Power generation scheduling optimization method and system based on power generation capacity prediction

Abstract

The invention discloses a power generation scheduling optimization method and system based on power generation capacity prediction, and solves the problems of low power generation strategy formulation accuracy and low demand change response capability in the prior art, and the method comprises the steps: constructing a power generation capacity prediction model, defining key influence factors, and constructing a power generation capacity prediction model; dynamically adjusting the weight of each influence factor in different periods of power generation; calculating and analyzing the variation trend of the power generation cost along with time, analyzing and predicting the future power generation cost trend by using a time sequence, and establishing a cost saving strategy; combining the assessment indexes with a compensation mechanism, establishing an excitation model, and adjusting excitation model parameters according to the influence of equipment defects on the power generation capacity; and formulating a combined power generation scheduling strategy, and dynamically adjusting and optimizing the power generation scheduling strategy in combination with different operation scenes according to the execution effect. And by enhancing an excitation mechanism, the flexibility and response speed of a power system are improved, and safe and stable operation of a power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatch automation, and in particular to a power generation dispatch optimization method and system based on power generation capacity prediction. Background Art

[0002] ‌Generation dispatch‌ refers to the process of adjusting the operating status of generators in the power system to ensure that the system power balance, system frequency and interconnection line exchange power remain at the specified value or range. However, the existing generation dispatch formulation mainly displays the power generation capacity in the form of pure numbers and reports, and does not adopt more intuitive visualization methods. In addition, the calculation of the generation dispatch strategy is based on estimates and lacks the support of actual operating data. In this case, the generation dispatch strategy may not be able to effectively cope with the fluctuation of power generation capacity, especially in the context of the increase in the proportion of renewable energy power generation. The randomness and volatility of power generation capacity require that the generation dispatch strategy must be more flexible and accurate. In terms of auxiliary decision-making, although certain generation dispatch strategies and dispatch plans are provided, there is a lack of monitoring and analysis functions for real-time operating data, and it is impossible to respond to potential problems in a timely manner, and there is no effective mechanism to evaluate the actual effect of the strategy. In terms of power generation capacity evaluation, the power generation capacity is mainly evaluated through static data reports, lacking the function of predicting the trend of future power generation capacity changes, and lacking the ability to simulate and deduce power generation capacity. Summary of the invention

[0003] The purpose of the present invention is to solve the problems of low accuracy in power generation scheduling and low responsiveness to demand changes in the prior art, and to provide a power generation scheduling optimization method and system based on power generation capacity prediction, which supports the prediction and planning of future power generation capacity, so that the formulation of power generation scheduling no longer relies on estimation, but is based on more scientific data analysis, thereby improving the accuracy and scientificity of power generation scheduling, and improving the flexibility and response speed of the power system by enhancing the incentive mechanism, ensuring the safe and stable operation of the power grid, and improving the comprehensive management capabilities of the power system.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A method for optimizing power generation scheduling based on power generation capacity prediction, characterized in that it comprises the following steps: Build a power generation capacity prediction model, define key influencing factors, and dynamically adjust the weights of each influencing factor at different power generation periods; Calculate and analyze the changing trend of power generation cost over time, use time series analysis to predict future power generation cost trends, and establish cost saving strategies; Combine the assessment indicators with the compensation mechanism to establish an incentive model, and adjust the incentive model parameters according to the impact of equipment defects on power generation capacity; Formulate a combined power generation dispatching strategy, and dynamically adjust and optimize the power generation dispatching strategy based on its execution effect and in combination with different operating scenarios.

[0005] The power generation dispatch optimization method based on power generation capacity prediction provided by the present invention constructs a power generation capacity prediction model and simulates the power generation performance under different conditions and influencing factor configurations, so that the formulation of the power generation dispatch strategy no longer relies on estimation, thereby improving the accuracy and scientificity of the power generation dispatch strategy. By calculating and analyzing various costs in the power generation process, controlling and reducing costs by optimizing the power generation dispatch strategy and improving equipment efficiency, the overall economic benefits are improved; in addition, through continuous post-evaluation analysis, the strategies and methods for power generation capacity management can be continuously improved and optimized to ensure that the strategies can be continuously improved and optimized, and the utilization efficiency of power generation resources and the reliability of power supply can be improved. By quantifying the effect of the incentive mechanism, the enthusiasm of power plants to participate in auxiliary services is stimulated, the flexibility and response speed of the power system are improved, the safe and stable operation of the power grid is ensured, and the comprehensive management capabilities of the power system are enhanced.

[0006] Preferably, in the early stage of the execution of the power generation dispatching strategy, the best power generation combination strategy is recommended according to the power generation scenario; in the middle stage of the execution of the power generation dispatching strategy, the operating data of the power generation equipment is monitored in real time. When it is detected that the execution result of the power generation dispatching strategy deviates from the expected target, the variance analysis method is used to quantify the degree of deviation. If the deviation exceeds the preset first threshold, the power generation dispatching strategy adjustment mechanism is triggered to provide real-time power generation dispatching strategy adjustment suggestions; in the late stage of the execution of the power generation dispatching strategy, the completed power generation equipment operating data and results are automatically analyzed, and the maximum and minimum output ranges of the power generation equipment under different conditions are calculated.

[0007] Preferably, the method of recommending the best power generation combination strategy according to the power generation scenario includes: presetting a combined power generation scheduling strategy and performing simulation deduction to combine the optimal power generation scheduling strategy according to different scenarios; evaluating whether the power generation capacity of the current power generation scheduling strategy meets the electricity demand of the designated area, and if there is a power supply gap, evaluating the feasibility of external power resources, and determining whether to recommend introducing external power to maintain the current regional power supply and demand balance.

[0008] Preferably, real-time operation data and principal component analysis are used to evaluate the impact of various influencing factors on power generation capacity and the mutual impact of various influencing factors, dynamically adjust the weight of each influencing factor in different power generation periods, and generate a report on the impact of influencing factors on power generation capacity in different time periods and weight changes.

[0009] Preferably, when constructing a power generation capacity prediction model, a cross-validation method is used to compare the real-time operating data and the prediction results of the power generation prediction model. If the difference between the real-time operating data and the prediction results is greater than a preset second threshold, the power generation prediction model parameters are automatically adjusted, and the power generation prediction model is periodically backtested using historical operating data.

[0010] As a preferred method, based on interval data, genetic algorithms are used to propose improvement suggestions for power generation scheduling strategies; when real-time operation data is missing, historical operation data and machine learning algorithms are used to generate simulation data to fill the missing real-time operation data.

