Intelligent power dispatching method of energy storage system

Abstract

The invention discloses an intelligent power dispatching method for an energy storage system, and relates to the field related to power distribution, and the method comprises the steps: determining time-of-use electricity price information of a power utilization region corresponding to a target energy storage system; collecting first power utilization demand information of the application area of the target energy storage system in the first time zone; collecting the energy storage capacity of the target energy storage system; executing a charging and discharging plan based on the energy storage capacity, the time-of-use electricity price information and the first electricity demand information, and generating the charging and discharging plan; and performing power dispatching control by using the target energy storage system based on the charging and discharging plan. The technical problem that power dispatching of an existing energy storage system is lack of accuracy and flexibility is solved, and the technical effects of accurate power dispatching and flexible control are achieved.

Description

Technical Field

[0001] The present application relates to fields related to power distribution, and in particular to an intelligent power dispatching method for an energy storage system. Background Art

[0002] Intelligent power dispatching of energy storage systems for industrial and commercial households is crucial for optimizing commercial and industrial electricity costs, improving energy efficiency, and enhancing the grid's ability to balance supply and demand. Currently, the main approach to addressing this problem is that most industrial and commercial households adopt charging and discharging strategies based on fixed time periods or perform simple power dispatch operations based on a single electricity price signal. This approach fails to fully account for the dynamic differences in time-of-use electricity prices in the areas where industrial and commercial households consume electricity, the significant fluctuations in actual electricity demand during different time periods, and the dynamic changes in the remaining capacity of the energy storage system due to operation. As a result, power dispatching lacks precision and flexibility, making it impossible to effectively minimize the electricity costs of industrial and commercial households and optimize the utilization efficiency of the energy storage system.

[0003] Among the current related technologies, the power dispatching of energy storage systems has the technical problem of lack of accuracy and flexibility. Summary of the Invention

[0004] This application provides an intelligent power dispatching method for an energy storage system. By determining the time-of-use electricity price information of the power consumption area where the energy storage system is located, collecting the actual power demand information of the application area in a specific time zone, and simultaneously obtaining the current energy storage capacity of the energy storage system, the application adopts technical means such as integrating these three key information to execute charging and discharging planning, generate a charging and discharging plan, and implement power dispatching control of the target energy storage system based on the plan. This method solves the technical problem of lack of accuracy and flexibility in the power dispatching of existing energy storage systems, and achieves the technical effect of accurate and flexible control of power dispatching.

[0005] The present application provides an intelligent power dispatching method for an energy storage system, comprising: determining time-of-use electricity price information for a power consumption area corresponding to a target energy storage system; collecting first power demand information for an application area of the target energy storage system in a first time zone; collecting the energy storage capacity of the target energy storage system; executing a charge and discharge plan based on the energy storage capacity, the time-of-use electricity price information, and the first power demand information to generate a charge and discharge plan; and performing power dispatching control using the target energy storage system based on the charge and discharge plan.

[0006] In a possible implementation, first electricity demand information of the application area of the target energy storage system in the first time zone is collected, and the following processing is performed: a first production task of the application area in the first time zone is collected; the first production task is parsed, and neighborhood aggregation of electricity demand is performed based on the start and stop status of electrical equipment to generate electricity demand information for multiple consecutive time zones; and the first electricity demand information is generated using the electricity demand information for multiple consecutive time zones.

[0007] In a possible implementation, the following processing is performed: the target energy storage system is an electric energy storage device in the application area, and the target energy storage system and the electrical equipment in the application area are both connected to a regional power grid, the regional power grid is connected to the electrical equipment in the application area through a first power supply line, and the target energy storage system is connected to the electrical equipment in the application area through a second power supply line, and the first power supply line and the second power supply line are independent of each other.

[0008] In a possible implementation method, the first production task is parsed, the electricity demand neighborhood is aggregated through the start and stop status of the electrical equipment, and multiple continuous time zone electricity demand information is generated, and the following processing is performed: the first production task is parsed to generate multiple start and stop statuses of the electrical equipment at multiple moments; based on the multiple start and stop statuses of the electrical equipment, the moments that are adjacent and have the same start and stop status of the electrical equipment are aggregated to generate the multiple continuous time zone electricity demand information.

[0009] In a possible implementation, the energy storage capacity of the target energy storage system is collected, and the following processing is performed: connecting to the battery management system of each battery pack in the target energy storage system and reading the battery health status of each battery pack; calculating the actual energy storage capacity of each battery pack based on the battery health status and the calibrated capacity of each battery pack; calculating the comprehensive capacity based on the actual energy storage capacity to generate the energy storage capacity.

[0010] In a possible implementation, the energy storage capacity is generated and the following processing is also performed: based on the battery health status of each battery group, a health impact analysis is performed at each step of charge and discharge depth to determine each battery performance degradation index; based on the each battery performance degradation index, a maximum charge and discharge depth that meets a preset degradation index threshold is determined; and the energy storage capacity is optimized based on the maximum charge and discharge depth that meets the preset degradation index threshold.

