A power control method and system based on power characteristic matrix

Abstract

The present invention relates to the field of power control technology, and in particular to a power control method and system based on a power characteristic matrix. The method comprises the following steps: obtaining distributed power plant equipment data, performing outlier replacement, generating complete equipment data, then screening out standard operating parameters, generating simulated power plant equipment units through equipment unit simulation processing, and performing combined power generation simulation to obtain a variety of simulation data, obtaining historical power output data and performing power demand analysis, generating predicted power output data and power demand peak and valley data, and based on these data, performing intensity adaptation screening on the combined power generation simulation, adjusting the operating cycle, and finally generating distributed equipment scheduling instructions to achieve efficient control of the intelligent virtual power plant; the present invention realizes a more efficient and reliable intelligent power control method.

Description

Technical Field

[0001] The present invention relates to the technical field of power control, and in particular to a power control method and system based on a power characteristic matrix. Background Art

[0002] Power regulation improves energy efficiency by integrating distributed power generation resources and loads, and provides flexible scheduling capabilities for the power market. However, virtual power plants currently face many challenges in the regulation process. Real-time acquisition and processing of equipment data, accurate demand forecasting, and effective scheduling strategies have not yet been fully addressed, and these issues seriously affect the operational efficiency and reliability of virtual power plants. Obtaining high-quality distributed power plant equipment data is the foundation for achieving intelligent regulation. Existing systems often face data anomalies and missing data, which makes it difficult to generate complete equipment data, which in turn affects the selection and application of standard operating parameters. In addition, the collaborative operation effects between devices have not been fully explored, resulting in insufficient data accuracy in combined power generation simulations and an inability to provide effective support for power demand. Summary of the Invention

[0003] Based on this, it is necessary to provide a power control method and system based on a power characteristic matrix to solve at least one of the above technical problems.

[0004] To achieve the above object, a power control method based on a power characteristic matrix includes the following steps:

[0005] Step S1: Obtain distributed power plant equipment data; replace abnormal values ​​in the distributed power plant equipment data to obtain complete equipment data; filter the complete equipment data for standard operating parameters to generate standard equipment operating parameters;

[0006] Step S2: performing equipment unit simulation processing on the standard equipment operating parameters to generate simulated power plant equipment units; performing combined power generation simulation on the simulated power plant equipment units to obtain multiple combined power generation simulation data;

[0007] Step S3: Acquire historical power output data; perform power demand analysis on the historical power output data to obtain historical power demand data; perform power generation demand forecasting on standard equipment operating parameters based on the historical power demand data to generate forecasted power output data; perform demand peak and valley analysis on the forecasted power output data to obtain power demand peak and valley data;

[0008] Step S4: Based on the predicted required power output data, multiple combined power generation simulation data are subjected to intensity adaptation screening to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptation equipment combination scheduling data;

[0009] Step S5: Generate distributed device dispatching instructions based on the adaptive device combination dispatching data; use the distributed device dispatching instructions to dispatch equipment operation of the virtual power plant to realize the intelligent power control method.

[0010] By acquiring data from distributed power plant equipment, the present invention ensures the comprehensiveness and accuracy of the data base. By replacing outliers on this data, it effectively eliminates inaccurate data caused by data collection errors or equipment failures, thereby improving overall data reliability. Subsequently, standard operating parameters are screened against the complete equipment data to ensure that the data used meets the normal operating requirements of the equipment, which is crucial for subsequent analysis and simulation. This data standardization not only improves data availability and consistency but also lays a solid foundation for subsequent simulation analysis, ensuring the effectiveness and accuracy of subsequent steps. Simulating the standard equipment operating parameters on a per-unit basis generates a variety of simulated power plant equipment units. This process enables simulation of various equipment combinations under different operating conditions, providing a more comprehensive operational perspective. This simulation not only helps identify synergies between different equipment but also generates a variety of combined power generation simulation data through combined power generation simulation, providing a rich basis for power plant scheduling decisions. This simulation data allows for in-depth analysis of power plant operating efficiency and optimization of equipment configurations, thereby improving the flexibility and efficiency of overall power production. By acquiring historical power output data and conducting power demand analysis, a critical foundation is established for understanding power demand fluctuations. This phase involves not only in-depth analysis of historical data but also extracting patterns and trends in power demand to enable more accurate power output forecasts. Power demand forecasts based on this historical data significantly improve forecast accuracy, helping power plants prepare for future fluctuations in power demand. Further peak-valley analysis identifies peak and valley periods in power demand, providing a crucial basis for optimizing scheduling strategies and ensuring stable and reliable power supply. Based on analysis of the predicted power output data, power generation simulation data from various combinations is adapted to ensure efficient power generation under various demand conditions. This process not only improves the adaptability of the power mix but also ensures flexible power plant scheduling strategies to meet varying power demands. Next, the operating cycles of the power generation intensity-adapted combination units are adjusted based on power demand peak-valley data to further optimize the scheduling efficiency of the power mix. By rationally scheduling the equipment's operating cycles, sufficient power output can be provided during peak demand periods while reducing unnecessary energy loss during valley demand periods, thereby achieving efficient resource utilization. The generated distributed equipment dispatch instructions enable intelligent management of the virtual power plant. These instructions dynamically dispatch equipment operations based on real-time data and forecasts, improving the plant's responsiveness and control capabilities. This process not only facilitates coordination between distributed power plants and the grid, but also enhances the stability and reliability of the entire power system.Driven by intelligent control methods, distributed power plants can more efficiently utilize renewable energy, promoting the development and application of green energy. At the same time, by increasing the flexibility of power supply, they can better meet the diverse power needs of users, thereby enhancing user satisfaction and optimizing the overall quality of power service.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Obtain distributed power plant equipment data; perform abnormal value interval detection on the distributed power plant equipment data to obtain abnormal value intervals of power plant equipment;

[0013] Step S12: Eliminate abnormal values ​​of power plant equipment and generate cleaning power plant equipment data;

[0014] Step S13: performing linear interpolation of abnormal intervals on the cleaning power plant equipment data to obtain complete equipment data;

[0015] Step S14: performing equipment operation simulation on the complete equipment data to obtain a simulation operation parameter set;

[0016] Step S15: clustering the simulation operation parameter set to obtain a clustered operation parameter set;

[0017] Step S16: Standardize the clustered operating parameter set to generate standard equipment operating parameters.

[0018] The present invention ensures the comprehensiveness and accuracy of the data source by acquiring the distributed power plant equipment data. By performing outlier interval detection on the distributed power plant equipment data, it can effectively identify the abnormal range in the data, thereby laying the foundation for subsequent data cleaning and processing. Generating the outlier interval of power plant equipment can help identify potential problems, improve the pertinence and effectiveness of data processing, ensure that the data based on which the subsequent analysis is based is reliable, and enhance the basic data quality of the entire system. The step of performing outlier elimination on the outlier interval of power plant equipment realizes further cleaning of the data. By eliminating data that does not meet the normal operating range, clean power plant equipment data is generated, and the integrity and accuracy of the data are ensured. This operation significantly improves the quality of the data, provides a more reliable data basis for subsequent analysis and simulation, reduces the analysis deviation caused by outliers, and ensures the effectiveness and Scientificity: The implementation of linear interpolation of abnormal intervals on the cleaning power plant equipment data can fill the gaps caused by the removal of abnormal values ​​in the data cleaning process and obtain complete equipment data. Through the linear interpolation method, not only the original trend of the data is retained, but also the continuity and integrity of the data are improved, providing high-quality data support for subsequent equipment operation simulation, enhancing the applicability of the data and the accuracy of the analysis, and ensuring the reliability of the simulation results. The steps of equipment operation simulation for complete equipment data, through the generation of simulated operation parameter sets, can deeply analyze the performance of the equipment under different operating conditions, provide targeted data support, and improve the operating efficiency and flexibility of the power plant. This simulation not only helps identify potential problems in equipment operation, but also provides a basis for optimizing equipment operation strategies, enhances the system's adaptability in the face of changes, and ensures the best power generation effect under different circumstances. The simulation operating parameter set is clustered. Through cluster analysis, different modes of equipment operation can be identified. Similar operating parameters can be grouped together to obtain a clustered operating parameter set. This step helps simplify data analysis, reveal the inherent laws of equipment operation, and enhance the depth and breadth of analysis. It lays the foundation for subsequent standardization processing and ensures that equipment performance can be effectively identified and managed under diverse operating conditions. The step of standardizing the clustered operating parameter set generates standard equipment operating parameters to ensure that parameters between different equipment are comparable. Standardization can eliminate analytical deviations caused by different dimensions and improve the accuracy and reliability of data analysis. This result provides a scientific basis for subsequent scheduling and decision-making, enhances the overall operating efficiency of the system, promotes the implementation of intelligent control, and ensures that the power plant can maintain an efficient and stable operating state under various operating conditions.

[0019] Preferably, step S2 includes the following steps:

[0020] Step S21: extracting features from the standard equipment operating parameters to obtain equipment characteristic parameters; performing correlation analysis on the equipment characteristic parameters to generate a parameter correlation matrix;

[0021] Step S22: performing an equipment unit operating condition analysis on the equipment characteristic parameters based on the parameter association matrix to obtain an operating condition parameter set; performing an operation trajectory calculation on the operating condition parameter set to obtain equipment operation trajectory data;

[0022] Step S23: performing equipment simulation processing on the equipment characteristic parameters according to the equipment operation trajectory data to generate a simulated power plant equipment unit;

[0023] Step S24: simulating the power generation operation of the simulated power plant equipment unit to obtain simulated equipment operating data;

[0024] Step S25: Based on the simulated equipment working data, feasible combined power generation simulation is performed on the simulated power plant equipment units to obtain multiple combined power generation simulation data.

