An industrial intelligent energy-saving power supply system

Abstract

The present invention relates to the technical field of energy-saving power supply, and specifically discloses an industrial intelligent energy-saving power supply system. The system analyzes and obtains the predicted energy-saving power supply feature matching value of the power grid system and the energy-saving power supply evaluation value of the power grid system by setting up a predicted energy-saving power supply feature matching value acquisition module, an actual energy-saving power supply feature matching value acquisition module, and a power grid system energy-saving power supply scheme acquisition module. The energy-saving power supply evaluation value of the power grid system is imported into the energy-saving power supply matching model to obtain the actual energy-saving power supply feature matching value of the power grid system. Based on the comparison result between the actual energy-saving power supply feature matching value of the power grid system and the predicted energy-saving power supply feature matching value of the power grid system, a scheme for energy-saving power supply to the power grid system is obtained. The present invention helps to carry out targeted optimization and adjustment, improve the energy-saving power supply capacity and operation efficiency of the power grid, and reduce the problem that the predicted optimization scheme does not match the scheme that actually needs to be optimized, resulting in the inability to achieve the expected energy-saving power supply effect.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving power supply, and specifically to an industrial intelligent energy-saving power supply system. Background Art

[0002] The research on industrial intelligent energy-saving power supply systems can effectively improve energy utilization efficiency, reduce energy waste, and reduce the impact on the environment. Industrial enterprises usually consume a large amount of electricity, and the electricity cost accounts for a part of the production cost. By researching industrial intelligent energy-saving power supply systems, it can help enterprises improve production efficiency, reduce energy consumption and costs, and enhance competitiveness. With the continuous development of technologies such as artificial intelligence, big data analysis, and the Internet of Things, intelligent applications have been widely used in the industrial field. Industrial intelligent energy-saving power supply systems combine these intelligent technologies and can achieve intelligent monitoring, optimized scheduling, and energy-saving control of electrical equipment. Energy conservation and emission reduction policies encourage enterprises to take energy-saving measures to reduce energy consumption and emissions.

[0003] For example, the invention patent with the publication number CN106208394B discloses an intelligent energy-saving power supply system and method based on cloud computing technology. The system includes an intelligent distribution box and a cloud computing control center. The intelligent distribution box includes an intelligent circuit breaker, a main controller, and an intelligent gateway. The intelligent circuit breaker monitors the power consumption data of the electrical load in the power supply circuit and sends it to the main controller; the main controller collects the power consumption data monitored by the intelligent circuit breaker and sends it to the intelligent gateway; the intelligent gateway sends the power consumption data to the cloud computing control center through the Internet; the cloud computing control center receives the power consumption data sent by the intelligent gateway, analyzes the power consumption data, generates a switch control instruction according to the energy-saving measure, and sends the switch control instruction to the intelligent gateway through the Internet; the main controller obtains the switch control instruction through the intelligent gateway and controls the intelligent circuit breaker to conduct or disconnect the power supply circuit of the electrical load according to the instruction. This kind of intelligent energy-saving power supply system has the advantages of low cost and no need for manual operation.

[0004] For example, in the invention patent with the publication number CN114597951 B, an optimization method for the energy-saving operation of electric vehicles participating in an AC urban rail power supply system is disclosed. Specifically, the electric vehicle is electrically connected to the traction substation. By setting the train traction and braking strategies and the charging and discharging strategies of the electric vehicle as optimization variables, and setting constraint conditions according to the design performance and the random access state of the electric vehicle, the optimization objective function is determined, and an optimization model for the energy-saving operation of the electric vehicle participating in the AC urban rail power supply system that meets single-objective optimization or multi-objective optimization is established. An intelligent optimization algorithm is used to automatically compare and select all possible train traction and braking strategies and the charging and discharging strategies of the electric vehicle to determine the optimal solution that meets the optimization objective. This invention can efficiently, quickly, and accurately determine the system energy-saving operation plan, improve the refinement level of the energy-saving operation of the electric vehicle accessing the AC urban rail power supply system, reduce the electricity cost of the AC urban rail power supply system, and meet the charging needs of the electric vehicle.

[0005] At present, there are still some deficiencies in the research on an industrial intelligent energy-saving power supply system. Specifically, the current research on an industrial intelligent energy-saving power supply system is limited to the analysis of real-time electricity consumption data, ignoring the analysis of historical electricity consumption conditions, unable to discover potential problems, and unable to evaluate the energy-saving effect. Historical electricity consumption data can reflect the long-term operation trend and seasonal changes of equipment. Ignoring the analysis of historical electricity consumption conditions is not conducive to formulating long-term energy-saving strategies, nor is the actual energy-saving power supply of the power grid system optimized. If the power grid system is not optimized, there may be a situation of energy waste. The unoptimized power grid system may lead to an increase in energy costs, there may be a risk of system instability in the unoptimized power grid system, and it may also cause the expected optimization plan to not match the plan that actually needs to be optimized, resulting in the inability to achieve the expected energy-saving power supply effect. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides an industrial intelligent energy-saving power supply system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: An industrial intelligent energy-saving power supply system includes an expected energy-saving power supply feature matching value acquisition module, an actual energy-saving power supply feature matching value acquisition module, and a power grid system energy-saving power supply solution acquisition module, where: The expected energy-saving power supply feature matching value acquisition module is used to obtain the power consumption situation data sets of each historical period of the power grid system, and based on the obtained power consumption situation data sets of each historical period of the power grid system, analyze and obtain the expected energy-saving power supply feature matching value of the power grid system; The actual energy-saving power supply feature matching value acquisition module is used to obtain the real-time power consumption situation data set of the power grid system, and based on the obtained real-time power consumption situation data set of the power grid system, analyze and obtain the energy-saving power supply evaluation value of the power grid system, import the energy-saving power supply evaluation value of the power grid system into the energy-saving power supply matching model, obtain the actual energy-saving power supply feature matching value of the power grid system, and compare the actual energy-saving power supply feature matching value of the power grid system with the expected energy-saving power supply feature matching value of the power grid system; The power grid system energy-saving power supply solution acquisition module is used to obtain a solution for energy-saving power supply to the power grid system based on the comparison result between the actual energy-saving power supply feature matching value of the power grid system and the expected energy-saving power supply feature matching value of the power grid system.

