Balance scheduling system for energy management and balance scheduling method thereof

Abstract

The invention discloses a balance scheduling system for energy management and a balance scheduling method thereof, and relates to the technical field of energy management scheduling, and the balance scheduling system comprises an energy data acquisition module, an energy demand trend analysis module, an energy supply and demand dynamic prediction module, an energy balance scheduling decision module, a scheduling execution module and a performance monitoring module. After the energy supply and demand dynamic prediction module integrates real-time external factor data of the energy data acquisition module and a long-term historical rule of the energy demand trend analysis module, a reference prediction baseline based on a long-term trend is generated, and then a dynamic correction value is superposed to respond to short-term disturbance. And meanwhile, through an error traceability mechanism for separating a trend baseline from external correction, the module performance can be optimized in a targeted manner, if an error is derived from long-term trend deviation, a trend analysis model is calibrated, and if external data is lagged, priority adjustment of a data acquisition module is triggered, so that system self-diagnosis and prediction precision closed-loop improvement are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and dispatching, and particularly to a balance dispatching system for energy management and its balance dispatching method. Background Art

[0002] With the diversification of the energy structure and the complexity of the power system, the management of energy supply-demand balance faces unprecedented challenges. Traditional energy dispatching systems mostly rely on static prediction models driven by historical data and fixed-priority data acquisition mechanisms, and it is difficult to adapt to dynamic disturbance factors such as sudden weather changes, market fluctuations, and emergencies. The existing technologies have the following limitations: Rigid data acquisition: Traditional systems mostly adopt data acquisition strategies with fixed frequencies and priorities, and cannot dynamically adjust the data source priorities according to real-time requirements (such as extreme weather warnings), resulting in insufficient timeliness of key data. Especially in the face of sudden disturbances, the problem of data lag is significant, affecting the accuracy of prediction and decision-making; Single prediction model: Existing prediction methods usually rely on long-term historical trend analysis and fail to effectively separate the influences of long-term patterns and short-term disturbances, resulting in insufficient response capabilities of prediction results to emergencies. For example, when a cold snap suddenly hits or the market price fluctuates violently, traditional models are prone to large deviations due to the lack of a dynamic correction mechanism, which may further lead to dispatching errors; Lack of flexibility in dispatching strategies: Most systems adopt deterministic dispatching schemes for a single scenario and do not consider the diverse possibilities of future supply and demand. When unpredictable disturbances (such as equipment failures or social events) occur, the system is difficult to quickly generate alternative solutions, resulting in rigid resource allocation and even the risk of supply-demand imbalance; Weak module collaboration and self-optimization capabilities: There is a lack of a closed-loop feedback mechanism between modules in existing systems, making it difficult to trace the source of prediction errors and accurately locate the root cause of problems (such as data acquisition delays or model biases). In addition, the reusability of historical dispatching experience is poor, and it is difficult to optimize decision rules through continuous learning, which limits the improvement of the overall performance of the system. Summary of the Invention

[0003] The purpose of the present invention is to provide a balance dispatching system for energy management and its balance dispatching method to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: A balance dispatching system for energy management, the system includes: An energy data acquisition module, used for real-time acquisition of historical energy production data, meteorological data, market price data, calendar information such as holidays and special events, grid status data, and energy storage device status data; The energy demand trend analysis module analyzes and identifies the periodic trends, seasonal variations, long-term growth, and decline trends of energy consumption based on the long-term historical data provided by the energy data collection module; The energy supply and demand dynamic prediction module is used to predict the short-cycle energy supply and demand in real time; The energy balance scheduling decision module formulates an optimal energy scheduling plan according to the prediction results provided by the energy supply and demand dynamic prediction module, as well as information such as real-time energy prices, energy storage device status, and grid restrictions; The scheduling execution module executes specific energy scheduling operations according to the instructions issued by the energy balance scheduling decision module, controlling the start, stop, and output of generators, the charge and discharge of energy storage devices, and external market transactions; The performance monitoring module is used to monitor the performance indicators of prediction accuracy, scheduling balance rate, cost, and revenue, and perform adaptive optimization; The performance monitoring module transmits the monitoring results to the energy supply and demand dynamic prediction module. When the prediction error exceeds the threshold (the actual demand is 10% higher than the prediction), the energy supply and demand dynamic prediction module will analyze the error source. If the trend baseline deviates from the actual, it is attributed to insufficient identification by the energy demand trend analysis module. If the correction value deviates from the actual, it is attributed to the lag in external factor collection by the energy data collection module. After obtaining the error source, the energy supply and demand dynamic prediction module will feedback the attribution result to the corresponding module.

