Demand control method and device of energy storage system, electronic equipment and medium

Abstract

The embodiment of the invention provides a demand control method and device of an energy storage system, electronic equipment and a medium. The method comprises the following steps: acquiring the residual electric quantity of an energy storage system, the current power of a power grid and the historical load of the power grid; determining a demand control target value of the next time period according to the residual electric quantity, the current power and the historical load; generating a power scheduling curve according to the demand control target value and the current power; and according to the power scheduling curve, controlling the energy storage system to execute corresponding charging and discharging operation through an edge controller so as to perform demand control on the energy storage system. According to the scheme, the demand control strategy matched with the real-time working parameters and the parameters of the power grid is dynamically generated according to the real-time working parameters of the energy storage system and the parameters of the power grid, so that demand control adapts to the actual working condition, and the accuracy of demand control is improved.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to a demand control method, device, electronic equipment and medium for an energy storage system. Background Technology

[0002] Energy storage systems can store electricity for use by electrical equipment. In practical applications, the power demand of electrical equipment changes dynamically, so demand control is required to ensure a stable power supply.

[0003] In related technologies, demand control of energy storage systems is achieved through fixed strategies.

[0004] However, this method suffers from low accuracy in demand control. Summary of the Invention

[0005] This application provides a demand control method, apparatus, electronic device, and medium for energy storage systems to improve the accuracy of demand control.

[0006] In a first aspect, embodiments of this application provide a demand control method for an energy storage system, comprising: acquiring the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid; determining a demand control target value for the next time period based on the remaining power, the current power, and the historical load; generating a power dispatch curve based on the demand control target value and the current power; and controlling the energy storage system to perform corresponding charging and discharging operations through an edge controller based on the power dispatch curve, so as to perform demand control on the energy storage system.

[0007] In one possible implementation, determining the demand control target value for the next time period based on the remaining power, the current power, and the historical load includes: determining the maximum capacity of the energy storage system; determining the available capacity of the energy storage system in the current time period based on the remaining power and the maximum capacity; determining the future load change trend of the energy storage system based on the current power and the historical load; and determining the demand control target value based on the available capacity and the load change trend.

[0008] In one possible implementation, determining the demand control target value based on the available capacity and the load change trend includes: determining the demand control value for the current time period; determining a demand control prediction model, wherein the demand control prediction model is obtained by training the model based on historical capacity, historical load change trend, and historical demand control value; and inputting the available capacity, the load change trend, and the demand control value into the demand control prediction model to obtain the demand control target value.

[0009] In one possible implementation, the available capacity, the load change trend, and the demand control value are input into the demand control prediction model to obtain the demand control target value, including: acquiring multiple environmental information and multiple business information that affect the power grid load; and inputting the available capacity, the load change trend, the demand control value, the multiple environmental information, and the multiple business information into the demand control prediction model to obtain the demand control target value.

[0010] In one possible implementation, generating a power dispatch curve based on the demand control target value and the current power includes: determining the predicted load for the next time period based on the load change trend of the energy storage system; generating a preliminary charge-discharge plan curve based on the demand control target value and the predicted load; obtaining the current load of the power grid; determining the deviation between the current load and the predicted load; and correcting the preliminary charge-discharge plan curve based on the current power, the remaining power, and the deviation to obtain the power dispatch curve.

[0011] In one possible implementation, controlling the energy storage system to perform corresponding charging and discharging operations via an edge controller includes: determining the operating state of the energy storage system, wherein the operating state is: working, backup power, or standby; if the operating state is working, then controlling the energy storage system to perform corresponding charging and discharging operations via the edge controller according to the power scheduling curve.

[0012] In one possible implementation, after controlling the energy storage system to perform the corresponding charging and discharging operation through the edge controller, the method further includes: obtaining the actual charging and discharging power of the energy storage system; verifying the actual charging and discharging power through the power scheduling curve to obtain a verification result, wherein the verification result is either verification passed or verification failed; if the verification result is verification failed, then an alarm is executed.