[0011] A power generation dispatch optimization system based on power generation capacity prediction, comprising: Multi-dimensional and multi-level power generation capacity management module, which establishes a power generation capacity model to simulate power generation performance under different conditions and configurations of power generation influencing factors; The power generation capacity multi-level system auxiliary decision-making module realizes the formulation and modification of power generation scheduling strategies by establishing power generation scheduling strategies and scheduling plans; The multi-dimensional and multi-level power generation capacity assessment module comprehensively assesses power generation capacity through power generation capacity detection, analyzes historical data and trends, and predicts future power generation capacity change trends.

[0012] Preferably, the multi-dimensional and multi-level power generation capacity management module includes: The power generation capacity model management module uses power generation equipment and historical data to establish a power generation capacity prediction model, compares the real-time data during the operation of the power generation equipment with the prediction results of the power generation capacity prediction model, and automatically adjusts the key parameters in the power generation capacity prediction model based on the comparison results; The power generation influencing factor management module determines the key influencing factors, calculates the weight of each influencing factor based on real-time monitoring data, uses the principal component analysis method to evaluate the contribution of each influencing factor to power generation capacity, and automatically adjusts the weight of the influencing factors; The incentive model management module builds an incentive model based on the supplementary algorithm, supplements the performance of the power generation vehicle and conducts rewards and penalties, and generates grid service calculation fees.

[0013] Preferably, the power generation capacity multi-level system auxiliary decision module includes: The multi-level dispatch coordination decision management module generates a variety of dispatch strategies based on real-time and historical data, monitors load changes in real time, and provides dispatch strategy adjustment suggestions; Risk assessment module: establishes a risk assessment model based on natural gas supply uncertainty, assesses risk levels in real time, and formulates corresponding emergency response strategies based on the assessment results; The pre-optimization decision-making module analyzes future load demand and power generation capacity based on historical data and real-time information, generates a load curve, recommends multiple power generation scheduling strategies based on the load curve, and selects the best solution; The in-process tracking and analysis module continuously monitors key indicators in the execution of power generation scheduling strategies, and provides dynamic adjustment suggestions when it is detected that the deviation between the execution of power generation scheduling strategies and expectations is greater than the threshold.

[0014] Preferably, the multi-dimensional and multi-level power generation capacity evaluation module includes: The power generation capacity prediction trend module builds a power generation capacity trend prediction model based on historical data and regularly updates the prediction model parameters, and uses the machine learning algorithm LSTM to perform load prediction and power generation capacity prediction; The power generation capacity simulation module simulates the change of power generation capacity by using the power generation capacity trend prediction model according to the scenario parameters set by the user; The power generation scheduling optimization module based on power generation capacity prediction collects all relevant data during the simulation and deduction using the power generation capacity prediction model, compares the actual results with the predicted targets of the power generation scheduling strategy, analyzes the deviations, and evaluates the effectiveness of the power generation scheduling strategy.

[0015] Therefore, the present invention has the following beneficial effects: 1. By building a power generation capacity prediction model and simulating the performance under different conditions and influencing factor configurations, it supports the prediction and planning of future power generation capacity, so that the formulation of power generation scheduling no longer relies on estimation, but is based on more scientific data analysis, thereby improving the accuracy and scientificity of power generation scheduling strategy formulation.

[0016] 2. By calculating and analyzing various costs in the power generation process, controlling and reducing costs by optimizing power generation scheduling strategies and improving equipment efficiency, the overall economic benefits can be improved. In addition, through continuous post-evaluation analysis, the strategies and methods for power generation capacity management can be continuously improved and optimized to ensure that scheduling strategies can be continuously improved and optimized, thereby improving the utilization efficiency of power generation resources and power supply reliability.

[0017] 3. By quantifying the effect of the incentive mechanism and collecting data on power plant incentives, we can stimulate the enthusiasm of power plants to participate in auxiliary services, improve the flexibility and response speed of the power system, ensure the safe and stable operation of the power grid, and improve the comprehensive management capabilities of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the steps of the power generation scheduling optimization method based on power generation capacity prediction in the present invention.

[0019] Figure 2 This is a workflow diagram for optimizing power generation scheduling based on power generation capacity prediction in the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments: Embodiment 1: This embodiment provides a method for optimizing power generation scheduling based on power generation capacity prediction. Figure 1As shown in the figure, the operation process is as follows: Step 1, construct a power generation capacity prediction model, define key influencing factors, and dynamically adjust the weights of each influencing factor in different power generation periods; Step 2, calculate and analyze the changing trend of power generation cost over time, use time series analysis to predict future power generation cost trends, and establish a cost saving strategy; Step 3, combine the assessment indicators with the compensation mechanism, establish an incentive model, and adjust the incentive model parameters according to the impact of equipment defects on power generation capacity; Step 4, formulate a combined power generation scheduling strategy, and dynamically adjust and optimize the power generation scheduling strategy according to the execution effect of the power generation scheduling strategy and combined with different operating scenarios.

[0021] The power generation scheduling optimization method based on power generation capacity prediction provided in this embodiment mainly addresses the following problems: (1) The formulation of power generation plans lacks scientific basis: In the existing power generation scheduling strategy formulation process, the power generation capacity is mainly displayed in the form of pure numbers and reports, which leads to the lack of intuitiveness and visualization of the power generation capacity. The calculation of power generation scheduling strategies is often based on estimates rather than actual data support, which makes the formulation of power generation scheduling strategies lack scientificity and accuracy. For example, when calculating power generation scheduling strategies, the scheduling planning profession lacks real-time data as a basis for calculation, making it difficult to accurately match the actual power generation capacity. In this case, the power generation scheduling strategy may not be able to effectively respond to the fluctuation of power generation capacity, especially in the context of the increase in the proportion of new energy power generation. The randomness and volatility of power generation capacity require that the power generation scheduling strategy must be more flexible and precise.

[0022] (2) Insufficient management of non-coordinated data: In the existing technical solutions, the management of non-coordinated data is relatively weak, and the multi-cycle regulation potential of non-coordinated energy is not fully tapped. The lack of an effective management and control mechanism for non-coordinated data has resulted in the failure to optimize the use of power generation resources. Due to the lack of comprehensive management and control of non-coordinated data, the power system is unable to achieve efficient allocation of power generation resources when faced with the volatility and uncertainty brought about by the access of new energy, thus affecting the stability and reliability of the power system.

[0023] (3) Inadequate cost control and incentive mechanisms In terms of power generation cost analysis and management, existing solutions fail to fully utilize data for cost control, resulting in limited room for power generation cost optimization. In addition, in the design of incentive mechanisms, there is a lack of quantitative analysis of the effectiveness of incentive mechanisms, which makes power plants less motivated to participate in auxiliary services, affecting the improvement of power system flexibility and response speed. Due to the lack of refined cost management and effective incentive mechanisms, the economy and flexibility of power generation plans are restricted.