[0011] In a possible implementation, a charge and discharge plan is executed based on the energy storage capacity, the time-of-use electricity price information and the first electricity demand information to generate a charge and discharge plan, and the following processing is performed: through the battery health status of each battery group, the battery health-charge and discharge parameter matching library is called to match the charge and discharge parameters to generate various optimal charge and discharge parameters; the charging time of the energy storage system is calculated in combination with the various optimal charge and discharge parameters and the energy storage capacity to generate a target charging time; based on the time-of-use electricity price information, with the minimum comprehensive electricity price as a constraint, the target charging time zone is determined according to the target charging time; based on the energy storage capacity, the first electricity demand information and the time-of-use electricity price information, the reserve energy access in the time zone with the maximum electricity price is executed to generate a target discharge time zone; the charge and discharge plan is generated based on the target charging time zone, the target discharge time zone and the various optimal charge and discharge parameters.

[0012] In a possible implementation, the target discharge time zone is generated and the following processing is further performed: line structure information of the second power supply line between the target energy storage system and the electrical equipment in the application area is obtained; the no-load losses of the line elements are accumulated and calculated based on the line structure information to generate a comprehensive line loss rate; and line loss analysis of the target discharge time zone is performed using the comprehensive line loss rate to correct the target discharge time zone.

[0013] In a possible implementation, the following processing is performed: the first power supply line is provided with a first line monitoring module, and the second power supply line is provided with a second line monitoring module; when the first line monitoring module prompts an abnormality, the energy storage capacity of the target energy storage system is read in real time, and when the energy storage capacity meets the preset minimum backup energy threshold, the first power supply line is switched to the second power supply line; after performing tolerance processing according to the preset minimum backup energy threshold, the charge and discharge of each battery group in the target energy storage system is classified to generate a charging identification battery group and a discharging identification battery group; the charging identification battery group is charged on the grid side, and the discharging identification battery group is discharged through the second power supply line.

[0014] In a possible implementation, the following processing is performed: when the target energy storage system is used for power dispatching control based on the charging and discharging plan and the power supply line is switched to the second power supply line, if the second line monitoring module prompts an abnormality, the power supply line is switched back to the first power supply line; if both the first line monitoring module and the second line monitoring module prompt an abnormality, a circuit maintenance reminder message is generated, and the power supply is forcibly stopped.

[0015] The proposed intelligent power dispatching method for an energy storage system first determines the time-of-use electricity price information for the target energy storage system's power consumption area, then collects first power demand information for the target energy storage system's application area in a first time zone, and then collects the target energy storage system's energy storage capacity. Based on the energy storage capacity, the time-of-use electricity price information, and the first power demand information, a charge and discharge plan is generated. Finally, power dispatch control is performed using the target energy storage system based on the charge and discharge plan. This achieves the technical effect of precise and flexible power dispatch control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flow chart of an intelligent power dispatching method for an energy storage system provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of a flow chart for generating a charge and discharge plan in an intelligent power dispatching method for an energy storage system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0022] The embodiment of the present application provides an intelligent power dispatching method for an energy storage system, such as Figure 1As shown, the method includes: Step S100: Determine the time-of-use electricity price information of the electricity consumption area corresponding to the target energy storage system.

[0023] Specifically, time-of-use electricity pricing refers to dividing the day into peak, off-peak, and normal hours based on the load characteristics of the power grid, and then pricing electricity differently for each time period. Peak electricity prices are higher, while off-peak prices are lower. The goal is to guide users to plan their electricity usage more efficiently and reduce the difference between peak and off-peak loads on the power grid.

[0024] The target energy storage system first activates the network communication module and establishes a connection with the power company's data interface (e.g., API) server using the preset IP address and port number. Following the interface protocol, it sends request data, requesting time-of-use electricity price information for the target power consumption area. This request data may include information such as the power consumption area's identification code. The system then receives the data returned by the power company and extracts the price information using a data parsing tool (e.g., a JSON parser). For example, it extracts data such as a peak price of 1.5 yuan / kWh, an off-peak price of 0.5 yuan / kWh, and a normal price of 1.0 yuan / kWh, and stores it in the system database.

[0025] Step S200: collecting first electricity demand information of an application area of the target energy storage system in a first time zone.

[0026] Specifically, the first time zone refers to a preset time period used for planning and forecasting electricity demand. This can be any set time range, such as a certain time period tomorrow or a certain weekday next week, depending on the scheduling requirements of the energy storage system and the factory's production schedule. The first electricity demand refers to the total amount of electricity required to be consumed by an application area, such as a factory or industrial park, within the first time zone. This first electricity demand is influenced by a variety of factors, including production plans, equipment operating status, and personnel activities.

[0027] In application areas such as factories or industrial parks, electricity demand information related to production plans is obtained by connecting to data interfaces with production management systems, such as enterprise resource planning (ERP) systems and manufacturing execution systems (MES). These interfaces can be based on network communications, such as using the HTTP protocol for data exchange, or obtaining data through shared folders on an internal LAN. For example, the target energy storage system control unit activates the network communication module and establishes a connection with the factory's ERP system or MES system according to the preset IP address and port number. According to the interface protocol requirements, request data is sent to request electricity demand information related to the production plan in the first time zone. The ERP system or MES system returns data, which the control unit receives and stores.