[0013] Secondly, embodiments of this application provide a demand control device for an energy storage system, comprising: an acquisition module for acquiring the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid; a prediction module for determining a demand control target value for the next time period based on the remaining power, the current power, and the historical load; a generation module for generating a power dispatch curve based on the demand control target value and the current power; and a dispatch module for controlling the energy storage system to perform corresponding charging and discharging operations through an edge controller based on the power dispatch curve, thereby performing demand control on the energy storage system.

[0014] In one possible implementation, the prediction module is specifically configured to determine the maximum capacity of the energy storage system; the prediction module is further configured to determine the available capacity of the energy storage system in the current time period based on the remaining power and the maximum capacity; the prediction module is further configured to determine the future load change trend of the energy storage system based on the current power and the historical load; and the prediction module is further configured to determine the demand control target value based on the available capacity and the load change trend.

[0015] In one possible implementation, the prediction module is specifically used to determine the demand control value for the current time period; the prediction module is further used to determine a demand control prediction model, which is obtained by training the model based on historical capacity, historical load change trends, and historical demand control values; the prediction module is further used to input the available capacity, the load change trend, and the demand control value into the demand control prediction model to obtain the demand control target value.

[0016] In one possible implementation, the prediction module is specifically used to acquire multiple environmental information and multiple business information that affect the power grid load; the prediction module is also specifically used to input the available capacity, the load change trend, the demand control value, the multiple environmental information, and the multiple business information into the demand control prediction model to obtain the demand control target value.

[0017] In one possible implementation, the generation module is specifically configured to determine the predicted load for the next time period based on the load change trend of the energy storage system; the generation module is further configured to generate a preliminary charge-discharge plan curve based on the demand control target value and the predicted load; the generation module is further configured to obtain the current load of the power grid; the generation module is further configured to determine the deviation between the current load and the predicted load; and the generation module is further configured to correct the preliminary charge-discharge plan curve based on the current power, the remaining power, and the deviation to obtain the power dispatch curve.

[0018] In one possible implementation, the scheduling module is specifically used to determine the operating state of the energy storage system, which is: working, backup power, or standby; the scheduling module is also specifically used to control the energy storage system to perform corresponding charging and discharging operations through the edge controller according to the power scheduling curve if the operating state is working.

[0019] In one possible implementation, the device further includes: a feedback module for acquiring the actual charging and discharging power of the energy storage system; the feedback module is further configured to verify the actual charging and discharging power through the power scheduling curve to obtain a verification result, wherein the verification result is either verification passed or verification failed; the feedback module is further configured to perform alarm processing if the verification result is verification failed.

[0020] Thirdly, embodiments of this application provide a demand control device for an energy storage system, including: a memory and a processor;

[0021] The memory stores computer-executed instructions;

[0022] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0023] Fourthly, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0024] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0025] The present application provides a demand control method, apparatus, electronic device, and medium for an energy storage system. The method includes: acquiring the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid; determining a demand control target value for the next time period based on the remaining power, the current power, and the historical load; generating a power dispatch curve based on the demand control target value and the current power; and controlling the energy storage system to perform corresponding charging and discharging operations via an edge controller based on the power dispatch curve, thereby performing demand control on the energy storage system. This solution dynamically generates a matching demand control strategy based on the real-time operating parameters of the energy storage system and the parameters of the power grid, adapting demand control to actual operating conditions and thus improving the accuracy of demand control. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0027] Figure 1 This is a schematic diagram illustrating an application scenario of a demand control method for an energy storage system provided in an embodiment of this application.

[0028] Figure 2 A flowchart illustrating a demand control method for an energy storage system provided in an embodiment of this application;

[0029] Figure 3 A flowchart illustrating another demand control method for an energy storage system provided in this application embodiment;

[0030] Figure 4 This is a schematic diagram illustrating the generation of demand control target values ​​provided in an embodiment of this application;

[0031] Figure 5 A schematic diagram of the generated power scheduling curve provided in an embodiment of this application;

[0032] Figure 6 A schematic diagram illustrating the demand control interaction provided in an embodiment of this application;

[0033] Figure 7 A schematic diagram of the structure of a demand control device for an energy storage system provided in an embodiment of this application;

[0034] Figure 8 A schematic diagram of the structure of another energy storage system demand control device provided in the embodiments of this application;

[0035] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0036] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0037] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0038] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0039] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0040] It should be noted that the demand control method, device, electronic equipment and medium of the energy storage system of this application can be used in the field of energy storage technology, and can also be used in any field other than energy storage. The application field of the demand control method, device, electronic equipment and medium of the energy storage system of this application is not limited.