[0008] As a further solution, the power consumption situation data sets of each historical period of the power grid system specifically include the power consumption quality data of each historical period of the power grid system and the power consumption quantity data of each historical period of the power grid system. Among them, the power consumption quality data of each historical period of the power grid system includes the number of instantaneous overvoltages in each historical period of the power grid system, the maximum difference in instantaneous frequency change in each historical period of the power grid system, the duration of short-term voltage sags in each historical period of the power grid system, and the number of short-term interruptions in each historical period of the power grid system. The power consumption quantity data of each historical period of the power grid system includes the total power consumption in each historical period of the power grid system, the power consumption for controlling the start and stop of equipment in each historical period of the power grid system, and the total power consumption during peak hours in each historical period of the power grid system.

[0009] As a further solution, based on the obtained power consumption situation data sets of each historical period of the power grid system, analyzing and obtaining the expected energy-saving power supply feature matching value of the power grid system, the specific analysis process is: comprehensively analyzing the obtained power consumption quality data of each historical period of the power grid system and the power consumption quantity data of each historical period of the power grid system to obtain the expected energy-saving power supply feature matching value of the power grid system, and the expected energy-saving power supply feature matching value of the power grid system is used as the analysis basis for obtaining a solution for energy-saving power supply to the power grid system.

[0010] As a further solution, the specific analysis process of the expected energy-saving power supply feature matching value of the power grid system is:

[0011]

[0012] In the formula, is the expected energy-saving power supply feature matching value of the power grid system, It is the matching value of the power quality characteristics of the power grid system in the j-th historical period. It is the matching value of the power consumption characteristics of the power grid system in the j-th historical period. ε1 is the weight factor of the matching value of the power quality characteristics of the power grid system set, ε2 is the weight factor of the matching value of the power consumption characteristics of the power grid system set, j is the number of each historical period, j = 1, 2, 3,..., n, n is the total number of historical periods, and e is the natural constant.

[0013] As a further solution, the real-time power consumption situation dataset of the power grid system specifically includes the real-time power factor of the power grid system, the real-time voltage of the power grid system, and the proportion of real-time new energy and traditional energy used in the power grid system.

[0014] As a further solution, based on the obtained real-time power consumption situation dataset of the power grid system, the energy-saving power supply evaluation value of the power grid system is analyzed. The specific analysis process is as follows: The energy-saving power supply evaluation value of the power grid system is comprehensively analyzed based on the obtained real-time power consumption situation dataset of the power grid system, and the energy-saving power supply evaluation value of the power grid system is used as the analysis basis for obtaining the actual energy-saving power supply characteristic matching value of the power grid system.

[0015] As a further solution, the energy-saving power supply evaluation value of the power grid system, the specific analysis process is as follows:

[0016]

[0017] In the formula, γ is the energy-saving power supply evaluation value of the power grid system, GY is the real-time power factor of the power grid system, D is the real-time voltage of the power grid system, BL is the proportion of real-time new energy and traditional energy used in the power grid system, GY0 is the reference power factor of the power grid system stored in the energy-saving power supply database, D0 is the reference voltage of the power grid system stored in the energy-saving power supply database, τ1 is the compensation factor of the real-time power factor of the power grid system set, τ2 is the compensation factor of the real-time voltage of the power grid system set, and τ3 is the compensation factor of the proportion of real-time new energy and traditional energy used in the power grid system set.

[0018] As a further solution, the energy-saving power supply evaluation value of the power grid system is imported into the energy-saving power supply matching model to obtain the actual energy-saving power supply characteristic matching value of the power grid system. The specific analysis process is as follows: The energy-saving power supply evaluation value of the power grid system is imported into the energy-saving power supply matching model to obtain the actual energy-saving power supply characteristic matching value of the power grid system, and the actual energy-saving power supply characteristic matching value of the power grid system is used as the analysis basis for obtaining the solution for energy-saving power supply to the power grid system.

[0019] As a further solution, a solution for energy-saving power supply to the power grid system is obtained. The specific analysis process is as follows: Compare the actual energy-saving power supply feature matching value of the power grid system with the expected energy-saving power supply feature matching value of the power grid system; if the actual energy-saving power supply feature matching value of the power grid system is lower than the expected energy-saving power supply feature matching value of the power grid system, then mark this power grid system as an excellent energy-saving power supply grid system, obtain the deviation value between the actual energy-saving power supply feature matching value of the power grid system and the reference energy-saving power supply feature matching value of the power grid system, record the deviation value between the actual energy-saving power supply feature matching value of the power grid system and the reference energy-saving power supply feature matching value of the power grid system as the energy-saving power supply feature matching deviation value of the power grid system, obtain the energy-saving power supply solution corresponding to this energy-saving power supply feature matching deviation value stored in the energy-saving power supply database, send an energy-saving power supply prompt to the power grid system, and apply the energy-saving power supply solution corresponding to this energy-saving power supply feature matching deviation value to the power grid system; if the actual energy-saving power supply feature matching value of the power grid system is equal to the expected energy-saving power supply feature matching value of the power grid system, then mark this power grid system as a good energy-saving power supply grid system, obtain the energy-saving power supply solution corresponding to this expected energy-saving power supply feature matching value of the power grid system stored in the energy-saving power supply database, send an energy-saving power supply prompt to the power grid system, and apply the energy-saving power supply solution corresponding to this expected energy-saving power supply feature matching value of the power grid system to the power grid system; if the actual energy-saving power supply feature matching value of the power grid system is higher than the expected energy-saving power supply feature matching value of the power grid system, then mark this power grid system as a poor energy-saving power supply grid system, optimize the expected energy-saving power supply feature matching value of the power grid system by relevant field professionals in combination with historical records, obtain the optimized expected energy-saving power supply feature matching value of the power grid system, denoted as the optimized expected energy-saving power supply feature matching value of the power grid system, obtain the energy-saving power supply solution corresponding to this optimized expected energy-saving power supply feature matching value of the power grid system stored in the energy-saving power supply database, send an energy-saving power supply prompt to the power grid system, and apply the energy-saving power supply solution corresponding to this optimized expected energy-saving power supply feature matching value of the power grid system to the power grid system.