[0028] For large-scale equipment in a factory, power demand can be predicted by analyzing the equipment's operating schedule (e.g., startup time, operating duration, power consumption, etc.). This can be achieved by reading data from the equipment's control system, such as a programmable logic controller (PLC). The PLC can record the equipment's operating parameters and transmit these parameters to the control unit of the target energy storage system. For example, the control unit establishes a communication connection with the factory equipment's PLC and, according to the communication protocol requirements, such as Modbus, sends a request to read the equipment's operating schedule. The PLC returns the equipment's operating schedule parameters, and the control unit calculates the equipment's power demand based on these parameters.

[0029] Future electricity demand is predicted using historical electricity consumption data combined with time series analysis algorithms (such as moving average and exponential smoothing). The control unit can store electricity consumption data from a past period (such as the past week or month) and analyze trends in this data to predict electricity demand in the first time zone. For example, the control unit retrieves historical electricity consumption data from a local database, such as power consumption data for the same time period each day over the past week. It then analyzes this historical data using a time series analysis algorithm. For example, the moving average method can be used to calculate the average power consumption for the same time period each day over the past week, which serves as a reference value for electricity demand in the first time zone. Furthermore, the reference value is adjusted to account for seasonal factors (such as increased air conditioning usage in summer) and differences between weekdays and holidays.

[0030] For example, consider a factory with an energy storage system to meet some of its electricity needs and reduce electricity costs. The factory's production schedule is managed through an ERP system. The factory also has several large production equipment whose operation plans are controlled by programmable logic controllers (PLCs). The energy storage system's control unit needs to collect electricity demand information for tomorrow (in the first time zone). The control unit connects to the factory's ERP system through a data interface and sends a request to obtain tomorrow's production schedule. The ERP system returns data indicating that two production lines will be operating tomorrow: Line 1, with a power of 200 kilowatts and an operating time of 8 hours; Line 2, with a power of 150 kilowatts and an operating time of 6 hours. The control unit stores this data. The control unit then establishes a communication connection with the PLCs of the factory equipment and sends a request to read the equipment's operating schedule. The PLCs return equipment operating parameters, indicating that large equipment A is scheduled to run for 8 hours at 100 kilowatts, while equipment B is scheduled to run for 4 hours at 80 kilowatts. The control unit calculates tomorrow's electricity demand based on these parameters. The control unit retrieves power consumption data for the same time period each day for the past week from a local database. The moving average method is used to calculate the average power consumption for the same time period each day over the past week. Considering that tomorrow is a workday and there are no special holidays, the control unit uses this average as a reference for tomorrow's power demand. Simultaneously, the reference value is adjusted based on production plan data and equipment operation plan analysis results to determine tomorrow's power demand (in the first time zone).

[0031] In one possible implementation, first power demand information for the target energy storage system's application area in a first time zone is collected. Step S200 further includes step S210 of collecting a first production task for the application area in the first time zone. Specifically, the production task information for the first time zone is obtained by connecting to a data interface with the factory's production management system. The production task data returned by the production management system is parsed to extract key information, such as the production task's start and end time, and the equipment and production lines involved.

[0032] Step S220: parse the first production task, aggregate the electricity demand in the neighborhood based on the start / stop status of the electrical equipment, and generate electricity demand information for multiple consecutive time zones. Specifically, the control unit analyzes the start / stop status and operation plan of each device based on the production task data. For example, device A runs for the first 4 hours of the first time zone with a power of 100 kilowatts; device B runs for the last 4 hours of the first time zone with a power of 80 kilowatts. Aggregate the electricity demand of adjacent time periods. For example, calculate the electricity demand for the first 4 hours and the last 4 hours of the first time zone separately to generate electricity demand information for two consecutive time zones.

[0033] Step S230, generating the first electricity demand information using the electricity demand information of the multiple consecutive time zones. Specifically, the electricity demand information of the multiple consecutive time zones generated in step S220 is integrated. For example, the electricity demand of the first 4 hours and the last 4 hours are added together to obtain the total electricity demand of the first time zone. The integrated electricity demand information is stored in the system database for subsequent charge and discharge planning. This implementation method can accurately predict the electricity demand of the first time zone by collecting production task information and performing neighborhood aggregation, which helps the target energy storage system to more reasonably plan charging and discharging operations.

[0034] In one possible implementation, the first production task is parsed, and neighborhood aggregation of electricity demand is performed based on the start / stop status of electrical devices to generate electricity demand information for multiple consecutive time zones. Step S220 further includes step S221, parsing the first production task to generate the start / stop status of multiple electrical devices at multiple time points. Specifically, the control unit parses the production task data and extracts the start / stop status of each electrical device at different time points. The parsed start / stop status data is stored in a system database.

[0035] Step S222: Based on the start / stop status of the plurality of electrical devices, adjacent moments with consistent start / stop status are aggregated to generate the plurality of continuous time zone power demand information. Specifically, the control unit aggregates adjacent moments with consistent start / stop status based on the start / stop status data. The aggregated data is integrated into the power demand information for the continuous time zone and stored in the system database.

[0036] For example, assuming that the production task data is as shown in Table 1, the corresponding aggregated continuous time zone electricity demand information is shown in Table 2.

[0037] Table 1: Example of start and stop status of electrical equipment Table 2: Example of aggregated electricity demand information for consecutive time zones This implementation details how to generate electricity demand information for multiple consecutive time zones by analyzing production tasks and performing neighborhood aggregation. Accurate electricity demand forecasting can optimize the charging and discharging planning of target energy storage systems, reducing electricity costs and improving energy management efficiency.