[0025] The present invention can effectively identify the key parameter characteristics of the equipment by performing feature extraction on the operating parameters of the standard equipment, thereby laying the foundation for subsequent analysis. By performing correlation analysis on the characteristic parameters of the equipment and generating a parameter correlation matrix, the intrinsic relationship between different parameters can be revealed, providing an important reference for subsequent working condition analysis, enhancing the depth and breadth of data analysis, ensuring that the operating characteristics of the equipment can be accurately identified in a complex operating environment, and improving the system's understanding and management capabilities of the equipment status. Based on the parameter correlation matrix, the equipment unit working condition analysis of the equipment characteristic parameters can be performed to gain an in-depth understanding of the performance of the equipment under different working conditions, obtain a working condition parameter set, and generate equipment operating trajectory data by performing operating trajectory calculation on the working condition parameter set. This process provides detailed dynamic data support for subsequent equipment simulation, ensuring that the actual operating status of the equipment can be accurately reflected during the simulation process, improving the authenticity and reliability of the simulation, and providing a scientific basis for optimizing the equipment operation strategy. Equipment simulation is performed on equipment characteristic parameters based on equipment operation trajectory data to generate simulated power plant equipment units. This can simulate the actual operation of the equipment in a virtual environment. Through this simulation, the performance of the equipment under different operating conditions can be evaluated, potential operating problems can be identified, and the understanding of equipment performance can be enhanced. This provides data support for the optimized operation of the power plant, ensuring that various challenges can be better addressed in actual operation and improving the overall flexibility and adaptability of the system. The operation of simulating the power generation operation of the simulated power plant equipment units can generate simulated equipment working data. Through this simulation, the power generation performance of the equipment under specific operating conditions can be evaluated, providing detailed data support to help identify the efficiency and stability of the equipment under different conditions, ensuring that reasonable scheduling and management can be carried out based on real simulation results during the decision-making process, and improving the overall operating efficiency and power generation capacity of the power plant. Based on the working data of the simulated equipment, feasible combination power generation simulation is carried out on the simulated power plant equipment units to generate a variety of combination power generation simulation data. This process can provide power plants with diversified power generation solutions by exploring the power generation potential of different equipment combinations, optimize resource allocation, and improve power generation efficiency. It ensures that when facing different power demands, the power plant can flexibly adjust its operating strategy, enhance the system's resilience and economic benefits, and ultimately achieve more efficient power production and supply.

[0026] Preferably, step S25 includes the following steps:

[0027] Step S251: classifying the simulated device working data into types to obtain a device classification data set; performing power characteristic analysis on the device classification data set to obtain a power characteristic matrix;

[0028] Step S252: performing independent operation simulation on the simulated power plant equipment units according to the power characteristic matrix to generate equipment independent operation data; performing permutations, combinations and groupings on the simulated power plant equipment units to generate multiple groups of permuted equipment units;

[0029] Step S253: performing a combined operation simulation on the arranged equipment units according to the power characteristic matrix to generate multiple sets of equipment combined operation data; performing an optimized combination screening on the equipment combined operation data based on the equipment independent operation data to obtain feasible equipment combined operation data;

[0030] Step S254: Based on the feasible equipment combination working data, a combined power generation simulation is performed on the simulated power plant equipment units to obtain a plurality of combined power generation simulation data.

[0031] The present invention can effectively classify the equipment according to different characteristics by dividing the working data of the simulated equipment into types, and obtain the equipment classification data set. By performing power characteristic analysis on the equipment classification data set and generating a power characteristic matrix, it can deeply understand the power output characteristics of various types of equipment under different working conditions, and provide a scientific basis for subsequent simulation and combination, ensuring that reasonable scheduling can be carried out according to the actual power characteristics when allocating resources, and improving the system's performance in power generation efficiency and resource utilization. According to the power characteristic matrix, the simulated power plant equipment unit is simulated to work independently, and the equipment independent working data is generated. It can evaluate the performance of each device without being affected by other devices, and provide detailed data support, ensuring that the independent operation capability of the equipment can be better understood in actual operation, identifying potential efficiency problems, and providing a basis for optimizing the working strategy of the equipment, which is of great significance to improving the overall performance of the system. The step of arranging, combining and grouping the simulated power plant equipment units can generate multiple groups of arranged equipment units. Through this combination method, the overall performance of the equipment can be evaluated under different configurations. The arranged equipment units are further simulated in combination according to the power characteristic matrix to generate multiple groups of equipment combination working data, ensuring that the power generation potential of the equipment combination under various operating conditions can be explored in the simulation, thereby enhancing the understanding of the performance of the equipment combination. Based on the independent working data of the equipment, the equipment combination working data is optimized and combined to obtain feasible equipment combination working data, which can identify the best configuration among multiple combination schemes, optimize resource allocation, improve power generation efficiency, and ensure that the power plant can flexibly adjust its operating strategy in the face of different power demands, thereby enhancing the economy and sustainability of the system. Through this series of steps, the combined power generation simulation of the simulated power plant equipment units based on the feasible equipment combination working data is finally realized, and multiple combined power generation simulation data are obtained, providing the power plant with diversified power generation schemes in actual operation, improving the flexibility and adaptability of the overall operation, and ensuring that the power needs of users can be efficiently met.

[0032] Preferably, step S3 includes the following steps:

[0033] Step S31: Acquire historical power output data; perform frequency domain transformation on the historical power output data to obtain a historical power feature sequence; perform power output pattern recognition on the historical power feature sequence to generate a historical power output pattern;

[0034] Step S32: Analyze the power demand of historical power output patterns to obtain historical power demand data; calculate the current power demand of standard equipment operating parameters based on the historical power demand data to obtain the power production data required by the current equipment;

[0035] Step S33: predicting the required power output of the standard equipment operating parameters based on the current required power generation data of the equipment to generate predicted required power output data;

[0036] Step S34: performing curve fitting on the predicted required power output data to obtain a predicted required power output curve; performing curve extreme point detection on the predicted required power output curve to obtain power demand peak and valley data.

[0037] The present invention ensures the breadth and reliability of data sources by obtaining historical power output data. By performing frequency domain transformation on the historical power output data, the key features of power output can be extracted to obtain a historical power feature sequence. The power output pattern recognition is then performed on the sequence to generate a historical power output pattern, laying the foundation for subsequent power demand analysis and ensuring that the laws of historical data can be accurately grasped when analyzing the power output pattern, thereby improving the accuracy and reliability of subsequent predictions. The step of performing power demand analysis on the historical power output pattern can provide an in-depth understanding of the changes in power demand under different power output modes, obtain historical power demand data, calculate the current power generation demand based on the standard equipment operating parameters based on these data, and generate the power generation data required by the current equipment. This series of operations ensures an accurate grasp of power demand changes, provides a scientific basis for subsequent power output predictions, and improves the flexibility and response speed of the system in responding to power demand fluctuations. The operation of predicting the required power output of standard equipment operating parameters based on the current equipment's required power production data can generate predicted required power output data, ensuring that scientific power output predictions can be made based on historical data and current demand in actual operation, improving the accuracy and effectiveness of the predictions, and providing reliable data support for power plants in scheduling and resource allocation, ensuring that reasonable decisions can be made in the face of a volatile power market, and enhancing the economy and effectiveness of the system. The step of curve fitting the predicted required power output data can generate a predicted required power output curve. By detecting extreme points on the predicted curve, power demand peak and valley data can be obtained, ensuring that the changing trend of power demand can be accurately identified during peak and valley periods of power demand, providing an important basis for optimizing the power plant's scheduling strategy, improving the responsiveness and flexibility of the overall system, and ensuring efficient power supply and management under different power demand conditions.

[0038] Preferably, step S4 includes the following steps:

[0039] Step S41: performing power superposition calculation on multiple combined power generation simulation data to generate combined power data; performing stable efficiency calculation on the combined power data to generate a combined efficiency index;

[0040] Step S42: Based on the predicted required power output data, the combined efficiency index is subjected to power generation intensity adaptation screening to obtain an efficiency adaptation combination; and according to the efficiency adaptation combination, the multiple combined power generation simulation data are subjected to device combination matching to obtain a power generation intensity adaptation combination unit;

[0041] Step S43: Associatively link the power demand peak and valley data and the power generation intensity adaptation combination unit to obtain a peak and valley adaptation device unit table;

[0042] Step S44: adjusting the operation cycle of the power generation intensity adaptation combination unit based on the peak-valley adaptation device unit table to generate adaptation device combination scheduling data.

[0043] The present invention can effectively integrate the power output of different equipment combinations in the power generation process and generate combined power data by performing power superposition calculations on multiple combined power generation simulation data. By performing stable efficiency calculations on the combined power data and generating combined efficiency indicators, it ensures that the synergy between the various devices can be fully considered when evaluating power generation efficiency, thereby improving the accuracy of the overall power generation efficiency assessment, providing a scientific basis for subsequent scheduling and resource allocation, and optimizing the power plant's operating strategy. Based on the predicted required power output data, the combined efficiency indicators are subjected to power generation intensity adaptation screening, ensuring that the optimal equipment combination is selected under different power demand conditions to obtain an efficiency adaptation combination. By matching the equipment combinations of multiple combined power generation simulation data according to the efficiency adaptation combination and generating a power generation intensity adaptation combination unit, the flexibility and adaptability of the equipment configuration are improved, ensuring that the power plant can achieve efficient power generation under different operating conditions, and enhancing the economy and resource utilization of the system. The steps of associating and linking power demand peak and valley data with power generation intensity adaptation combination units can effectively identify the optimal equipment combination under different power demand conditions, and obtain a peak and valley adaptation equipment unit table, ensuring that the appropriate equipment combination can be quickly matched during power demand peaks and valleys. This improves the dispatch flexibility of the power plant in the face of demand fluctuations, enhances the system's responsiveness to changes in power demand, and provides an important basis for subsequent dispatch decisions. Based on the peak and valley adaptation equipment unit table, the operation cycle of the power generation intensity adaptation combination unit is adjusted to generate adaptive equipment combination dispatch data, ensuring that the equipment operation cycle can be flexibly adjusted under different power demand conditions, optimizing resource allocation, and improving power generation efficiency. This ensures that the power plant can provide stable power supply during peak periods and reasonably reduce operating costs during valley periods, enhancing the operating efficiency and stability of the overall system, and providing solid technical support for the realization of intelligent power plant management.

