Balancing dispatching system for energy management and balancing dispatching method thereof

Abstract

The application discloses a kind of balanced scheduling system and its balanced scheduling method for energy management, it is related to energy management scheduling technical field, including energy data acquisition module, energy demand trend analysis module, energy supply and demand dynamic prediction module, energy balance scheduling decision module, scheduling execution module and performance monitoring module.The energy supply and demand dynamic prediction module of the present application generates a benchmark prediction baseline based on long-term trends after integrating the real-time external factor data of the energy data acquisition module and the long-term historical regularity of the energy demand trend analysis module, and then superimposes a dynamic correction value to respond to short-term disturbances. Meanwhile, through the error traceability mechanism of separating the trend baseline from external correction, the module performance can be optimized specifically. If the error is caused by the deviation of long-term trend, the trend analysis model needs to be calibrated. If the error is caused by the lag of external data, the priority adjustment of data acquisition module needs to be triggered to realize system self-diagnosis and closed-loop improvement of prediction accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management scheduling, in particular to a balance scheduling system for energy management and a balance scheduling method thereof. BACKGROUND

[0002] With the diversification of energy structure and the complication of power system, the energy supply and demand balance management is facing unprecedented challenges. The traditional energy scheduling system relies on static prediction models driven by historical data and fixed priority data collection mechanisms, which is difficult to adapt to dynamic disturbance factors such as meteorological mutations, market fluctuations and emergencies. The existing technology has the following limitations:

[0003] Data collection rigidity: Traditional systems mostly use fixed frequency and priority data collection strategies, which cannot dynamically adjust the data source priority according to real-time needs (such as extreme weather warnings), resulting in insufficient timeliness of key data, especially in the face of sudden disturbances, data lag is significant, affecting the accuracy of prediction and decision-making;

[0004] Single prediction model: Existing prediction methods are usually based on long-term historical trend analysis, which cannot effectively separate the influence of long-term regularity and short-term disturbance, resulting in insufficient response capability of prediction results to sudden events. For example, when a cold wave strikes or market prices fluctuate sharply, the traditional model lacks a dynamic correction mechanism, which is prone to large deviations, thereby causing scheduling errors;

[0005] Lack of flexibility in scheduling strategy: Most systems use single-scenario deterministic scheduling schemes and do not consider the diversification of future supply and demand. When unpredictable disturbances (such as equipment failure or social events) occur, the system is difficult to quickly generate alternative solutions, resulting in rigid resource allocation and even causing the risk of supply and demand imbalance;

[0006] Weak module coordination and self-optimization capability: The existing system lacks closed-loop feedback mechanism between modules, making it difficult to trace the source of prediction errors and accurately locate the problem root (such as data collection delay or model bias). In addition, the historical scheduling experience is poorly reusable, making it difficult to optimize decision rules through continuous learning, which limits the overall performance improvement of the system. SUMMARY

[0007] The purpose of the present application is to provide a balance scheduling system for energy management and a balance scheduling method thereof to solve the problems raised in the background art.

[0008] To achieve the above purpose, the present application provides the following technical solution: a balance scheduling system for energy management, the system comprising:

[0009] An energy data collection module for collecting real-time data on historical energy production, weather data, market price data, calendar information such as holidays and special events, grid state data, and energy storage device state data;

[0010] An energy demand trend analysis module for analyzing and identifying periodic trends, seasonal variations, long-term growth, and decline trends in energy consumption based on long-term historical data provided by the energy data collection module;

[0011] An energy supply and demand dynamic prediction module for real-time prediction of short-term energy supply and demand;

[0012] An energy balance scheduling decision module for developing an optimal energy scheduling plan based on prediction results provided by the energy supply and demand dynamic prediction module, as well as real-time energy prices, energy storage device states, and grid limitations;

[0013] A scheduling execution module for executing specific energy scheduling operations, such as starting and stopping generators, charging and discharging energy storage devices, and external market transactions, based on instructions from the energy balance scheduling decision module;

[0014] A performance monitoring module for monitoring performance indicators such as prediction accuracy, scheduling balance rate, cost, and revenue, and performing adaptive optimization;

[0015] The performance monitoring module transmits monitoring results to the energy supply and demand dynamic prediction module. When the prediction error exceeds a threshold (actual demand is 10% higher than prediction), the energy supply and demand dynamic prediction module analyzes the error source. If the trend baseline deviates from reality, it is attributed to insufficient identification by the energy demand trend analysis module, and if the correction value deviates from reality, it is attributed to lag in external factor collection by the energy data collection module. After identifying the error source, the energy supply and demand dynamic prediction module feeds back the attribution results to the corresponding modules.

