Battery redundancy duration and real-time capacity based demand response method and system

Abstract

The present application relates to the technical field of energy management, and particularly relates to a demand response method based on redundant duration of storage battery and real-time capacity, comprising the following steps: collecting real-time monitoring data including device running state, real-time capacity of 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 statistically analyzing response results; visually displaying the demand response plan, the response process and the response results; automatically adjusting the demand response plan according to real-time feedback of the power grid; and dynamically adjusting a participation strategy of the device by real-time calculation of available redundant duration of the storage battery, so as to maximize response capacity and safety of the system.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and more specifically to a demand response method based on battery redundancy duration and real-time capacity. Background Technology

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

[0003] Currently, traditional demand response technologies primarily rely on manual statistics and operation. These methods are typically based on manually entered equipment load information and fixed load control strategies. When the power grid needs to respond to demand, personnel manually select participating equipment and specify load adjustment strategies based on pre-entered load information. However, this method suffers from the following technical problems:

[0004] First, the accuracy and timeliness of load information are insufficient. Because the entry and updating of load information are often conducted offline, data lag and human error make it difficult to accurately implement demand response plans. When the power grid issues a demand request, the load information the system relies on may be outdated or inaccurate, failing to effectively support real-time decision-making.

[0005] Secondly, the dynamic adjustment capability of demand response plans is limited. Existing technologies lack the ability to monitor and analyze equipment operating status and load data in real time, making it difficult to dynamically adjust response plans according to changes in grid demand during execution. For example, when the actual load of equipment deviates from the planned value, it cannot be detected and corrected in a timely manner, thus increasing the risk of demand response failure.

[0006] Furthermore, existing methods lack intelligent analysis and optimization of response process data. In traditional systems, demand response results are often treated as single events, lacking in-depth analysis of historical response data. This prevents the system from learning and optimizing from past responses, making it difficult to continuously improve future response plans.

[0007] More importantly, existing technologies do not fully utilize the characteristics of energy storage devices, especially the redundancy duration and real-time capacity of batteries. As a crucial component of demand response, the performance parameters of energy storage devices directly affect the system's responsiveness and reliability. However, traditional methods typically do not consider the real-time capacity and redundancy duration of batteries during the planning and generation process, potentially leading to over-discharge or underload issues during the response, thereby reducing the system's operating efficiency and safety.

[0008] To address the aforementioned issues, this invention proposes a demand response method and system based on battery redundancy duration and real-time capacity. By introducing advanced data acquisition and analysis technologies, it enables real-time monitoring and optimization analysis of equipment load and battery status. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides a demand response method and system based on battery redundancy duration and real-time capacity, overcoming the shortcomings of traditional technologies. Specifically, this invention aims to solve the following technical problems: improve the accuracy and timeliness of load information, ensuring the system can acquire the operating status of equipment and the real-time capacity of batteries in real time; achieve dynamic adjustment and optimization of response plans, enhancing the flexibility and robustness of the system in executing demand response; intelligently analyze historical response data to optimize future response plans, improving the overall operating efficiency and economic benefits of the system; and fully utilize the battery redundancy duration and real-time capacity to avoid over-discharge or underload, ensuring the safety and reliability of equipment during the response process.

[0010] This invention protects 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] Based on the real-time monitoring data and historical load data, a demand response plan is generated;

[0013] The demand response plan is sent to the power grid, and the demand response plan is adjusted based on the information fed back by the power grid.

[0014] Monitor the execution process of demand response and perform statistical analysis on the response results;

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

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

[0017] Based on the battery capacity and historical load data of the equipment, determine the backup power redundancy duration of each device.

[0018] Based on real-time load data and historical load data within the demand response period, predict the average load for the response period.

[0019] Optimize equipment load allocation schemes by combining grid demand invitation information.

[0020] As a preferred option, it further includes:

[0021] Based on the optimized load distribution scheme, charge and discharge instructions are generated, which include discharge termination voltage and float charge voltage determined based on device type and number of battery cells.

[0022] Preferably, the steps of the monitoring demand response execution process include:

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

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

[0025] Preferably, the steps for statistically analyzing the response results include:

[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, which performs the method described above, includes:

[0029] The data acquisition unit is used in the acquisition steps of the demand response method to obtain real-time data such as equipment operating status, battery capacity, and historical load data.