[0024] (4) Inadequate management of generator unit defects: In the existing scheme, the management of generator unit defects is not detailed enough, and the impact of unit defects on power generation capacity is not fully analyzed. This results in the failure to effectively control the loss of power generation capacity when power generation equipment fails or unexpectedly shuts down, thus affecting the execution of power generation plans. Due to the lack of comprehensive management of unit defects, the operating efficiency and reliability of the power system are negatively affected.

[0025] (5) Insufficient real-time monitoring and evaluation of power generation capacity: In terms of real-time monitoring and evaluation of power generation capacity, existing technical solutions fail to provide a mechanism for real-time monitoring of the output capacity of power generation equipment, nor can they comprehensively evaluate the bandwidth of power generation capacity. This makes the power system lack effective data support when responding to changes in demand, making it difficult to continuously improve and optimize the strategy for power generation capacity management. Due to the lack of real-time monitoring and comprehensive evaluation, the formulation of power generation capacity management strategies lacks sufficient data support, which affects the flexibility and response speed of the power system.

[0026] According to the above, the present embodiment provides a method for optimizing power generation scheduling based on power generation capacity prediction. First, by constructing a power generation capacity model, key input factors such as weather and load are defined, and then real-time power generation-related operating data and external environmental parameters are obtained, including weather data, equipment status information, and grid operation data. When real-time data (including real-time operating data and external environmental parameters) is missing, historical data and machine learning algorithms are used to generate simulated data to fill in the data gaps, thereby maintaining the continuous operation of the prediction model, and combining cross-validation and backtesting techniques to test the prediction accuracy of the power generation capacity prediction model.

[0027] At the same time, various factors affecting power generation, including climate, fuel supply and equipment status, are managed in detail. Through real-time monitoring and principal component analysis methods, the impact of each factor on power generation capacity is evaluated, and its weight is dynamically adjusted to generate a report on the impact of power generation factors on power generation capacity in different time periods and weight changes, ensuring that the power generation capacity prediction model can accurately reflect the current power generation situation and provide data support for power generation decisions.

[0028] In addition, the power generation scheduling optimization method provided in this embodiment also takes into account the power generation cost, classifies the power generation cost in detail, calculates and analyzes the cost change trend over time, and uses time series analysis to predict future cost trends, and proposes cost saving strategies based on cost-benefit analysis.

[0029] When monitoring the operating status of power generation equipment in real time, equipment defects are recorded and analyzed, and the power generation capacity prediction model is adjusted based on the performance impact assessment results, including possible power generation reduction, cost increase and impact on grid stability. In addition, measures such as starting standby units, adjusting power generation scheduling strategies or using external electricity are recommended for different situations. In addition, the incentive model is adjusted based on the impact of defects on power generation capacity to ensure that rewards or penalties for power plants are fair and effective. The introduction of the "two detailed rules" assessment and compensation algorithm as an incentive model management to evaluate power plant performance can effectively encourage power plants to improve their operating processes. The two detailed rules refer to the implementation rules for the grid-connected operation management of power generation projects and the implementation rules for the management of auxiliary services for power generation projects.

[0030] For different power generation scenarios (such as weather changes, market demand fluctuations, etc.), users are provided with a free combination of power generation dispatching strategy entry. Through real-time monitoring, dynamic adjustment, intelligent analysis and multi-level collaborative decision-making, the utilization efficiency of power generation resources and power supply reliability are improved to ensure the economic, environmental and stable operation of the power system.

[0031] The technical scheme and technical effects of the present invention will be further described below through specific examples. The following examples are explanations of the present invention and the present invention is not limited to the following examples.

[0032] Specifically, Figure 1 As shown, a power generation scheduling optimization method based on power generation capacity prediction includes the following steps: Step 1: Build a power generation capacity prediction model, define key influencing factors, and dynamically adjust the weights of each influencing factor in different power generation periods.

[0033] Establish and maintain power generation capacity prediction models to dynamically predict power generation capacity.

[0034] The power generation capacity prediction model process is as follows: Use power generation equipment and historical data to build a power generation capacity model, define power generation influencing factors (such as weather and load); regularly collect real-time data during the operation of power generation equipment, including equipment status and external environment, compare real-time data with the prediction results of the power generation capacity prediction model, and calculate the deviation. According to the size of the deviation, automatically adjust the key parameters in the power generation capacity prediction model (such as equipment performance coefficient and load sensitivity). If the deviation exceeds the set threshold, generate an alarm record. At the same time, set a fixed time to verify the power generation capacity prediction model, and use historical data to backtest the model to ensure its accuracy.

[0035] Furthermore, according to the historical influence and real-time changes of each influencing factor, its weight in the model is adjusted to ensure the accuracy of the prediction model.

[0036] Step 2: Calculate and analyze the changing trend of power generation cost over time, use time series analysis to predict future power generation cost trends, and establish cost-saving strategies.

[0037] Calculate and analyze various costs in the power generation process, including fuel costs, operation and maintenance costs, environmental compliance costs, etc., calculate the proportion of various costs and their changing trends over time, and provide strategies such as fuel procurement optimization suggestions based on the cost analysis results to help power plants reduce operating costs.

[0038] Step 3: Combine the assessment indicators with the compensation mechanism to establish an incentive model, and adjust the incentive model parameters according to the impact of equipment defects on power generation capacity.

[0039] Combine the assessment indicators with the compensation mechanism to generate a corresponding incentive model. Calculate the compensation or penalty that the power plant should receive based on its actual performance (such as power generation and response time), and generate a settlement cost report.

[0040] Step 4: Formulate a combined power generation dispatching strategy, and dynamically adjust and optimize the power generation dispatching strategy based on the execution effect of the power generation dispatching strategy and combined with different operating scenarios.

[0041] Specific: In the early stage of the power generation dispatching strategy execution, the best power generation dispatching strategy is recommended according to the power generation scenario; in the middle stage of the power generation dispatching strategy execution, the power generation equipment operation data is detected and analyzed in real time. When the power generation dispatching strategy execution result is detected to be different from the expected target, the variance analysis method is used to quantify the degree of such deviation. Once the deviation exceeds the preset threshold, the adjustment mechanism is triggered to provide real-time power generation dispatching strategy adjustment suggestions; after the power generation dispatching strategy is executed, the complete operation data and results are automatically analyzed, and the maximum and minimum output ranges (bandwidth) of the equipment under different conditions are calculated. Based on the bandwidth data, the genetic algorithm is used to propose power generation dispatching strategy improvement suggestions and the strategy execution effect is displayed through a visual interface.