[0041] Figure 1 This is a schematic diagram illustrating an application scenario of a demand control method for an energy storage system provided in an embodiment of this application. The scenario illustrated is as follows: the energy storage system receives electricity from the grid and supplies electricity to multiple electrical devices based on their power consumption needs.

[0042] In practical applications, the electricity demand of electrical equipment changes dynamically; for example, the electricity demand during peak hours is much higher than at other times. Demand control of energy storage systems is necessary to ensure that the energy storage system generates a large peak-valley difference when charging from the grid.

[0043] For example, the basic principle of demand control is peak shaving and valley filling, controlling the energy storage system to charge and discharge according to electricity demand. For instance, during peak electricity demand periods, the energy storage system is controlled to discharge to the load, thereby reducing the charging power of the energy storage system and controlling demand. During off-peak electricity demand periods, the energy storage system is controlled to charge and store energy from the grid.

[0044] In related technologies, demand control is achieved through fixed strategies. For example, charging or discharging at a fixed power during fixed time periods.

[0045] However, the timing and extent of actual peak and off-peak electricity consumption periods are dynamic, and fixed strategies may not be suitable for actual electricity demand, leading to energy waste or increased costs.

[0046] The demand control method for energy storage systems provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0047] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0048] Figure 2 This application provides a flowchart illustrating a demand control method for an energy storage system, which includes the following steps:

[0049] S201. Obtain the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid.

[0050] For example, the remaining power of an energy storage system represents the actual value of the system's current power level. This remaining power is used to constrain demand control and prevent the energy storage system from overcharging or over-discharging.

[0051] For example, the current power of the grid represents the power that the energy storage system is currently charging from the grid. The current power is used to determine whether demand control is needed and the specific parameters for demand control.

[0052] For example, historical load represents the power grid's historical power supply records. Based on historical load, the patterns of grid load can be determined, thereby accurately predicting the future load of the grid. Adaptive demand control strategies can then be developed based on the future grid load.

[0053] S202. Determine the demand control target value for the next period based on the remaining power, current power, and historical load.

[0054] For example, the next time period is determined based on the current time and a preset period. The preset period could be a day, an hour, etc. The period can be dynamically adjusted.

[0055] For example, the demand control target value is the demand that is adapted to the operation of the energy storage system and the grid in the next cycle. The demand control target value is affected by multiple factors, and is determined by a combination of multiple parameters.

[0056] With the example of a scenario, if the historical load shows that the peak electricity consumption is about to begin and the current power is rising rapidly, but the remaining power is sufficient, the demand control target value for the next period can be lowered to avoid overcharging of the energy storage system.

[0057] Conversely, if the remaining battery power is already very low, even if the current power is high, the demand control target value is increased to store enough energy for the electrical equipment, prevent the battery from being over-discharged, and prepare for charging during the next off-peak period.

[0058] Based on the above implementation methods, multiple parameters are dynamically adjusted to obtain accurate demand control target values.

[0059] S203. Generate a power scheduling curve based on the demand control target value and the current power.

[0060] For example, the power dispatch curve is a command curve with time on the horizontal axis and power on the vertical axis. The power dispatch curve defines the charging and discharging power of the energy storage system at each time point in the next time period.

[0061] For example, the demand control target value is a constraint condition of the power dispatch curve from the perspective of the energy storage system, so that the charging and discharging power at each moment is less than or equal to the demand control target value when charging and discharging according to the power adjustment curve.

[0062] For example, the power dispatch curve can be adjusted by considering the current power level and the constraints from the grid perspective. For instance, if the current power level is already high, the power dispatch curve can immediately indicate discharge; if the current power level is low, the power dispatch curve can immediately indicate discharge and charging. This ensures the real-time responsiveness of demand control.

