Demand response method and system based on redundant duration and real-time capacity of storage battery

Abstract

The invention relates to the technical field of energy management, in particular to a demand response method based on redundant duration and real-time capacity of a storage battery, which comprises the following steps of: acquiring real-time monitoring data including an equipment running state, the real-time capacity of the storage battery and historical load data; generating a demand response plan based on the real-time monitoring data and the historical load data; sending the demand response plan to a power grid, and adjusting the demand response plan according to information fed back by the power grid; monitoring a demand response execution process, and carrying out statistics and analysis on response results; a demand response plan, a response process and a response result are visually displayed, and the demand response plan can be automatically adjusted according to real-time feedback of a power grid; by calculating the available redundant duration of the storage battery in real time, the participation strategy of the equipment can be dynamically adjusted, and the response capability and safety of the system are maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a demand response method based on battery redundancy duration and real-time capacity. Background Art

[0002] With the continuous adjustment of the global energy structure and the rapid development of the power market, the demand for load regulation of power grids is growing. In this context, virtual power plants, as a new type of load management technology, provide demand response services for power grids by aggregating and optimizing distributed power sources, controllable loads and energy storage equipment. However, traditional virtual power plant demand response methods have many limitations, which significantly limit the flexibility, accuracy and reliability of the system in practical applications.

[0003] At present, traditional demand response technologies mainly rely on manual statistics and manual operations. These methods are usually based on manually entered equipment load information and fixed load control strategies. When the power grid needs to respond to demand, relevant personnel manually select participating equipment and specify load adjustment strategies based on the pre-entered load information. However, this method has the following technical problems:

[0004] First, the accuracy and timeliness of load information are insufficient. Since the input and update process of load information is often done offline, data lag and human errors make it difficult to accurately implement demand response plans. When the power grid makes a demand invitation, the load information that the system relies on may be outdated or inaccurate, and cannot effectively support real-time decision-making.

[0005] Secondly, the dynamic control capability of the demand response plan is limited. The existing technology lacks the ability to monitor and analyze the equipment operating status and load data in real time, which makes it difficult to dynamically adjust the response plan according to the changes in grid demand during execution. For example, when the actual load of the equipment deviates from the planned value, it cannot be discovered and corrected in time, thereby increasing the risk of failure of demand response.

[0006] In addition, existing methods lack intelligent analysis and optimization of response process data. In traditional systems, demand response results are often only treated as single events, lacking in-depth analysis of historical response data. This results in the system being unable to learn and optimize from past responses, making it difficult to achieve continuous improvement of future response plans.

[0007] More importantly, existing technologies do not fully utilize the characteristics of energy storage devices, especially the redundancy time and real-time capacity of batteries. As an important component of demand response, the performance parameters of energy storage devices directly affect the responsiveness and reliability of the system. However, traditional methods usually do not consider the real-time capacity and redundancy time of batteries when generating plans, which may lead to over-discharge or under-load problems during the response process, thereby reducing the operating efficiency and safety of the system.

[0008] In order to solve the above problems, the present invention proposes a demand response method and system based on battery redundancy time and real-time capacity, and realizes real-time monitoring and optimization analysis of equipment load and battery status by introducing advanced data acquisition and analysis technology. Summary of the invention

[0009] In order to solve the above technical problems, a demand response method and system based on battery redundancy time and real-time capacity overcome the above defects in traditional technologies. Specifically, the present invention aims to solve the following technical problems: improve the accuracy and timeliness of load information, ensure that the system can obtain the operating status of the equipment and the real-time capacity of the battery in real time; realize dynamic adjustment and optimization of the response plan, and enhance the flexibility and robustness of the system in the process of executing demand response; perform intelligent analysis of historical response data, optimize future response plans, and improve the overall operating efficiency and economic benefits of the system; make full use of the redundant time and real-time capacity of the battery to avoid over-discharge or insufficient load, and ensure the safety and reliability of the equipment during the response process.

[0010] The present invention provides a demand response method based on battery redundancy duration and real-time capacity, comprising the following steps:

[0011] Collect real-time monitoring data including equipment operating status, real-time battery capacity, and historical load data;

[0012] generating a demand response plan based on the real-time monitoring data and the historical load data;

[0013] Sending the demand response plan to the power grid, and adjusting the demand response plan according to information fed back by the power grid;

[0014] Monitor the demand response execution process and collect statistics and analyze the response results;

[0015] Visually display the demand response plan, response process and response results.