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

[0031] The virtual power plant interface unit is connected to the data AI analysis and statistics unit and is used to receive grid demand invitation information and send response plans and results.

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

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

[0034] The data filtering module is used to filter out the key data required for demand response from the collected data;

[0035] The load forecasting module is used to predict equipment load for different time periods based on the filtered data.

[0036] The plan generation module is used to generate demand response plans that meet the requirements of the power grid invitation.

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

[0038] The information receiving module is used to receive demand invitation information from the power grid;

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

[0040] Preferably, the data display unit includes:

[0041] The statistics display module is used to display statistical information such as load allocation, response success rate, and annual response revenue during the demand response process;

[0042] The real-time monitoring module is used to display the equipment load status and response process in real time.

[0043] Preferably, the system further includes an exception handling module, which generates a callback instruction and activates a backup device to replace the faulty device in the response when an exception is detected during the demand response process.

[0044] The beneficial effects of this invention are:

[0045] (1) The data acquisition unit can collect key parameters of the equipment in real time, including the real-time capacity, voltage, and current of the battery, as well as the load status of the equipment. This high-frequency acquisition method ensures the accuracy and timeliness of the data, providing a solid data foundation for subsequent load forecasting and response plan generation. Preferably, by sampling every 15 minutes or less, the load trend of the equipment and the health status of the battery 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 multiple regression algorithm and a neural network model for accurate load prediction and planning optimization, achieving high-precision prediction of future load changes. This algorithm can effectively integrate historical data, real-time data, and trend data, solving the problems of large fluctuations and low accuracy in prediction results in traditional methods.

[0047] (3) The virtual power plant interface unit enables rapid response and dynamic adjustment to grid demand through bidirectional 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 allocation strategy of the equipment within 5 minutes to meet the new demand.

[0048] (4) This invention makes full use of the battery's redundancy time and real-time capacity to ensure that the equipment will not experience over-discharge or underload during the response process. For example, by calculating the available redundancy time of the battery in real time, the equipment's participation strategy can be dynamically adjusted to maximize the system's responsiveness and safety.

[0049] (5) Through the visualization of response results by the data display unit, this 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 of the system and the efficiency of its use, but also provides valuable reference for future optimization and improvement.

[0050] In summary, this invention solves key problems in traditional demand response systems, such as delayed load information, inflexible plan execution, and underutilization of equipment performance, through innovative data acquisition, analysis, and optimization technologies. By leveraging advanced algorithms and module collaboration, this invention not only improves the system's response efficiency and security but also significantly enhances the power grid's load regulation capabilities, providing technical support for achieving more efficient and reliable demand response management. Attached Figure Description

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

[0052] Figure 2 This is a schematic diagram illustrating the interaction between the virtual power plant interface unit and the data AI analysis and statistics unit of the present invention.

[0053] Figure 3 This is a flowchart of the demand response decision analysis and execution process of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this 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, this invention relates to a demand response method based on battery redundancy duration and real-time capacity, aiming to improve the real-time performance and accuracy of demand response by optimizing the data acquisition and analysis process. First, the data acquisition unit 1 is responsible for collecting key information from various devices in the system in real time, including operating status, battery capacity, voltage, current, power, and historical load data. For example, in a communication base station, the battery capacity is 80%, the equipment operating load is 8kW, and the voltage is operating stably at 48V. These parameters are uploaded to the system in real time through the acquisition unit.

[0056] Next, based on the real-time and historical load data, the system generates a preliminary demand response plan through the data AI analysis and statistics unit 2. It is important to note that the time span of the historical load data can be flexibly set according to different scenarios. Preferably, load data from the past 5 days is used for trend analysis to ensure the accuracy of the plan. For example, if the average load of a certain device fluctuates between 9kW and 10kW during the same time period each day over the past 5 days, the system can relatively accurately predict load changes during the response period by monitoring its current load at 9.5kW in real time.

[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 the load by 15kW between 14:00 and 16:00 in the afternoon, the system generates a reasonable plan based on the collected and analyzed real-time and historical data, and the interface unit provides timely feedback.

[0058] During the execution of the plan, the system continuously monitors the performance of the demand response, collects real-time equipment response load data, and dynamically adjusts the response plan. Finally, data visualization unit 4 visualizes the response process and results, displaying information such as response success rate, load achievement rate, and equipment status, providing users with intuitive feedback. For example, in the aforementioned load reduction case, the data visualization unit will show that the load on each piece of equipment decreased from 8kW to 7kW, with real-time charts showing a response success rate of 95%.