[0005] Furthermore, the energy data collection module dynamically adjusts the data collection priority based on the real-time demand feedback from the energy supply and demand dynamic prediction module and the energy balance scheduling decision module.

[0006] Furthermore, the energy data collection module collaborates with the performance monitoring module to automatically block low-reliability data sources according to the historical data quality assessment results.

[0007] Furthermore, the energy demand trend analysis module decomposes the energy demand into predictable trends and unpredictable disturbances, and analyzes the disturbance sources.

[0008] Furthermore, after obtaining the data transmitted by the energy data collection module and the energy demand trend analysis module, the energy supply and demand dynamic prediction module will output a baseline prediction based on the data of the energy demand trend analysis module, and then add a correction value to the baseline prediction based on the external factor data obtained by the energy data collection module.

[0009] Furthermore, the energy balance scheduling decision module uses the probabilistic prediction information from the energy supply and demand dynamic prediction module to construct multiple future supply and demand scenarios, and evaluates the performance of each scheduling plan under different scenarios.

[0010] Furthermore, during the execution of instructions, the scheduling execution module will monitor the execution process in real time and feedback the execution status, equipment operation status, and actual energy supply and demand situation to the energy balance scheduling decision module.

[0011] Furthermore, the scheduling execution module is synchronized and calibrated with external systems to achieve real-time data synchronization and status calibration with external systems such as the power grid control system and the generator control system, ensuring the effective execution of instructions.

[0012] Furthermore, the performance monitoring module will regularly initiate collaborative diagnosis tasks. The performance monitoring module combines the energy data acquisition module and the energy supply and demand dynamic prediction module to analyze whether the prediction error stems from data acquisition delay or model deviation. The performance monitoring module combines the energy balance scheduling decision module and the scheduling execution module to review the differences between the scheduling instructions and the actual execution, and whether there are situations such as equipment response lag or overly idealized instructions.

[0013] A balancing scheduling method for an energy management balancing scheduling system, which is applied to an energy management balancing scheduling system. The method includes: S1: The energy data acquisition module dynamically acquires multi-source data and dynamically adjusts the priority according to real-time requirements. For example, during extreme weather, it actively improves the acquisition frequency and accuracy of meteorological data, and at the same time automatically shields low-reliability data sources based on the data quality assessment of the performance monitoring module; S2: The collected data is decomposed into predictable trends and unpredictable disturbances by the energy demand trend analysis module. If a disturbance is detected, the multi-hypothesis prediction mode is started jointly with the energy supply and demand dynamic prediction module to generate multiple potential evolution paths, and the balancing scheduling decision module is synchronously triggered to generate a flexible scheduling plan based on probabilistic scenario assessment; S3: The energy supply and demand dynamic prediction module outputs short-term supply and demand forecasts by combining the baseline forecast and external factor correction values. When the performance monitoring module detects that the prediction error exceeds the threshold, it automatically traces back to the deviation of the trend analysis or data acquisition module and feedbacks optimization instructions; S4: The scheduling execution module controls the generator set, energy storage equipment, and external transactions in real time according to the decision instructions, synchronizes and calibrates the status with the power grid control system. If the execution is blocked, it dynamically adjusts according to the alternative plan and feeds back the execution log to the performance monitoring module; S5: The performance monitoring module regularly initiates multi-module collaborative diagnosis, analyzes whether the prediction error stems from data delay or model deviation, reviews the differences between the scheduling instructions and the actual execution, and converts the results into rule updates to form a closed-loop adaptive optimization.