[0044] Preferably, step S44 includes the following steps:

[0045] Step S441: performing time series decomposition on the peak-valley adaptation device unit table to obtain a basic cycle sequence; performing principal component extraction on the basic cycle sequence to obtain a core cycle pattern;

[0046] Step S442: Divide the core cycle mode into time windows to obtain scheduling time segments; perform device state mapping on the power generation intensity adaptation combination unit according to the scheduling time segments to generate a device state sequence;

[0047] Step S443: Calculate the state transition cost of the device state sequence to obtain a state transition matrix;

[0048] Step S444: perform shortest step detection on the state transition matrix to generate shortest path data; perform equipment operation cycle adjustment on the shortest path data to generate adaptive equipment combination scheduling data.

[0049] The present invention can effectively identify the basic cycle sequences in equipment operation by performing time series decomposition on the peak-valley adaptation equipment unit table. After obtaining these basic cycle sequences, the core cycle patterns are generated through principal component extraction, ensuring that the most representative cycle features can be extracted when analyzing the equipment operation rules, improving the understanding of the equipment operation status, enhancing the scientificity and rationality of subsequent scheduling strategies, and providing an important basis for optimizing equipment operation. The step of dividing the core cycle mode into time windows can divide the equipment operation data into scheduling time segments. Through this division, a corresponding scheduling strategy can be formulated for each time segment. The equipment status of the power generation intensity adaptation combination unit is mapped according to the scheduling time segment to generate an equipment status sequence, ensuring that the equipment operation status can be accurately reflected in different scheduling time periods, improving the real-time and flexibility of the scheduling process, and laying the foundation for subsequent status analysis. The state transition cost calculation operation is performed on the equipment status sequence to generate a state transition matrix. Through this matrix, the cost of the equipment transitioning between different states can be effectively evaluated, providing a quantitative basis for subsequent scheduling decisions, ensuring that the economy and feasibility of the equipment state transition can be fully considered when formulating the scheduling strategy, improving the efficiency and rationality of the overall scheduling, and providing support for the efficient use of power plant resources. The step of performing the shortest step length detection on the state transition matrix can identify the optimal path in equipment operation and generate the shortest path data, ensuring that the lowest-cost equipment state transition plan can be selected in actual scheduling. The equipment operation cycle is adjusted based on the shortest path data, and adaptive equipment combination scheduling data is generated to ensure that the power plant can flexibly adjust the equipment operation cycle during peak and trough periods, optimize resource allocation, improve power generation efficiency, enhance the adaptability and economy of the power plant in responding to changes in electricity demand, and provide technical support for efficient power plant management.

[0050] Preferably, step S443 includes the following steps:

[0051] Performing state quantization processing on the device state sequence to obtain a quantized state sequence;

[0052] Perform transfer frequency statistics on the quantized state sequence to generate a state transfer frequency table;

[0053] Based on the preset state transition weight data, the state transition frequency table is cost calculated to obtain the basic state transition cost; the basic state transition cost is matrix constructed to generate the state transition cost matrix;

[0054] Identify unreasonable costs on the state transfer cost matrix and obtain unreasonable conversion costs;

[0055] The cost branches of the state transfer cost matrix are cleaned up according to the unreasonable conversion costs to generate the state transition matrix.

[0056] The present invention can convert the operating state of the equipment into a quantifiable value by performing state quantization processing on the equipment state sequence, and obtain a quantized state sequence. Through this quantization, it is ensured that the operating performance of the equipment can be evaluated in a data-driven manner in subsequent analysis, which improves the analyzability and accuracy of the data, provides a basis for subsequent state transition frequency statistics, and enhances the comprehensive understanding of the equipment operating state. The step of performing transition frequency statistics on the quantized state sequence generates a state transition frequency table, which can reveal the frequency of equipment transitions between different states, provides a real basis for subsequent cost calculation, ensures that the actual operating conditions can be reflected when analyzing the effectiveness of equipment operation, improves the grasp of equipment operation dynamics, and lays the foundation for formulating more reasonable scheduling strategies. Based on the preset state transition weight data, the state transition frequency table is cost-calculated to obtain the basic state transition cost, ensuring that the cost of transitions between each state can be fully considered when evaluating the economic efficiency of equipment state transitions. The state transition cost matrix is ​​constructed, which can clearly present the transition costs between different states, promotes in-depth analysis of equipment operating efficiency, and provides a quantitative basis for subsequent scheduling optimization. The operation of identifying unreasonable costs in the state transfer cost matrix can effectively identify economic problems in equipment state transitions and obtain unreasonable conversion costs, which provides direction for subsequent cost branch cleaning, ensuring that unnecessary resource waste can be avoided when formulating scheduling strategies, and improving the rationality and efficiency of overall scheduling. According to the steps of clearing the cost branches of the state transfer cost matrix based on unreasonable conversion costs, a state transition matrix is ​​generated to ensure that reasonable state transition costs are reflected in the final model, thereby providing a scientific basis for subsequent scheduling decisions, improving the flexibility and economy of power plants in resource allocation and equipment scheduling, and ensuring efficient and stable power generation output under different power demand conditions.

[0057] The present invention further provides a power control system based on a power characteristic matrix, which is used to execute the power control method based on the power characteristic matrix described above. The power control system based on the power characteristic matrix includes:

[0058] The data preprocessing module is used to obtain distributed power plant equipment data; replace outliers in the distributed power plant equipment data to obtain complete equipment data; and filter the complete equipment data for standard operating parameters to generate standard equipment operating parameters;

[0059] The combined simulation module is used to simulate the equipment units of the standard equipment operating parameters to generate simulated power plant equipment units; it performs combined power generation simulation on the simulated power plant equipment units to obtain various combined power generation simulation data;

[0060] The power generation and demand analysis module is used to obtain historical power output data; perform power demand analysis on the historical power output data to obtain historical power demand data; perform power generation demand forecasting on the standard equipment operating parameters based on the historical power demand data to generate forecasted power output data; perform demand peak and valley analysis on the forecasted power output data to obtain power demand peak and valley data;

[0061] The scheduling analysis module is used to perform intensity adaptation screening on multiple power generation simulation data combinations based on the predicted required power output data to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptive equipment combination scheduling data;

[0062] The operation scheduling module is used to generate distributed equipment scheduling instructions based on the adaptive equipment combination scheduling data; and use the distributed equipment scheduling instructions to schedule the equipment operation of the virtual power plant to realize the intelligent power control method.

[0063] The present invention obtains distributed power plant equipment data through the data preprocessing module to ensure the comprehensiveness and reliability of the data. The implementation of abnormal value replacement effectively eliminates data deviations caused by equipment failure or data collection errors, thereby generating complete equipment data. These complete data are further screened for standard operating parameters to ensure that the data based on which subsequent analysis is based meets the normal operating standards of the equipment, thereby enhancing the consistency and effectiveness of the data, laying a solid foundation for subsequent analysis and optimization, and improving the technical level and reliability of the overall system. The combined simulation module simulates the equipment unit by simulating the standard equipment operating parameters to generate a variety of simulated power plant equipment units. This process is The power plant's operating strategy provides rich simulation data, which can deeply analyze the synergy between different equipment combinations. With the help of combined power generation simulation, a variety of combined power generation simulation data are generated, which provides diverse options for decision-making, optimizes the flexibility and efficiency of power plant operation, ensures the best power generation effect under different conditions, and improves the adaptability and response speed of the technology. The power demand analysis module establishes an important foundation for changes in power demand by acquiring and analyzing historical power output data. The in-depth analysis of historical data not only extracts the law of power demand, but also predicts power demand for standard equipment operating parameters, thereby generating the power output data required for prediction, and further implementing demand peaks and valleys. The analysis helps identify the peak and trough periods of electricity demand, provides a scientific basis for the optimization of subsequent dispatch strategies, ensures accurate dispatch decisions in the face of fluctuations in electricity demand, and improves the overall responsiveness of the system. The dispatch analysis module is based on the analysis of the predicted power output data, and performs intensity adaptation screening on a variety of combined power generation simulation data to ensure efficient power generation of different equipment combinations under various power demand conditions. It further adjusts the operating cycle based on the peak and trough data of electricity demand to generate adaptive equipment combination dispatch data. This highly adaptable dispatch strategy optimizes the operating efficiency of the equipment, ensuring that sufficient power output can be provided during peak electricity demand periods and sufficient power output during low demand periods. It effectively reduces unnecessary waste of resources and improves the overall operational efficiency and benefits of the power plant. The operation scheduling module generates distributed equipment scheduling instructions based on the adaptive equipment combination scheduling data to realize the intelligent management of the virtual power plant. These scheduling instructions can dynamically adjust the operating status of the equipment based on real-time data and prediction results, improve the degree of automation of equipment operation, and enhance the flexibility and response speed of power supply. The overall system not only promotes the coordination between distributed power plants and power grids, improves the stability and reliability of the power system, but also supports the efficient use of renewable energy, promotes the development and application of green energy, and ultimately meets the diversified power needs of users and enhances user satisfaction and service quality.

[0064] The present invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the power control method based on the power characteristic matrix as described in any one of the above items are implemented.