[0016] Further, the energy data collection module dynamically adjusts data collection priorities based on real-time demand feedback from the energy supply and demand dynamic prediction module and the energy balance scheduling decision module.

[0017] Further, the energy data collection module cooperates with the performance monitoring module to automatically shield low-reliability data sources based on historical data quality assessment results.

[0018] Further, the energy demand trend analysis module decomposes energy demand into predictable trends and unpredictable disturbances, and analyzes the sources of disturbances.

[0019] Further, the energy supply and demand dynamic prediction module will output a baseline prediction based on the data from the energy demand trend analysis module, and then add a correction value based on the external factor data obtained by the energy data collection module.

[0020] Further, the energy balance scheduling decision module uses the probability prediction information from the energy supply and demand dynamic prediction module to construct multiple future supply and demand scenarios and evaluate the performance of each scheduling scheme under different scenarios.

[0021] Further, the scheduling execution module will monitor the execution process in real time and feedback the execution status, device operating status, and actual energy supply and demand situation to the energy balance scheduling decision module.

[0022] Further, the scheduling execution module synchronizes and calibrates with external systems to realize real-time data synchronization and state calibration with external systems such as power grid control systems and generator control systems, ensuring effective execution of instructions.

[0023] Further, the performance monitoring module will periodically initiate a collaborative diagnosis task. The performance monitoring module, in conjunction with the energy data collection module and the energy supply and demand dynamic prediction module, analyzes whether the prediction error is caused by data collection delay or model bias. The performance monitoring module, in conjunction with the energy balance scheduling decision module and the scheduling execution module, reviews the differences between the scheduling instructions and the actual execution, whether there is a device response lag or the instructions are too idealistic.

[0024] A balance scheduling method of a balance scheduling system for energy management, applied to a balance scheduling system for energy management, the method comprising:

[0025] S1: The energy data collection module dynamically collects multi-source data and dynamically adjusts the priority according to real-time demand feedback, such as actively increasing the frequency and accuracy of weather data collection during extreme weather, and automatically shielding low-reliability data sources based on the data quality evaluation of the performance monitoring module;

[0026] 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 energy supply and demand dynamic prediction module is started in multiple hypothesis prediction mode to generate multiple potential evolution paths, and the balance scheduling decision module is triggered to generate an elastic scheduling scheme based on probabilistic scenario evaluation;

[0027] S3: The energy supply and demand dynamic prediction module outputs short-period supply and demand prediction combining baseline prediction and external factor correction value. When the performance monitoring module detects that the prediction error exceeds the threshold, it automatically traces the bias of the trend analysis or data collection module and feeds back optimization instructions;

[0028] S4: The scheduling execution module controls the generator set, energy storage device and external transaction in real time according to the decision instruction, synchronizes and calibrates the state with the power grid control system, if the execution is blocked, the alternative scheme is dynamically adjusted and the execution log is fed back to the performance monitoring module;

[0029] S5: The performance monitoring module initiates multi-module collaborative diagnosis periodically, analyzes and predicts the error source from data delay or model deviation, reviews the difference between the scheduling instruction and the actual execution, and converts the result into rule update to form a closed-loop adaptive optimization.

[0030] The application provides a balance scheduling system and method for energy management, which has the following beneficial effects:

[0031] 1、The energy data acquisition module can dynamically optimize data acquisition priority and parameter configuration by receiving the demand feedback of the energy supply and demand dynamic prediction module and the energy balance scheduling decision module in real time, for example, when the extreme weather warning is triggered, the module will automatically improve the collection frequency and accuracy of meteorological data, through this intelligent adaptive mechanism to ensure the timeliness and fineness of key data, which can provide accurate information for subsequent prediction model to reduce prediction deviation, and actively capture the initial signal of disturbance to shorten the system response delay, thereby providing sufficient data support for multi-hypothesis prediction deduction and flexible scheduling decision, and effectively reducing the risk of scheduling failure caused by insufficient information.