[0014] The present invention provides an energy management balancing scheduling system and its balancing scheduling method, which have the following beneficial effects: 1. The energy data acquisition module of the present invention can dynamically optimize the data acquisition priority and parameter configuration by receiving demand feedback from the energy supply and demand dynamic prediction module and the energy balance scheduling decision module in real time. For example, when an extreme weather warning is triggered, the module will automatically increase the frequency and accuracy of meteorological data collection. The timeliness and precision of key data are ensured through this intelligent adaptation mechanism. It can not only provide accurate information for subsequent prediction models to reduce prediction deviations, but also actively capture early disturbance signals and shorten system response delays, thereby providing sufficient data support for multi-hypothesis prediction and deduction and flexible scheduling decisions, and effectively reducing the risk of scheduling errors due to insufficient information.

[0015] 2. After integrating the real-time external factor data of the energy data acquisition module (such as sudden weather changes, market price fluctuations) and the long-term historical laws (such as seasonal cycles) of the energy demand trend analysis module, the energy supply and demand dynamic prediction module of the present invention generates a benchmark prediction baseline based on the long-term trend, and then superimposes the dynamic correction value to respond to short-term disturbances. For example, when a cold wave suddenly hits, the system superimposes the real-time temperature drop correction value through the winter benchmark load forecast to accurately capture the impact of extreme weather on the power load. At the same time, through the error tracing mechanism of separating the trend baseline and the external correction, the module performance can be optimized in a targeted manner. If the error is caused by the long-term trend deviation, the trend analysis model is calibrated. If it is due to external data lag, the data acquisition module priority adjustment is triggered to achieve system self-diagnosis and closed-loop improvement of prediction accuracy.

[0016] 3. The energy balance scheduling decision module of the present invention constructs a multi-scenario deduction model based on the probabilistic prediction of the energy supply and demand dynamic prediction module, and selects the optimal scheduling plan based on the evaluation data of the performance monitoring module and pushes it to the scheduling execution module. The module synchronously monitors the equipment status and supply and demand dynamics during the execution process. When the execution deviation exceeds the preset threshold, it automatically calls the alternative plan or implements local dynamic adjustment according to the priority to ensure the flexible implementation of the scheduling strategy. At the same time, through the performance monitoring coordination mechanism, the historical scheduling is reviewed in multiple dimensions, the high-efficiency model is precipitated into a rule base, and the inefficient decision-making path is eliminated to form a continuously iterative closed-loop optimization system, and finally the accuracy and intelligence of the scheduling strategy are spirally improved.

[0017] 4. The energy demand trend analysis module of the present invention classifies energy demand into predictable trends and unpredictable disturbances, dynamically analyzes the disturbance source and activates the response mechanism. When an unpredictable disturbance is detected, the energy supply and demand dynamic prediction module is linked to switch to the multi-hypothesis prediction mode to generate a set of disturbance evolution paths. At the same time, the energy balance scheduling decision module generates a flexible resource allocation plan based on the path risk gradient. During the process, the performance monitoring module synchronously evaluates the disturbance impact domain and duration assumptions, covers potential scenarios through multi-hypothesis prediction, and avoids the limitations of a single prediction. At the same time, the flexible plan dynamically allocates resources according to the risk gradient, improves the response speed and utilization efficiency, and continuously optimizes the rule base by tracing the disturbance data to gradually reduce the deviation from human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a schematic diagram of the working process of a balancing scheduling system for energy management according to the present invention. DETAILED DESCRIPTION

[0019] See also Figure 1 The present invention provides a technical solution: a balanced scheduling system and a balanced scheduling method for energy management, the system comprising: Energy data collection module, used for real-time collection of historical energy production data, meteorological data, market price data, calendar information such as holidays and special events, power grid status data, and energy storage equipment status data; The energy demand trend analysis module analyzes and identifies the cyclical trends, seasonal changes, long-term growth and decline trends of energy consumption based on the long-term historical data provided by the energy data acquisition module.