[0020] As a further solution, an industrial intelligent energy-saving power supply system further includes an energy-saving power supply database, which is used to store the reference power factor of the power grid system, the reference voltage of the power grid system, the energy-saving power supply solutions corresponding to the energy-saving power supply feature matching deviation values of each power grid system, the energy-saving power supply solutions corresponding to the expected energy-saving power supply feature matching values of each power grid system, and the energy-saving power supply solutions corresponding to the optimized expected energy-saving power supply feature matching values of each optimized power grid system.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) By providing an industrial intelligent energy-saving power supply system, the present invention can analyze historical power consumption situations, obtain the predicted energy-saving power supply characteristic matching values of the power grid system, identify the power consumption patterns and characteristics of the power grid system, thereby improving the prediction ability for future power consumption demands, helping to more accurately evaluate the current energy-saving power supply capacity of the power grid. The analysis based on historical power consumption data can help determine the energy-saving potential of the power grid system, that is, under the current power consumption situation, through which measures and optimizations can energy conservation be achieved, so as to guide the formulation and implementation of energy-saving power supply strategies. The analysis of historical power consumption data can help detect abnormal situations and emergencies, such as abnormal fluctuations in power consumption, and take timely measures to avoid a decline in energy-saving power supply capacity caused thereby. It can also discover problems and bottlenecks existing in the power supply of the power grid system, and conduct targeted optimization and adjustment to improve the energy-saving power supply capacity and operation efficiency of the power grid.

[0022] (2) By re-analyzing the energy-saving power supply schemes corresponding to excellent energy-saving power grid systems, the present invention can find more economical and energy-saving power supply schemes through re-analysis, which helps to reduce the energy consumption cost and the electricity consumption cost of enterprises. Re-optimizing the energy-saving power supply scheme can reduce energy consumption and environmental impacts such as carbon dioxide emissions, which is beneficial to environmental protection and sustainable development. The optimized energy-saving power supply scheme can improve the stability and reliability of the power grid system, reduce the risk of system failures, and ensure the stability of power supply. Re-analyzing the schemes of excellent energy-saving power grid systems can promote technological innovation and development, and drive the power grid system to develop towards a more intelligent and efficient direction. By re-analyzing and optimizing the energy-saving power supply scheme, enterprises can improve their own energy-saving levels and energy utilization efficiency, enhance their competitiveness, and gain a market advantage.

[0023] (3) By optimizing the energy-saving power supply schemes corresponding to poor energy-saving power grid systems, the optimized energy-saving power supply scheme can improve the stability and reliability of the power grid system, reduce the risk of system failures, and ensure the stability of power supply. It helps to reduce the problem that the expected optimization scheme does not match the scheme actually to be optimized, resulting in the inability to achieve the expected energy-saving power supply effect. The optimized energy-saving power supply scheme can improve the energy-saving level and energy utilization efficiency of enterprises, enhance their competitiveness, and gain a market advantage. The optimized energy-saving power supply scheme can reduce energy waste, improve energy utilization efficiency, and thus achieve more effective energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0025] Figure 1 Schematic diagram of system module connection for the present invention. Specific implementation manners

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 , the embodiments of the present invention provide an industrial intelligent energy-saving power supply technical solution: an industrial intelligent energy-saving power supply system, including an expected energy-saving power supply feature matching value acquisition module, an actual energy-saving power supply feature matching value acquisition module, and a power grid system energy-saving power supply scheme acquisition module.

[0028] The expected energy-saving power supply feature matching value acquisition module is used to acquire the power consumption situation data sets of each historical period of the power grid system, and based on the acquired power consumption situation data sets of each historical period of the power grid system, analyze and obtain the expected energy-saving power supply feature matching value of the power grid system.

[0029] Specifically, the dataset of the electricity consumption situation of each historical period of the power grid system specifically includes the power quality data of each historical period of the power grid system and the electricity consumption data of each historical period of the power grid system. Among them, the power quality data of each historical period of the power grid system includes the number of instantaneous overvoltage times in each historical period of the power grid system, the maximum difference in instantaneous frequency change in each historical period of the power grid system, the duration of short-term voltage sags in each historical period of the power grid system, and the number of short-term interruptions in each historical period of the power grid system. The indicator of the number of instantaneous overvoltage times in the historical period represents the number of instantaneous overvoltages that occur in the power grid system during the historical period. An instantaneous overvoltage refers to a situation where the voltage instantaneously exceeds the rated value, which may cause damage to the equipment and facilities in the power grid system. Therefore, too many instantaneous overvoltage times may affect the stability of the power grid system and the lifespan of the equipment. The indicator of the maximum difference in instantaneous frequency change in the historical period represents the maximum difference in frequency change in the power grid system during the historical period. Frequency is an important parameter in the power system, and the stability of frequency is crucial for the operation of the power grid system. Excessive frequency changes may lead to abnormal operation or damage of the equipment. The indicator of the duration of short-term voltage sags in the historical period represents the duration of short-term voltage sags that occur in the power grid system during the historical period. A short-term voltage sag refers to a situation where the voltage or frequency instantaneously drops within a short period of time, which may cause short-term power outages or abnormal operation of the equipment. The indicator of the number of short-term interruptions in the historical period represents the number of short-term interruptions that occur in the power grid system during the historical period. A short-term interruption refers to a short power outage situation that occurs in the power grid system, which may affect users and the reliability of the power grid system. The electricity consumption data of each historical period of the power grid system includes the total electricity consumption in each historical period of the power grid system, the electricity consumption for controlling the start and stop of equipment in each historical period of the power grid system, and the total electricity consumption during peak hours in each historical period of the power grid system. The indicator of the total electricity consumption in the historical period represents the total electricity consumption of the entire power grid system during the historical period. The total electricity consumption is an important indicator for measuring the energy consumption of the power grid system, reflecting the load demand and power utilization situation of the power grid system. The indicator of the electricity consumption for controlling the start and stop of equipment in the historical period represents the electricity consumption for controlling the start and stop of equipment during the historical period. The electricity consumption for controlling the start and stop of equipment usually includes control signal transmission, energy consumption during the start and stop process of the equipment, etc. The indicator of the total electricity consumption during peak hours in the historical period represents the total electricity consumption of the power grid system during peak hours in the historical period. Peak hours usually refer to the period when the load demand of the power grid system is the highest, and at this time, the power grid system needs to provide the maximum power supply to meet the user's demand. The total electricity consumption during peak hours reflects the power consumption situation of the power grid system during peak load.