[0065] The present invention can optimize power production and distribution through real-time data collection and analysis, improve the overall operating efficiency of the power system, ensure that it can quickly respond and adjust the power generation combination when power demand fluctuates, effectively reduce operating costs, and improve economic benefits. Through in-depth mining and pattern recognition of equipment operation data, it can accurately identify power output patterns and demand changes, optimize equipment scheduling strategies, and enable various types of equipment to perform at their best under different working conditions, ensuring the stability and reliability of power supply. It adopts advanced algorithms for state conversion and cost analysis, can monitor equipment status in real time and make reasonable assessments, identify unreasonable operating costs, ensure the rational allocation and use of resources, reduce equipment maintenance costs and unnecessary power waste, and achieve Based on the prediction and simulation of historical data, through accurate prediction and analysis of electricity demand, it can provide a scientific basis for the dispatching decision of the power plant, ensuring that electricity demand can be met during peak periods, and effectively adjust equipment operation during off-peak periods to improve resource utilization. Through state quantification and frequency statistics, it can record the operating state changes of the equipment in detail and generate a clear state transfer matrix, providing accurate data support for subsequent dispatch optimization and decision-making, thereby improving the intelligence and automation level of power plant management, and providing a flexible strategy adjustment mechanism for power dispatch. Through dynamic optimization and adjustment of equipment combinations, it can respond quickly to changes in real-time electricity demand, ensure the efficiency and economy of power supply, and promote the integration and development of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic flow chart of the steps of a power control method based on a power characteristic matrix;

[0067] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0068] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0069] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0070] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0072] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve this, please refer to Figures 1 to 3 , a power control method based on a power characteristic matrix, comprising the following steps:

[0074] Step S1: Obtain distributed power plant equipment data; replace abnormal values ​​in the distributed power plant equipment data to obtain complete equipment data; filter the complete equipment data for standard operating parameters to generate standard equipment operating parameters;

[0075] Step S2: performing equipment unit simulation processing on the standard equipment operating parameters to generate simulated power plant equipment units; performing combined power generation simulation on the simulated power plant equipment units to obtain multiple combined power generation simulation data;

[0076] Step S3: Acquire historical power output data; perform power demand analysis on the historical power output data to obtain historical power demand data; perform power generation demand forecasting on standard equipment operating parameters based on the historical power demand data to generate forecasted power output data; perform demand peak and valley analysis on the forecasted power output data to obtain power demand peak and valley data;

[0077] Step S4: Based on the predicted required power output data, multiple combined power generation simulation data are subjected to intensity adaptation screening to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptation equipment combination scheduling data;

[0078] Step S5: Generate distributed device dispatching instructions based on the adaptive device combination dispatching data; use the distributed device dispatching instructions to dispatch equipment operation of the virtual power plant to realize the intelligent power control method.

[0079] By acquiring data from distributed power plant equipment, the present invention ensures the comprehensiveness and accuracy of the data base. By replacing outliers on this data, it effectively eliminates inaccurate data caused by data collection errors or equipment failures, thereby improving overall data reliability. Subsequently, standard operating parameters are screened against the complete equipment data to ensure that the data used meets the normal operating requirements of the equipment, which is crucial for subsequent analysis and simulation. This data standardization not only improves data availability and consistency but also lays a solid foundation for subsequent simulation analysis, ensuring the effectiveness and accuracy of subsequent steps. Simulating the standard equipment operating parameters on a per-unit basis generates a variety of simulated power plant equipment units. This process enables simulation of various equipment combinations under different operating conditions, providing a more comprehensive operational perspective. This simulation not only helps identify synergies between different equipment but also generates a variety of combined power generation simulation data through combined power generation simulation, providing a rich basis for power plant scheduling decisions. This simulation data allows for in-depth analysis of power plant operating efficiency and optimization of equipment configurations, thereby improving the flexibility and efficiency of overall power production. By acquiring historical power output data and conducting power demand analysis, a critical foundation is established for understanding power demand fluctuations. This phase involves not only in-depth analysis of historical data but also extracting patterns and trends in power demand to enable more accurate power output forecasts. Power demand forecasts based on this historical data significantly improve forecast accuracy, helping power plants prepare for future fluctuations in power demand. Further peak-valley analysis identifies peak and valley periods in power demand, providing a crucial basis for optimizing scheduling strategies and ensuring stable and reliable power supply. Based on analysis of the predicted power output data, power generation simulation data from various combinations is adapted to ensure efficient power generation under various demand conditions. This process not only improves the adaptability of the power mix but also ensures flexible power plant scheduling strategies to meet varying power demands. Next, the operating cycles of the power generation intensity-adapted combination units are adjusted based on power demand peak-valley data to further optimize the scheduling efficiency of the power mix. By rationally scheduling the equipment's operating cycles, sufficient power output can be provided during peak demand periods while reducing unnecessary energy loss during valley demand periods, thereby achieving efficient resource utilization. The generated distributed equipment dispatch instructions enable intelligent management of the virtual power plant. These instructions dynamically dispatch equipment operations based on real-time data and forecasts, improving the plant's responsiveness and control capabilities. This process not only facilitates coordination between distributed power plants and the grid, but also enhances the stability and reliability of the entire power system.Driven by intelligent control methods, distributed power plants can more efficiently utilize renewable energy, promoting the development and application of green energy. At the same time, by increasing the flexibility of power supply, they can better meet the diverse power needs of users, thereby enhancing user satisfaction and optimizing the overall quality of power service.

[0080] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a power control method based on a power characteristic matrix of the present invention. In this example, the power control method based on a power characteristic matrix includes the following steps:

[0081] Step S1: Obtain distributed power plant equipment data; replace abnormal values ​​in the distributed power plant equipment data to obtain complete equipment data; filter the complete equipment data for standard operating parameters to generate standard equipment operating parameters;

[0082] In this embodiment, the operation of obtaining distributed power plant equipment data is performed by using a data acquisition system to read sensor data from each distributed device in real time, and using industrial communication protocols such as Modbus or OPC UA (OLE for Process Control Unified Architecture) for data transmission to ensure the accuracy and timeliness of the data. Outliers are replaced in the acquired equipment data, and statistical methods such as Z-score or IQR (Interquartile Range) are used to detect outliers. Data that exceeds the normal range is replaced or eliminated to finally obtain complete equipment data. The complete equipment data is then screened for standard operating parameters, and data mining techniques such as cluster analysis or principal component analysis (PCA) are used to identify standard operating parameters of the equipment to ensure that subsequent analysis is based on accurate equipment performance data.

[0083] Step S2: performing equipment unit simulation processing on the standard equipment operating parameters to generate simulated power plant equipment units; performing combined power generation simulation on the simulated power plant equipment units to obtain multiple combined power generation simulation data;

[0084] In this embodiment, equipment unit simulation processing is performed on standard equipment operating parameters, and simulation software such as MATLAB / Simulink or ANSYS is used to build a model to construct a simulated power plant model that includes the characteristics of various types of equipment. Standard operating parameters are input into the model to perform dynamic simulation to generate simulated power plant equipment units. Next, a combined power generation simulation is performed on the simulated power plant equipment units. The power generation performance of different equipment combinations is analyzed using methods such as Monte Carlo simulation to obtain a variety of combined power generation simulation data. This data provides a real reference basis for subsequent power demand and resource scheduling.

[0085] Step S3: Acquire historical power output data; perform power demand analysis on the historical power output data to obtain historical power demand data; perform power generation demand forecasting on standard equipment operating parameters based on the historical power demand data to generate forecasted power output data; perform demand peak and valley analysis on the forecasted power output data to obtain power demand peak and valley data;

[0086] In this embodiment, the operation of obtaining historical power output data is performed by calling the historical data storage of the power company or the smart meter system. The data format is usually CSV (Comma-Separated Values) or database format. Next, the power demand analysis is performed on the historical power output data. The trend and cyclical changes of power demand are identified using time series analysis methods such as ARIMA (Autoregressive Integrated Moving Average model) to obtain historical power demand data. Based on the historical power demand data, the power generation demand is predicted for the standard equipment operating parameters. Linear regression or machine learning models such as Random Forest are used for prediction to generate the power output data required for the prediction. Then, the demand peak and valley analysis is performed on the power output data required for the prediction. The peak and valley analysis method is used to identify the peaks and valleys of power demand and obtain the peak and valley data of power demand to provide a basis for subsequent scheduling.

[0087] Step S4: Based on the predicted required power output data, multiple combined power generation simulation data are subjected to intensity adaptation screening to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptation equipment combination scheduling data;

[0088] In this embodiment, intensity adaptation screening is performed on multiple combinations of power generation simulation data based on the predicted required power output data, and an optimization algorithm such as a genetic algorithm or particle swarm optimization is used to evaluate the power generation intensity of different combinations to generate a power generation intensity adaptation combination unit. Subsequently, the operation cycle of the power generation intensity adaptation combination unit is adjusted based on the peak and valley data of power demand, and scheduling optimization is performed using linear programming or integer programming methods to generate adaptive equipment combination scheduling data to ensure that the operating time of the equipment can be reasonably configured during peak and valley periods, thereby improving power generation efficiency.

[0089] Step S5: Generate distributed device dispatching instructions based on the adaptive device combination dispatching data; use the distributed device dispatching instructions to dispatch equipment operation of the virtual power plant to realize the intelligent power control method.

[0090] In this embodiment, distributed device dispatching instructions are generated based on the combined dispatching data of the adapted devices, and a dispatching management system such as SCADA (Supervisory Control and Data Acquisition) is used to generate and issue instructions. The dispatching instructions include equipment startup, shutdown and operation parameter settings. The distributed device dispatching instructions are used to dispatch the equipment operation of the virtual power plant to ensure that each device works together according to the predetermined plan, monitor the equipment status in real time, and make dynamic adjustments through the feedback mechanism to ensure the efficient operation of the virtual power plant, ultimately realizing an intelligent power control method and improving the flexibility and reliability of the power system.