[0020] The energy supply and demand dynamic prediction module is used to predict short-term energy supply and demand in real time.

[0021] The energy balance scheduling decision module formulates the optimal energy scheduling plan based on the prediction results provided by the energy supply and demand dynamic prediction module, as well as real-time energy prices, energy storage equipment status, grid restrictions and other information.

[0022] The dispatch execution module performs specific energy dispatch operations according to the instructions issued by the energy balance dispatch decision module, controls the start and stop and output of generators, the charging and discharging of energy storage equipment, and external market transactions.

[0023] The performance monitoring module is used to monitor the performance indicators of prediction accuracy, scheduling balance rate, cost, and benefit, and perform adaptive optimization.

[0024] The human-computer interaction module is used to display system status, prediction results, scheduling plans and performance indicators to users.

[0025] The specific operations are as follows. After the energy data acquisition module has completed the real-time acquisition of historical energy production data, meteorological data, market price data, calendar information such as holidays and special events, grid status data, and energy storage device status data, it will transfer the above data to the energy demand trend analysis module. During the use of the energy data acquisition module, it can dynamically adjust the data acquisition priority based on the real-time demand feedback from the energy supply and demand dynamic prediction module and the energy balance scheduling decision module. For example, when the system receives an extreme weather warning, the energy data acquisition module will actively increase the acquisition frequency and accuracy of meteorological data. By dynamically increasing the acquisition frequency and accuracy of data, the system can obtain more accurate data information, which can provide more accurate data support for the subsequent energy supply and demand dynamic prediction module, thus significantly reducing the prediction deviation caused by data lag or roughness. In addition, dynamically adjusting the data acquisition priority enables the system to have an active response ability. When the system senses an extreme weather warning, the energy data acquisition module can immediately focus on key data sources to ensure capturing key signals at the initial stage of the disturbance, providing sufficient information support for the subsequent multi-hypothesis prediction and flexible scheduling scheme of the system. This can significantly shorten the delay from the system's perception to response and reduce the risk of scheduling errors caused by insufficient data. The energy demand trend analysis module can use machine learning algorithms to perform real-time analysis and prediction on the data transmitted by the energy demand trend analysis, and train the model through a large amount of historical data to analyze and identify the periodic trends, seasonal variations, long-term growth, and downward trends of energy consumption. Among them, the energy demand trend analysis module will first collect the daily electricity consumption data of the past 5 years. After collecting the data, the energy demand trend analysis module uses a computer program to label the rules according to three types: periodic trends, seasonal variations, and long-term growth. Among them, periodic trends are used to mark recurring patterns (for example, the electricity consumption in July each year increases by 20% compared to June), seasonal variations are used to mark the electricity changes caused by seasons (for example, the electricity consumption in summer is 25% higher than in winter), and long-term growth marks the items that have been growing due to year-on-year changes (such as the electricity consumption in 2023 is 5% higher than in 2022). After obtaining the data, the system will generate a spline curve based on year-on-year changes. The energy supply and demand dynamic prediction module will predict the energy supply and demand for this year according to the curve change trend. During the operation of the system, the energy data acquisition module will work in coordination with the performance monitoring module to automatically block low-reliability data sources based on the evaluation results of historical data quality. This can reduce the impact of poor data credibility on the analysis results of the energy demand trend analysis module. When the system checks the data quality, if it finds that the data is missing by more than 20% (such as the sensor has not sent data for 3 consecutive days), the data is significantly incorrect (such as the electricity consumption record is low at the peak electricity consumption time), and the data delay exceeds 1 hour, etc., the data source will be blocked. At the same time, after the system blocks the abnormal data, it will automatically switch to the backup data source (such as when the main weather station fails, it will automatically switch to the backup data of the meteorological bureau); After the Energy Supply and Demand Dynamic Prediction Module obtains the data transmitted by the Energy Data Acquisition Module and the Energy Demand Trend Analysis Module, it will output a baseline prediction based on the data of the Energy Demand Trend Analysis Module, and then add a correction value to the baseline prediction based on the external factor data obtained by the Energy Data Acquisition Module. In the above operations, the baseline prediction provides a stable prediction baseline based on long-term historical trends (such as periodic demand, seasonal changes), while the external factor correction value (such as real-time meteorological mutations, market price fluctuations, emergencies) is a dynamically injected real-time influencing factor. This enables the system to retain long-term patterns and respond to short-term perturbations. For example, when a cold snap strikes, the baseline prediction will give an electricity load prediction based on the normal winter demand, and the correction value will adjust the load increase through real-time temperature drop data to avoid prediction errors caused by ignoring extreme weather. In addition, by separating the two dimensions of the trend baseline and external corrections, the Performance Monitoring Module can accurately identify the sources of prediction