[0032] 2、The energy supply and demand dynamic prediction module integrates the real-time external factor data (such as meteorological mutation and market price fluctuation) of the energy data acquisition module and the long-term historical law (such as seasonal cycle) of the energy demand trend analysis module, generates a baseline prediction baseline based on long-term trend, and then superimposes a dynamic correction value to respond to short-term disturbance, for example, when a cold wave strikes, the system superimposes a real-time temperature drop correction value on the winter baseline load prediction to accurately capture the impact of extreme weather on electricity load, and through the error traceability mechanism of separating trend baseline and external correction, the module performance can be optimized, if the error is caused by deviation of long-term trend, the trend analysis model is calibrated, if the error is caused by external data lag, the priority of the data acquisition module is adjusted, realizing system self-diagnosis and closed-loop improvement of prediction accuracy.

[0033] 3、The energy balance scheduling decision module constructs a multi-scenario deduction model based on the probability prediction of the energy supply and demand dynamic prediction module, filters the optimal scheduling scheme in combination with the evaluation data of the performance monitoring module, and pushes the scheduling scheme to the scheduling execution module. The module synchronously monitors the equipment state and supply and demand dynamics during the execution process. When the execution deviation exceeds the preset threshold, the alternative scheme is automatically called or the local dynamic adjustment is implemented according to the priority, so that the scheduling strategy is flexibly landed. Meanwhile, the performance monitoring mechanism is used to multi-dimensionally review the historical scheduling, so that the high-efficiency mode is deposited into the rule library, the low-efficiency decision path is eliminated, a closed-loop optimization system is formed through continuous iteration, and finally the spiral improvement of the scheduling strategy accuracy and intelligence is realized.

[0034] 4、The energy demand trend analysis module classifies the 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 switched to a multi-hypothesis prediction mode to generate a set of disturbance evolution paths. Meanwhile, the energy balance scheduling decision module generates an elastic resource allocation scheme based on the path risk gradient. The performance monitoring module synchronously evaluates the disturbance influence domain and duration assumption during the process. The multi-hypothesis prediction covers potential scenarios, avoids the limitations of single prediction, dynamically allocates resources according to the risk gradient, improves the response speed and utilization efficiency, and continuously optimizes the rule library through the disturbance data, thereby gradually reducing the human intervention deviation. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is a working flow diagram of the balance scheduling system for energy management of the application. DETAILED DESCRIPTION

[0036] Please refer to Figure 1 The application provides a technical scheme: a balance scheduling system for energy management and a balance scheduling method thereof. The system comprises:

[0037] An energy data acquisition module is used to acquire historical energy production data, meteorological data, market price data, calendar information such as holidays and special events, power grid state data, and energy storage device state data in real time.

[0038] An energy demand trend analysis module is used to analyze and identify the periodic trend, seasonal change, long-term growth, and downward trend of energy consumption based on long-term historical data provided by the energy data acquisition module.

[0039] An energy supply and demand dynamic prediction module is used to predict short-period energy supply and demand in real time.

[0040] An energy balance scheduling decision module is used to formulate an optimal energy scheduling scheme according to the prediction results provided by the energy supply and demand dynamic prediction module, as well as real-time energy prices, energy storage device states, and power grid limitations.

[0041] A dispatch execution module, according to the instruction issued by the energy balance scheduling decision module, executes specific energy scheduling operation, controls the start-stop and output of the generator, the charging and discharging of the energy storage device and the external market transaction.

[0042] A performance monitoring module, used for monitoring the performance indicators of prediction accuracy, scheduling balance rate, cost and benefit, and performing adaptive optimization.

[0043] A human-computer interaction module, used for showing the system state, prediction result, scheduling scheme and performance indicator to the user.