[0038] In a possible implementation, the target energy storage system is further included as follows: the target energy storage system is an electric energy storage device in the application area, and the target energy storage system and the electrical equipment in the application area are both connected to a regional power grid, the regional power grid is connected to the electrical equipment in the application area via a first power supply line, and the target energy storage system is connected to the electrical equipment in the application area via a second power supply line, and the first power supply line and the second power supply line are independent of each other.

[0039] Specifically, in this implementation, the target energy storage system serves as the energy storage device for the application area, and both it and the electrical users in the application area are connected to the regional power grid. The regional power grid is connected to the electrical users in the application area via a first power supply line, while the target energy storage system is connected to the electrical users in the application area via a second power supply line, and the first and second power supply lines are independent of each other. This design allows power to be supplied by the regional power grid under normal circumstances, but to be switched to the target energy storage system in the event of a grid failure or high electricity prices.

[0040] Specifically, the regional power grid provides primary power and connects to the electrical equipment in the application area via a first power line. The target energy storage system serves as a backup or auxiliary power supply and connects to the electrical equipment in the application area via a second power line. The first and second power lines are independent, ensuring no interference when switching power sources. Intelligent monitoring equipment is installed on the first power line to monitor the regional power grid in real time and transmit the monitoring data to the control unit of the target energy storage system. The control unit determines whether to switch power sources based on the monitoring data. If a regional power grid failure or excessively high electricity prices is detected, the control unit sends a command to switch to the second power line (the target energy storage system). If the regional power grid returns to normal or the electricity price decreases, the control unit sends a command to switch back to the first power line (the regional power grid). This implementation, through independent power lines and an intelligent switching mechanism, ensures seamless power switching to the target energy storage system in the event of a regional power grid failure, reducing power outages and improving power supply reliability. Switching to the target energy storage system during periods of high electricity prices allows the target energy storage system to utilize the energy stored during periods of low electricity prices, effectively reducing electricity costs. By real-time monitoring of regional power grid status and intelligent switching control, energy optimization management is achieved, energy utilization efficiency is improved, and dependence on the regional power grid is reduced.

[0041] In a possible implementation, it further includes: the first power supply line is provided with a first line monitoring module, and the second power supply line is provided with a second line monitoring module; when the first line monitoring module prompts an abnormality, the energy storage capacity of the target energy storage system is read in real time, and when the energy storage capacity meets the preset minimum backup energy threshold, the first power supply line is switched to the second power supply line; after performing tolerance processing according to the preset minimum backup energy threshold, the battery groups in the target energy storage system are classified for charge and discharge, and a charging mark battery group and a discharging mark battery group are generated; the charging mark battery group is charged on the grid side, and the discharging mark battery group is discharged through the second power supply line.

[0042] Specifically, a first line monitoring module and a second line monitoring module are installed on the first and second power supply lines, respectively. These monitoring modules integrate multiple sensors, such as voltage, current, and power sensors, enabling real-time and accurate acquisition of line electrical parameters. Furthermore, a data acquisition and transmission module converts the collected analog signals into digital signals and transmits the data to the control unit via wired (e.g., Ethernet) or wireless (e.g., Wi-Fi, ZigBee) communication. When the first line monitoring module detects an abnormality in line parameters, such as a voltage fluctuation exceeding ±10% of the rated voltage or a current exceeding 80% of the line's rated carrying capacity, a switching command is triggered. For example, the first line monitoring data is read every 100ms. If three consecutive sampling points are abnormal, a "grid fault" is flagged. Simultaneously, the second line status is read to confirm that there is no fault and prepare for switching.

[0043] Before switching, the current energy storage capacity of the target energy storage system is read in real time. The remaining power of the target energy storage system must meet a preset minimum backup energy threshold (for example, the ratio of the target energy storage system's remaining power to the total energy storage capacity must be ≥30%). The preset minimum backup energy threshold is determined based on factors such as the minimum power demand of the electrical equipment in the application area in an emergency and the expected duration of the abnormality. If the conditions are met, the target energy storage system will send a control signal to the power supply line switching device, which will quickly disconnect the first power supply line from the electrical equipment and simultaneously connect the second power supply line to the electrical equipment, achieving automatic switching of the power supply lines.

[0044] Each battery pack in the target energy storage system is subjected to tolerance processing based on a preset minimum reserve energy threshold. Tolerance processing involves increasing the preset minimum reserve energy threshold, for example, to 50%. Remaining charge information for each battery pack is obtained by communicating with the battery management system. The remaining charge of each battery pack is then compared with the tolerance-processed threshold. If the remaining charge of a battery pack is higher than the tolerance-processed threshold, it is classified as a discharge-marked battery pack. If the remaining charge of a battery pack is lower than the tolerance-processed threshold, it is classified as a charge-marked battery pack.