errors. If the errors are mainly caused by deviations from the long-term trend, the Energy Demand Trend Analysis Module will be optimized. If the errors are due to lags in external factor collection, it will be fed back to the Energy Data Acquisition Module to increase the priority. This strategy enables the system to have self-diagnostic capabilities and avoid blind predictions. After the Energy Supply and Demand Dynamic Prediction Module completes the prediction, it will upload the prediction results to the Energy Balance Scheduling Decision Module. The Energy Balance Scheduling Decision Module will use the probabilistic prediction information from the Energy Supply and Demand Dynamic Prediction Module to construct multiple future supply and demand scenarios, evaluate the performance of each scheduling plan under different scenarios, and select the optimal plan based on the data feedback from the Performance Monitoring Module. After obtaining the completed plan, the Energy Balance Scheduling Decision Module will upload the plan to the Scheduling Execution Module. During the execution of the instructions, the Scheduling Execution Module will monitor the execution process in real time and feedback the execution status, equipment operation status (generator failures, decreased charging efficiency of energy storage devices), and actual energy supply and demand conditions to the Energy Balance Scheduling Decision Module. When the instructions issued by the Energy Balance Scheduling Decision Module cannot be fully executed or do not meet the execution requirements, the Scheduling Execution Module will make local adjustments according to the alternative plans or priorities provided by the Energy Balance Scheduling Decision Module. When deviating from the trend baseline, the system will decompose the prediction error into two parts, namely the trend baseline deviation and the correction value deviation. The trend baseline deviation is due to inaccurate prediction of long-term patterns (such as predicting 1000 degrees of electricity consumption per day in summer, but actually lasting 1200 degrees), and the correction value deviation is due to the failure to capture short-term sudden factors (such as a cold snap causing a surge in electricity consumption, but the system did not receive real-time temperature data). After an error occurs, the Energy Demand Trend Analysis Module will initiate self-diagnostic optimization steps. If the proportion of the trend deviation > 70%, the system will retrain the trend analysis model (for example, changing the data from the past 5 years to the most recent 3 years). If the proportion of the correction value deviation > 70%, the system will increase the data collection speed (for example, increasing the meteorological data collection frequency from once an hour to once every half hour). Through this design, the system's plan decision-making can be made more flexible, reducing the probability that the plan cannot be implemented normally.In addition, during the use of the energy balance scheduling decision-making module, it can cooperate with the performance monitoring module to conduct a review and analysis of historical scheduling decisions, identify successful scheduling patterns and lessons from failures. Through the review and analysis of historical scheduling decisions, the system can accurately identify highly beneficial scheduling patterns or inefficient decisions, solidify the successful patterns into the rule base, and avoid lessons from failures, gradually forming a more intelligent scheduling strategy. In addition, the scheduling execution module synchronizes and calibrates with external systems to achieve real-time data synchronization and status calibration with external systems such as the power grid control system and the generator control system, ensuring the effective execution of instructions. In addition, the scheduling execution module provides detailed execution logs and status data to the performance monitoring module as the basis for performance evaluation; During the use of the performance monitoring module, it will regularly initiate collaborative diagnosis tasks. The performance monitoring module combines with the energy data acquisition module and the energy supply and demand dynamic prediction module to analyze whether the prediction error stems from data acquisition delays or model biases. The performance monitoring module combines with the energy balance scheduling decision-making module and the scheduling execution module to review the differences between the scheduling instructions and the actual execution, and whether there are situations such as equipment response lags or overly idealized instructions. The diagnostic results of the performance monitoring module will be transformed into updates of the collaborative rules between modules. In addition, during the use of the energy demand trend analysis module, it can decompose the energy demand into predictable trends (such as policy changes, extreme weather) and unpredictable disturbances (such as sudden social events), analyze the sources of disturbances. When detecting unpredictable disturbances, it can actively combine with the energy supply and demand dynamic prediction module and the performance monitoring module to initiate the disturbance response mode. In this mode, the energy demand trend analysis module provides disturbance characteristics such as the scope of influence and duration assumptions. The energy supply and demand dynamic prediction module temporarily switches to the multi-hypothesis prediction mode to generate multiple potential disturbance evolution paths. The energy balance scheduling decision-making module generates a flexible scheduling plan and evaluates risks based on the multi-path prediction. Through the above operations, the system can decompose the demand and identify unpredictable disturbances, enabling the system to respond more quickly to emergencies, reduce reliance on the pre-determined plan, and the multi-hypothesis prediction mode can consider multiple potential scenarios to avoid the limitations of single prediction. The flexible scheduling plan can reasonably allocate resources according to different risk scenarios, improve resource utilization efficiency. In addition, by evaluating the risks of different evolution paths, countermeasures can be formulated in advance to reduce the negative impact of emergencies, and the decision-making based on data analysis and multi-scenario simulation can effectively reduce the error of human judgment.