[0016] Preferably, the step of generating a demand response plan comprises:

[0017] Determine the backup power redundancy duration of each device based on the device's battery capacity and historical load data;

[0018] Based on the real-time load data and historical load data during the demand response period, the average load during the response period is predicted;

[0019] Combined with the grid demand invitation information, optimize the equipment load distribution plan.

[0020] As a preference, it further comprises:

[0021] According to the optimized load distribution scheme, a charge and discharge instruction is generated, wherein the charge and discharge instruction includes a discharge termination voltage and a floating charge voltage determined based on the device type and the number of battery cells.

[0022] Preferably, the step of monitoring the demand response execution process comprises:

[0023] During the demand response execution process, the load data of each device is monitored in real time;

[0024] When an abnormal situation is detected, the response of the abnormal device is terminated and a callback instruction is generated to replace the device to participate in the response.

[0025] Preferably, the step of counting and analyzing the response results comprises:

[0026] Collect and organize the actual load data and response time of each device during the response process;

[0027] The success rate of demand response and the load achievement rate are calculated based on the data.

[0028] A demand response system based on battery redundancy duration and real-time capacity for executing the method includes:

[0029] A data collection unit, used in the collection step of the demand response method, to obtain real-time data such as equipment operating status, battery capacity, historical load data, etc.;

[0030] A data AI analysis and statistics unit, connected to the data acquisition unit, for the analysis step;

[0031] A virtual power plant interface unit, connected to the data AI analysis and statistics unit, for receiving grid demand invitation information and sending response plans and results;

[0032] The data display unit is used to visually display the demand response plan and response results.

[0033] Preferably, the data AI analysis and statistics unit includes:

[0034] A data screening module is used to screen out key data required for demand response from the collected data;

[0035] Load forecasting module, used to forecast equipment load in each time period based on filtered data;

[0036] The plan generation module is used to generate a demand response plan that meets the grid invitation requirements.

[0037] Preferably, the virtual power plant interface unit comprises:

[0038] An information receiving module, used for receiving demand invitation information from the power grid;

[0039] The result feedback module is used to feed back the response plan and actual response results to the power grid.

[0040] Preferably, the data display unit comprises:

[0041] The statistical display module is used to display the load distribution, response success rate, annual response income and other statistical information during the demand response process;

[0042] Real-time monitoring module, used to display equipment load status and response process in real time.

[0043] Preferably, the device further includes an exception handling module for generating a callback instruction and starting a backup device to replace the abnormal device to participate in the response when an abnormality is detected during the demand response process.

[0044] The beneficial effects of the present invention are:

[0045] (1) The data acquisition unit can collect key parameters of the equipment in real time, including the real-time capacity, voltage, current of the battery, and the load status of the equipment. This high-frequency acquisition method ensures the accuracy and timeliness of the data, and provides a solid data foundation for subsequent load forecasting and response plan generation. Preferably, by sampling every 15 minutes or less, the load trend and battery health status of the equipment can be detected in advance before demand response, thereby ensuring the accuracy of the plan.

[0046] (2) The data AI analysis and statistics unit introduces a multivariate regression algorithm and a neural network model to accurately predict and optimize the load plan, achieving high-precision prediction of future load changes. The algorithm can effectively integrate historical data, real-time data, and trend data, solving the problem of large fluctuations and low accuracy in prediction results in traditional methods.

[0047] (3) The virtual power plant interface unit achieves rapid response and dynamic adjustment to grid demand through two-way data interaction. The system can automatically adjust the demand response plan based on real-time feedback from the grid. For example, when grid demand changes, the system can re-optimize the load distribution strategy of the equipment within 5 minutes to meet the new demand.

[0048] (4) The present invention makes full use of the redundant time and real-time capacity of the battery to ensure that the device will not be over-discharged or under-loaded during the response process. For example, by calculating the available redundant time of the battery in real time, the device's participation strategy can be dynamically adjusted to maximize the system's responsiveness and safety.