[0042] At the same time, after the combined power generation dispatch strategy is preset, simulation will be performed to simulate the performance of power generation capacity, and the simulation data will be displayed in a visual interface to help users combine the optimal power generation dispatch strategy according to different scenarios. Then it will be evaluated whether the power generation capacity of the current power generation dispatch strategy meets the power demand of the specified area. If the analysis results show that there is a power supply gap, the availability of external power resources will be evaluated to determine whether it is recommended to introduce external power to maintain the current regional power supply and demand balance.

[0043] The power generation scheduling optimization method based on power generation capacity prediction provided in this embodiment has the following advantages: (1) The cross-validation power generation capacity prediction model based on the control cloud ensures the power generation capacity prediction and improves the accuracy of the power generation capacity prediction.

[0044] Based on the control cloud platform, the system obtains power generation-related operation data and external environmental parameters in real time, including meteorological data, equipment status information, and power grid operation data. When real-time data is missing, the system can use historical data and machine learning algorithms to generate simulated data to fill in the data gaps, thereby maintaining the continuous operation of the prediction model. Then, the real-time data and model prediction results are compared by cross-validation, and the model parameters are automatically adjusted after the deviation is identified. Finally, the model is backtested regularly using historical data to ensure the accuracy of the power generation capacity model prediction.

[0045] (2) Evaluation of the impact of power generation factors based on principal component analysis optimization to improve overall operating efficiency and reliability.

[0046] The principal component analysis method is used to integrate real-time / historical power generation data, real-time environmental data and equipment status to evaluate the impact of power generation factors on power generation capacity and the mutual impact between power generation factors. The system can dynamically adjust the weights of power generation factors in different periods and generate detailed evaluation reports on the impact on power generation capacity, providing strong data support for power generation decisions, thereby helping power plants optimize power generation scheduling strategies and improve overall operating efficiency and reliability.

[0047] (3) Incentive models and strategies based on the “two detailed rules” assessment and compensation algorithms can improve the utilization rate of power generation resources and the reliability of power supply.

[0048] Using the assessment and compensation algorithms of the "two rules", we build and manage incentive models based on different assessment and compensation algorithms. For different power generation scenarios, we automatically recommend the best power generation combination strategy in the early stage. In the mid-term of strategy execution, we detect and analyze the operating data of power generation equipment in real time. When it is detected that there is a deviation between the strategy execution result and the expected target, we use variance analysis to quantify the degree of this deviation. Once the deviation exceeds the preset threshold, the adjustment mechanism is triggered to provide real-time strategy adjustment suggestions. After the strategy is executed, we automatically analyze the complete operating data and results, and calculate the maximum and minimum output range (bandwidth) of the equipment under different conditions. We use bandwidth data and combine the optimization capabilities of genetic algorithms to propose suggestions for improving the scheduling strategy. We review and evaluate the power generation scheduling strategy to ensure that the strategy can be continuously improved and optimized, thereby improving the utilization rate of power generation resources and the reliability of power supply.

[0049] Embodiment 2: This embodiment provides a power generation scheduling optimization system based on power generation capacity prediction, which is used to implement the power generation scheduling optimization method based on power generation capacity prediction in embodiment 1 and solve the following technical problems: (1) The display of power generation capacity is not intuitive enough: The existing display method of power generation capacity mainly relies on pure numbers and report presentations, lacking intuitive and visual means to view the overall power generation situation.

[0050] (2) Lack of actual data in power generation plan calculation: The scheduling and planning profession currently relies mainly on estimates when formulating power generation plans, lacking the support of actual operating data, which makes it difficult to guarantee the accuracy of power generation plans.

[0051] (3) Lack of effective management and control of non-coordinated data: Insufficient management of non-coordinated data has resulted in failure to fully tap the multi-cycle regulation potential of non-coordinated energy, resulting in failure to optimize the use of power generation resources.

[0052] Specifically, a power generation scheduling optimization system based on power generation capacity prediction includes a multi-dimensional and multi-level power generation capacity management module, a power generation capacity multi-level system decision-making auxiliary module and a multi-dimensional and multi-level power generation capacity evaluation module. The multi-dimensional and multi-level power generation capacity management module simulates the power generation performance under different conditions and factor configurations by establishing a power generation capacity model, thereby realizing the performance simulation of the power generation capacity; the power generation capacity multi-level system decision-making auxiliary module realizes the process functions such as strategy formulation, modification, and effectiveness by adding power generation scheduling strategies and scheduling plans to support various types of strategy management based on cost, environmental protection, reliability, etc.; the multi-dimensional and multi-level power generation capacity evaluation module realizes a comprehensive evaluation of the power generation capacity by adding power generation capacity detection, and adds analysis technology for historical data and trends to realize the prediction of future power generation capacity change trends.

[0053] The overall workflow of the power generation dispatch optimization system based on power generation capacity prediction is as follows: Figure 2 As shown: The optimization system first builds a power generation capacity prediction model, defines key input factors, and then obtains power generation-related operating data and external environmental parameters in real time based on the control cloud platform, and tests the prediction accuracy of the model in combination with cross-validation and backtesting technology. At the same time, the optimization system manages various factors affecting power generation in detail, and evaluates the impact of each power generation factor on power generation capacity through real-time monitoring and principal component analysis methods, dynamically adjusts its weight and generates a report on the impact of power generation factors on power generation capacity in different time periods and weight changes, ensuring that the model can accurately reflect the current power generation status and provide data support for power generation decisions.

[0054] In addition, the optimization system will also take into account the cost of power generation, classify the power generation cost in detail, calculate and analyze the cost change trend over time, and use time series analysis to predict future cost trends, and propose cost saving strategies based on cost-benefit analysis.

[0055] When monitoring the operating status of power generation equipment in real time, the optimization system will record and analyze equipment defects, and adjust the power generation capacity prediction model based on the performance impact assessment results, including possible power generation reduction, cost increase and impact on grid stability, and recommend measures such as starting backup units, adjusting power generation scheduling strategies or using external electricity for different situations. In addition, the optimization system will also adjust the incentive model based on the impact of defects on power generation capacity to ensure that rewards or penalties for power plants are fair and effective, and the introduction of the "two detailed rules" assessment and compensation algorithm as an incentive model management to evaluate power plant performance can effectively encourage power plants to improve their operating processes.