[0063] Optionally, the charging and discharging power at multiple times in the next time period can be generated, and then the power scheduling curve can be generated by interpolation.

[0064] S204. Based on the power dispatch curve, the edge controller controls the energy storage system to perform corresponding charging and discharging operations in order to control the demand of the energy storage system.

[0065] For example, the edge controller is a computing and control device deployed locally on the energy storage system. The implementing entity of this application can be a central server or a cloud server, which sends power scheduling curves to the edge controller to control the energy storage system to perform charging and discharging operations with high reliability and low latency.

[0066] Optionally, the edge controller analyzes the power dispatch curve, generates specific control commands (such as voltage and current commands), and drives the energy storage system to charge and discharge according to the control commands.

[0067] The demand control method for energy storage systems provided in this application involves acquiring the remaining energy of the energy storage system, the current power of the power grid, and the historical load of the power grid; determining the demand control target value for the next time period based on the remaining energy, current power, and historical load; generating a power dispatch curve based on the demand control target value and the current power; and controlling the energy storage system to perform corresponding charging and discharging operations through an edge controller based on the power dispatch curve, thereby controlling the demand of the energy storage system. This scheme dynamically generates a matching demand control strategy based on the real-time operating parameters of the energy storage system and the parameters of the power grid, adapting the demand control to the actual operating conditions and thus improving the accuracy of demand control.

[0068] Based on any of the above embodiments, the following, in conjunction with Figure 3 The detailed process of demand control for energy storage systems is explained.

[0069] Figure 3 This is a flowchart illustrating another demand control method for an energy storage system provided in an embodiment of this application. Figure 3 As shown, the method includes:

[0070] S301. Obtain the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid.

[0071] It should be noted that the execution process of S301 is the same as that of S201, and will not be repeated here.

[0072] S302. Determine the maximum capacity of the energy storage system.

[0073] For example, maximum capacity is a property parameter of the energy storage system, representing the maximum amount of electricity that the energy storage system can store.

[0074] Optionally, the maximum capacity can be determined directly based on the rated capacity of the energy storage system.

[0075] Optionally, the maximum capacity can be calculated based on the rated capacity of the energy storage system and the current decay rate.

[0076] S303. Determine the available capacity of the energy storage system for the current period based on the remaining power and maximum capacity.

[0077] For example, available capacity represents the amount of electricity that the energy storage system can charge and discharge during the current period.

[0078] Optionally, the state of charge window of the energy storage system can be obtained, the energy window can be determined based on the maximum capacity and the state of charge window, and the available capacity can be determined based on the energy window and the remaining energy, so as to avoid overcharging and over-discharging of the energy storage system.

[0079] To illustrate with a scenario example, if the maximum capacity is 10000kWh and the state of charge window is 20%-90%, then the energy window is 2000 kWh-9000 kWh. If the remaining energy is 4000 kWh, then the energy storage system can discharge 2000 kWh and charge 5000 kWh during the current period.

[0080] S304. Determine the future load change trend of the energy storage system based on the current power and historical load.

[0081] For example, based on real-time data, namely current power and historical load reflecting long-term patterns, prediction algorithms are used to infer the rising and falling trends and possible peak values ​​of the total power grid load in the next period.

[0082] Optionally, the prediction algorithm includes, but is not limited to, at least one of the following: time series analysis, machine learning model, etc.

[0083] Based on the above implementation methods, by predicting load change trends in advance, demand control strategies that match these trends can be formulated in advance, thereby improving the accuracy of demand control.

[0084] S305. Determine the demand control value for the current time period.

[0085] For example, the demand control value for the current period serves as the starting point or baseline state for demand control. The demand control value provides contextual information for forecasting, enabling forecasting to consider the continuity of control and achieve a smooth transition.

[0086] Optionally, the demand control value for the current period can be either predicted from the previous period or actually measured in the current period.

[0087] S306. Determine the demand control prediction model. The demand control prediction model is obtained by training the model based on historical capacity, historical load change trends, and historical demand control values.