[0049] (5) Through the visual display of the response results by the data display unit, the present invention can provide users with intuitive analysis reports, including load achievement rate, response success rate and equipment performance evaluation. This multi-dimensional data display method not only improves users' understanding and usage efficiency of the system, but also provides valuable reference for future optimization and improvement.

[0050] In summary, the present invention solves the key problems of load information lag, inflexible plan execution, and underutilized equipment performance in traditional demand response systems through innovative data collection, analysis, and optimization technologies. With the help of advanced algorithms and module collaboration, the present invention not only improves the response efficiency and safety of the system, but also significantly enhances the load regulation capability of the power grid, providing technical support for more efficient and reliable demand response management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the architecture of the system of the present invention;

[0052] Figure 2 It is a flow chart of the interaction between the virtual power plant interface unit and the data AI analysis and statistics unit of the present invention;

[0053] Figure 3 It is a demand response decision analysis and execution flow chart of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] like Figure 1-3As shown, the present invention relates to a demand response method based on battery redundancy time and real-time capacity, which aims to improve the real-time and accuracy of demand response by optimizing the data collection and analysis process. First, the data acquisition unit 1 is responsible for real-time collection of key information including operating status, battery capacity, voltage, current, power and historical load data from various devices in the system. For example, in a certain communication base station, the battery capacity is 80%, the equipment operating load is 8kW, and the voltage is stable at 48V. These parameters are uploaded to the system in real time through the acquisition unit.

[0056] Next, based on the real-time data and historical load data, the system generates a preliminary demand response plan through the data AI analysis and statistics unit 2. It should be noted that the time span of the historical load data can be flexibly set according to different scenarios. Preferably, the load data of the past 5 days is used for trend analysis to ensure the accuracy of the plan. For example, the average load fluctuation of a certain device is between 9kW and 10kW in the same time period every day for the past 5 days. The system monitors its current load in real time as 9.5kW, and can more accurately predict the load changes during the response period.

[0057] The generated demand response plan is then sent to the power grid through the virtual power plant interface unit 3. The interface unit is responsible for receiving the power grid's demand invitation information and returning the response plan. In specific operations, for example, if the power grid proposes to reduce 15kW load between 14:00 and 16:00 in the afternoon, the system generates a reasonable plan based on the real-time data and historical data collected and analyzed, and the interface unit provides timely feedback.

[0058] During the execution of the plan, the system continuously monitors the execution of the demand response, collects the response load data of the equipment in real time, and dynamically adjusts the response plan. Finally, the data display unit 4 visualizes the response process and results, including the response success rate, load achievement rate, equipment status, etc., to provide intuitive feedback to users. For example, in the above-mentioned load reduction case, the data display unit will show that the load of each device is reduced from 8kW to 7kW, and the real-time chart shows that the response success rate is 95%.

[0059] Figure 1 The overall system architecture of the present invention is shown. Figure 1 It can be seen that the data flow of the entire system flows from the acquisition unit to the analysis unit, then interacts with the power grid through the interface unit, and finally provides user-friendly data visualization support in the display unit. This modular design ensures the real-time, reliability and efficiency of the system.

[0060] The data acquisition unit 1 acquires device data in real time through high-frequency sampling technology, such as device operating status and battery parameters, and transmits these data to the data AI analysis and statistics unit 2. The data AI analysis and statistics unit 2 predicts the device load through a multivariate regression model and a neural network model, and generates a demand response plan. The two-way interaction between the virtual power plant interface unit 3 and the power grid realizes rapid response and plan feedback of power grid demand. The data display unit 4 provides users with a multi-dimensional display of the response results.

[0061] This architecture enables efficient data flow and close coordination of functional modules, thereby greatly improving response speed and decision-making accuracy during the demand response process.

[0062] In one embodiment of the present invention, a specific method for generating a demand response plan is further specified. The core of generating a response plan is to accurately calculate the battery redundancy time of the equipment. Preferably, the equipment redundancy time T 备电 Determined by the following formula:

[0063] T 备电 =T 总续航 -T 需求

[0064] Among them, T 总续航 Indicates the maximum endurance of the device under the current capacity and load conditions. For example, a battery with a capacity of 100Ah can support a battery life of 5 hours under full load. 总续航 = 5 hours. If the demand response plan requires the device to reduce part of the load within the next 3 hours, the corresponding T 需求 is 3 hours, and finally the redundant time T is calculated 备电 =2 hours.