[0045] For battery packs with a discharge indicator, the system controls them to supply power to the electrical equipment in the application area via a second power supply line. Specifically, the energy storage system control unit sends instructions to the battery pack's discharge control module, which adjusts the battery pack's output parameters, such as voltage and current, based on the instructions to meet the needs of the electrical equipment. Simultaneously, the system monitors the battery pack's remaining charge, voltage, current, and other parameters in real time to ensure that the discharge process remains within the battery pack's safe operating range. For battery packs with a charge indicator, the energy storage system control unit controls the charging device to charge the battery pack from the grid. The charging device uses an intelligent charger that can adopt an appropriate charging strategy, such as constant current-constant voltage charging, based on the battery pack's characteristics and current status. During the constant current charging phase, the charger charges the battery pack at a constant current, gradually increasing the battery voltage. Once the battery voltage reaches the set value, the system enters the constant voltage charging phase, maintaining a constant voltage and gradually reducing the current until the battery pack is fully charged. This implementation utilizes dual-line monitoring and intelligent classification management to switch to energy storage power in the event of a regional grid fault. This allows healthy battery packs to be discharged and low-charged battery packs to be charged, ensuring uninterrupted operation of critical loads and extending battery life.

[0046] In one possible implementation, the method further includes: when the target energy storage system is used for power dispatching control based on the charge and discharge plan and the power supply line is switched to the second power supply line, if the second line monitoring module indicates an abnormality, the power supply line is switched back to the first power supply line; if both the first line monitoring module and the second line monitoring module indicate an abnormality, a circuit maintenance reminder message is generated, and the power supply is forcibly stopped.

[0047] Specifically, if the second power supply line's monitoring module detects an anomaly (such as a voltage drop or current overload) during operation, the battery management system suspends current charging and discharging operations (for example, by stopping the output of the discharge indicator battery pack) and switches the power supply back to the first power supply line. After the switchover, the voltage, current, and other parameters of the first power supply line are re-read to confirm whether they have returned to normal. If the first power supply line's monitoring module still reports an anomaly (such as a voltage fluctuation exceeding ±15%) after switching back to the first power supply line, and the second power supply line's monitoring module has not recovered, a systemic failure is determined. All power supply lines are disconnected from the powered equipment to prevent damage. An alert message (such as a text message or email) is sent to maintenance personnel, including the time of the anomaly, line parameters, and possible fault causes (such as line aging or short circuit). The abnormal event and system status data are logged for subsequent analysis. This implementation maximizes power supply continuity through a hierarchical fallback strategy from the second power supply line to the first power supply line. Forced power outages in the event of dual-line anomalies prevent damage to powered equipment caused by operating under unstable voltage.

[0048] Step S300: collecting the energy storage capacity of the target energy storage system.

[0049] Specifically, energy storage capacity refers to the total amount of electrical energy that a target energy storage system (such as a battery pack) can store. For battery energy storage systems, this is constrained by multiple factors, including battery capacity and health (such as the number of cycles and self-discharge rate).

[0050] The target energy storage system is equipped with a battery management system (BMS), a system used to manage and monitor battery packs. It can monitor battery power, voltage, current, and other parameters in real time to ensure safe battery operation and provide battery health status information. The control unit of the target energy storage system establishes a communication connection with the BMS and sends a request to read the energy storage capacity in accordance with the communication protocol requirements, such as through a specific frame format on the CAN bus. The BMS returns the current energy storage capacity data, which the control unit receives and calibrates based on factors such as the battery self-discharge rate. For example, if the BMS shows that the current energy storage capacity is 1000 kWh and the last calibration time was 24 hours ago, then after taking into account the self-discharge rate (assuming 0.5% per day), the actual available energy storage capacity is 1000×(1-0.5%)=995 kWh.

[0051] In one possible implementation, the energy storage capacity of the target energy storage system is collected, and step S300 further includes step S310, connecting to the battery management system of each battery pack in the target energy storage system and reading the battery health status of each battery pack. Specifically, the BMS of each battery pack in the target energy storage system is connected via the CAN bus or Modbus protocol, and the following key parameters are read in real time: battery state of health (SOH), battery temperature, and number of cycles. Among them, SOH reflects the percentage of the current battery capacity relative to the initial capacity (for example, SOH = 85% means that the battery capacity has decayed to 85% of the initial capacity). Battery temperature affects the accuracy of SOH calculation (for example, the SOH correction coefficient is 1.0 at 25°C and 0.95 at 40°C). The number of cycles refers to the cumulative number of charge and discharge cycles (for example, the SOH decays to 80% after 2000 cycles).

[0052] Assuming that the target energy storage system contains 4 battery packs (each battery pack contains 100 single cells), the SOH data read by the BMS is shown in Table 3.

[0053] Table 3: Example of SOH data Step S320 calculates the actual energy storage capacity of each battery pack based on the battery health status and the rated capacity of each battery pack. Specifically, actual energy storage capacity = rated capacity × SOH × correction factor, where rated capacity is the factory-rated capacity of the battery pack (e.g., the rated capacity of each battery pack is 500 kWh). Table 4 shows an example of calculating the actual energy storage capacity of each battery pack based on the data in Table 3.

[0054] Table 4: Example of actual energy storage capacity data Step S330 calculates a comprehensive capacity based on the actual energy storage capacities to generate the energy storage capacity. Specifically, the actual energy storage capacities of the individual battery packs are summed to obtain the target energy storage system's energy storage capacity. This implementation improves the accuracy of energy storage capacity calculations through corrections, effectively preventing overcharging of battery packs due to insufficient actual energy storage capacity, thereby extending their lifespan. It also ensures that the depth of discharge does not exceed the actual energy storage capacity, thus guaranteeing power supply reliability.