[0026] It should be noted that in this article, the term "including", "containing" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device.

[0027] In this article, specific examples are used to elaborate on the principles and implementation modes of the present invention. The description of the above examples is only for helping to understand the method and its core idea of the present invention. The above is only the preferred implementation mode of the present invention. It should be noted that due to the limitation of literal expression and objectively there are infinite specific structures, for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements, embellishments or changes can be made, and the above technical features can also be combined in an appropriate way; these improvements, embellishments, changes or combinations, or directly applying the concept and technical solution of the invention to other occasions without improvement, shall all be regarded as the protection scope of the present invention.

Claims

1. A balanced scheduling system for energy management, characterized in that, The system includes: An energy data acquisition module, which is used to collect historical energy production data, meteorological data, market price data, calendar information such as holidays and special events, grid status data, and energy storage device status data in real time; An energy demand trend analysis module, which analyzes and identifies the periodic trends, seasonal variations, long-term growth, and decline trends of energy consumption based on the long-term historical data provided by the energy data acquisition module; An energy supply and demand dynamic prediction module, which is used to predict the short-cycle energy supply and demand in real time; An energy balance scheduling decision-making module, which formulates an optimal energy scheduling plan according to the prediction results provided by the energy supply and demand dynamic prediction module, as well as information such as real-time energy prices, energy storage device status, and grid restrictions; A scheduling execution module, which executes specific energy scheduling operations according to the instructions issued by the energy balance scheduling decision-making module, and controls the start, stop, and output of generators, the charge and discharge of energy storage devices, and external market transactions; A performance monitoring module, which is used to monitor the performance indicators of prediction accuracy, scheduling balance rate, cost, and revenue, and perform adaptive optimization; The performance monitoring module transmits the monitoring results to the energy supply and demand dynamic prediction module. When the prediction error exceeds the threshold, the energy supply and demand dynamic prediction module will analyze the error source. If the trend baseline deviates from the actual value, it is attributed to insufficient identification by the energy demand trend analysis module. If the correction value deviates from the actual value, it is attributed to the lag in the acquisition of external factors by the energy data acquisition module. After obtaining the error source, the energy supply and demand dynamic prediction module will feedback the attribution result to the corresponding module.