[0088] For example, the demand control forecasting model learns the mapping relationship between three key features from historical data. Historical capacity represents the available energy of the energy storage system at the decision point in the past; historical load change trends represent the forecast of future load; and historical demand control values ​​represent the control actions taken under historical conditions.

[0089] For example, through model training, the demand control prediction model intrinsically constructs a complex nonlinear mapping function between the energy storage system state and the current control baseline to the optimal control objective. This function encapsulates the optimal decision-making experience implicit in historical data.

[0090] S307. Input the available capacity, load change trend, and demand control value into the demand control prediction model to obtain the demand control target value.

[0091] For example, during the inference process of the demand control prediction model, the current energy storage system status (i.e., available capacity and load change trends) and the current control baseline (i.e., the demand control value for the current period) are used as inputs to enable the demand control prediction model to make decisions.

[0092] For example, the demand control prediction model performs forward propagation calculations on the input features based on the internally learned mapping relationship, and directly outputs an optimized demand control target value for the next time period.

[0093] In related technologies, demand control is carried out through fixed strategies, but the target value of demand control may not be accurately adapted to the actual operating conditions of the energy storage system and the power grid.

[0094] Based on the above implementation methods, a demand control prediction model is used to describe the multidimensional and nonlinear mapping relationship between the actual operating state of the energy storage system and the power grid and the optimal demand control target value, thereby improving the accuracy of demand control.

[0095] One feasible implementation method is to generate the demand control target value by: acquiring multiple environmental information and multiple business information that affect the power grid load; inputting the available capacity, load change trend, demand control value, multiple environmental information, and multiple business information into the demand control prediction model to obtain the demand control target value.

[0096] For example, multiple environmental and business information items are associated with fluctuations in grid load.

[0097] Optionally, environmental information refers to external natural physical conditions that affect the power grid load, including but not limited to at least one of the following: meteorological data (e.g., temperature, humidity, light intensity), time status (e.g., season, day and night), air quality index, etc.

[0098] With the help of scenario examples, it can be seen that in environments with high temperature, strong sunlight, extreme weather, daytime, and poor air quality index, there are more electrical devices and the power grid load is high.

[0099] Optionally, business information refers to production activities that affect the power grid load, including but not limited to at least one of the following: production plans and schedules, facility operation information (such as building operating hours, public area lighting), etc.

[0100] With the help of scenario examples, it can be seen that during peak production periods, building operation periods, and lighting operation periods, there are more electrical devices and the grid load is higher.

[0101] Optionally, after acquiring multiple environmental and business information, data cleaning can be performed to remove invalid data.

[0102] For example, historical environmental and operational information is used as input during the training phase of the demand control prediction model. During the inference phase, multiple pieces of environmental and operational information are combined to predict the demand control target value through richer mapping relationships.

[0103] Below, in conjunction with Figure 4 The generation of demand control target values ​​is explained.

[0104] Figure 4 This is a schematic diagram illustrating the generation of demand control target values ​​provided in an embodiment of this application. For example... Figure 4 As shown, data cleaning is performed on multiple environmental information sources, multiple business information sources, available capacity, load change trends, and demand control values ​​to obtain processed data. This processed data is then input into a pre-trained demand control prediction model to obtain the demand control target value.

[0105] In this feasible implementation, environmental and business information are introduced, enabling the demand control forecasting model to capture and quantify the direct impact of these key external factors on the load, thereby improving the accuracy of demand control.

[0106] S308. Generate a power dispatch curve based on the demand control target value and the current power.

[0107] One feasible implementation method is to generate a power dispatch curve by: determining the predicted load for the next period based on the load change trend of the energy storage system; generating a preliminary charge-discharge plan curve based on the demand control target value and the predicted load; obtaining the current load of the power grid; determining the deviation between the current load and the predicted load; and correcting the preliminary charge-discharge plan curve based on the current power, remaining power, and deviation to obtain the power dispatch curve.

[0108] For example, the process of generating power scheduling curves includes day-ahead forecasting and intraday correction.