[0056] For different power generation scenarios, users are provided with an entry for freely combining power generation scheduling strategies. After the combined power generation scheduling strategy is preset, the optimization system will perform simulation and deduction to simulate the performance of power generation capacity, and display the deduction data in a visual interface to help users combine the optimal power generation scheduling strategy according to different scenarios. Then it will evaluate whether the power generation capacity of the current power generation scheduling strategy meets the power demand of the specified area. If the analysis results show that there is a power supply gap, the system will evaluate the availability of external power resources and determine whether to recommend the introduction of external power to maintain the current regional power supply and demand balance.

[0057] The entire optimization system is designed to improve the utilization efficiency of power generation resources and power supply reliability through real-time monitoring, dynamic adjustment, intelligent analysis and multi-level collaborative decision-making, and ensure the economic, environmentally friendly and stable operation of the power system.

[0058] The power generation scheduling optimization system based on power generation capacity prediction provided by this embodiment is further described in detail below.

[0059] 1. Multi-dimensional and multi-level power generation capacity management module.

[0060] The multi-dimensional and multi-level power generation capacity management module includes: (1) Power generation capacity model management module.

[0061] The power generation capacity model management module is used to establish and maintain the power generation capacity model and dynamically predict the power generation capacity.

[0062] Its workflow includes: A. Model construction: Establish a power generation capacity model based on power generation equipment and historical data, and define input factors (such as weather and load).

[0063] B. Dynamically adjust model parameters: During the operation of power generation equipment, real-time data, including equipment status and external environment, is collected regularly (e.g., every 5 minutes), and the real-time data is compared with the model prediction results to calculate the deviation. According to the deviation, key parameters in the model (such as equipment performance coefficient, load sensitivity) are automatically adjusted. If the deviation exceeds the set threshold, the system generates an alarm record. C. Model Verification: Perform model verification daily / weekly and backtest the model using historical data to ensure its accuracy.

[0064] (2) Power generation influencing factor management module.

[0065] The power generation influencing factor management module is used to manage external and internal factors that affect power generation capabilities (such as climate, fuel supply, and equipment status).

[0066] Its specific work content includes: A. Factor identification: Determine key influencing factors, such as temperature, wind speed, equipment status, etc.

[0067] B. Dynamic weight adjustment: Collect real-time monitoring data and calculate the weight of each factor. Use principal component analysis to evaluate the contribution of each factor to power generation capacity and automatically adjust its weight.

[0068] C. Report generation: Generate factor impact reports regularly to show the impact of each factor on power generation capacity and its changing trend.

[0069] (3) Power generation factor analysis module.

[0070] The power generation factor analysis module is used to collect and analyze power generation factor data, calculate the impact of each factor on the overall power generation capacity through an algorithm, and analyze and derive the optimal power generation factor ratio.

[0071] Its specific work content includes: A. Dynamic adjustment of factor weights: According to the historical influence and real-time changes of each factor, adjust its weight in the model to ensure the accuracy of the power generation capacity prediction model.

[0072] B. Visual display: Provide a visual report to show the weight changes of various factors in different time periods and their impact on power generation capacity.

[0073] (4) Power generation cost analysis and management module.

[0074] The power generation cost analysis and management module is used to calculate and analyze various costs in the power generation process, including fuel costs, operation and maintenance costs, environmental compliance costs, etc., to help power plants optimize operating costs.

[0075] Its specific work content includes: A. Cost classification: The power generation cost is divided into fuel cost, operation and maintenance cost, environmental compliance cost, etc.

[0076] B. Cost calculation: The system calculates the proportion of various types of costs and their changing trends over time.

[0077] C. Optimization suggestions: Based on the cost analysis results, provide strategies such as fuel procurement optimization suggestions to help power plants reduce operating costs.

[0078] (5) Incentive model management module.

[0079] The incentive model management module is used to build an incentive model based on the assessment and compensation algorithms of the "two rules", evaluate the performance of power plants and provide rewards or penalties, and generate service settlement fees for power plants.

[0080] Its specific work content includes: A. Construction of assessment and compensation algorithm: Based on the standards of the "two rules", the system determines the key parameters of the assessment, such as power generation, active output, etc.

[0081] B. Model construction: The system combines the assessment indicators with the compensation mechanism to generate the corresponding incentive model.

[0082] C. Incentive calculation and implementation: Based on the actual performance of the power plant (such as power generation and response time), the system calculates the compensation or penalty it should receive and generates a settlement cost report.

[0083] (6) Unit defect management module.

[0084] The unit defect management module is used to manage unit defect data, record equipment defect data, analyze the impact of defects on power generation capacity, and generate defect management reports.

[0085] Its specific work content includes: A. Defect record: The system records the defect information of all equipment and tracks the progress of defect repair.

[0086] B. Defect analysis: Analyze the impact of defects on power generation capacity and equipment performance, and automatically generate defect impact reports.

[0087] C. Improvement suggestions: Based on the defect analysis results, the system makes maintenance or equipment upgrade suggestions to improve equipment performance.

[0088] (7) Power generation capacity verification and monitoring module.

[0089] The power generation capacity verification and monitoring module is used to monitor the output capacity of power generation equipment in real time, ensure that it meets the predetermined performance and safety standards, and verify whether the power generation capacity meets market demand.

[0090] Its specific work content includes: A. Real-time monitoring: Continuously monitor the output power of the equipment and record any deviations.

[0091] B. Abnormal warning: When equipment performance deviates from model prediction, the system will automatically issue an alarm.

[0092] C. Fault diagnosis: The system analyzes the cause of the deviation, generates a fault diagnosis report, and provides equipment maintenance suggestions.

[0093] (8) Power generation capacity bandwidth analysis module.

[0094] The power generation capacity bandwidth analysis module is used to detect the actual output of power generation equipment in real time, calculate the maximum and minimum output (bandwidth) of power generation equipment under different operating conditions, analyze the impact of load fluctuations on bandwidth, and display the bandwidth change trend in a graphical interface; regularly analyze bandwidth changes, generate optimized scheduling strategies, and provide reference for power scheduling. Its specific work content includes: A. Bandwidth calculation: The system calculates the output bandwidth of the device under different operating conditions in real time and displays the fluctuations.

[0095] B. Bandwidth optimization: By analyzing bandwidth data, the system provides dispatchers with suggestions for optimizing dispatch strategies.

[0096] (9) Post-evaluation analysis module.

[0097] The post-evaluation analysis module is used to review and evaluate the implemented power generation dispatching strategies, continuously improve and optimize the strategies and methods of power generation capacity management, ensure that the strategies can be continuously improved and optimized, and improve the utilization efficiency of power generation resources and power supply reliability.

[0098] Its specific work content includes: A. Data collection: Collect all data during the strategy execution process, such as power generation, equipment status, market price, etc.

[0099] B. Deviation analysis: The system compares the actual results with the strategic goals to find out the deviations.