[0091] Preferably, step S1 includes the following steps:

[0092] Step S11: Obtain distributed power plant equipment data; perform abnormal value interval detection on the distributed power plant equipment data to obtain abnormal value intervals of power plant equipment;

[0093] Step S12: Eliminate abnormal values ​​of power plant equipment and generate cleaning power plant equipment data;

[0094] Step S13: performing linear interpolation of abnormal intervals on the cleaning power plant equipment data to obtain complete equipment data;

[0095] Step S14: performing equipment operation simulation on the complete equipment data to obtain a simulation operation parameter set;

[0096] Step S15: clustering the simulation operation parameter set to obtain a clustered operation parameter set;

[0097] Step S16: Standardize the clustered operating parameter set to generate standard equipment operating parameters.

[0098] In this embodiment, the operation of obtaining distributed power plant equipment data is to use a data acquisition system to collect real-time data from each distributed device. The specific method includes using Modbus protocol or OPC UA (OLE for Process Control Unified Architecture) for data communication to ensure that the data transmission between devices is real-time and accurate. The collected data includes multiple parameters such as voltage, current, power, temperature, etc. Next, the distributed power plant equipment data is detected for outlier intervals, and statistical methods such as Z-score detection or box plot (Box Plot) method is used to identify abnormal points in the data, determine the upper and lower limits of abnormal values, generate abnormal value intervals for power plant equipment, and perform abnormal elimination operations on abnormal value intervals for power plant equipment. First, according to the determined abnormal value interval, by writing a data processing script, use Python or R language to implement data filtering, eliminate data that is not in the normal interval, and generate cleaning power plant equipment data. The specific method includes using the DataFrame in the Pandas library to perform data operations, traversing each data point, determining whether it is within the normal range, ensuring that valid data is retained, and after eliminating abnormal data, the generated data set should contain historical data of each device under normal operating conditions, perform abnormal interval linear interpolation operations on the cleaning power plant equipment data, and apply the linear interpolation algorithm to fill in missing or incomplete data. The specific steps are to first identify the location of the missing value in the data set, and then calculate the interpolation based on the valid data points before and after. Use the interpolation function of the NumPy library to perform linear interpolation calculations to generate complete device data and ensure data continuity during the interpolation process. The specific method includes setting the value of the missing data point to the weighted average of the valid data points before and after it, performing device operation simulation operations on the complete device data, using simulation software such as MATLAB / Simulink to build a dynamic model of the device, establishing a mathematical model including device characteristics and operating parameters, inputting complete device data, performing time domain simulation, and generating a simulation operation parameter set. The specific implementation steps include defining the physical parameters, operating constraints and environmental conditions of the device, setting the simulation time, running the simulation model and recording the operating status of the device under different conditions, and finally obtaining a simulation operation data set containing multiple parameters such as device output power and temperature change. The simulation operation parameter set is clustered using clustering algorithms such as K-means or hierarchical clustering. Clustering) is used to analyze the simulation data. First, the simulation operation parameter set is standardized to eliminate the influence of different dimensions on the clustering results. Then, the parameters of the clustering algorithm are set, such as the number of clusters. Then, the standardized data is clustered to generate a cluster operation parameter set. Each cluster represents a group of similar equipment operation states. In the specific implementation, the Python Scikit-learn library is used to cluster the data.Ensure that the clustering results can effectively reflect the operating characteristics of the equipment under different working conditions, providing a basis for subsequent standardization. The operation of standardizing the cluster operating parameter set uses standardization techniques such as Z-score standardization or Min-Max scaling to unify the clustering results to eliminate the dimensional influence between different characteristics. The specific method is to calculate the mean and standard deviation of each cluster parameter, use a formula to standardize, and generate standard equipment operating parameters. This ensures that all parameters are on the same scale, facilitating subsequent analysis and comparison. The final standard equipment operating parameters will provide basic data for equipment performance evaluation and optimization, ensuring the effectiveness of subsequent scheduling and decision-making.

[0099] Preferably, step S2 includes the following steps:

[0100] Step S21: extracting features from the standard equipment operating parameters to obtain equipment characteristic parameters; performing correlation analysis on the equipment characteristic parameters to generate a parameter correlation matrix;

[0101] Step S22: performing an equipment unit operating condition analysis on the equipment characteristic parameters based on the parameter association matrix to obtain an operating condition parameter set; performing an operation trajectory calculation on the operating condition parameter set to obtain equipment operation trajectory data;

[0102] Step S23: performing equipment simulation processing on the equipment characteristic parameters according to the equipment operation trajectory data to generate a simulated power plant equipment unit;

[0103] Step S24: simulating the power generation operation of the simulated power plant equipment unit to obtain simulated equipment operating data;

[0104] Step S25: Based on the simulated equipment working data, feasible combined power generation simulation is performed on the simulated power plant equipment units to obtain multiple combined power generation simulation data.

[0105] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0106] Step S21: extracting features from the standard equipment operating parameters to obtain equipment characteristic parameters; performing correlation analysis on the equipment characteristic parameters to generate a parameter correlation matrix;

[0107] In this embodiment, the feature extraction operation of the standard equipment operating parameters is first performed by using a data processing tool such as the Pandas library in Python to clean and organize the equipment operating parameter data, and extract key features such as the power, efficiency, temperature, etc. of the equipment. Then, a feature selection algorithm such as Random Forest or Principal Component Analysis (PCA) is used to screen out the most influential features to ensure that the extracted features can effectively reflect the equipment performance and generate equipment characteristic parameters. Subsequently, a correlation analysis is performed on the equipment characteristic parameters, and a correlation coefficient matrix and a visualization tool such as the Seaborn library are used to draw a heat map to generate a parameter correlation matrix. By analyzing the correlation between different features, the key parameters affecting the equipment performance are identified to ensure the accuracy and effectiveness of subsequent analysis.

[0108] Step S22: performing an equipment unit operating condition analysis on the equipment characteristic parameters based on the parameter association matrix to obtain an operating condition parameter set; performing an operation trajectory calculation on the operating condition parameter set to obtain equipment operation trajectory data;

[0109] In this embodiment, the operation of analyzing the operating condition of the equipment unit based on the equipment characteristic parameters is first performed by using a machine learning algorithm such as a support vector machine (SVM) or a decision tree to classify the equipment characteristic parameters to obtain an operating condition parameter set. Then, the operating trajectory of the operating condition parameter set is calculated, and a simulation tool such as MATLAB / Simulink is used to construct a dynamic model to simulate the operating behavior of the equipment under different operating conditions and generate equipment operating trajectory data to ensure that the simulation process takes into account the actual working environment and dynamic changes of the equipment. By analyzing the operating trajectory, the operating characteristics of the equipment under various operating conditions are identified, providing real basic data for subsequent simulations.

[0110] Step S23: performing equipment simulation processing on the equipment characteristic parameters according to the equipment operation trajectory data to generate a simulated power plant equipment unit;

[0111] In this embodiment, the operation of performing device simulation processing on the device characteristic parameters based on the device operation trajectory data first establishes a mathematical model of the device in the simulation software, inputs the acquired device operation trajectory data, selects a suitable simulation algorithm, such as discrete event simulation (Discrete Event Simulation) or continuous simulation, runs the simulation model, and generates a simulated power plant equipment unit to ensure that the simulation model can truly reflect the operating characteristics and behavior of the equipment. During the simulation process, various operating parameters of the equipment, such as power output, load changes, etc., are recorded, and a detailed simulation report is generated to provide necessary data support for subsequent power generation operation simulation, thereby ensuring the accuracy and reliability of the simulation results.

[0112] Step S24: simulating the power generation operation of the simulated power plant equipment unit to obtain simulated equipment operating data;

[0113] In this embodiment, the operation of power generation operation simulation is performed on the simulated power plant equipment unit. Simulation software such as MATLAB or Simulink is used to build a comprehensive simulation model containing all equipment parameters. Various data of the simulated power plant equipment units are input to perform dynamic operation simulation to ensure that the power generation efficiency and output power of each device can be simulated under different operating conditions. The equipment working data of each simulation operation, such as power output, energy consumption and operating time, are recorded to generate simulated equipment working data to ensure that the simulation results cover different operating scenarios and can truly reflect the performance of the equipment in actual operation, providing an accurate data basis for subsequent combined power generation simulation.

[0114] Step S25: Based on the simulated equipment working data, feasible combined power generation simulation is performed on the simulated power plant equipment units to obtain multiple combined power generation simulation data.

[0115] In this embodiment, a feasible combination power generation simulation operation is performed on the simulated power plant equipment units based on the simulated equipment working data. First, the simulated equipment working data is analyzed to identify the power generation potential and operating efficiency of the equipment. Then, a combinatorial optimization algorithm, such as a genetic algorithm (Genetic Algorithm) or a mixed integer linear programming (MILP), is used to evaluate different equipment combinations and generate a variety of combined power generation simulation data to ensure that each combination takes into account the synergy and load balancing between the equipment. Finally, the power generation capacity, operating cost and efficiency of each combination are output to form a feasible equipment combination plan, provide a scientific basis for the scheduling and management of the power plant, and ensure the comprehensiveness and effectiveness of the combined power generation simulation results.

[0116] Preferably, step S25 includes the following steps:

[0117] Step S251: classifying the simulated device working data into types to obtain a device classification data set; performing power characteristic analysis on the device classification data set to obtain a power characteristic matrix;

[0118] Step S252: performing independent operation simulation on the simulated power plant equipment units according to the power characteristic matrix to generate equipment independent operation data; performing permutations, combinations and groupings on the simulated power plant equipment units to generate multiple groups of permuted equipment units;

[0119] Step S253: performing a combined operation simulation on the arranged equipment units according to the power characteristic matrix to generate multiple sets of equipment combined operation data; performing an optimized combination screening on the equipment combined operation data based on the equipment independent operation data to obtain feasible equipment combined operation data;

[0120] Step S254: Based on the feasible equipment combination working data, a combined power generation simulation is performed on the simulated power plant equipment units to obtain a plurality of combined power generation simulation data.