[0055] In one possible implementation, the energy storage capacity is generated, and step S300 further includes step S340, in which a health impact analysis is performed at each step charge and discharge depth based on the battery health status of each battery group to determine the performance degradation index of each battery. Specifically, different charge and discharge depth (DoD) gradients (such as 20%, 40%, 60%, 80%, and 100%) are set for each battery group in the target energy storage system. The health impact of the battery at different charge and discharge depths is analyzed using a specific algorithm model. The algorithm model is trained based on a large amount of battery experimental data and historical operation data, and can accurately simulate the performance changes of the battery under different operating conditions. Based on the results of the health impact analysis, the performance degradation index of each battery group at different step charge and discharge depths, such as the annual capacity degradation rate, is obtained, and a degradation rate matrix of each battery group at different DoDs is generated.

[0056] Step S350 determines the maximum depth of charge / discharge (DOC) that meets a preset degradation threshold based on the battery performance degradation indicators. Specifically, a degradation threshold is set (e.g., annual capacity degradation rate ≤ 5% / year). The degradation rate matrix of each battery pack is checked in ascending DoD gradient order. The first DoD gradient that exceeds the threshold is marked, and the gradient preceding the first DoD that exceeds the threshold is used as the maximum allowable DoD. The maximum allowable DoD for each battery pack that meets the preset degradation threshold is output.

[0057] Step S360 optimizes the energy storage capacity based on the maximum depth of charge / discharge (DOD) that meets a preset degradation threshold. Specifically, the available capacity of each battery pack is converted to the maximum allowable DoD (DoD), where available capacity = actual energy storage capacity × maximum DoD. The available capacities of all battery packs are summed to generate the optimized energy storage capacity. This implementation, through quantitative DoD analysis, strictly controls battery performance degradation within the preset degradation threshold, avoiding accelerated degradation caused by overdischarge and extending battery life.

[0058] Step S400 , executing charging and discharging planning based on the energy storage capacity, the time-of-use electricity price information, and the first electricity demand information to generate a charging and discharging plan.

[0059] Specifically, the collected time-of-use electricity price information, the initial electricity demand information, and the energy storage capacity data are input into a charge-discharge planning algorithm (an optimization algorithm, such as a linear programming algorithm or a genetic algorithm). For example, consider a peak electricity price of 1.5 yuan / kWh, an off-peak price of 0.5 yuan / kWh, a normal price of 1.0 yuan / kWh, a current energy storage capacity of 995 kWh, and a power demand of 500 kW in the first time zone. The algorithm performs calculations based on an objective function (such as minimizing electricity costs) and constraints (such as the energy storage capacity must not exceed the upper limit of the battery capacity or fall below a safety limit). For example, a linear programming algorithm uses a mathematical model to determine the charge and discharge amounts for different time periods to minimize total cost. The algorithm then outputs a charge-discharge plan, including the number of kWh to charge during off-peak hours and the number of kWh to discharge during peak hours.

[0060] like Figure 2 As shown, in one possible implementation, charge and discharge planning is performed based on the energy storage capacity, the time-of-use electricity price information, and the first electricity demand information to generate a charge and discharge plan. Step S400 further includes step S410, whereby a battery health status-charge and discharge parameter matching library is invoked based on the battery health status of each battery pack to match charge and discharge parameters and generate optimal charge and discharge parameters. Specifically, a machine learning algorithm (e.g., a neural network) is used to construct a matching model between battery health status and charge and discharge parameters. Specifically, charge and discharge experimental data (e.g., number of cycles, remaining charge change, power response, etc.) is collected from different battery packs under different health statuses. The neural network model is trained using a deep learning framework (e.g., TensorFlow), which inputs the battery health status (e.g., number of cycles) and outputs optimal charge and discharge parameters (e.g., charge current, discharge current, charge cutoff voltage, etc.). The battery health status data of each battery pack is read in real time, and the battery health-charge and discharge parameter matching library is invoked via an API to obtain the corresponding optimal charge and discharge parameters. For example, the optimal charge current for battery pack BMS_1 is 150A, and the discharge current is 200A. The optimal charging current of battery pack BMS_2 is 100A, and the discharging current is 150A.

[0061] Step S420 calculates the energy storage system charging duration, combining the optimal charge and discharge parameters with the energy storage capacity, to generate a target charging duration. Specifically, the charging duration is calculated based on the optimal charging current, energy storage capacity, and initial remaining charge (SOC) of each battery pack. For battery pack BMS_1, the energy storage capacity is 500 kWh, the initial SOC is 50%, and the optimal charging current is 150 A. Therefore, the charging duration is calculated as 500 × (1 - 50%) ÷ 150, which is approximately 1.67 hours.