[0030] Specifically, based on the dataset of the power consumption situation of the power grid system in each historical period obtained, the predicted energy-saving power supply feature matching value of the power grid system is analyzed. The specific analysis process is as follows: Based on the comprehensive analysis of the power quality data and the power consumption data of the power grid system in each historical period, the predicted energy-saving power supply feature matching value of the power grid system is obtained, and the predicted energy-saving power supply feature matching value of the power grid system is used as the analysis basis for obtaining the energy-saving power supply scheme for the power grid system.

[0031] In a specific embodiment, by analyzing the historical power consumption situation and obtaining the predicted energy-saving power supply feature matching value of the power grid system, the power consumption patterns and characteristics of the power grid system can be identified, thereby improving the prediction ability of future power consumption demands, helping to more accurately evaluate the current energy-saving power supply capacity of the power grid. The analysis based on historical power consumption data can help determine the energy-saving potential of the power grid system, that is, under the current power consumption situation, which measures and optimizations can be adopted to achieve energy conservation, so as to guide the formulation and implementation of energy-saving power supply strategies. The analysis of historical power consumption data can also help discover abnormal situations and emergencies, such as abnormal fluctuations in power consumption, and take timely measures to avoid the decline of energy-saving power supply capacity caused thereby. It can also discover the problems and bottlenecks existing in the power supply of the power grid system, and make targeted optimization adjustments to improve the energy-saving power supply capacity and operation efficiency of the power grid.

[0032] It should be explained that the above-mentioned predicted energy-saving power supply feature matching value of the power grid system can not only be further analyzed through a machine learning integrated model, using an integrated method such as Gradient Boosting Machine or random forest to combine the prediction results of multiple basic models to obtain a more accurate predicted energy-saving power supply feature matching value of the power grid system, but also can be calculated through the following method. The calculation formula of the predicted energy-saving power supply feature matching value of the power grid system is:

[0033]

[0034] In the formula, is the predicted energy-saving power supply feature matching value of the power grid system, is the power quality feature matching value of the power grid system in the jth historical period, is the power consumption feature matching value of the power grid system in the jth historical period, ε1 is the set weight factor of the power quality feature matching value of the power grid system, ε2 is the set weight factor of the power consumption feature matching value of the power grid system, j is the number of each historical period, j = 1, 2, 3,..., n, n is the total number of historical periods, and e is the natural constant.

[0035] In a specific embodiment, the predicted energy-saving power supply feature matching value of the power grid system is obtained by analyzing the power consumption data sets of each historical cycle of the power grid system, and comprehensively analyzing the power quality data and power consumption data of each historical cycle of the power grid system. By analyzing the power consumption data of historical cycles, the power quality and power consumption characteristics of the power grid system at different time periods can be discovered, thereby predicting future power consumption situations. The predicted energy-saving power supply feature matching value can help grid operators predict future energy-saving power supply requirements and characteristics, and formulate energy-saving power supply strategies accordingly. Comprehensively analyzing the power quality data and power consumption data of historical cycles can more accurately evaluate the power consumption demand and characteristic change trends of the power grid system. Through the predicted energy-saving power supply feature matching value, grid operators can optimize the energy-saving power supply plan, reasonably arrange power production and supply, improve power supply efficiency and energy-saving level. By analyzing the power consumption data of historical cycles, the power quality situation of the power grid system at different time periods can be understood, and the power supply quality and stability can be improved accordingly. The predicted energy-saving power supply feature matching value can help grid operators adjust the power production and supply methods, ensure power supply quality and stability, reduce energy waste and power supply risks. Through the predicted energy-saving power supply feature matching value, grid operators can better plan power production and supply, avoid overproduction or underproduction situations, reduce operating costs and resource waste. Optimizing the energy-saving power supply plan helps to save energy resources, improve resource utilization efficiency, and reduce economic costs.

[0036] It should be noted that the power quality feature matching value of each historical cycle of the above power grid system is used to evaluate the power quality of each historical cycle of the power grid system, and serves as an analysis basis for obtaining a solution for energy-saving power supply to the power grid system. The power quality feature matching value of each historical cycle of the power grid system can not only be further analyzed through a machine learning integration model. Using integration methods such as Gradient Boosting Machine or random forest, the prediction results of multiple basic models are combined to obtain a more accurate power quality feature matching value of each historical cycle of the power grid system. It can also be calculated through the following method. The calculation formula for the power quality feature matching value of each historical cycle of the power grid system is:

[0037]

[0038] In the formula, is the power quality feature matching value of the j-th historical cycle of the power grid system, SG j is the number of instantaneous overvoltages in the j-th historical cycle of the power grid system, SP j is the maximum difference in instantaneous frequency change in the j-th historical cycle of the power grid system, ZT j is the short-term voltage sag duration in the j-th historical cycle of the power grid system, DN j$N_j$ is the number of short-time interruptions in the $j$-th historical period of the power grid system, $SG_0$ is the defined number of instantaneous overvoltages of the power grid system stored in the energy-saving power supply database, $SP_0$ is the maximum difference in the defined instantaneous frequency change of the power grid system stored in the energy-saving power supply database, $ZT_0$ is the permitted short-time voltage sag duration of the power grid system stored in the energy-saving power supply database, $DN_0$ is the permitted number of short-time interruptions of the power grid system stored in the energy-saving power supply database, $\sigma_1$ is the compensation factor for the defined number of instantaneous overvoltages of the power grid system, $\sigma_2$ is the compensation factor for the maximum difference in the defined instantaneous frequency change of the power grid system, $\sigma_3$ is the compensation factor for the short-time voltage sag duration of the power grid system, $\sigma_4$ is the compensation factor for the number of short-time interruptions of the power grid system, $j$ is the number of each historical period, $j = 1, 2, 3,\cdots, n$, $n$ is the total number of historical periods, and $e$ is the natural constant.