[0044] The specific operation is as follows: after the energy data acquisition module completes the real-time collection of historical energy production data, meteorological data, market price data, calendar information such as holidays and special events, power grid state data, and energy storage device state data, it will pass the above data to the energy demand trend analysis module. During use, the energy data acquisition module can dynamically adjust the data collection priority based on the real-time demand feedback of 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 collection frequency and accuracy of meteorological data. By dynamically enhancing the collection 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, thereby significantly reducing the prediction deviation caused by data lag or roughness. In addition, dynamically adjusting the data collection priority enables the system to have active response capability. When the system senses an extreme weather warning, the energy data acquisition module can immediately focus on key data sources to ensure that critical signals are captured in the early stages of disturbance, providing sufficient information support for the system's subsequent multi-hypothesis prediction and flexible scheduling scheme. This can significantly reduce the delay from sensing to responding and reduce the risk of scheduling errors due to insufficient data. The energy demand trend analysis module can use machine learning algorithms to analyze and predict the data passed by the energy demand trend analysis module in real time, and train the model using a large amount of historical data to analyze and identify the periodic trends, seasonal changes, long-term growth, and declining trends of energy consumption. First, the energy demand trend analysis module collects daily electricity consumption data for the past five years. After collecting the data, the energy demand trend analysis module uses computer programs to label the rules according to three types of periodic trends, seasonal changes, and long-term growth. Periodic trends are used to mark recurring patterns (e.g., a 20% increase in electricity consumption in July compared to June), seasonal changes are used to mark changes in electricity consumption due to seasons (e.g., a 25% increase in summer electricity consumption compared to winter), and long-term growth marks projects that have been growing over the years (e.g., a 5% increase in electricity consumption in 2023 compared to 2022). After obtaining the data, the system generates a spline curve based on the year-to-year changes, and the energy supply and demand dynamic prediction module predicts the energy supply and demand based on the curve trend. During operation, the energy data acquisition module works in coordination with the performance monitoring module to automatically shield low-reliability data sources based on the evaluation results of historical data quality. This can reduce the impact of low data reliability on the analysis results of the energy demand trend analysis module. When the system checks data quality, if it finds that the data is missing more than 20% (e.g., a sensor has not sent data for 3 consecutive days), the data is obviously incorrect (e.g., the electricity consumption is recorded low during the peak electricity consumption period), and the data is delayed for more than 1 hour, etc., the data source will be shielded. At the same time, after shielding the abnormal data, the system automatically switches to a backup data source (e.g., when the main weather station fails, it automatically switches to the backup data of the weather bureau);

[0045] 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 operation, the baseline prediction provides a stable prediction baseline based on long-term historical trends (such as periodic demand and seasonal changes), while the external factor correction value (such as real-time weather anomalies, market price fluctuations, and unexpected events) dynamically injects real-time influencing factors, which makes the system retain long-term regularity and respond to short-term disturbances. For example, when a cold wave strikes, the baseline prediction will give a power load prediction based on the regular winter demand, and the correction value will adjust the load increase through real-time temperature drop data to avoid prediction deviation caused by ignoring extreme weather. In addition, by separating the trend baseline and external correction into two dimensions, the performance monitoring module can accurately identify the source of prediction error. If the error is mainly caused by deviation from long-term trends, the energy demand trend analysis module will be optimized. If the error is caused by lag in external factor acquisition, the energy data acquisition module will be fed back to improve the priority. This strategy enables the system to have self-diagnostic capabilities, avoiding blind predictions. After completing the prediction, the energy supply and demand dynamic prediction module uploads the prediction results to the energy balance dispatching decision module. The energy balance dispatching decision module uses the probability prediction information from the energy supply and demand dynamic prediction module to construct multiple future supply and demand scenarios, evaluates the performance of each dispatching scheme under different scenarios, and selects the optimal scheme according to the data feedback from the performance monitoring module. After obtaining the scheme, the energy balance dispatching decision module uploads the scheme to the dispatching execution module. The dispatching execution module monitors the execution process in real time and feeds back the execution status, device operating status (generator failure, energy storage device charging efficiency decline), and actual energy supply and demand situation to the energy balance dispatching decision module. When the instructions issued by the energy balance dispatching decision module cannot be fully executed or do not meet the execution requirements, the dispatching execution module will make local adjustments based on the alternative schemes or priorities provided by the energy balance dispatching decision module. When the trend baseline is deviated, the system will decompose the prediction error into two parts: trend baseline deviation and correction value deviation. The trend baseline deviation is the inaccuracy of long-term regularity prediction (e.g., predicting 1000 degrees of daily electricity consumption in summer, but the actual consumption is 1200 degrees), and the correction value deviation is the failure to capture short-term unexpected factors (e.g., a sudden increase in electricity consumption due to a cold wave, but the system did not receive real-time temperature data). After the error occurs, the energy demand trend analysis module will start the self-diagnostic optimization step. If the trend deviation ratio is > 70%, the system will retrain the trend analysis model (e.g., changing the data from the past 5 years to the last 3 years). If the correction value deviation ratio is > 70%, the system will increase the data acquisition speed (e.g., increasing the weather data acquisition frequency from once an hour to once every half hour). Through this design, the system's scheme decision can be flexible, reducing the probability that the scheme cannot be implemented normally.In addition, during use, the energy balance scheduling decision module can collaborate with the performance monitoring module to review and analyze historical scheduling decisions, identify successful scheduling patterns and lessons from failure. Through the review and analysis of historical scheduling decisions, the system will accurately identify high-efficiency scheduling patterns or inefficient decisions, and solidify successful patterns into a rule base, while avoiding failure lessons and 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 generator control system to ensure the effective execution of instructions. In addition, the scheduling execution module will provide detailed execution logs and status data to the performance monitoring module as the basis for performance evaluation;