[0059] Figure 1 This demonstrates the overall system architecture of the present invention, from Figure 1 As can be seen, the data flow of the entire system moves 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 system's real-time performance, reliability, and efficiency.

[0060] Data acquisition unit 1 acquires equipment data in real time, such as equipment operating status and battery parameters, using high-frequency sampling technology, and transmits this data to data AI analysis and statistics unit 2. Data AI analysis and statistics unit 2 uses multiple regression models and neural network models to predict equipment load and generate demand response plans. The virtual power plant interface unit 3 enables bidirectional interaction with the power grid, achieving rapid response and plan feedback to grid demand. Data display unit 4 provides users with a multi-dimensional display of the response results.

[0061] This architecture enables efficient data flow and close collaboration among functional modules, thereby significantly improving response speed and decision-making accuracy during demand response.

[0062] In one embodiment of the present invention, a specific method for generating a demand response plan is further specified. The core of generating the response plan lies in the accurate calculation of the battery redundancy duration of the equipment. Preferably, the equipment redundancy duration 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 This indicates the maximum runtime of the device under current capacity and load conditions. For example, if a battery has a capacity of 100Ah and supports 5 hours of runtime under full load, then T... 总续航 = 5 hours. If the demand response plan requires equipment to reduce some load within the next 3 hours, then the corresponding T 需求 The redundancy duration T is calculated to be 3 hours. 备电 = 2 hours.

[0065] Meanwhile, this invention proposes an efficient processing method for calculating real-time load data. Specifically, by continuously collecting load data from the equipment every 15 minutes through a monitoring module, the average load within the demand response period is calculated based on the following formula:

[0066]

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

[0068] To achieve more efficient load management, this invention proposes a method for generating charge and discharge commands. The response plan generation combines equipment type, load conditions, and battery characteristics to generate optimal charge and discharge commands. For example, for a 48V battery pack with 24 individual battery cells, the system automatically calculates its discharge termination voltage and float charge voltage.

[0069] Specifically, the discharge termination voltage can be calculated using 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 battery cell is 1.75V, the total discharge termination voltage of a 24-cell battery pack is 42V. Similarly, the float charge voltage is preferably set as the recommended value of the single-cell float charge voltage multiplied by the number of battery cells. When the recommended value for a single cell in a device is 2.25V, the float charge voltage is 54V.

[0072] In the generated charge and discharge commands, the discharge termination voltage is set according to the discharge termination voltage set in the dictionary table based on the battery type and the number of individual cells. The recharge float voltage is preferentially set to the float voltage setting value read when the battery is in float charging state. If the read float voltage value is greater than 53V, then that value is used. If it is less than 53V or there is no value, then the corresponding charging float voltage setting value in the dictionary table is used to send the command according to the battery type and the number of individual cells.

[0073] Before issuing a remote adjustment command to adjust the float charge voltage of the switching power supply for charging / discharging or not charging / discharging, if the "DC output voltage" of the switching power supply is greater than or equal to the "equalizing charge voltage setting value", then a remote control command with a value of 1 for "remote equalizing float charging" needs to be issued before issuing the corresponding remote adjustment command for the "float charge voltage setting value". Otherwise, this remote control command is not required. (The return result of the "remote equalizing float charging" command can be ignored, as some switching power supplies may not have this measurement point or may not respond.) During charging, the "remote equalizing float charging" command is not required; simply issue the original remote adjustment command for the "float charge voltage setting value".

[0074] When emergency plans or equipment alarms occur, the intelligent analysis system automatically terminates the device's response and sends a callback command. It then intelligently analyzes and selects alternative devices, recording the response results.

[0075] These charge and discharge commands play a crucial role in demand response execution, ensuring not only dynamic load adjustment but also avoiding equipment risks such as over-discharge.

[0076] To ensure the smooth implementation of the plan during the demand response process, this invention incorporates dynamic monitoring and anomaly handling mechanisms into the system. The real-time monitoring module continuously monitors equipment load changes during execution to detect whether the response is proceeding normally.

[0077] For example, if the actual load of a device fluctuates from the planned value of 10kW to 7kW during the execution of the planned 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 grid demand.