[0039] It should be noted that the matching values of the power quality characteristics of each historical period of the above power grid system are calculated through the number of instantaneous overvoltages in each historical period of the power grid system, the maximum difference in the instantaneous frequency change in each historical period of the power grid system, the short-time voltage sag duration in each historical period of the power grid system, and the number of short-time interruptions in each historical period of the power grid system. By comprehensively considering factors such as the number of instantaneous overvoltages in each historical period of the power grid system, the maximum difference in the instantaneous frequency change in each historical period of the power grid system, the short-time voltage sag duration in each historical period of the power grid system, and the number of short-time interruptions in each historical period of the power grid system, and by comprehensively considering multiple power quality characteristic matching values, the power quality of the power grid system can be evaluated more comprehensively and objectively, so as to more accurately understand the operating status and existing problems of the power grid. Considering factors such as the number of instantaneous overvoltages and the maximum difference in frequency change can help detect abnormal situations in the operation of the power grid, take timely measures to maintain the stable operation of the power grid, and reduce the risks of equipment damage and power outages. By comprehensively considering factors such as the short-time voltage sag duration and the number of short-time interruptions, the operating conditions of the equipment can be better understood, equipment management and maintenance can be carried out targeted, the service life of the equipment can be extended, and the reliability of the equipment can be improved. Comprehensively considering multiple power quality characteristic matching values helps to formulate a more reasonable power grid operation and dispatching plan, optimize the power grid operation efficiency, improve the power supply quality and energy-saving power supply capacity, reduce energy waste, and comprehensively considering multiple factors can help predict potential safety risks and take preventive measures in advance to prevent accidents and ensure the safe and stable operation of the power grid system.

[0040] It should be noted that the matching values of the power consumption characteristics of each historical period of the above power grid system are used to evaluate the power consumption of each historical period of the power grid system and serve as the analysis basis for obtaining the energy-saving power supply plan for the power grid system. The matching values of the power consumption characteristics of each historical period of the power grid system can not only be further analyzed through a machine learning integration model, using integration methods such as the K-means clustering model, the support vector machine model, or CatBoost, to combine the prediction results of multiple basic models to obtain more accurate matching values of the power consumption characteristics of each historical period of the power grid system, but also can be calculated through the following method. The calculation formula for the matching values of the power consumption characteristics of each historical period of the power grid system is as follows:

[0041]

[0042] In the formula, is the matching value of the power consumption characteristics of the j-th historical period of the power grid system, QZ j is the total power consumption of the j-th historical period of the power grid system, QT j is the power consumption for controlling the start and stop of equipment in the j-th historical period of the power grid system, GQ j is the total power consumption during the peak period of the j-th historical period of the power grid system, GQ0 is the reference power consumption during the peak period of the power grid system stored in the energy-saving power supply database, σ5 is the compensation factor for the proportion of the power consumption for controlling the start and stop of equipment in the total power consumption of the power grid system, σ6 is the compensation factor for the total power consumption during the peak period of the power grid system, j is the number of each historical period, j = 1, 2, 3,..., n, and n is the total number of historical periods.

[0043] It should be noted that the matching values of the electricity consumption characteristics of each historical period of the above power grid system are calculated through the total electricity consumption of each historical period of the power grid system, the electricity consumption for controlling the start and stop of equipment in each historical period of the power grid system, and the total electricity consumption during peak hours in each historical period of the power grid system. By comprehensively considering factors such as the total electricity consumption of each historical period of the power grid system, the electricity consumption for controlling the start and stop of equipment in each historical period of the power grid system, and the total electricity consumption during peak hours in each historical period of the power grid system, and through comprehensively considering various electricity consumption characteristic matching values, the electricity consumption situation of the power grid system can be more comprehensively understood, energy conservation and emission reduction measures can be formulated targeted, the energy consumption of the power grid system can be reduced, and the impact on the environment can be reduced. Considering factors such as the electricity consumption for controlling the start and stop of equipment can help identify the power consumption of equipment, optimize the operation strategy of equipment targeted, reduce unnecessary energy consumption, and extend the service life of equipment. Comprehensively considering factors such as the total electricity consumption during peak hours helps to better understand the power grid load situation, adjust the power grid operation strategy targeted, improve the load balancing ability of the power grid, and reduce problems caused by overload. By comprehensively considering various electricity consumption characteristic matching values, the electricity consumption cost of the power grid system can be more effectively managed, the electricity consumption structure can be optimized, the electricity consumption cost can be reduced, and the electricity consumption efficiency can be improved. Comprehensively considering factors such as the total electricity consumption can help predict the electricity demand of the power grid system, adjust the power supply plan targeted, improve the power supply reliability, and ensure the normal electricity consumption of users.

[0044] The actual energy-saving power supply characteristic matching value acquisition module is used to obtain the real-time electricity consumption situation dataset of the power grid system. Based on the obtained real-time electricity consumption situation dataset of the power grid system, the energy-saving power supply evaluation value of the power grid system is analyzed, the energy-saving power supply evaluation value of the power grid system is imported into the energy-saving power supply matching model to obtain the actual energy-saving power supply characteristic matching value of the power grid system, and the actual energy-saving power supply characteristic matching value of the power grid system is compared with the expected energy-saving power supply characteristic matching value of the power grid system.

[0045] Specifically, the real-time electricity consumption situation dataset of the power grid system specifically includes the real-time power factor of the power grid system, the real-time voltage of the power grid system, and the usage ratio of real-time new energy and traditional energy in the power grid system.

[0046] It should be noted that the above real-time power factor of the power grid system is a parameter that measures the relationship between active power and reactive power in the power grid system. A power factor of 1 indicates that the active power and reactive power in the power grid system are completely synchronized, and a power factor lower than 1 indicates the existence of a certain degree of reactive power. The real-time power factor can reflect the power quality and stability of the power grid system, and helps to evaluate the operation efficiency of the power grid system. The real-time voltage of the power grid system refers to the voltage levels of each node or device in the power grid system. A stable voltage level is crucial for the normal operation of the power grid system. Too high or too low voltage may cause equipment damage or abnormal operation. Monitoring the real-time voltage can help the power grid operator detect voltage abnormalities in a timely manner and take corresponding measures. The indicator of the real-time proportion of new energy and traditional energy used in the power grid system represents the proportion of new energy and traditional energy used in the power grid system. New energy includes renewable energy such as solar energy and wind energy, and traditional energy includes traditional energy such as coal and natural gas. Monitoring the proportion of new energy and traditional energy used can help evaluate the energy structure of the power grid system, guide energy dispatching and planning work, and promote the development and utilization of renewable energy.