[0121] In this embodiment, the operation of classifying the simulated equipment operating data by type is carried out by using data mining technology. The equipment operating data includes the equipment's operating parameters, power output, and operating status. Then, the data is classified using the Pandas library in Python, grouped according to equipment type and operating status, and an equipment classification data set is generated. Next, the equipment classification data set is subjected to power characteristic analysis. Statistical analysis tools such as NumPy and SciPy are used to calculate the power output of different equipment and generate a power characteristic matrix. This ensures that the matrix contains the power output characteristics of each equipment under different operating conditions, providing the necessary parameters for subsequent independent operation simulations. Based on the power characteristic matrix, an independent operation simulation is performed on the simulated power plant equipment units. A dynamic model containing all equipment parameters is established using simulation software such as MATLAB / Simulink. The data in the power characteristic matrix is ​​input to perform operation simulations of individual equipment. The output data of each equipment in the independent operation state is recorded to generate equipment independent operation data. This ensures that the dynamic response and load changes of the equipment are taken into account during the simulation process. The obtained independent operation data will provide a basis for subsequent equipment combination simulations. The steps of arranging, combining and grouping the simulated power plant equipment units are as follows: by writing an algorithm, using Python's itertools library to fully arrange and combine the equipment, generating multiple groups of arranged equipment units, ensuring that each combination takes into account the operating characteristics and power output limitations of the equipment, then simulating the combined operation of the arranged equipment units according to the power characteristic matrix, using the above-mentioned simulation model to simulate the operation of each combination, recording the output data of each group of equipment in the combined working state, generating multiple groups of equipment combination working data, and ensuring that the actual operation of each group of combinations is effectively evaluated. The operation of optimizing and combining the equipment combination working data based on the independent working data of the equipment is first evaluated for each group of equipment combination working data, and using a multi-objective optimization algorithm, such as a genetic algorithm or particle swarm optimization (Particle Swarm Optimization), to analyze the power generation efficiency and operating cost of the combination, screen out the optimal combination, and obtain feasible equipment combination working data, ensuring that the synergy and load balance of the equipment are taken into account during the screening process. Based on the working data of feasible equipment combinations, the simulated power plant equipment units are simulated for combined power generation. The obtained feasible combination data is used to simulate power generation using simulation software. A comprehensive simulation model containing all feasible combinations is established. The operating parameters of the equipment combination are input, dynamic simulation is performed, and the power generation performance and efficiency of each combination are recorded. Finally, multiple combinations of power generation simulation data are generated to provide a scientific basis for the scheduling and management of the power plant, ensuring that under different power demand conditions, the equipment combination can be flexibly adjusted to achieve efficient power generation.

[0122] Preferably, step S3 includes the following steps:

[0123] Step S31: Acquire historical power output data; perform frequency domain transformation on the historical power output data to obtain a historical power feature sequence; perform power output pattern recognition on the historical power feature sequence to generate a historical power output pattern;

[0124] Step S32: Analyze the power demand of historical power output patterns to obtain historical power demand data; calculate the current power demand of standard equipment operating parameters based on the historical power demand data to obtain the power production data required by the current equipment;

[0125] Step S33: predicting the required power output of the standard equipment operating parameters based on the current required power generation data of the equipment to generate predicted required power output data;

[0126] Step S34: performing curve fitting on the predicted required power output data to obtain a predicted required power output curve; performing curve extreme point detection on the predicted required power output curve to obtain power demand peak and valley data.

[0127] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0128] Step S31: Acquire historical power output data; perform frequency domain transformation on the historical power output data to obtain a historical power feature sequence; perform power output pattern recognition on the historical power feature sequence to generate a historical power output pattern;

[0129] In this embodiment, the operation of obtaining historical power output data is achieved by extracting data from a power management system or a smart meter. The data format is usually CSV (Comma-Separated Values) or a database format, which contains power output records over a period of time in the past. Next, the historical power output data is subjected to frequency domain transformation processing. The time domain data is converted into frequency domain data using Fast Fourier Transform (FFT), the main frequency components in the power output signal are identified, and a historical power feature sequence is generated. Subsequently, power output pattern recognition is performed on the historical power feature sequence. A clustering algorithm such as K-means or DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to analyze the data, identify different power output patterns, and finally generate a historical power output pattern to provide basic data for subsequent power demand analysis.

[0130] Step S32: Analyze the power demand of historical power output patterns to obtain historical power demand data; calculate the current power demand of standard equipment operating parameters based on the historical power demand data to obtain the power production data required by the current equipment;

[0131] In this embodiment, the operation of performing power demand analysis on historical power output patterns first models the power demand change trend based on the identified historical power output patterns using time series analysis methods, such as ARIMA (autoregressive integrated moving average model), to obtain historical power demand data. Then, based on the historical power demand data, the current power generation demand is calculated for the standard equipment operating parameters, and the current power demand is predicted using linear regression or machine learning models, such as random forest (RandomForest), to ensure that the calculated power generation data required by the current equipment can accurately reflect the changes in power demand, thereby providing real data support for subsequent power output predictions.

[0132] Step S33: predicting the required power output of the standard equipment operating parameters based on the current required power generation data of the equipment to generate predicted required power output data;

[0133] In this embodiment, the required power output of the standard equipment operating parameters is predicted based on the current equipment required power production data. First, the standard equipment operating parameters are combined with the current power production demand, and a multivariate linear regression or neural network model is used for prediction. The input of the model includes historical power output, equipment performance parameters and current power demand, and the predicted required power output data is generated to ensure that the prediction process fully considers the efficiency and load characteristics of the equipment. The final output of the predicted required power output data will provide a basis for the scheduling and management of the power plant, ensuring that the equipment operation strategy can be adjusted in time when the power demand changes.

[0134] Step S34: performing curve fitting on the predicted required power output data to obtain a predicted required power output curve; performing curve extreme point detection on the predicted required power output curve to obtain power demand peak and valley data.

[0135] In this embodiment, a curve fitting operation is performed on the predicted required power output data. A data analysis tool such as SciPy or MATLAB is used to perform curve fitting on the predicted data using a polynomial fitting or spline fitting method to generate a smooth predicted required power output curve. It is ensured that an appropriate degree of fitting is selected during the fitting process to avoid overfitting or underfitting. The predicted required power output curve is then subjected to curve extreme point detection. The extreme points of the curve are identified using a derivative method or a second-order derivative method to obtain peak and valley data of power demand. It is ensured that the identified peak and valley data can accurately reflect the peaks and valleys of power demand, provide a scientific basis for the scheduling and resource allocation of power plants, and ensure that reasonable decisions can be made under different power demand conditions.

[0136] Preferably, step S4 includes the following steps:

[0137] Step S41: performing power superposition calculation on multiple combined power generation simulation data to generate combined power data; performing stable efficiency calculation on the combined power data to generate a combined efficiency index;

[0138] Step S42: Based on the predicted required power output data, the combined efficiency index is subjected to power generation intensity adaptation screening to obtain an efficiency adaptation combination; and according to the efficiency adaptation combination, the multiple combined power generation simulation data are subjected to device combination matching to obtain a power generation intensity adaptation combination unit;

[0139] Step S43: Associatively link the power demand peak and valley data and the power generation intensity adaptation combination unit to obtain a peak and valley adaptation device unit table;

[0140] Step S44: adjusting the operation cycle of the power generation intensity adaptation combination unit based on the peak-valley adaptation device unit table to generate adaptation device combination scheduling data.

[0141] In this embodiment, the power superposition calculation operation is performed on the power generation simulation data of multiple combinations. First, the power generation simulation data of all combinations are collected to ensure that the data is complete and in the same format. Then, the NumPy library in Python is used to superimpose the power generation of each combination, and the total power output of each combination in different time periods is calculated to generate the combined power data. Next, the stable efficiency of the combined power data is calculated using the efficiency formula, that is, efficiency is equal to the ratio of output power to input power. The calculation results are sorted using the Pandas library to generate a combined efficiency index to ensure that the efficiency index of each combination can accurately reflect its performance in actual operation, providing a basis for subsequent adaptation screening. Based on the predicted required power output data, the power generation intensity adaptation screening of the combined efficiency index is performed. First, the predicted required power output data and the combined efficiency index are cross-analyzed. The conditional screening algorithm is used to ensure that the efficiency of all combinations meets the set threshold standard to obtain the efficiency adaptation combination. Then, the power generation simulation data of multiple combinations are matched with the equipment combination according to the efficiency adaptation combination. A matching algorithm, such as a greedy algorithm or dynamic programming, is used to combine the equipment that meets the conditions to generate The power generation intensity adaptation combination unit is formed to ensure that the matching process takes into account the power output and stability of the equipment, improve the overall efficiency of the combination, and perform correlation linking operations on the power demand peak and valley data and the power generation intensity adaptation combination unit. First, the peak and valley data are integrated with the power generation intensity adaptation combination unit. The two are connected according to time series or specific conditions using data framework tools such as Pandas' merge function to generate a peak and valley adaptation equipment unit table to ensure that each combination unit can correspond to the corresponding power demand peak and valley data. Through this association, the operating characteristics of different equipment combinations during peak and valley periods are clarified, providing a basis for subsequent scheduling decisions and ensuring the accuracy and real-time nature of the data. Based on the peak and valley adaptation equipment unit table, the operation cycle of the power generation intensity adaptation combination unit is adjusted. First, the operating time and power demand of each combination unit in the peak and valley adaptation equipment unit table are analyzed. Optimization algorithms such as linear programming are used to adjust the operating cycle of the equipment to ensure that during peak power demand periods, equipment combinations with high efficiency can be scheduled first. Adaptive equipment combination scheduling data is generated and scheduling management system software such as SCADA (Supervisory The generated scheduling data is visualized to ensure the rationality and feasibility of the scheduling plan, and ultimately form a set of efficient equipment combination scheduling strategies to cope with changes in power demand.