[0109] For example, day-ahead forecasting infers future conditions based on historical conditions. Data for the next time period is extracted from load change trends to obtain the forecasted load for the next time period. By combining the demand control target value from the perspective of the energy storage system and the forecasted load from the perspective of the power grid, a preliminary discharge plan curve for day-ahead forecasting is generated.

[0110] Optionally, a preliminary discharge plan curve can be generated using a time series model or a machine learning model.

[0111] For example, the current load represents the actual operating state of the power grid.

[0112] Optionally, the current load on the power grid can be measured in real time using smart meters and / or power sensors installed on the grid.

[0113] For example, deviation quantification is used to differentiate between the predicted load and the actual load, thereby analyzing the error in the preliminary discharge plan curve.

[0114] For example, by quantifying the current power, remaining power, and deviation, the predicted preliminary charge and discharge plan curve is corrected intraday, adjusting the parameters in the preliminary charge and discharge plan curve to values ​​that conform to the actual state of the energy storage system and the power grid, thereby obtaining a more accurate power dispatch curve.

[0115] Below, in conjunction with Figure 5 The generated power scheduling curve is explained.

[0116] Figure 5 This is a schematic diagram of the generated power scheduling curve provided in an embodiment of this application. Figure 5 As shown, the cloud platform acquires data according to a preset collection time frequency. The cloud platform cleans the acquired data through interpolation to ensure data integrity. Load forecasting is performed according to a preset cycle. The demand control target value is calculated by integrating multiple parameters. An optimization strategy is determined for the day-ahead planning curve and intraday hourly optimization. Using the demand control target value as a constraint, and the daily load curve and daily energy storage power curve as parameters, the day-ahead planning curve is calculated and adjusted through the optimization strategy to generate the intraday planning curve, i.e., the power dispatch curve. The intraday planning curve is distributed in real time for demand control of the energy storage system.

[0117] This feasible implementation addresses the problem of fixed strategies being unable to cope with real-time fluctuations through prediction and correction. This allows for proactive and dynamic adjustment of the scheduling strategy, thereby improving the accuracy of demand control.

[0118] S309. Based on the power dispatch curve, the edge controller controls the energy storage system to perform corresponding charging and discharging operations in order to control the demand of the energy storage system.

[0119] Below, in conjunction with Figure 6 The demand control interaction is explained.

[0120] Figure 6 This is a schematic diagram illustrating the demand control interaction provided in an embodiment of this application. Figure 6 As shown, the energy storage system reports its real-time information to the edge controller. The edge controller reports the energy storage system's real-time information and business information to the cloud platform. Other platforms collect environmental information and send it to the cloud platform. The cloud platform generates a power scheduling curve based on the energy storage system's real-time information, business information, and environmental information, and sends it to the edge controller. The edge controller generates corresponding control commands based on the power adjustment curve and controls the energy storage system to perform corresponding charging and discharging operations according to the control commands.

[0121] One feasible implementation method is to perform charging and discharging operations by: determining the operating status of the energy storage system, which is: working, backup power, or standby; if the operating status is working, then controlling the energy storage system to perform the corresponding charging and discharging operations through the edge controller according to the power scheduling curve.

[0122] For example, when in active operation, the energy storage system actively responds to dispatch commands and performs charging and discharging. When in standby operation, the energy storage system operates with the goal of ensuring power supply reliability to guarantee a reliable power supply to electrical equipment. When in standby operation, the energy storage system operates with low power consumption.

[0123] Optionally, the current operating status of the energy storage system can be determined through the working logic of the energy storage system or the mode switching command of the power management system.

[0124] For example, the operating status is used as a criterion for determining the power scheduling curve, thus avoiding conflicts between the power scheduling curve and the operating status of the energy storage system.

[0125] In this feasible implementation, the operating status of the energy storage system is used as a judgment condition to ensure that demand control does not affect the normal operation of the energy storage system, thereby improving the reliability of demand control.