[0047] Furthermore, based on the obtained dataset of the real-time power consumption situation of the power grid system, an energy-saving power supply evaluation value of the power grid system is analyzed. The specific analysis process is as follows: The energy-saving power supply evaluation value of the power grid system is comprehensively analyzed based on the obtained dataset of the real-time power consumption situation of the power grid system, and the energy-saving power supply evaluation value of the power grid system is used as the analysis basis for obtaining the matching value of the actual energy-saving power supply characteristics of the power grid system.

[0048] It should be explained that the above energy-saving power supply evaluation value of the power grid system can not only be further analyzed through a machine learning integration model. Using an integration method such as a support vector machine model, the prediction results of multiple basic models are combined to obtain a more accurate energy-saving power supply evaluation value of the power grid system, but also can be calculated through the following method. The calculation formula for the energy-saving power supply evaluation value of the power grid system is:

[0049]

[0050] In the formula, γ is the energy-saving power supply evaluation value of the power grid system, GY is the real-time power factor of the power grid system, D is the real-time voltage of the power grid system, BL is the real-time proportion of new energy and traditional energy used in the power grid system, GY0 is the reference power factor of the power grid system stored in the energy-saving power supply database, D0 is the reference voltage of the power grid system stored in the energy-saving power supply database, τ1 is the compensation factor for the real-time power factor of the power grid system set, τ2 is the compensation factor for the real-time voltage of the power grid system set, and τ3 is the compensation factor for the real-time proportion of new energy and traditional energy used in the power grid system set.

[0051] It should be noted that the above energy-saving power supply evaluation value of the power grid system is calculated through the real-time power factor of the power grid system, the real-time voltage of the power grid system, and the real-time proportion of new energy and traditional energy used in the power grid system. By comprehensively considering factors such as the real-time power factor of the power grid system, the real-time voltage of the power grid system, and the real-time proportion of new energy and traditional energy used in the power grid system, the energy utilization efficiency of the power grid system can be comprehensively evaluated. The energy-saving power supply evaluation value can reflect the overall performance of the power grid system in terms of energy utilization, help to discover potential problems of energy waste and optimize energy utilization. By monitoring and evaluating the energy-saving power supply evaluation value, grid operators can timely detect energy waste and inefficiency problems existing in the power grid system and take corresponding energy-saving measures for optimization. This helps to improve the energy-saving level of the power grid system, reduce energy consumption, and lower operating costs. Comprehensively considering the proportion of new energy and traditional energy used can help grid operators better plan and dispatch new energy resources, promote the rational use of new energy and increase the proportion of new energy in the power grid system. This helps to promote the development of renewable energy, reduce dependence on traditional energy, lower carbon emissions, and achieve clean energy supply. Comprehensively considering factors such as power factor and voltage can help grid operators timely adjust the operating state of the power grid system, maintain the stability and reliability of the power grid system. Optimizing the power factor and voltage level helps to reduce the energy loss and the risk of equipment damage in the power grid system, improve the power supply quality and stability, and contribute to the energy-saving power supply of the power grid system.

[0052] Furthermore, import the energy-saving power supply evaluation value of the power grid system into the energy-saving power supply matching model to obtain the actual energy-saving power supply characteristic matching value of the power grid system. The specific analysis process is as follows: Import the energy-saving power supply evaluation value of the power grid system into the energy-saving power supply matching model to obtain the actual energy-saving power supply characteristic matching value of the power grid system. The actual energy-saving power supply characteristic matching value of the power grid system serves as the analysis basis for obtaining the scheme for energy-saving power supply to the power grid system.

[0053] It should be noted that the above actual energy-saving power supply characteristic matching value of the power grid system can not only be further analyzed through a machine learning integration model. Use an integration method such as LightGBM to combine the prediction results of multiple basic models to obtain a more accurate actual energy-saving power supply characteristic matching value of the power grid system. It can also be calculated through the following method. The energy-saving power supply matching model is:

[0054]

[0055] Wherein, E is the actual energy-saving power supply feature matching value of the power grid system, γ is the energy-saving power supply evaluation value of the power grid system, δ is the compensation factor of the set energy-saving power supply evaluation value of the power grid system, τ1 is the compensation factor of the set real-time power factor of the power grid system, τ2 is the compensation factor of the set real-time voltage of the power grid system, τ3 is the compensation factor of the set proportion of real-time new energy and traditional energy used in the power grid system, and α is the correction factor of the set actual energy-saving power supply feature matching value of the power grid system.

[0056] It should be noted that by importing the energy-saving power supply evaluation value of the power grid system into the energy-saving power supply matching model, the actual energy-saving power supply feature matching value of the power grid system is obtained. The actual energy-saving power supply feature matching value of the power grid system is used as the analysis basis for obtaining the energy-saving power supply plan for the power grid system. By using the actual energy-saving power supply feature matching value as the analysis basis, the current energy-saving power supply requirements and features of the power grid system can be understood more accurately. This helps to formulate a more precise energy-saving power supply plan, avoid resource waste and improve the energy-saving effect. The actual energy-saving power supply feature matching value reflects the actual situation of the current power grid system, and the energy-saving power supply plan can be adjusted in time to cope with the changing power consumption demands and grid states. This helps to improve the response speed and flexibility of the power grid system. By comparing the actual energy-saving power supply feature matching value with the predicted value, the actual effect and accuracy of the energy-saving power supply plan can be evaluated. This helps to detect problems in time and make adjustments to improve the implementation effect of the energy-saving power supply plan. Based on the analysis of the actual energy-saving power supply feature matching value, the resource allocation can be better optimized, the power supply efficiency and energy-saving level can be improved. This helps to reduce costs, reduce energy waste, and achieve the optimal utilization of resources. The actual energy-saving power supply feature matching value provides an important basis for decision-making, helps to formulate a more scientific and effective energy-saving power supply plan. This is conducive to improving the scientificity and feasibility of decision-making and promoting the development of the power grid system towards the direction of intelligence, high efficiency, and cleanliness.

[0057] The energy-saving power supply plan acquisition module for the power grid system is used to obtain the energy-saving power supply plan for the power grid system based on the comparison result between the actual energy-saving power supply feature matching value and the predicted energy-saving power supply feature matching value of the power grid system.