[0065] At the same time, the present invention proposes an efficient processing method for calculating real-time load data. Specifically, the monitoring module continuously collects the load data of the equipment every 15 minutes, and calculates the average load within the demand response time period based on the following formula:

[0066]

[0067] Among them, P i is each load value collected during the time period, and n is the number of sampling times. For example, during the response time period, the collected load data are 8.5kW, 9kW, 8.8kW and 9.2kW, then the average load L 平均 =8.875kW. This calculation method ensures that the response plan can accurately respond to the dynamic load changes of the equipment.

[0068] In order to achieve more efficient load management, the present invention proposes a method for generating charge and discharge instructions. The generation of the response plan will generate the optimal charge and discharge instructions based on the device type, load conditions and battery characteristics. For example, for a 48V battery pack with 24 cells, the system automatically calculates its discharge termination voltage and floating charge voltage.

[0069] Specifically, the discharge termination voltage can be calculated according to the following formula:

[0070] V 放电终止 =N 节数 ×V 单体放电终止

[0071] If the discharge termination voltage of a single cell is 1.75 V, the total discharge termination voltage of a 24-cell battery pack is 42 V. Similarly, the float charge voltage is preferably set to the recommended value of the single cell float charge voltage multiplied by the number of cells. When the recommended value of a single cell of a device is 2.25 V, the float charge voltage is 54 V.

[0072] In the generated charge and discharge instructions, the termination voltage of the discharge is set according to the discharge termination voltage set in the dictionary table based on the battery type and the number of cells. The floating charge voltage of the recharge is preferentially set to the floating charge voltage setting value read when the battery is in the floating charge state. If the floating charge voltage value read is greater than 53V, this value is taken. If it is less than 53V or there is no value, the corresponding charging floating charge voltage setting value in the dictionary table is taken according to the battery type and the number of cells and sent down.

[0073] Before issuing a remote control instruction to adjust the floating charge voltage of the switching power supply for charging and discharging or neither charging nor discharging, if the "DC output voltage" of the switching power supply is greater than or equal to the "equalizing charge voltage setting value", it is necessary to increase the remote control instruction of "remote control equalizing and floating charge" with a value of 1 and then issue the corresponding remote control instruction of "floating charge voltage setting value", otherwise it is not necessary to issue the remote control instruction; (the remote control instruction of "remote control equalizing and floating charge" can be issued regardless of the return result, because some switching power supplies may not have this measurement point or will not respond); when charging, it is not necessary to issue the remote control instruction of "remote control equalizing and floating charge", and the original remote control instruction of only issuing the "floating charge voltage setting value" can be maintained;

[0074] When emergency plans or equipment alarms occur, the intelligent analysis system automatically terminates the equipment's response and sends a callback command. It also intelligently analyzes and selects other replacement equipment and records the response results.

[0075] These charging and discharging instructions play a key role in the execution of demand response, not only ensuring the dynamic adjustment of the load, but also avoiding equipment risks such as over-discharge.

[0076] In the process of implementing demand response, in order to ensure the smooth implementation of the plan, the present invention adds dynamic monitoring and exception handling mechanisms to the system. The real-time monitoring module continuously monitors the load changes of the equipment during the implementation process to detect whether the response is normal.

[0077] For example, when the actual load of a device fluctuates from the planned value of 10kW to 7kW during the execution period, exceeding the preset deviation threshold of ±5%, the system will immediately trigger the abnormal callback mechanism. The core of the abnormal callback mechanism is to dynamically adjust the affected equipment and introduce backup equipment into the response process to ensure that the overall load adjustment can meet the needs of the power grid.

[0078] After the response plan is executed, the present invention provides a detailed statistical and analytical method for the response results. The system collects key data in each demand response, including the actual response load of the equipment, the response time, and the load deviation. The statistical analysis module processes these data in depth to generate a series of key indicators, such as the response success rate and the load achievement rate. The demand side response is evaluated and the analysis operations performed are counted. The statistical results include no response, successful response, failed response, timeout response, etc.