[0100] C. Report generation: The system generates a post-evaluation report, analyzes the causes of deviations and provides optimization suggestions.

[0101] 2. Multi-level system decision-making auxiliary module for power generation capacity.

[0102] The power generation capacity multi-level system auxiliary decision module includes: (1) Multi-level scheduling coordination decision management module.

[0103] The multi-level dispatch coordination decision management module is used to provide a variety of dispatch strategy options (such as economic priority, environmental protection priority, load tracking, etc.). The dispatch algorithm model based on the power grid load and equipment historical data dynamically adjusts the dispatch strategy to cope with instantaneous changes in power demand and ensure the flexibility and applicability of the strategy.

[0104] Its specific work content includes: A. Strategy formulation: Based on real-time and historical data, generate a variety of dispatching strategies for dispatchers to choose from.

[0105] B. Strategy adjustment: In the real-time monitoring system, load changes are automatically monitored and adjustment suggestions are provided. The dispatcher can manually adjust the strategy based on the suggestions or enable the system's automatic adjustment function.

[0106] (2) Auxiliary strategy scenario management module.

[0107] The auxiliary strategy scenario management module is used to formulate a combined power generation dispatch strategy, dynamically adjust and optimize it according to different operating scenarios (such as weather changes, market demand fluctuations, etc.), and improve the flexibility of power supply.

[0108] Its specific work content includes: A. Scenario creation: allows users to customize and save multiple operating scenarios (such as high load, equipment failure), and the scenario can be bound to one or more scheduling strategies.

[0109] B. Scene switching: When specific conditions are detected (such as load exceeding the preset range), the system automatically switches to the corresponding emergency scene.

[0110] C. Real-time adjustment: Provide real-time feedback, analyze the effects after scene switching and make adjustments.

[0111] (3) Risk assessment model management module.

[0112] The risk assessment model management module is used to establish a risk assessment model based on the uncertainty of natural gas supply, consider the impact of supply interruptions on the power generation system, establish a risk assessment model, consider the impact of factors such as supply interruptions on the operation of the power generation system, assess potential risks, and formulate emergency measures.

[0113] Its specific work content includes: A. Risk Identification: Identify and record potential risks associated with natural gas supply (e.g., supply disruptions, price fluctuations).

[0114] B. Dynamic assessment: Combine real-time data with historical data to assess risk levels in real time.

[0115] C. Emergency strategy formulation: Based on the assessment results, formulate corresponding emergency response strategies, such as activating backup fuel or adjusting power generation plans.

[0116] D. Emergency strategy response: When risks occur, the power generation dispatch optimization system based on power generation capacity prediction can automatically activate the corresponding emergency strategy and generate alarm data.

[0117] E. Emergency strategy evaluation: After risk assessment and emergency response, evaluate the real-time effectiveness of the emergency strategy and update the risk assessment model and emergency response plan in a timely manner.

[0118] (4) Pre-optimization decision-making module.

[0119] The pre-optimization decision-making module is used to formulate the best power generation scheduling strategy and scheduling plan in advance based on historical data and real-time information to cope with various operating conditions that may arise in the future and ensure the efficiency and reliability of power supply.

[0120] Its specific work content includes: A. Trend forecasting: Analyze future load demand and power generation capacity based on historical data and real-time information to generate load curves.

[0121] B. Power generation scheduling strategy matching: recommend multiple power generation scheduling strategies based on the load curve.

[0122] C. Scheme comparison and selection: Compare the recommended power generation dispatch strategies, select the best scheme, and confirm it before dispatch execution.

[0123] (5) In-process tracking and analysis module.

[0124] The in-process tracking and analysis module is used to monitor and analyze operating data in real time, promptly identify potential problems and adjust strategies to ensure the stability and security of power supply.

[0125] Its specific work content includes: A. Real-time monitoring: Continuously monitor key indicators in the execution of power generation scheduling strategies (such as power generation, load, and equipment status).

[0126] B. Dynamic adjustment suggestions: When it is monitored that the execution of the power generation dispatching strategy deviates from the expected, dynamic adjustment suggestions are provided to ensure the effectiveness of the power generation dispatching strategy.

[0127] C. Execution status report: Generate execution status reports regularly to analyze the execution effect of power generation scheduling strategies and record abnormal situations during the execution process.

[0128] (6) Post-evaluation improvement module.

[0129] The post-evaluation improvement module is used to review and summarize historical operating data, evaluate the actual effects of various strategies, identify problems and propose improvement measures to improve the accuracy and effectiveness of future decisions.

[0130] Its specific work content includes: A. Data review and analysis: Collect data after the execution of the power generation dispatch strategy and compare and analyze it with the preset goals.

[0131] B. Performance evaluation: Evaluate the effectiveness of strategy execution based on prior forecast data.

[0132] C. Improvement suggestion generation: Generate improvement suggestions and provide optimization ideas for future power generation (dispatching) strategies based on the evaluation results.

[0133] D. Report and feedback: Generate post-evaluation reports, share them with relevant teams, and discuss improvement measures.

[0134] (7) Auxiliary strategy effectiveness evaluation module.

[0135] The auxiliary strategy effect evaluation module detects the real-time data during strategy execution, compares the actual execution data with the preset targets, analyzes deviations, evaluates the implementation effect of the strategy, and displays the evaluation results through a visual interface to help users intuitively understand the strategy effect; at the same time, it determines its impact on the operation, efficiency and reliability of the power generation system and continuously optimizes the strategy.

[0136] Its specific work content includes: A. Data collection: During the implementation of the power generation dispatch strategy, the optimization system continuously saves relevant data, including power generation, response time, market electricity price, equipment status, etc.

[0137] B. Data analysis and comparison: Compare the actual execution data with the expected goals before the strategy is implemented, calculate the deviation and analyze the influencing factors, and use the variance analysis method to quantify the effect of the auxiliary strategy.

[0138] C. Report generation: Generate a detailed effect evaluation report to show the changes in various indicators and their impact on power generation capacity. The report contains visual charts to help dispatchers quickly understand the execution effect of the strategy.

[0139] D. Improvement suggestions: Generate improvement suggestions based on the evaluation results to promote the optimization and adjustment of subsequent strategies.

[0140] (8) Multi-level collaborative assessment management module.

[0141] The multi-level collaborative assessment management module integrates power generation capacity data at all levels, conducts accurate capacity assessment and identification, ensures the fairness and accuracy of the assessment, and responds to capacity changes in a timely manner, making adjustments and audits.

[0142] Its specific work content includes: A. Formulation of assessment standards: formulate assessment standards based on industry standards and clarify the assessment indicators of power generation units at all levels (such as power generation, equipment utilization rate, environmental protection indicators, etc.).