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

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

[0080]

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

[0082] This invention combines key technologies such as data acquisition, analysis, dynamic adjustment, and visualization, solving technical problems in traditional demand response methods such as inaccurate load forecasting, inflexible response adjustments, and ineffective utilization of historical data. Furthermore, through a tightly integrated modular design and efficient algorithm application, this invention significantly improves the load regulation capability and response efficiency of the power system, providing a strong guarantee for the stable operation of the power grid.

[0083] This invention not only proposes a demand response method based on battery redundancy duration and real-time capacity, but also provides a demand response system capable of executing this 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 illustrated in detail, including key steps such as request processing, load declaration, load control, and feedback. In this process, the input and output data at each stage are clearly labeled, ensuring the system's accurate response to grid demand. When the grid sends a demand request through the virtual power plant interface unit 3, the information receiving module first parses the request data, including the request number, response time period, load demand, and corresponding price. This data is 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 future load trends and generates a response plan that meets grid demand.

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

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

[0087] Data acquisition unit 1, as the system's front-end module, is responsible for real-time acquisition of key parameters such as equipment operating status, battery capacity, voltage, and current. This data is transmitted via a communication network to data AI analysis and statistics unit 2, providing foundational support for subsequent demand response plan generation. Preferably, to ensure the accuracy of the collected data, the data acquisition frequency can be set to once per minute, and in scenarios with large load fluctuations, it can even be increased to once every 10 seconds. This high-frequency acquisition method can effectively capture dynamic changes in the load.

[0088] The data AI analysis and statistics unit 2 is the core of the entire system, containing multiple sub-modules for data filtering, load forecasting, and plan generation. Specifically, this unit can filter key data related to demand response from the collected data, predict future load trends based on this data, and ultimately generate an optimized response plan that meets the grid's needs.

[0089] The virtual power plant interface unit 3 serves as the interaction hub between the system and the power grid. Its main functions include receiving demand invitation information from the power grid and providing feedback on response results. For example, when the power grid needs to reduce its load by 20kW during a specific time period, the interface unit can quickly transmit the demand information to the data AI analysis and statistics unit, which will then generate a corresponding response plan and return it to the power grid.

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

[0091] Figure 3 This invention demonstrates the complete process from receiving power grid invitation information, generating response plans, monitoring execution, to recording response results. It mainly includes:

[0092] Data Acquisition and Decision Analysis: Collect real-time data from the equipment, match it with the power 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: Monitor the device in real time according to the generated response plan and adjust the response strategy as necessary.

[0095] Emergency plans and callbacks: If an anomaly is detected during execution, such as unstable equipment load or failure to meet planned requirements, the system will immediately activate the emergency plan to replace the faulty equipment and ensure overall response effectiveness.

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

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

[0098] During execution, the real-time monitoring module detects 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 abnormal handling mechanism to automatically adjust other devices to supplement the load and ensure that the overall response load meets the grid requirements.

[0099] The advantage of this process design is that the system can dynamically adapt to complex and ever-changing grid demands, ensuring the reliability and efficiency of the response. Simultaneously, by recording response results, the system can continuously optimize load forecasting models and plan generation strategies, improving 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. This unit consists of multiple functional modules, including a data filtering module, a load forecasting module, and a plan generation module, which work closely together through data flow.

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

[0102] The load forecasting module uses multiple regression models or deep learning algorithms to predict future loads. The forecasting 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) represents the predicted load value at time t, and α, β, and γ are the weighting coefficients for historical data, real-time data, and environmental factors, respectively. These weighting 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 specific response plans based on the forecast results. For example, if the forecast load may reach 20kW between 14:00 and 16:00, and the power 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 subdivided into an information receiving module and a result feedback module. This unit serves as 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 demand invitation information sent by the power grid. This information typically includes the invitation number, load demand, response time period, and corresponding price. For example, in a demand response, the power grid sends an invitation requesting a 15kW load reduction between 10:00 and 12:00. The information receiving module will accurately transmit this demand to the data AI analysis and statistics unit 2.