[0058] Specifically, a solution for energy-saving power supply to the power grid system is obtained, and the specific analysis process is as follows: Compare the actual energy-saving power supply feature matching value of the power grid system with the expected energy-saving power supply feature matching value of the power grid system; if the actual energy-saving power supply feature matching value of the power grid system is lower than the expected energy-saving power supply feature matching value of the power grid system, then mark this power grid system as an excellent energy-saving power supply grid system, obtain the deviation value between the actual energy-saving power supply feature matching value of the power grid system and the reference energy-saving power supply feature matching value of the power grid system, record the deviation value between the actual energy-saving power supply feature matching value of the power grid system and the reference energy-saving power supply feature matching value as the energy-saving power supply feature matching deviation value of the power grid system, obtain the energy-saving power supply solution corresponding to this energy-saving power supply feature matching deviation value stored in the energy-saving power supply database, send an energy-saving power supply prompt to the power grid system, and apply the energy-saving power supply solution corresponding to this energy-saving power supply feature matching deviation value to the power grid system; if the actual energy-saving power supply feature matching value of the power grid system is equal to the expected energy-saving power supply feature matching value of the power grid system, then mark this power grid system as a good energy-saving power supply grid system, obtain the energy-saving power supply solution corresponding to this expected energy-saving power supply feature matching value of the power grid system stored in the energy-saving power supply database, send an energy-saving power supply prompt to the power grid system, and apply the energy-saving power supply solution corresponding to this expected energy-saving power supply feature matching value to the power grid system; if the actual energy-saving power supply feature matching value of the power grid system is higher than the expected energy-saving power supply feature matching value of the power grid system, then mark this power grid system as a poor energy-saving power supply grid system, and let professionals in the relevant field optimize the expected energy-saving power supply feature matching value of the power grid system in combination with historical records, obtain the optimized expected energy-saving power supply feature matching value of the power grid system, denoted as the optimized expected energy-saving power supply feature matching value of the power grid system, obtain the energy-saving power supply solution corresponding to this optimized expected energy-saving power supply feature matching value stored in the energy-saving power supply database, send an energy-saving power supply prompt to the power grid system, and apply the energy-saving power supply solution corresponding to this optimized expected energy-saving power supply feature matching value to the power grid system.

[0059] It should be noted that by re-analyzing the energy-saving power supply solution corresponding to the excellent energy-saving power supply grid system, through re-analysis, a more economical and energy-saving power supply solution can be found, which helps to reduce the energy consumption cost and the electricity cost of the enterprise. Re-optimizing the energy-saving power supply solution can reduce energy consumption and the environmental impact such as carbon dioxide emissions, which is beneficial to environmental protection and sustainable development. The optimized energy-saving power supply solution can improve the stability and reliability of the power grid system, reduce the risk of system failures, and ensure the stability of power supply. Re-analyzing the solution of the excellent energy-saving power supply grid system can promote technological innovation and development, and drive the power grid system to develop in a more intelligent and efficient direction. By re-analyzing and optimizing the energy-saving power supply solution, the enterprise can improve its own energy-saving level and energy utilization efficiency, enhance its competitiveness, and gain a market advantage.

[0060] It should be noted that by optimizing the energy-saving power supply scheme corresponding to the poor energy-saving power supply grid system as described above, the optimized energy-saving power supply scheme can improve the stability and reliability of the grid system, reduce the risk of system failures, ensure the stability of power supply, and help reduce the problem that the expected optimization scheme does not match the scheme actually to be optimized, resulting in the inability to achieve the expected energy-saving power supply effect. The optimized energy-saving power supply scheme can improve the energy-saving level and energy utilization efficiency of enterprises, enhance competitiveness, and gain a market advantage. The optimized energy-saving power supply scheme can reduce energy waste, improve energy utilization efficiency, and thus achieve more effective energy consumption.

[0061] An industrial intelligent energy-saving power supply system further includes an energy-saving power supply database, which is used to store the reference power factor of the grid system, the reference voltage of the grid system, the energy-saving power supply schemes corresponding to the matching deviation values of the energy-saving power supply characteristics of each grid system, the energy-saving power supply schemes corresponding to the expected energy-saving power supply characteristic matching values of each grid system, the energy-saving power supply schemes corresponding to the expected energy-saving power supply characteristic matching values of each optimized grid system, the defined number of instantaneous overvoltages of the grid system, the maximum difference in the defined instantaneous frequency change of the grid system, the permitted short-time voltage sag duration of the grid system, the permitted number of short-time interruptions of the grid system, and the reference power consumption during peak hours of the grid system.

[0062] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods to replace the specific embodiments described, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. An industrial intelligent energy-saving power supply system, characterized in that: It includes an estimated energy-saving power supply feature matching value acquisition module, an actual energy-saving power supply feature matching value acquisition module and a power grid system energy-saving power supply plan acquisition module, wherein: The module for obtaining the matching value of the expected energy-saving power supply characteristics is used to obtain the power consumption data set of each historical period of the power grid system, and to analyze and obtain the matching value of the expected energy-saving power supply characteristics of the power grid system based on the obtained power consumption data set of each historical period of the power grid system; An actual energy-saving power supply feature matching value acquisition module is used to acquire a real-time power consumption data set of a power grid system, analyze and obtain an energy-saving power supply evaluation value of the power grid system based on the acquired real-time power consumption data set of the power grid system, import the energy-saving power supply evaluation value of the power grid system into an energy-saving power supply matching model, obtain an actual energy-saving power supply feature matching value of the power grid system, and compare the actual energy-saving power supply feature matching value of the power grid system with the expected energy-saving power supply feature matching value of the power grid system; A power grid system energy-saving power supply scheme acquisition module is used to acquire a plan for energy-saving power supply to the power grid system based on a comparison result of an actual energy-saving power supply feature matching value of the power grid system and an expected energy-saving power supply feature matching value of the power grid system; The power consumption data set of each historical period of the power grid system specifically includes power quality data of each historical period of the power grid system and power consumption data of each historical period of the power grid system, wherein the power quality data of each historical period of the power grid system includes the number of instantaneous overvoltages in each historical period of the power grid system, the maximum difference of instantaneous frequency changes in each historical period of the power grid system, the duration of short-term temporary drops in each historical period of the power grid system, and the number of short-term interruptions in each historical period of the power grid system; the power consumption data of each historical period of the power grid system includes the total power consumption of each historical period of the power grid system, the power consumption used to control the start and stop of equipment in each historical period of the power grid system, and the total power consumption during peak hours of each historical period of the power grid system; Based on the acquired power consumption data set of each historical period of the power grid system, the expected energy-saving power supply feature matching value of the power grid system is analyzed. The specific analysis process is as follows: Based on the acquired power quality data of each historical period of the power grid system and the power consumption data of each historical period of the power grid system, a comprehensive analysis is performed to obtain the expected energy-saving power supply feature matching value of the power grid system, and the expected energy-saving power supply feature matching value of the power grid system is used as an analysis basis for obtaining a plan for energy-saving power supply to the power grid system; The real-time power consumption data set of the power grid system specifically includes the real-time power factor of the power grid system, the real-time voltage of the power grid system, and the real-time usage ratio of new energy and traditional energy in the power grid system; Based on the acquired real-time power consumption data set of the power grid system, the energy-saving power supply evaluation value of the power grid system is analyzed, and the specific analysis process is as follows: Based on the comprehensive analysis of the acquired real-time power consumption data set of the power grid system, the energy-saving power supply evaluation value of the power grid system is obtained, and the energy-saving power supply evaluation value of the power grid system is used as the analysis basis for obtaining the actual energy-saving power supply feature matching value of the power grid system.