[0079] For example, if the planned load reduction of a device is 10kW and the actual response load is 9.5kW, the response success rate can be calculated by the following formula:

[0080]

[0081] In this case, R 成功 =95%. The statistical results will be presented to users intuitively through the data display unit to help users understand the response execution effect and provide data support for subsequent system optimization.

[0082] The present invention combines key technologies such as data collection, analysis, dynamic adjustment and visual display, and solves technical problems in traditional demand response methods such as inaccurate load forecasting, inflexible response adjustment, and ineffective use of historical data. Furthermore, through closely connected module design and efficient algorithm application, the present invention significantly improves the load control capability and response efficiency of the power system, providing a strong guarantee for the stable operation of the power grid.

[0083] The present invention not only proposes a demand response method based on battery redundancy time and real-time capacity, but also provides a demand response system capable of executing the method. The system design includes multiple functional modules, including a data acquisition unit 1, a data AI analysis and statistics unit 2, a virtual power plant interface unit 3, and a data display unit 4. These modules work together to form an efficient and reliable closed-loop system.

[0084] Figure 2The interaction process between the virtual power plant interface unit 3 and the data AI analysis and statistics unit 2 is shown in detail, including key steps such as invitation processing, load declaration, load control and feedback. In the process, the input and output data of each link are clearly marked to ensure the accurate response of the system to the power grid demand. When the power grid sends a demand invitation through the virtual power plant interface unit 3, the information receiving module first parses the invitation data, including the invitation number, response time period, load demand and corresponding price. These data are then transmitted to the data AI analysis and statistics unit 2 for load forecasting and plan generation. The load forecasting module in the data AI analysis and statistics unit calculates the future load change trend and generates a response plan that meets the power grid demand.

[0085] For example, when the grid invitation requires a 10kW load reduction between 10:00 and 12:00, the system finds through prediction that the average load of a certain device can be reduced by 8kW and another device can be reduced by 2kW. The virtual power plant interface unit 3 then feeds back the response plan to the grid.

[0086] This two-way interactive mode ensures that the system can dynamically adapt to changes in grid demand and achieve accurate and timely load control.

[0087] As the front-end module of the system, the data acquisition unit 1 is responsible for real-time acquisition of key parameters such as the operating status of the equipment, battery capacity, voltage, current, etc. These data are transmitted to the data AI analysis and statistics unit 2 through the communication network to provide basic support for the subsequent generation of demand response plans. Preferably, in order to ensure the accuracy of the collected data, the data collection frequency can be set to sample once per minute, and can even be increased to once every 10 seconds in scenarios with large load fluctuations. This high-frequency acquisition method can effectively capture the dynamic changes of the load.

[0088] The data AI analysis and statistics unit 2 is the core of the entire system, which contains multiple submodules for data screening, load forecasting and plan generation. Specifically, this unit can filter out key data related to demand response from data collection, and predict future load trends based on these data, and finally generate an optimized response plan that meets the needs of the power grid.

[0089] The virtual power plant interface unit 3 is the interactive hub between the system and the power grid. Its main functions include receiving demand invitation information from the power grid and feeding back response results. For example, when the power grid needs to reduce 20kW load in a certain period of time, the interface unit can quickly transmit the demand information to the data AI analysis and statistics unit, which generates the corresponding response plan and returns it to the power grid.

[0090] The data display unit 4 provides rich visualization functions, which can intuitively display the data and analysis results generated during the operation of the system to the user. The user can view the load status, response effect and overall operation of each device in the form of charts and reports. This intuitive display method greatly facilitates the user's understanding and decision-making.

[0091] Figure 3 The complete process of the present invention from receiving grid invitation information, generating response plan, executing monitoring to recording response results is shown. It mainly includes:

[0092] Data collection and decision analysis: Collect real-time data from equipment, match it with the grid invitation information, and analyze whether the bid can be won.

[0093] Response plan generation: If the invitation conditions are met, a specific response plan is generated; otherwise, the invitation information is recorded for subsequent analysis.

[0094] Execution and monitoring: According to the generated response plan, the device is monitored in real time and the response strategy is adjusted when necessary.

[0095] Emergency plan and callback: If an abnormality is detected during the execution process, such as unstable equipment load or failure to meet the planned requirements, the system immediately initiates the emergency plan and replaces the faulty equipment to ensure the overall response effect.