[0046] During the use of the performance monitoring module, it will regularly initiate collaborative diagnosis tasks. The performance monitoring module will work with the energy data acquisition module and the energy supply and demand dynamic prediction module to analyze whether the prediction error is caused by data acquisition delay or model deviation. The performance monitoring module will work with the energy balance scheduling decision module and the scheduling execution module to review the difference between the scheduling instructions and the actual execution, and whether there is a lag in equipment response or the instructions are too ideal. The diagnostic results of the performance monitoring module will be converted into collaborative rule updates between modules. In addition, the energy demand trend analysis module can decompose energy demand into predictable trends (such as policy changes, extreme weather) and unpredictable disturbances (such as sudden social events) during use, analyze the source of the disturbance, and when an unpredictable disturbance is detected, it can actively work with the energy supply and demand dynamic prediction module and the performance monitoring module to start the disturbance response mode. In this mode, In this formula, the energy demand trend analysis module provides disturbance characteristics such as the impact range 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 module generates a flexible scheduling plan and evaluates risks based on the multi-path prediction. Through the above operations, the system can decompose demand and identify unpredictable disturbances, which enables the system to respond to emergencies faster and reduce dependence on predetermined plans. The multi-hypothesis prediction model can consider multiple potential scenarios and avoid the limitations of a single prediction. The flexible scheduling plan can reasonably allocate resources according to different risk scenarios and improve resource utilization efficiency. In addition, by evaluating the risks of different evolution paths, response measures can be formulated in advance to reduce the negative impact of emergencies. Decisions based on data analysis and multi-scenario simulation can effectively reduce errors in human judgment.

[0047] It should be noted that, in this article, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements that are inherent to such process, method, article or apparatus.

[0048] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea. The above description is only the preferred implementation manner of the present application, and it should be noted that, due to the limited expression of the text, there are objectively infinite specific structures, and for ordinary skilled persons in the technical field, some improvements, refinements or changes can be made without departing from the principles of the present application, and the above technical features can also be combined in an appropriate manner; the improvements, refinements, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, shall be regarded as the protection scope of the present application.