[0143] B. Data collection and monitoring: Regularly collect power generation data, equipment operating conditions and related indicators of each power plant to ensure the accuracy and timeliness of the data.

[0144] C. Assessment Execution: Regularly assess power generation units, evaluate their performance based on preset standards, and use assessment algorithms to calculate the overall score of each unit.

[0145] D. Feedback on assessment results: Feedback the assessment results to power generation units at all levels, provide report cards and improvement suggestions, and help them identify deficiencies.

[0146] (9) Multi-level collaborative process management module.

[0147] The multi-level collaborative process management module manages related processes such as exemption from examination for non-centrally dispatched power plants, reporting of grid-connected power consumption, and unit participation in assessments, thereby realizing process management operations for non-centrally dispatched power plants.

[0148] Its specific work content includes: A. Provide a data upload interface: Provide clear data upload formats and templates (such as Excel, CSV, etc.) to ensure the standardization of data, and a concise and intuitive data upload page so that users can easily find the upload entrance.

[0149] B. Data collection and input: supports uploading various types of data, such as exemption from examination, online power reporting, unit participation in assessment, etc. During the uploading process, the system performs data verification to ensure the integrity and accuracy of the input data.

[0150] C. Data storage and management: Uploaded data is stored in the central database in real time to facilitate subsequent query and analysis.

[0151] D. Anomaly detection and early warning: The system monitors the uploaded data in real time, detects anomalies and generates early warning data.

[0152] 3. Multi-dimensional and multi-level power generation capacity assessment module.

[0153] The multi-dimensional and multi-level power generation capacity assessment module includes: (1) Comprehensive evaluation module of power generation capacity.

[0154] The power generation capacity comprehensive assessment module comprehensively assesses the power generation capacity, covering the power generation capacity of different areas of the equipment / power plant / region / province in different periods.

[0155] Its specific work content includes: A. Application of evaluation model: Based on the established power generation capacity model and combined with real-time data, a linear regression model is used to evaluate the linear relationship between power generation capacity and power generation factors, and a neural network model is used to analyze the nonlinear relationship between power generation capacity and power generation factors.

[0156] B. Result analysis: Generate power generation capacity assessment results and analyze the matching degree between current power generation capacity and demand. Compare actual power generation with predicted power generation, calculate deviations and analyze causes.

[0157] C. Report generation: Automatically generate an assessment report, including comprehensive assessment results, analysis charts and recommendations.

[0158] (2) Power generation capacity trend prediction module.

[0159] The power generation capacity trend forecasting module uses historical data and technical means to predict the changing trend of future power generation capacity and provide forward-looking guidance for power generation plan formulation.

[0160] Its specific work content includes: A. Data preparation: Collect past power generation records, load data, and meteorological conditions.

[0161] B. Trend prediction model construction: Build an event sequence prediction model based on historical data and use the machine learning algorithm LSTM to predict load and power generation capacity.

[0162] C. Real-time update: Regularly update the prediction model parameters to ensure that they adapt to the latest data changes.

[0163] D. Forecast result analysis: Generate forecast reports on future power generation capacity and load demand to show trend changes. Provide forecast results under different scenarios for dispatchers to make reference.

[0164] (3) Power generation capacity simulation module.

[0165] The power generation capacity simulation module simulates different operating scenarios, evaluates the power generation capacity performance under different strategies, and performs simulations in a visual form to help decision makers intuitively understand the effects of strategies.

[0166] Its specific work content includes: A. Scenario setting: Users define multiple simulation scenarios (such as extreme weather, high load, equipment failure, etc.).

[0167] B. Run the simulation model: According to the scenario parameters set by the user, run the power generation capacity trend prediction model to simulate the changes in power generation capacity.

[0168] C. Result collection and analysis: Collect simulation results and analyze the power generation capacity and impact on the power grid under different conditions.

[0169] D. Visualization: Generate visualization charts to intuitively display simulation results and help users understand the impact of different scenarios.

[0170] (4) Power generation scheduling optimization and review module based on power generation capacity prediction.

[0171] The power generation dispatch optimization and review module based on power generation capacity forecast puts forward optimization suggestions for power generation dispatch based on power generation capacity forecast, monitors the implementation effect, summarizes experience and lessons, and continuously iterates and improves strategies.

[0172] Its specific work content includes: A. Data review: Collect all relevant data during the simulation using the power generation capacity prediction model, such as power generation, equipment status, market response, etc.

[0173] B. Performance evaluation: Compare actual results with strategy prediction targets, analyze deviations, and evaluate effectiveness.

[0174] C. Generate review report: Automatically generate a review report that includes a summary of the strategy implementation effect and improvement suggestions.

[0175] D. Feedback and optimization: Feedback the review results into the incentive model and decision support system to provide reference for the formulation of future strategies.

[0176] (5) External power resource management module.

[0177] The external power resource management module manages external power contract data, evaluates its impact on internal power generation capacity, ensures the rational use of external power, and improves the flexibility of overall power supply.

[0178] Its specific work content includes: A. Provide a data upload interface: Provide clear data upload formats and templates (such as Excel, CSV, etc.) to ensure the standardization of data, and a concise and intuitive data upload page so that users can easily find the upload entrance.

[0179] B. Data collection and input: During the upload process, the system performs data verification to ensure the integrity and accuracy of the input data.

[0180] C. Data storage and management: Uploaded data is stored in the central database in real time to facilitate subsequent query and analysis.

[0181] The power generation dispatch optimization system based on power generation capacity prediction provided by this embodiment has the following beneficial effects: (1) Improving the scientificity and accuracy of power generation planning: By building a power generation capacity model and simulating the performance under different conditions and factor configurations, it supports the prediction and planning of future power generation capacity. This makes the formulation of power generation plans no longer rely on estimates, but on more scientific data analysis, thereby improving the accuracy and scientificity of power generation plans.

[0182] (2) Optimize power generation costs and strategies to improve economic benefits: By calculating and analyzing the various costs in the power generation process, optimizing power generation plans and improving equipment efficiency to control and reduce costs, the overall economic benefits can be improved. In addition, through continuous post-evaluation analysis, the strategies and methods for power generation capacity management can be continuously improved and optimized to ensure that the strategies can be continuously improved and optimized, thereby improving the utilization efficiency of power generation resources and the reliability of power supply.

[0183] (3) Real-time monitoring and comprehensive evaluation of power generation capacity: By real-time monitoring of the output capacity of power generation equipment, ensuring that it meets predetermined performance and safety standards, and evaluating the ability of the power generation system to respond to changes in demand, the stability of the power system under different operating conditions can be ensured; at the same time, through power generation capacity bandwidth analysis, the system's responsiveness and adaptability to demand fluctuations can be further enhanced.