[0126] One feasible implementation method, after controlling the energy storage system to perform the corresponding charging and discharging operation, may further include: obtaining the actual charging and discharging power of the energy storage system; verifying the actual charging and discharging power through the power scheduling curve to obtain the verification result, which is either verification passed or verification failed; if the verification result is verification failed, then performing alarm processing.

[0127] For example, after controlling the energy storage system to perform charging and discharging operations, it is verified whether the charging and discharging operations have achieved the preset target, so as to provide feedback on demand control.

[0128] Optionally, the actual charging and discharging power can be obtained through sensors (such as current transformers or voltage transformers) or a battery management system.

[0129] For example, the execution of the power scheduling curve can be verified by comparing whether the charging and discharging power indicated at the corresponding time in the actual charging and discharging pattern is equal or whether the error is within a preset range.

[0130] In terms of specific scenarios, when the energy storage system experiences problems such as a faulty energy storage converter, limitations in the battery management system, abnormal physical wiring connections, or communication issues with the edge controller, the actual charging and discharging power may not conform to the power scheduling curve.

[0131] Optionally, alarms can be handled through methods such as audible and visual alarms, log recording, or remote reporting.

[0132] In this feasible implementation method, the specific execution of demand control is verified and feedback is provided, thereby improving the accuracy of demand control.

[0133] Figure 7 This is a schematic diagram of the structure of a demand control device for an energy storage system provided in an embodiment of this application. Figure 7 As shown, the demand control device 70 of the energy storage system may include: an acquisition module 71, a prediction module 72, a generation module 73, and a scheduling module 74.

[0134] The acquisition module 71 is used to acquire the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid.

[0135] The prediction module 72 is used to determine the demand control target value for the next period based on the remaining power, current power, and historical load.

[0136] The generation module 73 is used to generate a power scheduling curve based on the demand control target value and the current power.

[0137] The scheduling module 74 is used to control the energy storage system to perform corresponding charging and discharging operations through the edge controller according to the power scheduling curve, so as to control the demand of the energy storage system.

[0138] Optionally, module 71 can be executed. Figure 2 S201 in the embodiment.

[0139] Optionally, prediction module 72 can perform... Figure 2 S202 in the embodiment.

[0140] Optionally, the generation module 73 can be executed. Figure 2 S203 in the embodiment.

[0141] Optionally, scheduling module 74 can execute Figure 2 S204 in the embodiment.

[0142] It should be noted that the demand control device of the energy storage system shown in the embodiments of this application can execute the technical solution shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0143] In one possible implementation, the prediction module 72 is specifically used for:

[0144] Determine the maximum capacity of the energy storage system;

[0145] Determine the available capacity of the energy storage system for the current time period based on the remaining power and maximum capacity;

[0146] Determine the future load change trend of the energy storage system based on current power and historical load;

[0147] Determine the demand control target value based on available capacity and load change trends.

[0148] In one possible implementation, the prediction module 72 is specifically used for:

[0149] Determine the demand control value for the current time period;

[0150] The demand control forecasting model is determined by training the model based on historical capacity, historical load change trends, and historical demand control values.

[0151] By inputting available capacity, load change trends, and demand control values ​​into the demand control prediction model, the demand control target value can be obtained.

[0152] In one possible implementation, the prediction module 72 is specifically used for:

[0153] Acquire multiple environmental and business information sources that affect the power grid load;

[0154] By inputting available capacity, load change trends, demand control values, multiple environmental information, and multiple business information into the demand control prediction model, the demand control target value is obtained.

[0155] In one possible implementation, generation module 73 is specifically used for:

[0156] Based on the load change trend of the energy storage system, determine the predicted load for the next period.

[0157] Based on the demand control target value and the predicted load, a preliminary charge and discharge plan curve is generated;

[0158] Obtain the current load of the power grid;

[0159] Determine the deviation between the current load and the predicted load;

[0160] Based on the current power, remaining power, and deviation, the preliminary charge and discharge plan curve is corrected to obtain the power dispatch curve.

[0161] In one possible implementation, the scheduling module 74 is specifically used for:

[0162] Determine the operating status of the energy storage system, which can be: active, backup power, or standby.