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

[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 statistics display module can showcase the system's operational performance from multiple dimensions, including annual, monthly, and daily. For example, users can view the response success rate, total load reduction, and revenue over the past year. These statistics are presented in chart and report formats, providing users with multi-dimensional information for easy analysis. Charts display the following information, including annual response statistics (annual response revenue, annual response invitations, annual response duration, annual response load), annual demand response qualification rate (annual demand load), annual top 10 response loads, real-time load, daily response statistics (total number of sites, number of active sites, sites responding, number of responded invitations, number of responded invitations), latest invitation information, latest response results, and alerts. Statistics are displayed on an annual and daily basis.

[0111] The real-time monitoring module focuses on displaying 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 piece of equipment has been adjusted from the planned 8kW to 7.5kW, with a deviation rate of -6.25%. This real-time feedback can help users quickly identify and resolve potential problems.

[0112] To improve the robustness of the system, this invention introduces an anomaly handling module into the data AI analysis and statistics unit. This module 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 a set threshold (e.g., more than 10%), the exception handling module triggers a callback mechanism to select a backup device to supplement the response load. For example, in a 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] This invention addresses the problems of inaccurate data acquisition, inflexible response plans, and insufficient anomaly handling capabilities in traditional demand response systems through the coordinated operation of multiple modules, including data acquisition, analysis, optimization, and visualization. Through advanced algorithms and dynamic control strategies, this invention significantly improves the load regulation capabilities of power systems, providing technical assurance for the stable operation of the power grid.

[0115] Those skilled in the art will readily understand that the above description is merely 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 within the scope of protection of the present invention.

Claims

1. A demand response method based on battery redundancy duration and real-time capacity, characterized in that, Includes the following steps: Collect real-time monitoring data, including equipment operating status, real-time battery capacity, and historical load data; Based on the real-time monitoring data and historical load data, a demand response plan is generated; The demand response plan is sent to the power grid, and the demand response plan is adjusted based on the information fed back by the power grid. Monitor the execution process of demand response and perform statistical analysis on the response results; Visualize the demand response plan, response process, and response results; The steps for generating a demand response plan include: determining the backup power redundancy duration for each device based on the device's battery capacity and historical load data; predicting the average load during the response period based on real-time load data and historical load data; and optimizing the device load allocation scheme by combining grid demand invitation information. Further, it includes: generating charge and discharge instructions based on the optimized load allocation scheme, wherein the charge and discharge instructions include a discharge termination voltage and a float charge voltage determined based on the device type and the number of battery cells; The steps of monitoring the demand response execution process include: monitoring the load data of each device in real time during the demand response execution process; when an abnormal situation is detected, terminating the response of the abnormal device and generating a callback instruction to replace the device in the response; The steps for statistical analysis of the response results include: collecting and organizing the actual load data and response duration of each device during the response process; calculating the success rate of demand response and load achievement rate based on the load data and response duration; and calculating the backup power redundancy duration. Determined by the following formula: ， in, This indicates the device's maximum battery life under current capacity and load conditions. This indicates that the demand response plan requires equipment to be available in the future. Internally reduce some load.

2. A demand response system based on battery redundancy duration and real-time capacity that performs the method of claim 1, characterized in that, include: The data acquisition unit is used for the acquisition steps of the demand response method, and the real-time data acquired includes equipment operating status, battery capacity, and historical load data; the data AI analysis and statistics unit is connected to the data acquisition unit and is used for the analysis steps; the virtual power plant interface unit is connected to the data AI analysis and statistics unit and is used to receive grid demand invitation information and send response plans and results. The data visualization unit is used to visually present the demand response plan and response results.

3. The system according to claim 2, characterized in that, The data AI analysis and statistics unit includes: a data filtering module, used to filter out key data required for demand response from the collected data; a load forecasting module, used to forecast equipment load for each time period based on the filtered data; and a plan generation module, used to generate a demand response plan that meets the grid invitation requirements.

4. The system according to claim 3, characterized in that, The virtual power plant interface unit includes: an information receiving module for receiving demand invitation information from the power grid; and a result feedback module for feeding back the response plan and actual response results to the power grid.

5. The system according to claim 4, characterized in that, The data display unit includes: a statistical display module, used to display statistical information during the demand response process, including load allocation, response success rate, and annual response revenue; and a real-time monitoring module, used to display the equipment load status and response progress in real time.

6. The system according to any one of claims 2 to 5, characterized in that, It further includes an exception handling module, which generates a callback instruction and activates a backup device to replace the faulty device in the response when an exception is detected during the demand response process.

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