[0142] Preferably, step S44 includes the following steps:

[0143] Step S441: performing time series decomposition on the peak-valley adaptation device unit table to obtain a basic cycle sequence; performing principal component extraction on the basic cycle sequence to obtain a core cycle pattern;

[0144] Step S442: Divide the core cycle mode into time windows to obtain scheduling time segments; perform device state mapping on the power generation intensity adaptation combination unit according to the scheduling time segments to generate a device state sequence;

[0145] Step S443: Calculate the state transition cost of the device state sequence to obtain a state transition matrix;

[0146] Step S444: perform shortest step detection on the state transition matrix to generate shortest path data; perform equipment operation cycle adjustment on the shortest path data to generate adaptive equipment combination scheduling data.

[0147] In this embodiment, the time series decomposition operation of the peak-valley adaptation equipment unit table is first performed by using the statsmodels library in Python to perform seasonal decomposition on the equipment operation data, and adopting an additive or multiplicative model to obtain a basic cycle series. The basic cycle series contains trend components, seasonal components, and residual components. The next step is to extract the principal components of the basic cycle series and use principal component analysis (PCA) to extract the principal components of the basic cycle series. Analysis), use the PCA tool in the Scikit-learn library to extract the core cycle pattern that best represents the data variability, ensure that the extracted principal components can effectively reflect the main characteristics of the equipment operation status, generate the core cycle pattern, and provide a basis for the subsequent scheduling time segment division. The core cycle pattern is divided into time windows. First, according to the actual needs of the equipment operation and historical data, a reasonable time window length is set. Use Python's Pandas library to slice the core cycle pattern and generate scheduling time segments to ensure that each time segment can represent a complete equipment operation cycle. Next, the power generation intensity adaptation combination unit is mapped to the device state according to the scheduling time segment. Using the mapping algorithm, each time segment is matched with the corresponding device state to generate a device state sequence. Ensure that the state sequence can truly reflect the operating state of the equipment in each time period and provide basic data for the subsequent state transition cost calculation. The state transition cost calculation operation is performed on the device state sequence. First, define the cost function of state transition The cost function can consider factors such as the energy consumption, start and stop time of the equipment, and then construct a state transition matrix. Use the NumPy library in Python to count the transitions between the states in the equipment state sequence and generate a state transition matrix to ensure that each element in the matrix can accurately reflect the cost of transitioning from one state to another. After completion, the state transition matrix obtained provides necessary data support for the subsequent shortest step detection. The shortest step detection operation is performed on the state transition matrix. The Dijkstra algorithm or the A* algorithm is used to analyze the state transition matrix to find the shortest path from the initial state to the target state, ensuring that the algorithm can effectively handle the transition cost of each state in the matrix and generate the shortest path data. Next, the equipment operation cycle is adjusted according to the obtained shortest path data. The scheduling optimization tool is used to generate adaptive equipment combination scheduling data by adjusting the operating time and sequence of the equipment to ensure that the final scheduling plan can maximize the power generation efficiency of the equipment and reduce the operating cost. The obtained scheduling data will provide a scientific basis for the intelligent scheduling of the power plant.

[0148] Preferably, step S443 includes the following steps:

[0149] Performing state quantization processing on the device state sequence to obtain a quantized state sequence;

[0150] Perform transfer frequency statistics on the quantized state sequence to generate a state transfer frequency table;

[0151] Based on the preset state transition weight data, the state transition frequency table is cost calculated to obtain the basic state transition cost; the basic state transition cost is matrix constructed to generate the state transition cost matrix;

[0152] Identify unreasonable costs on the state transfer cost matrix and obtain unreasonable conversion costs;

[0153] The cost branches of the state transfer cost matrix are cleaned up according to the unreasonable conversion costs to generate the state transition matrix.

[0154] In this embodiment, the operation of performing state quantization processing on the device state sequence first classifies the continuous states in the device state sequence, defines the quantization standard for each state, and uses the NumPy library or Pandas library in Python to convert the state sequence into a quantized state sequence to ensure that each state is mapped to a discrete numerical value. Next, the transition frequency of the quantized state sequence is counted, and the groupby function in the Pandas library is used to count the transitions between adjacent states to generate a state transition frequency table. The frequency table includes the number of transitions between each state to ensure that the statistical results can truly reflect the changing patterns of the device state and provide a data basis for subsequent cost calculations. The cost calculation process for the state transition frequency table is performed based on the preset state transition weight data. First, weight data for each state transition is set. These weights can be set based on the device's energy consumption, maintenance costs, and operating efficiency. The frequency in the state transition frequency table is then multiplied by the corresponding weight to obtain the basic state transition cost. The Pandas library in Python is used for data processing to ensure that the cost of each state transition accurately reflects the actual situation. After completion, a matrix is ​​constructed for the basic state transition cost to generate a state transition cost matrix. Each element of the matrix corresponds to the transition cost between each pair of states in the state transition frequency table, ensuring that the matrix is ​​structured for subsequent analysis. The state transition cost matrix is ​​used to identify unreasonable costs. First, thresholds for reasonable costs are set. These thresholds can be based on industry standards or historical device data. Then, using the NumPy library in Python, each element in the state transition cost matrix is ​​compared to identify costs that exceed the reasonable thresholds. This generates unreasonable conversion costs, ensuring that the identified unreasonable costs accurately reflect abnormalities in device operation and providing a basis for subsequent cost cleanup. The cost branch cleaning operation of the state transition cost matrix is ​​performed based on the unreasonable conversion costs. First, the identified unreasonable costs are analyzed to determine the state transitions that need to be cleaned up. The drop function in the Pandas library is used to remove the unreasonable state transitions from the cost matrix to generate a cleaned state transition matrix. It is ensured that the cleaned matrix only contains reasonable state transfer costs. The final state transition matrix will provide accurate data support for subsequent scheduling optimization and equipment management, ensuring cost control and efficiency improvement during equipment operation.

[0155] The present invention further provides a power control system based on a power characteristic matrix, which is used to execute the power control method based on the power characteristic matrix described above. The power control system based on the power characteristic matrix includes:

[0156] The data preprocessing module is used to obtain distributed power plant equipment data; replace outliers in the distributed power plant equipment data to obtain complete equipment data; and filter the complete equipment data for standard operating parameters to generate standard equipment operating parameters;

[0157] The combined simulation module is used to simulate the equipment units of the standard equipment operating parameters to generate simulated power plant equipment units; it performs combined power generation simulation on the simulated power plant equipment units to obtain various combined power generation simulation data;

[0158] The power generation and demand analysis module is used to obtain historical power output data; perform power demand analysis on the historical power output data to obtain historical power demand data; perform power generation demand forecasting on the standard equipment operating parameters based on the historical power demand data to generate forecasted power output data; perform demand peak and valley analysis on the forecasted power output data to obtain power demand peak and valley data;

[0159] The scheduling analysis module is used to perform intensity adaptation screening on multiple power generation simulation data combinations based on the predicted required power output data to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptive equipment combination scheduling data;

[0160] The operation scheduling module is used to generate distributed equipment scheduling instructions based on the adaptive equipment combination scheduling data; and use the distributed equipment scheduling instructions to schedule the equipment operation of the virtual power plant to realize the intelligent power control method.

[0161] The present invention obtains distributed power plant equipment data through the data preprocessing module to ensure the comprehensiveness and reliability of the data. The implementation of abnormal value replacement effectively eliminates data deviations caused by equipment failure or data collection errors, thereby generating complete equipment data. These complete data are further screened for standard operating parameters to ensure that the data based on which subsequent analysis is based meets the normal operating standards of the equipment, thereby enhancing the consistency and effectiveness of the data, laying a solid foundation for subsequent analysis and optimization, and improving the technical level and reliability of the overall system. The combined simulation module simulates the equipment unit by simulating the standard equipment operating parameters to generate a variety of simulated power plant equipment units. This process is The power plant's operating strategy provides rich simulation data, which can deeply analyze the synergy between different equipment combinations. With the help of combined power generation simulation, a variety of combined power generation simulation data are generated, which provides diverse options for decision-making, optimizes the flexibility and efficiency of power plant operation, ensures the best power generation effect under different conditions, and improves the adaptability and response speed of the technology. The power demand analysis module establishes an important foundation for changes in power demand by acquiring and analyzing historical power output data. The in-depth analysis of historical data not only extracts the law of power demand, but also predicts power demand for standard equipment operating parameters, thereby generating the power output data required for prediction, and further implementing demand peaks and valleys. The analysis helps identify the peak and trough periods of electricity demand, provides a scientific basis for the optimization of subsequent dispatch strategies, ensures accurate dispatch decisions in the face of fluctuations in electricity demand, and improves the overall responsiveness of the system. The dispatch analysis module is based on the analysis of the predicted power output data, and performs intensity adaptation screening on a variety of combined power generation simulation data to ensure efficient power generation of different equipment combinations under various power demand conditions. It further adjusts the operating cycle based on the peak and trough data of electricity demand to generate adaptive equipment combination dispatch data. This highly adaptable dispatch strategy optimizes the operating efficiency of the equipment, ensuring that sufficient power output can be provided during peak electricity demand periods and sufficient power output during low demand periods. It effectively reduces unnecessary waste of resources and improves the overall operational efficiency and benefits of the power plant. The operation scheduling module generates distributed equipment scheduling instructions based on the adaptive equipment combination scheduling data to realize the intelligent management of the virtual power plant. These scheduling instructions can dynamically adjust the operating status of the equipment based on real-time data and prediction results, improve the degree of automation of equipment operation, and enhance the flexibility and response speed of power supply. The overall system not only promotes the coordination between distributed power plants and power grids, improves the stability and reliability of the power system, but also supports the efficient use of renewable energy, promotes the development and application of green energy, and ultimately meets the diversified power needs of users and enhances user satisfaction and service quality.