Claims

1. A balancing scheduling system for energy management, characterized in that: The system comprises: Energy data collection module, used for real-time collection of historical energy production data, meteorological data, market price data, holidays, special event calendar information, grid status data, and energy storage equipment status data; Energy demand trend analysis module, which analyzes and identifies cyclical trends, seasonal changes, long-term growth and decline trends in energy consumption based on the long-term historical data provided by the energy data acquisition module; The energy supply and demand dynamic forecasting module is used to forecast short-term energy supply and demand in real time. After obtaining data from the energy data acquisition module and the energy demand trend analysis module, the energy supply and demand dynamic forecasting module will output a baseline forecast based on the data from the energy demand trend analysis module. It will then add correction values ​​to the baseline forecast based on the external factor data obtained by the energy data acquisition module. In the above operations, the baseline forecast provides a stable forecast baseline based on long-term historical trends, while the external factor correction values ​​are dynamically injected into the real-time influencing factors, which enables the system to retain long-term patterns while responding to short-term disturbances. The energy balance scheduling decision module formulates the optimal energy scheduling plan based on the forecast results provided by the energy supply and demand dynamic forecast module, as well as real-time energy prices, energy storage equipment status, and grid restriction information; The dispatch execution module executes specific energy dispatch operations according to the instructions issued by the energy balance dispatch decision module, controlling the start and stop and output of generators, the charging and discharging of energy storage devices, and external market transactions; Performance monitoring module, used to monitor performance indicators such as 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 source of the error. If the trend baseline deviates from the actual, it is attributed to insufficient recognition of the energy demand trend analysis module. If the correction value deviates from the actual, it is attributed to the external factor collection lag of the energy data acquisition module. After obtaining the source of the error, the energy supply and demand dynamic prediction module will feed back the attribution result to the corresponding module.

2. The energy management balancing and scheduling system according to claim 1, characterized in that: 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 module.

3. The energy management balancing and scheduling system according to claim 1, characterized in that: The energy data acquisition module cooperates with the performance monitoring module to automatically shield low-reliability data sources based on historical data quality assessment results.

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

5. The energy management balancing and scheduling system 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 energy management balancing and scheduling system according to claim 1, characterized in that: 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 evaluate the performance of each scheduling scheme under different scenarios.

7. The energy management balancing and scheduling system according to claim 1, characterized in that: During the execution of instructions, the scheduling execution module will monitor the execution process in real time and provide feedback to the energy balance scheduling decision module on the execution status, equipment operation status and actual energy supply and demand.

8. The energy management balancing and scheduling system according to claim 1, characterized in that: The dispatch execution module is synchronized and calibrated with the external system to achieve real-time data synchronization and status calibration with the power grid control system and the generator control system external system to ensure the effective execution of the instructions.

9. The energy management balancing and scheduling system according to claim 1, characterized in that: The performance monitoring module will regularly initiate collaborative diagnostic tasks. The performance monitoring module will work with the energy data acquisition module and the energy supply and demand dynamic prediction module to analyze whether the prediction error is caused by data acquisition delay or model deviation. The performance monitoring module will work with the energy balance scheduling decision module and the scheduling execution module to review the differences between scheduling instructions and actual execution, and whether there is a lag in equipment response or the instructions are too idealistic.

10. A balancing scheduling method for a balancing scheduling system for energy management, characterized in that: The method applied to a balancing scheduling system for energy management according to any one of claims 1 to 9 comprises: S1: The energy data acquisition module dynamically collects data from multiple sources and adjusts priorities based on real-time demand feedback. It proactively increases the frequency and accuracy of meteorological data collection during extreme weather conditions and automatically blocks low-reliability data sources based on data quality assessments from 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 energy supply and demand dynamic prediction module will start the multi-hypothesis prediction mode to generate multiple potential evolution paths, and simultaneously trigger the balance scheduling decision module to generate a flexible scheduling plan based on probabilistic scenario assessment; S3: The energy supply and demand dynamic forecasting module combines the baseline forecast with the external factor correction value to output a short-term supply and demand forecast. When the performance monitoring module detects that the forecast error exceeds the threshold, it automatically traces the deviation to the trend analysis or data acquisition module and provides feedback on optimization instructions. S4: The dispatch execution module controls the generator sets, energy storage equipment, and external transactions in real time according to the decision instructions, synchronizing the status with the grid control system. If execution is blocked, it dynamically adjusts according to the alternative plan and feeds the execution log to the performance monitoring module. S5: The performance monitoring module regularly initiates multi-module collaborative diagnosis to analyze prediction errors caused by data delays or model deviations, review the differences between scheduling instructions and actual execution, and convert the results into rule updates to form a closed-loop adaptive optimization.

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