe power threshold and limits the power supply based on the state of charge to prevent overcharging or over-discharging of the backup battery. This not only quickly smooths out grid frequency fluctuations, ensuring grid stability, but also extends the life of energy storage equipment, achieving coordinated optimization of grid safety and energy storage protection.

[0082] Corresponding to the collaborative scheduling method based on independent energy storage in the above embodiment, Figure 2 This is a structural block diagram of a collaborative scheduling device based on independent energy storage provided in one embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. 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.

[0083] Among them, the first charge and discharge power determination module 21 is used to determine the energy storage scheduling comprehensive 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 charge and discharge power of the independent energy storage power station is obtained based on the energy storage scheduling comprehensive index; the power stability index is an indicator that measures the degree of power fluctuation of the power grid, and the energy storage life loss index represents the degree of life loss of the independent energy storage power station during the charging and discharging process; the predicted active power is based on the first historical active power of the power grid and the current active power predicted by the autoregressive integral moving average model; A second charge and discharge power determination module 22 is configured to adjust the initial charge and discharge power of the independent energy storage power station based on the deviation between the real-time active power of the power grid and the predicted active power, thereby obtaining a target charge and discharge power of the independent energy storage power station; The scheduling module 23 is used to schedule the independent energy storage power station based on the target charging and discharging power.

[0084] In one embodiment of the present application, the first historical active power of the power grid is the active power corresponding to the first historical period; The independent energy storage coordinated scheduling device 20 further 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 is 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, and the second historical period is earlier than the first historical period; In response to the stationarity being greater than the stationarity threshold, taking the order of the differential processing corresponding to the stationarity as the differential order; Determining an autoregressive order based on a first correlation degree between the active power at the plurality of lag moments and the active power at the target moment within the second historical period; the first correlation degree indicates consistency between a changing trend of the active power at the plurality of lag moments and a changing trend of the active power at the target moment; Determining the moving average order based on a second degree of correlation between the historical predicted active power errors at multiple lag moments and the predicted active power errors at the target moment within the second historical period; the second degree of correlation indicates consistency between a changing trend of the historical predicted active power errors at multiple lag moments within the second historical period and a changing trend of the predicted active power errors at the target moment; wherein the historical predicted active power error refers to a difference between the predicted active power at the target moment obtained by the historical prediction model and the actual active power at the target moment within the second historical period; An autoregressive integrated moving average model is constructed based on the autoregressive order, difference order and moving average order.

[0085] In one embodiment of the present application, the first charge and discharge power determination module 21 is specifically configured to: Based on the first historical period, obtaining multiple historical predicted active powers; Calculate the fluctuation variance of multiple historically predicted active powers and determine the current power stability index of the power grid based on the inverse of the fluctuation variance; The energy storage life loss index of the independent energy storage power station is determined based on the current charge and discharge depth and the cumulative charge and discharge times of the independent energy storage power station.

[0086] In one embodiment of the present application, the first charge and discharge power determination module 21 is further configured to: Determine the weight corresponding to the power stability index and the weight corresponding to the energy storage life loss index; Based on the power stability index of the power grid and the energy storage life loss index of the independent energy storage power station, as well as their respective corresponding weights, a weighted fusion is performed to obtain a comprehensive energy storage scheduling index corresponding to the predicted active power.

[0087] In one embodiment of the present application, 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 groups of parallel-connected batteries; The scheduling module 23 is specifically used for: In response to the predicted active power being less than the first preset power, charging the plurality of parallel-connected storage batteries based on the charging power; In response to the predicted active power being greater than a second preset power, determining the number of storage batteries to be discharged based on the discharge power, determining a battery group to be discharged from a plurality of parallel-connected battery groups based on the number, and discharging the battery group to be discharged into the grid; The first preset power is less than the second preset power.

[0088] In one embodiment of the present application, the scheduling module 23 is further configured to: Obtaining the remaining power data corresponding to each group of batteries to be discharged; Calculating the difference in power between the groups of batteries to be discharged based on the remaining power data; In response to the electric quantity difference being greater than the difference threshold, balancing management is performed on the electric storage batteries that participate in discharging to the power grid.

[0089] In one embodiment of the present application, the energy storage equipment of the independent energy storage power station further includes a backup battery pack; the scheduling module 23 is further specifically configured to: In response to a real-time deviation between the grid frequency and the preset frequency being greater than the preset frequency deviation, determining a frequency-regulated charging and discharging power based on the real-time deviation, where the frequency-regulated charging and discharging power includes a charging power when the grid charges the backup battery pack or a power when the backup battery pack discharges the power to the grid; Independent energy storage power stations are dispatched based on frequency-adjusted charging and discharging power.

[0090] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3 The electronic device 300 in the embodiment shown 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 memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of the first charge and discharge power determination module 21, the second charge and discharge power determination module 22 and the scheduling module 23 are shown.

[0091] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), 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, etc.

[0092] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0093] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information such as historical predicted active power.

[0094] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in the collaborative scheduling method based on independent energy storage provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0095] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0096] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned 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, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage 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 is about to be output.