2. The balanced scheduling system for energy management according to claim 1, wherein The energy data acquisition module dynamically adjusts the data acquisition priority based on the real-time demand feedback from the energy supply and demand dynamic prediction module and the energy balance scheduling decision-making module.

3. A balanced scheduling system for energy management according to claim 1, characterized in that, The energy data acquisition module collaborates with the performance monitoring module to automatically block low-reliability data sources according to the historical data quality assessment results.

4. A balanced scheduling system for energy management according to claim 1, characterized in that, The energy demand trend analysis module decomposes the energy demand into predictable trends and unpredictable disturbances, and analyzes the sources of disturbances.

5. The balanced scheduling system for energy management according to claim 1, characterized in that, After obtaining the data transmitted by the energy data acquisition module and the energy demand trend analysis module, the energy supply and demand dynamic prediction module will output a baseline prediction based on the data of the energy demand trend analysis module, and then add a correction value to the baseline prediction based on the external factor data obtained by the energy data acquisition module.

6. The balanced scheduling system for energy management according to claim 1, wherein, The energy balance scheduling decision-making module uses the probabilistic prediction information from the energy supply and demand dynamic prediction module to construct multiple future supply and demand scenarios, and evaluates the performance of each scheduling plan under different scenarios.

7. A balanced scheduling system for energy management according to claim 1, characterized in that, During the execution of the instructions, the scheduling execution module will monitor the execution process in real time, and feedback the execution status, device operation status, and actual energy supply and demand situation to the energy balance scheduling decision-making module.

8. The balanced scheduling system for energy management according to claim 1, wherein The scheduling execution module is synchronized and calibrated with external systems to achieve real-time data synchronization and status calibration with external systems such as the grid control system and the generator control system, ensuring the effective execution of the instructions.

9. The balanced scheduling system for energy management according to claim 1, wherein The performance monitoring module periodically initiates collaborative diagnostic tasks. The performance monitoring module collaborates with the energy data acquisition module and the energy supply and demand dynamic prediction module to analyze whether the prediction error is due to data acquisition delay or model deviation. The performance monitoring module collaborates with the energy balance dispatch decision-making module and the dispatch execution module to review the differences between the dispatch instructions and the actual execution, and whether there are situations such as equipment response lags or overly idealized instructions.

10. A balancing scheduling method for a balancing scheduling system for energy management, characterized in that, Applied to a balance dispatch system for energy management according to any one of claims 1-9, the method includes: S1: The energy data acquisition module dynamically acquires multi-source data and dynamically adjusts the priority according to real-time demand feedback. For example, during extreme weather, it actively increases the acquisition frequency and accuracy of meteorological data, and at the same time automatically shields low-reliability data sources based on the data quality assessment of the performance monitoring module; S2: The collected data is decomposed by the energy demand trend analysis module into predictable trends and unpredictable disturbances. If a disturbance is detected, the multi-hypothesis prediction mode is started jointly with the energy supply and demand dynamic prediction module to generate multiple potential evolution paths, and the balance dispatch decision-making module is synchronously triggered to generate a flexible dispatch plan based on probabilistic scenario assessment; S3: The energy supply and demand dynamic prediction module outputs short-term supply and demand forecasts by combining the baseline forecast and the external factor correction value. When the performance monitoring module detects that the prediction error exceeds the threshold, it automatically traces back to the deviation of the trend analysis or data acquisition module and feeds back optimization instructions; S4: The dispatch execution module controls the generator sets, energy storage devices, and external transactions in real time according to the decision-making instructions, and synchronously calibrates the status with the power grid control system. If the execution is blocked, it dynamically adjusts according to the alternative plan and feeds back the execution log to the performance monitoring module; S5: The performance monitoring module periodically initiates multi-module collaborative diagnosis, analyzes whether the prediction error is due to data delay or model deviation, reviews the differences between the dispatch instructions and the actual execution, and converts the results into rule updates to form a closed-loop adaptive optimization.

Images