[0163] If the operating status is active, the energy storage system will be controlled by the edge controller to perform the corresponding charging and discharging operations according to the power scheduling curve.

[0164] Figure 8 This is a schematic diagram of the structure of a demand control device for another energy storage system provided in an embodiment of this application. Figure 7 Based on the illustrated embodiments, as Figure 8 As shown, the demand control device 70 of the energy storage system also includes a feedback module 75.

[0165] Feedback module 75 is used for:

[0166] Obtain the actual charging and discharging power of the energy storage system;

[0167] The actual charging and discharging power is verified by the power scheduling curve, and the verification result is either verification passed or verification failed.

[0168] If the verification result is that the verification failed, an alarm will be triggered.

[0169] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 9 As shown, the electronic device includes:

[0170] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0171] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0172] The memory 292, as a non-volatile computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0173] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0174] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0175] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0176] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0177] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0178] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0179] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0180] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0181] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0182] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0183] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0184] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A demand control method for an energy storage system, characterized in that, include: Obtain the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid; Based on the remaining power, the current power, and the historical load, determine the demand control target value for the next time period; A power scheduling curve is generated based on the demand control target value and the current power. Based on the power scheduling curve, the energy storage system is controlled by the edge controller to perform corresponding charging and discharging operations in order to control the demand of the energy storage system.

2. The method according to claim 1, characterized in that, Based on the remaining power, the current power, and the historical load, determine the demand control target value for the next time period, including: Determine the maximum capacity of the energy storage system; Based on the remaining power and the maximum capacity, determine the available capacity of the energy storage system in the current time period; Based on the current power and the historical load, determine the future load change trend of the energy storage system; The demand control target value is determined based on the available capacity and the load change trend.

3. The method according to claim 2, characterized in that, Determining the demand control target value based on the available capacity and the load change trend includes: Determine the demand control value for the current time period; A demand control prediction model is determined, which is obtained by training the model based on historical capacity, historical load change trends, and historical demand control values. The available capacity, the load change trend, and the demand control value are input into the demand control prediction model to obtain the demand control target value.

4. The method according to claim 3, characterized in that, The available capacity, the load change trend, and the demand control value are input into the demand control prediction model to obtain the demand control target value, including: Acquire multiple environmental and business information sources that affect the power grid load; The available capacity, the load change trend, the demand control value, the multiple environmental information, and the multiple service information are input into the demand control prediction model to obtain the demand control target value.

5. The method according to claim 1, characterized in that, Based on the demand control target value and the current power, a power scheduling curve is generated, including: Based on the load change trend of the energy storage system, determine the predicted load for the next period. Based on the demand control target value and the predicted load, a preliminary charge and discharge plan curve is generated; Obtain the current load of the power grid; Determine the deviation between the current load and the predicted load; Based on the current power, the remaining power, and the deviation, the preliminary charge-discharge plan curve is corrected to obtain the power scheduling curve.

6. The method according to claim 1, characterized in that, The energy storage system is controlled by an edge controller to perform corresponding charging and discharging operations, including: Determine the operating status of the energy storage system, wherein the operating status is: working, backup power, or standby; If the operating state is active, then according to the power scheduling curve, the edge controller controls the energy storage system to perform the corresponding charging and discharging operations.

7. The method according to any one of claims 1-6, characterized in that, After controlling the energy storage system to perform the corresponding charging and discharging operations via the edge controller, the method further includes: Obtain the actual charging and discharging power of the energy storage system; The actual charging and discharging power is verified using the power scheduling curve to obtain a verification result, which is either verification passed or verification failed. If the verification result is that the verification fails, an alarm will be triggered.

8. A demand control device for an energy storage system, characterized in that, include: The acquisition module is used to acquire the remaining power of the energy storage system, the current power of the power grid, and the historical load of the power grid; The prediction module is used to determine the demand control target value for the next period based on the remaining power, the current power, and the historical load. The generation module is used to generate a power scheduling curve based on the demand control target value and the current power. The scheduling module is used to control the energy storage system to perform corresponding charging and discharging operations through the edge controller according to the power scheduling curve, so as to control the demand of the energy storage system.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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