[0097] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0098] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] 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 schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0100] The units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0101] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each module may exist physically separately, or two or more modules may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0102] The above are only specific embodiments of the present application, but the scope of protection of the present 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 such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A collaborative 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 the independent energy storage power station, the comprehensive energy storage scheduling index corresponding to the predicted active power is determined; The initial charge and discharge power of the independent energy storage power station is obtained based on the comprehensive energy storage scheduling index; the power stability index is an indicator that measures the degree of power fluctuation of the power grid, and the energy storage life loss index represents the degree of life 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, adjusting the initial charge and discharge power of the independent energy storage power station to obtain the target charge and discharge power of the independent energy storage power station; The independent energy storage power station is dispatched based on the target charging and discharging power.

2. The coordinated scheduling method based on independent energy storage according to claim 1, characterized in that: The first historical active power of the power grid is the active power corresponding to the first historical period; The autoregressive integrated moving average model is obtained by the following method: Determining the stability of a second historical active power of the power grid after differential processing; the stability is the stability of an 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 a second historical period, the second historical period being earlier than the first historical period; In response to the stationarity being greater than a stationarity threshold, taking the order of the differential processing corresponding to the stationarity as the differential order; Determining an autoregressive order based on a first correlation degree between the active power at the plurality of lag moments and the active power at the target moment within the second historical period; the first correlation degree indicating consistency between a changing trend of the active power at the plurality of lag moments and a changing trend of the active power at the target moment; Determining the moving average order based on a second degree of correlation between the historical predicted active power errors at multiple lag moments and the predicted active power errors at the target moment within the second historical period; the second degree of correlation indicates consistency between a changing trend of the historical predicted active power errors at multiple lag moments within the second historical period and a changing trend of the predicted active power errors at the target moment; wherein the historical predicted active power error refers to a difference between the predicted active power at the target moment obtained by the historical prediction model and the actual active power at the target moment within the second historical period; An autoregressive integrated moving average model is constructed based on the autoregressive order, the difference order and the moving average order.

3. The coordinated scheduling method based on independent energy storage according to claim 1, characterized in that: The method for determining the current power stability index of the power grid includes: Based on the first historical period, obtaining multiple historical predicted active powers; Calculating the fluctuation variance of multiple historically predicted active powers, and determining the current power stability index of the power grid based on the inverse of the fluctuation variance; The method for determining the current energy storage life loss index of the independent energy storage power station includes: An energy storage life loss index of the independent energy storage power station is determined based on the current charge and discharge depth and the accumulated charge and discharge times of the independent energy storage power station.

4. The coordinated scheduling method based on independent energy storage according to claim 3, characterized in that: The energy storage scheduling comprehensive index corresponding to the predicted active power is determined based on the power stability index of the power grid and the energy storage life loss index of the independent energy storage power station, including: Determining a weight corresponding to the power stability index and a weight corresponding to the energy storage life loss index; Based on the power stability index of the power grid and the energy storage life loss index of the independent energy storage power station, and their respective corresponding weights, a weighted fusion is performed to obtain a comprehensive energy storage scheduling index corresponding to the predicted active power.

5. The coordinated scheduling method based on independent energy storage according to 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 groups of parallel-connected batteries; Scheduling the independent energy storage power station based on the target charge and discharge power includes: In response to the predicted active power being less than a first preset power, charging the plurality of parallel-connected storage batteries based on the charging power; In response to the predicted active power being greater than a second preset power, determining the number of storage batteries to be discharged based on the discharge power, determining a battery group to be discharged from the plurality of parallel battery groups based on the number, and discharging the battery group to be discharged to the grid; The first preset power is less than the second preset power.

6. The coordinated dispatching method based on independent energy storage according to claim 5, characterized in that: The battery group to be discharged is at least two groups of batteries. In the process of discharging the power grid based on the battery group to be discharged, the method further includes: Obtaining the remaining power data corresponding to each group of batteries to be discharged; Calculating the difference in power between the groups of batteries to be discharged based on the remaining power data; In response to the electrical quantity difference being greater than a difference threshold, balancing management is performed on the electrical storage batteries that participate in discharging to the power grid.

7. The coordinated scheduling method based on independent energy storage according to claim 5, characterized in that: The energy storage equipment of the independent energy storage power station further includes a backup battery pack; the method further includes: In response to a real-time deviation between a grid frequency and a preset frequency being greater than a preset frequency deviation, determining a frequency-regulated charging and discharging power based on the real-time deviation, the frequency-regulated charging and discharging power including a charging power when the grid charges the backup battery pack or a power when the backup battery pack discharges the backup battery pack to the grid; Independent energy storage power stations are dispatched based on frequency-adjusted charging and discharging power.

8. A collaborative scheduling device based on independent energy storage, characterized in that: include: A first charge and discharge power determination module is configured to determine a comprehensive energy storage scheduling index corresponding to the predicted active power based on a current power stability index of the power grid and a current energy storage life loss index of the independent energy storage power station; The initial charge and discharge power of the independent energy storage power station is obtained based on the comprehensive energy storage scheduling index; the power stability index is an indicator that measures the degree of power fluctuation of the power grid, and the energy storage life loss index represents the degree of life 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; a second charge and discharge power determination module, configured to adjust the initial charge and discharge power of the independent energy storage power station based on a deviation between the real-time active power of the power grid and the predicted active power, to obtain a target charge and discharge power of the independent energy storage power station; A scheduling module is used to schedule the independent energy storage power station based on the target charging and discharging power.

9. 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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