[0096] Response result recording: After the response is completed, the response results and all data during the execution process are recorded to provide a basis for future optimization decisions.

[0097] For example, in a demand response, the system receives an invitation to reduce the load by 15kW. The data acquisition module first obtains the actual load status of the current device, combines this data with historical data, and uses the load forecasting model to analyze whether the reduction demand can be met. The analysis results show that one device can reduce 10kW and another device can reduce 5kW. The system then generates a response plan and enters the execution monitoring stage.

[0098] During the execution process, the real-time monitoring module finds that the actual load of a certain device deviates from the planned value by more than 5% (for example, the planned load is 10kW, but the actual load is 9.4kW), triggering the exception handling mechanism to automatically adjust other devices to supplement the load and ensure that the overall response load meets the grid requirements.

[0099] The beneficial effect of this process design is that the system can dynamically adapt to complex and changing grid demands, ensuring the reliability and efficiency of the response. At the same time, by recording the response results, the system can continuously optimize the load forecasting model and plan generation strategy to improve long-term response performance.

[0100] In one embodiment of the present invention, the internal structure and workflow of the data AI analysis and statistics unit 2 are further described in detail. The unit is composed of multiple functional modules, including a data screening module, a load forecasting module, and a plan generation module, and the modules work closely together through data flows.

[0101] The data screening module is responsible for filtering out the key data required for demand response from data collection. To ensure the accuracy and timeliness of the data, the screening criteria include parameters such as real-time load, battery status, and ambient temperature. For example, in a specific demand response scenario, the filtered data may show that the current load of a device is 12kW, while its historical load fluctuates between 10kW and 13kW in the same time period. Based on these filtered data, the system can more accurately predict load change trends.

[0102] The load forecasting module uses a multivariate regression model or a deep learning algorithm to predict future loads. The prediction formula is as follows:

[0103] L 预测 (t) = α·L 历史 (t)+β·L 实时 (t)+γ·L 环境 (t)

[0104] Among them, L 预测 (t) is the predicted load value at time t, and α, β, and γ are the weight coefficients of historical data, real-time data, and environmental factors, respectively. These weight coefficients can be dynamically adjusted through regression analysis of historical data, and are preferably set to 0.5, 0.3, and 0.2 to adapt to different demand response scenarios.

[0105] The plan generation module generates a specific response plan based on the forecast results. For example, when the forecast load may reach 20kW between 14:00 and 16:00, and the grid requires a 10kW reduction, the system will distribute the load among different devices to ensure that the overall response target is achieved.

[0106] The virtual power plant interface unit 3 is further divided into an information receiving module and a result feedback module. This unit is a communication bridge between the power grid and the system, ensuring the timely transmission of demand information and response results.

[0107] The information receiving module is responsible for receiving the demand invitation information sent by the power grid, which usually includes the invitation number, load demand, response time period and corresponding price. For example, in a certain demand response, the invitation information sent by the power grid requires a 15kW load reduction between 10:00 and 12:00. The information receiving module will accurately transmit the demand to the data AI analysis and statistics unit 2.

[0108] After the demand response is completed, the result feedback module returns the response results to the power grid. The feedback content includes the execution of the response plan, the actual load reduction, and the operating status of each device. This two-way communication method ensures that the power grid can understand the system's response capabilities and execution effects in a timely manner.

[0109] The data display unit 4 is designed to provide users with comprehensive and intuitive system operation data. The data display unit further includes a statistical display module and a real-time monitoring module.

[0110] The statistical display module can display the system's operating performance from multiple dimensions such as annual, monthly and daily. For example, users can view the response success rate, total load reduction and revenue of the past year. These statistical data are presented in the form of charts and reports, providing users with multi-dimensional information that is easy to analyze. The following information can be displayed in charts, including this year's response statistics (annual response revenue, annual response invitation, annual response time, annual response load), annual demand response qualification rate (annual demand load), annual response load Top10, real-time load, daily response statistics (total number of sites, number of enabled sites, responding sites, number of invitations responded, number of invitations responded), latest invitation information, latest response results, and attention alarms. Statistics are displayed in the dimensions of year and day.