[0062] Step S430 determines the target charging time zone based on the time-of-use electricity price information, with the minimum comprehensive electricity price as a constraint, and according to the target charging duration. Specifically, based on the time-of-use electricity price information, a dynamic programming algorithm is used to determine the target charging time zone. The day is divided into multiple time periods (e.g., one hourly period), the electricity price for each period is calculated, and the period with the lowest electricity price is selected as the starting charging period, ensuring that the charging duration does not exceed the duration of the period with the lowest electricity price. For example, the time-of-use electricity price is: 0.3 yuan / kWh between 00:00 and 04:00, 0.4 yuan / kWh between 04:00 and 08:00, and 0.6 yuan / kWh between 08:00 and 12:00. The target charging duration is 1.67 hours, and 00:00-04:00 is selected as the charging time zone.

[0063] Step S440: Based on the energy storage capacity, the first electricity demand information, and the time-of-use electricity price information, reserve energy is connected to the time zone with the highest electricity price, generating a target discharge time zone. Specifically, an optimization algorithm is used to determine the target discharge time zone based on the energy storage capacity, the first electricity demand information, and the time-of-use electricity price information. The time zone with the highest electricity price is selected as the discharge time zone, ensuring that the discharge duration does not exceed the maximum discharge duration allowed by the energy storage capacity.

[0064] Step S450 generates the charge and discharge plan based on the target charging time zone, target discharging time zone, and the optimal charge and discharge parameters. Specifically, the target charging time zone, target discharging time zone, and optimal charge and discharge parameters (such as current and voltage) are written into a scheduling instruction file and integrated to generate the charge and discharge plan. This implementation method maximizes battery performance by precisely matching battery health status with charge and discharge parameters, selecting charging during the lowest electricity price period and discharging during the highest electricity price period, thereby achieving cost optimization.

[0065] In one possible implementation, after generating the target discharge time zone, step S400 further includes step S460 of obtaining line structure information of a second power supply line between the target energy storage system and the electrical equipment in the application area. Specifically, the structural information of the second power supply line, including topology and conductor parameters (e.g., model, length, and cross-sectional area), is obtained through a power grid GIS (Geographic Information System) platform or a local database.

[0066] Step S470: Calculate the no-load losses of the line components based on the line structure information to generate a comprehensive line loss rate. Specifically, the no-load loss of each line component is calculated based on the length, wire type, and cross-sectional area of the second power supply line. For example, if the wire type is LGJ-240 and the resistivity is 0.0172Ω·mm 2 / m, the line length is 1200m, and the cross-sectional area is 240mm 2, then the resistance is 0.0172×1200÷240=0.086Ω, if the current is 100A, then the no-load loss is 100 2 × 0.086 = 860W. The total line loss is calculated by summing the no-load losses of all line components. Based on the total transmitted power, the comprehensive line loss ratio is calculated. The comprehensive line loss ratio is the ratio of the total line loss to the total transmitted power.

[0067] Step S480 performs a line loss analysis of the target discharge time zone using the comprehensive line loss rate, and modifies the target discharge time zone. Specifically, a modified discharge power is calculated based on the comprehensive line loss rate. The modified discharge power is equal to the original discharge power multiplied by (comprehensive line loss rate + 1). An analysis is performed to determine whether the modified discharge power is within the capacity of the target energy storage system. If the modified discharge power exceeds the capacity of the target energy storage system, the target discharge time zone is adjusted, and another suitable time period is selected for discharge. For example, assuming the maximum discharge power of the target energy storage system is 210 kW, the modified discharge power of 200.86 kW is within the capacity range. Discharge is performed during the time period with the highest electricity price, such as 12:00-14:00. If the modified discharge power exceeds the capacity range, another time period (such as 13:00-15:00) is selected for discharge. This implementation method modifies the target discharge time zone based on line loss, avoids power shortages caused by line loss, and improves system reliability.

[0068] Step S500: performing power dispatch control using the target energy storage system based on the charge and discharge plan.

[0069] Specifically, based on the charge and discharge plan, the control unit generates corresponding control signals. Charging and discharging operations are performed by controlling the power electronics devices (such as inverters and converters) in the target energy storage system. The control signals are converted into analog signals via a digital-to-analog converter (DAC) to drive the power electronics devices. After receiving the control signals, the power electronics devices execute the charge and discharge operations. During the power dispatch process, the operating status of the target energy storage system (such as battery temperature and charge and discharge current) is monitored in real time, and the control strategy is adjusted based on the feedback information. For example, if the battery temperature is too high, the charge and discharge power is reduced to protect the battery.

[0070] For example, if off-peak charging is planned, the control unit sends a charging instruction to the inverter, including parameters such as charging power. The inverter converts AC power to DC power and charges the energy storage battery at the set power. During the charging process, the control unit monitors the battery status in real time. If it detects an abnormally high battery voltage, the control unit uses a feedback mechanism to adjust the charging current, reduce the charging power, or even suspend charging to ensure battery safety.

[0071] The embodiment of the present application adopts technical means such as determining time-of-use electricity price information of the power consumption area where the energy storage system is located, collecting actual electricity demand information of the application area in a specific time zone, and obtaining the current energy storage capacity of the energy storage system. These three key information are integrated to execute charging and discharging planning, generate a charging and discharging plan, and implement power dispatch control of the target energy storage system based on the plan. These technical means solve the technical problem of lack of accuracy and flexibility in power dispatch of existing energy storage systems, and achieve the technical effect of accurate and flexible power dispatch control.