[0162] The present invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the power control method based on the power characteristic matrix as described in any one of the above items are implemented.

[0163] The present invention can optimize power production and distribution through real-time data collection and analysis, improve the overall operating efficiency of the power system, ensure that it can quickly respond and adjust the power generation combination when power demand fluctuates, effectively reduce operating costs, and improve economic benefits. Through in-depth mining and pattern recognition of equipment operation data, it can accurately identify power output patterns and demand changes, optimize equipment scheduling strategies, and enable various types of equipment to perform at their best under different working conditions, ensuring the stability and reliability of power supply. It adopts advanced algorithms for state conversion and cost analysis, can monitor equipment status in real time and make reasonable assessments, identify unreasonable operating costs, ensure the rational allocation and use of resources, reduce equipment maintenance costs and unnecessary power waste, and achieve Based on the prediction and simulation of historical data, through accurate prediction and analysis of electricity demand, it can provide a scientific basis for the dispatching decision of the power plant, ensuring that electricity demand can be met during peak periods, and effectively adjust equipment operation during off-peak periods to improve resource utilization. Through state quantification and frequency statistics, it can record the operating state changes of the equipment in detail and generate a clear state transfer matrix, providing accurate data support for subsequent dispatch optimization and decision-making, thereby improving the intelligence and automation level of power plant management, and providing a flexible strategy adjustment mechanism for power dispatch. Through dynamic optimization and adjustment of equipment combinations, it can respond quickly to changes in real-time electricity demand, ensure the efficiency and economy of power supply, and promote the integration and development of renewable energy.

[0164] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0165] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A power control method based on a power characteristic matrix, characterized in that: The following steps are involved: Step S1: Obtaining distributed power plant equipment data; Replace outliers in distributed power plant equipment data to obtain complete equipment data; Screen the complete equipment data for standard operating parameters and generate standard equipment operating parameters; Step S2: performing equipment unit simulation processing on the standard equipment operating parameters to generate simulated power plant equipment units; performing combined power generation simulation on the simulated power plant equipment units to obtain multiple combined power generation simulation data; Step S3: Obtain historical power output data; Performing power demand analysis on historical power output data to obtain historical power demand data; Based on historical power demand data, the power generation demand is predicted for the standard equipment operating parameters to generate the predicted power output data; the predicted power output data is analyzed for demand peaks and valleys to obtain power demand peak and valley data; Step S4: Based on the predicted required power output data, multiple combined power generation simulation data are subjected to intensity adaptation screening to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptation equipment combination scheduling data; Step S5: Generate distributed device dispatching instructions based on the adaptive device combination dispatching data; use the distributed device dispatching instructions to dispatch equipment operation of the virtual power plant to realize the intelligent power control method.

2. The power control method based on the power characteristic matrix according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtain distributed power plant equipment data; perform abnormal value interval detection on the distributed power plant equipment data to obtain abnormal value intervals of power plant equipment; Step S12: Eliminate abnormal values ​​of power plant equipment and generate cleaning power plant equipment data; Step S13: performing linear interpolation of abnormal intervals on the cleaning power plant equipment data to obtain complete equipment data; Step S14: performing equipment operation simulation on the complete equipment data to obtain a simulation operation parameter set; Step S15: clustering the simulation operation parameter set to obtain a clustered operation parameter set; Step S16: Standardize the clustered operating parameter set to generate standard equipment operating parameters.

3. The power control method based on the power characteristic matrix according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting features from the standard equipment operating parameters to obtain equipment characteristic parameters; performing correlation analysis on the equipment characteristic parameters to generate a parameter correlation matrix; Step S22: performing an equipment unit operating condition analysis on the equipment characteristic parameters based on the parameter association matrix to obtain an operating condition parameter set; performing an operation trajectory calculation on the operating condition parameter set to obtain equipment operation trajectory data; Step S23: performing equipment simulation processing on the equipment characteristic parameters according to the equipment operation trajectory data to generate a simulated power plant equipment unit; Step S24: simulating the power generation operation of the simulated power plant equipment unit to obtain simulated equipment operating data; Step S25: Based on the simulated equipment working data, feasible combined power generation simulation is performed on the simulated power plant equipment units to obtain multiple combined power generation simulation data.

4. The power control method based on the power characteristic matrix according to claim 3 is characterized in that: Step S25 includes the following steps: Step S251: classifying the simulated device working data into types to obtain a device classification data set; performing power characteristic analysis on the device classification data set to obtain a power characteristic matrix; Step S252: performing independent operation simulation on the simulated power plant equipment units according to the power characteristic matrix to generate equipment independent operation data; performing permutations, combinations and groupings on the simulated power plant equipment units to generate multiple groups of permuted equipment units; Step S253: performing a combined operation simulation on the arranged equipment units according to the power characteristic matrix to generate multiple sets of equipment combined operation data; performing an optimized combination screening on the equipment combined operation data based on the equipment independent operation data to obtain feasible equipment combined operation data; Step S254: Based on the feasible equipment combination working data, a combined power generation simulation is performed on the simulated power plant equipment units to obtain a plurality of combined power generation simulation data.

5. The power control method based on the power characteristic matrix according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Acquire historical power output data; perform frequency domain transformation on the historical power output data to obtain a historical power feature sequence; perform power output pattern recognition on the historical power feature sequence to generate a historical power output pattern; Step S32: Analyze the power demand of historical power output patterns to obtain historical power demand data; calculate the current power demand of standard equipment operating parameters based on the historical power demand data to obtain the power production data required by the current equipment; Step S33: predicting the required power output of the standard equipment operating parameters based on the current required power generation data of the equipment to generate predicted required power output data; Step S34: performing curve fitting on the predicted required power output data to obtain a predicted required power output curve; performing curve extreme point detection on the predicted required power output curve to obtain power demand peak and valley data.

6. The power control method based on the power characteristic matrix according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing power superposition calculation on multiple combined power generation simulation data to generate combined power data; performing stable efficiency calculation on the combined power data to generate a combined efficiency index; Step S42: Based on the predicted required power output data, the combined efficiency index is subjected to power generation intensity adaptation screening to obtain an efficiency adaptation combination; and according to the efficiency adaptation combination, the multiple combined power generation simulation data are subjected to device combination matching to obtain a power generation intensity adaptation combination unit; Step S43: Associatively link the power demand peak and valley data and the power generation intensity adaptation combination unit to obtain a peak and valley adaptation device unit table; Step S44: adjusting the operation cycle of the power generation intensity adaptation combination unit based on the peak-valley adaptation device unit table to generate adaptation device combination scheduling data.

7. The power control method based on the power characteristic matrix according to claim 6, characterized in that: Step S44 includes the following steps: Step S441: performing time series decomposition on the peak-valley adaptation device unit table to obtain a basic cycle sequence; performing principal component extraction on the basic cycle sequence to obtain a core cycle pattern; Step S442: Divide the core cycle mode into time windows to obtain scheduling time segments; perform device state mapping on the power generation intensity adaptation combination unit according to the scheduling time segments to generate a device state sequence; Step S443: Calculate the state transition cost of the device state sequence to obtain a state transition matrix; Step S444: perform shortest step detection on the state transition matrix to generate shortest path data; perform equipment operation cycle adjustment on the shortest path data to generate adaptive equipment combination scheduling data.

8. The power control method based on the power characteristic matrix according to claim 7, characterized in that: Step S443 includes the following steps: Performing state quantization processing on the device state sequence to obtain a quantized state sequence; Perform transfer frequency statistics on the quantized state sequence to generate a state transfer frequency table; Based on the preset state transition weight data, the state transition frequency table is cost calculated to obtain the basic state transition cost; the basic state transition cost is matrix constructed to generate the state transition cost matrix; Identify unreasonable costs on the state transfer cost matrix and obtain unreasonable conversion costs; The cost branches of the state transfer cost matrix are cleaned up according to the unreasonable conversion costs to generate the state transition matrix.

9. A power control system based on a power characteristic matrix, characterized in that: For executing the power control method based on the power characteristic matrix according to claim 1, the power control system based on the power characteristic matrix comprises: The data preprocessing module is used to obtain distributed power plant equipment data; replace outliers in the distributed power plant equipment data to obtain complete equipment data; and filter the complete equipment data for standard operating parameters to generate standard equipment operating parameters; The combined simulation module is used to simulate the equipment units of the standard equipment operating parameters to generate simulated power plant equipment units; it performs combined power generation simulation on the simulated power plant equipment units to obtain various combined power generation simulation data; The power generation and demand analysis module is used to obtain historical power output data; perform power demand analysis on the historical power output data to obtain historical power demand data; perform power generation demand forecasting on the standard equipment operating parameters based on the historical power demand data to generate forecasted power output data; perform demand peak and valley analysis on the forecasted power output data to obtain power demand peak and valley data; The scheduling analysis module is used to perform intensity adaptation screening on multiple power generation simulation data combinations based on the predicted required power output data to obtain power generation intensity adaptation combination units; based on the power demand peak and valley data, the operation cycle of the power generation intensity adaptation combination units is adjusted to generate adaptive equipment combination scheduling data; The operation scheduling module is used to generate distributed equipment scheduling instructions based on the adaptive equipment combination scheduling data; and use the distributed equipment scheduling instructions to schedule the equipment operation of the virtual power plant to realize the intelligent power control method.

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