[0184] (4) Enhance the effectiveness of the incentive mechanism and improve system flexibility: By quantifying the effect of the incentive mechanism and collecting data on the two detailed incentives for power plants, we can stimulate the enthusiasm of power plants to participate in auxiliary services, improve the flexibility and response speed of the power system, ensure the safe and stable operation of the power grid, and improve the comprehensive management capabilities of the power system.

[0185] The above-described embodiment is only a preferred solution of the present invention and does not limit the present invention in any form. There are other variations and modifications without exceeding the technical solution described in the claims.

Claims

1. A method for optimizing power generation scheduling based on power generation capacity prediction, characterized in that: The following steps are involved: Build a power generation capacity prediction model, define key influencing factors, and dynamically adjust the weights of each influencing factor at different power generation periods; Calculate the changing trend of power generation cost over time, use time series analysis to predict future power generation cost trends, and establish cost-saving strategies; Combine the assessment indicators with the compensation mechanism to establish an incentive model, and adjust the incentive model parameters according to the impact of equipment defects on power generation capacity; Formulate a combined power generation dispatching strategy, and dynamically adjust and optimize the power generation dispatching strategy based on its execution effect and in combination with different operating scenarios.

2. A power generation scheduling optimization method based on power generation capacity prediction according to claim 1, characterized in that: In the early stage of the execution of the power generation dispatching strategy, the best power generation dispatching combination strategy is recommended according to the power generation scenario; in the middle stage of the power generation dispatching strategy execution, the power generation equipment operation data is monitored in real time. When it is detected that the execution result of the power generation dispatching strategy deviates from the expected target, the variance analysis method is used to quantify the degree of deviation. If the deviation exceeds the preset first threshold, the power generation dispatching strategy adjustment mechanism is triggered to provide real-time power generation dispatching strategy adjustment suggestions; in the late stage of the power generation dispatching strategy execution, the completed power generation equipment operation data and results are automatically analyzed, and the maximum and minimum output ranges of the power generation equipment under different conditions are calculated.

3. The power generation scheduling optimization method based on power generation capacity prediction according to claim 2 is characterized in that: The method of recommending the best power generation scheduling combination strategy according to the power generation scenario includes: presetting the combined power generation scheduling strategy and performing simulation deduction to combine the optimal power generation scheduling strategy according to different scenarios; evaluating whether the power generation capacity of the current power generation scheduling strategy meets the power demand of the designated area, and if there is a power supply gap, determining whether to recommend introducing external power to maintain the current regional power supply and demand balance.

4. A power generation scheduling optimization method based on power generation capacity prediction according to claim 1, 2 or 3, characterized in that: Using real-time operation data and principal component analysis, the impact of various influencing factors on power generation capacity and the mutual influence of various influencing factors are evaluated, the weights of various influencing factors in different power generation periods are dynamically adjusted, and a report on the impact of influencing factors on power generation capacity in different time periods and weight changes is generated.

5. A power generation scheduling optimization method based on power generation capacity prediction according to claim 1, 2 or 3, characterized in that: When constructing a power generation capacity prediction model, the cross-validation method is used to compare the real-time operating data and the prediction results of the power generation prediction model. If the difference between the real-time operating data and the prediction results is greater than the preset second threshold, the power generation prediction model parameters are automatically adjusted, and the power generation prediction model is backtested using historical operating data at regular intervals.

6. A power generation scheduling optimization method based on power generation capacity prediction according to claim 2 or 3, characterized in that: Based on interval data, genetic algorithms are used to propose improvement suggestions for power generation scheduling strategies; when real-time operation data is missing, historical operation data and machine learning algorithms are used to generate simulated data to fill the missing real-time operation data.

7. A power generation dispatch optimization system based on power generation capacity prediction, adopting a power generation dispatch optimization method based on power generation capacity prediction according to any one of claims 1 to 6, characterized in that: include: Multi-dimensional and multi-level power generation capacity management module, which establishes a power generation capacity model to simulate power generation performance under different conditions and configurations of power generation influencing factors; The power generation capacity multi-level system auxiliary decision-making module realizes the formulation and modification of power generation scheduling strategies by adding power generation scheduling strategies; The multi-dimensional and multi-level power generation capacity assessment module comprehensively assesses power generation capacity through power generation capacity detection, analyzes historical data and trends, and predicts future power generation capacity change trends.

8. A power generation dispatch optimization system based on power generation capacity prediction according to claim 7, characterized in that: The multi-dimensional and multi-level power generation capacity management module uses power generation equipment and historical data to establish a power generation capacity prediction model, compares the real-time data during the operation of the power generation equipment with the prediction results of the power generation capacity prediction model, and automatically adjusts the key parameters in the power generation capacity prediction model according to the comparison results; at the same time, it determines the key influencing factors, calculates the weight of each influencing factor according to the real-time monitoring data, and uses the principal component analysis method to evaluate the contribution of each influencing factor to the power generation capacity, and automatically adjusts the weight of the influencing factor; An incentive model is constructed based on the compensation algorithm to generate grid service calculation fees.

9. A power generation dispatch optimization system based on power generation capacity prediction according to claim 7, characterized in that: The power generation capacity multi-level system auxiliary decision-making module generates a variety of power generation scheduling strategies based on real-time operation data and historical operation data, monitors load changes in real time, and provides power generation scheduling strategy adjustment suggestions; Establish a risk assessment model based on natural gas supply uncertainty, assess the risk level in real time, and formulate corresponding emergency response strategies based on the assessment results; Analyze future load demand and power generation capacity based on historical data and real-time information, generate load curves, recommend multiple power generation scheduling strategies based on the load curves and select the best option; continuously monitor key indicators in the execution of power generation scheduling strategies, and provide dynamic adjustment suggestions when it is detected that the deviation between the execution of power generation scheduling strategies and expectations is greater than the threshold.

10. A power generation dispatch optimization system based on power generation capacity prediction according to claim 7, 8 or 9, characterized in that: The multi-dimensional and multi-level power generation capacity assessment module constructs a power generation capacity trend prediction model based on historical operating data and regularly updates the trend prediction model parameters, uses the machine learning algorithm LSTM to perform load forecasting and power generation capacity forecasting, and uses the power generation capacity trend prediction model to simulate power generation capacity changes based on scenario parameters set by the user; collects all relevant data during the simulation and deduction of the power generation capacity prediction model, compares the actual results with the power generation strategy prediction targets, analyzes deviations, and evaluates the effectiveness of the power generation strategy.

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