[0072] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An intelligent power dispatching method for an energy storage system, characterized in that: include: Determine the time-of-use electricity price information for the electricity consumption area corresponding to the target energy storage system; Collecting first electricity demand information of an application area of the target energy storage system in a first time zone; Acquiring the energy storage capacity of the target energy storage system; Executing a charge and discharge plan based on the energy storage capacity, the time-of-use electricity price information, and the first electricity demand information to generate a charge and discharge plan; The target energy storage system is used to perform power dispatch control based on the charge and discharge plan.

2. The intelligent power dispatching method for an energy storage system according to claim 1, characterized in that: Collecting first electricity demand information of an application area of the target energy storage system in a first time zone includes: collecting a first production task of the application area in the first time zone; Analyze the first production task, aggregate the electricity demand in neighborhoods based on the start / stop status of the electrical equipment, and generate electricity demand information for multiple consecutive time zones; The first power demand information is generated using the power demand information of the multiple consecutive time zones.

3. The intelligent power dispatching method of an energy storage system according to claim 2, characterized in that: include: The target energy storage system is an electric energy storage device in the application area, and both the target energy storage system and the electrical equipment in the application area are connected to a regional power grid. The regional power grid is connected to the electrical equipment in the application area via a first power supply line, and the target energy storage system is connected to the electrical equipment in the application area via a second power supply line. The first power supply line and the second power supply line are independent of each other.

4. The intelligent power dispatching method for an energy storage system according to claim 2, wherein: Analyze the first production task, aggregate the electricity demand in the neighborhood based on the start and stop status of the electrical equipment, and generate electricity demand information for multiple consecutive time zones, including: Analyze the first production task and generate start and stop status of a plurality of electrical equipment at a plurality of moments; Based on the start and stop states of the plurality of electric devices, adjacent moments with consistent start and stop states of the electric devices are aggregated to generate the plurality of continuous time zone electricity demand information.

5. The intelligent power dispatching method of an energy storage system according to claim 1, characterized in that: Acquiring the energy storage capacity of the target energy storage system includes: Connecting to the battery management system of each battery pack in the target energy storage system to read the battery health status of each battery pack; Calculating the actual energy storage capacity of each battery pack based on the battery health status and the rated capacity of each battery pack; The comprehensive capacity is calculated based on the actual energy storage capacities to generate the energy storage capacity.

6. The intelligent power dispatching method of an energy storage system according to claim 5, characterized in that: Generating the energy storage capacity further includes: Perform health impact analysis at each step of charge and discharge depth based on the battery health status of each battery pack to determine the performance degradation index of each battery; Based on the respective battery performance degradation indicators, determining a maximum charge / discharge depth that meets a preset degradation indicator threshold; The energy storage capacity is optimized with a maximum charge and discharge depth that meets a preset degradation indicator threshold.

7. The intelligent power dispatching method for an energy storage system according to claim 1, wherein: Executing a charge and discharge plan based on the energy storage capacity, the time-of-use electricity price information, and the first electricity demand information to generate a charge and discharge plan includes: Based on the battery health status of each battery pack, the battery health-charge and discharge parameter matching library is called to match the charge and discharge parameters and generate the optimal charge and discharge parameters. Calculating the charging time of the energy storage system by combining the optimal charging and discharging parameters with the energy storage capacity to generate a target charging time; Based on the time-of-use electricity price information, with the minimum comprehensive electricity price as a constraint, determining the target charging time zone according to the target charging time; Based on the energy storage capacity, the first electricity demand information, and the time-of-use electricity price information, access to the reserve energy in the time zone with the highest electricity price is performed to generate a target discharge time zone; The charge and discharge plan is generated based on the target charge time zone, the target discharge time zone, and the optimal charge and discharge parameters.

8. The intelligent power dispatching method for an energy storage system according to claim 7, characterized in that: Generate target discharge time zones, including: Acquiring line structure information of a second power supply line between the target energy storage system and the electrical equipment in the application area; Accumulate and calculate the no-load losses of line components according to the line structure information to generate a comprehensive line loss rate; A line loss analysis of the target discharge time zone is performed using the comprehensive line loss rate, and the target discharge time zone is corrected.

9. The intelligent power dispatching method for an energy storage system according to claim 3, characterized in that: The first power supply line is provided with a first line monitoring module, and the second power supply line is provided with a second line monitoring module; When the first line monitoring module indicates an abnormality, the energy storage capacity of the target energy storage system is read in real time. When the energy storage capacity meets the preset minimum backup energy threshold, the first power supply line is switched to the second power supply line; After performing tolerance processing according to the preset minimum backup energy threshold, charging and discharging classification is performed on each battery group in the target energy storage system to generate a charging identification battery group and a discharging identification battery group; The battery pack with the charging mark is charged on the grid side, and the battery pack with the discharging mark is discharged through the second power supply line.

10. The intelligent power dispatching method of an energy storage system according to claim 9, characterized in that: When the target energy storage system is used for power dispatch control based on the charge and discharge plan and the power supply line is switched to the second power supply line, if the second line monitoring module indicates an abnormality, the power supply line is switched back to the first power supply line; If both the first line monitoring module and the second line monitoring module indicate an abnormality, a circuit maintenance reminder message is generated and the power supply is forcibly stopped.

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