[0111] The real-time monitoring module focuses on displaying the dynamic data of the system during the demand response process, such as the current load of the equipment, response time and load deviation. For example, in a certain response, the real-time monitoring module shows that the actual load of a certain equipment is adjusted from the planned 8kW to 7.5kW, with a deviation rate of -6.25%. This real-time feedback can help users quickly discover and solve possible problems.

[0112] In order to improve the robustness of the system, the present invention introduces an exception handling module in the data AI analysis and statistics unit, which is responsible for detecting anomalies and taking remedial measures during the demand response execution process.

[0113] When the actual load of a device deviates from the planned load by more than the set threshold (for example, the deviation exceeds 10%), the exception handling module will trigger the callback mechanism and select a backup device to supplement the response load. For example, in a certain demand response, the planned load reduction is 12kW, but the actual response load is only 10kW. After the system detects that the deviation exceeds 10%, it immediately adds another backup device with a load of 2kW to the response to ensure that the overall response target is achieved.

[0114] The present invention solves the problems of inaccurate data collection, inflexible response plan, insufficient exception handling capability, etc. in traditional demand response systems through the collaborative work of multiple modules such as data collection, analysis, optimization and display. Through advanced algorithms and dynamic control strategies, the present invention significantly improves the load control capability of the power system and provides technical guarantee for the stable operation of the power grid.

[0115] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A demand response method based on battery redundancy duration and real-time capacity, characterized in that: The following steps are involved: Collect real-time monitoring data including equipment operating status, real-time battery capacity, and historical load data; generating a demand response plan based on the real-time monitoring data and the historical load data; Sending the demand response plan to the power grid, and adjusting the demand response plan according to information fed back by the power grid; Monitor the demand response execution process and collect statistics and analyze the response results; Visually display the demand response plan, response process and response results.

2. The method according to claim 1, characterized in that The step of generating a demand response plan comprises: Determine the backup power redundancy duration of each device based on the battery capacity and historical load data of the device; predict the average load of the response time period based on the real-time load data and historical load data during the demand response time period; Combined with the grid demand invitation information, optimize the equipment load distribution plan.

3. The method according to claim 2, characterized in that Further including: According to the optimized load distribution scheme, a charge and discharge instruction is generated, wherein the charge and discharge instruction includes a discharge termination voltage and a floating charge voltage determined based on the device type and the number of battery cells.

4. The method according to any one of claims 1 to 3, characterized in that: The step of monitoring the demand response execution process includes: During the demand response execution process, the load data of each device is monitored in real time; When an abnormal situation is detected, the response of the abnormal device is terminated and a callback instruction is generated to replace the device to participate in the response.

5. The method according to claim 4, characterized in that The steps of counting and analyzing the response results include: Collect and organize the actual load data and response time of each device during the response process; The success rate of demand response and the load achievement rate are calculated based on the data.

6. A demand response system based on battery redundancy duration and real-time capacity for executing the method of claim 1, characterized in that: include: A data collection unit, used in the collection step of the demand response method, to obtain real-time data such as equipment operating status, battery capacity, historical load data, etc.; A data AI analysis and statistics unit, connected to the data acquisition unit, for analysis steps; a virtual power plant interface unit, connected to the data AI analysis and statistics unit, for receiving grid demand invitation information and sending response plans and results; The data display unit is used to visually display the demand response plan and response results.

7. The system according to claim 6, characterized in that The data AI analysis and statistics unit includes: The data screening module is used to screen out the key data required for demand response from the collected data; the load forecasting module is used to forecast the equipment load in each time period based on the screened data; The plan generation module is used to generate a demand response plan that meets the grid invitation requirements.

8. The system according to claim 7, characterized in that The virtual power plant interface unit comprises: An information receiving module, used for receiving demand invitation information from the power grid; The result feedback module is used to feed back the response plan and actual response results to the power grid.

9. The system according to claim 6, characterized in that The data display unit comprises: The statistical display module is used to display the load distribution, response success rate, annual response income and other statistical information during the demand response process; Real-time monitoring module, used to display equipment load status and response process in real time.

10. The system according to any one of claims 6 to 9, characterized in that: It further includes an exception handling module, which is used to generate a callback instruction and start a backup device to replace the abnormal device to participate in the response when an abnormality is detected during the demand response process.

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