2. The industrial intelligent energy-saving power supply system according to claim 1, characterized in that: The power grid system predicts the energy-saving power supply characteristic matching value, and the specific analysis process is as follows: In the formula, Predict energy-saving power supply feature matching values ​​for the power grid system, is the power quality characteristic matching value of the jth historical period of the power grid system, is the power consumption characteristic matching value of the jth historical period of the power grid system, ε1 is the weight factor of the set power quality characteristic matching value of the power grid system, ε2 is the weight factor of the set power consumption characteristic matching value of the power grid system, j is the number of each historical period, j=1, 2, 3, ..., n, n is the total number of historical periods, and e is a natural constant.

3. The industrial intelligent energy-saving power supply system according to claim 1 is characterized in that: The specific analysis process of the energy-saving power supply evaluation value of the power grid system is as follows: Wherein, γ is the energy-saving power supply evaluation value of the power grid system, GY is the real-time power factor of the power grid system, D is the real-time voltage of the power grid system, BL is the real-time proportion of new energy and traditional energy use in the power grid system, GY0 is the reference power factor of the power grid system stored in the energy-saving power supply database, D0 is the reference voltage of the power grid system stored in the energy-saving power supply database, τ1 is the compensation factor of the set real-time power factor of the power grid system, τ2 is the compensation factor of the set real-time voltage of the power grid system, and τ3 is the compensation factor of the set real-time proportion of new energy and traditional energy use in the power grid system.

4. The industrial intelligent energy-saving power supply system according to claim 3 is characterized in that: The energy-saving power supply evaluation value of the power grid system is introduced into the energy-saving power supply matching model to obtain the actual energy-saving power supply feature matching value of the power grid system. The specific analysis process is as follows: The energy-saving power supply evaluation value of the power grid system is imported into the energy-saving power supply matching model to obtain the actual energy-saving power supply characteristic matching value of the power grid system. The actual energy-saving power supply characteristic matching value of the power grid system is used as the analysis basis for obtaining the energy-saving power supply plan for the power grid system.

5. The industrial intelligent energy-saving power supply system according to claim 4, characterized in that: The specific analysis process of obtaining the scheme for energy-saving power supply to the power grid system is as follows: comparing the actual energy-saving power supply characteristic matching value of the power grid system with the expected energy-saving power supply characteristic matching value of the power grid system; If the actual energy-saving power supply feature matching value of the power grid system is lower than the expected energy-saving power supply feature matching value of the power grid system, the power grid system is marked as an excellent energy-saving power supply power grid system, the deviation value between the actual energy-saving power supply feature matching value of the power grid system and the reference energy-saving power supply feature matching value of the power grid system is obtained, the deviation value between the actual energy-saving power supply feature matching value of the power grid system and the reference energy-saving power supply feature matching value of the power grid system is recorded as the power grid system energy-saving power supply feature matching deviation value, the energy-saving power supply plan corresponding to the energy-saving power supply feature matching deviation value of the power grid system stored in the energy-saving power supply database is obtained, an energy-saving power supply prompt is issued to the power grid system, and the energy-saving power supply plan corresponding to the energy-saving power supply feature matching deviation value of the power grid system is applied to the power grid system; If the actual energy-saving power supply feature matching value of the power grid system is equal to the expected energy-saving power supply feature matching value of the power grid system, the power grid system is marked as a good energy-saving power supply power grid system, the energy-saving power supply plan corresponding to the expected energy-saving power supply feature matching value of the power grid system stored in the energy-saving power supply database is obtained, an energy-saving power supply prompt is issued to the power grid system, and the energy-saving power supply plan corresponding to the expected energy-saving power supply feature matching value of the power grid system is applied to the power grid system; If the actual energy-saving power supply feature matching value of the power grid system is higher than the expected energy-saving power supply feature matching value of the power grid system, the power grid system will be marked as a poor energy-saving power supply power grid system, and professionals in related fields will optimize the expected energy-saving power supply feature matching value of the power grid system based on historical records to obtain the optimized expected energy-saving power supply feature matching value of the power grid system, which will be recorded as the optimized expected energy-saving power supply feature matching value of the power grid system, and the energy-saving power supply plan corresponding to the optimized expected energy-saving power supply feature matching value of the power grid system stored in the energy-saving power supply database will be obtained, an energy-saving power supply prompt will be issued to the power grid system, and the energy-saving power supply plan corresponding to the optimized expected energy-saving power supply feature matching value of the power grid system will be applied to the power grid system.

6. The industrial intelligent energy-saving power supply system according to claim 1, characterized in that: An industrial intelligent energy-saving power supply system also includes an energy-saving power supply database, which is used to store a reference power factor of the power grid system, a reference voltage of the power grid system, energy-saving power supply plans corresponding to the matching deviation values ​​of the energy-saving power supply characteristics of each power grid system, energy-saving power supply plans corresponding to the expected energy-saving power supply characteristic matching values ​​of each power grid system, and energy-saving power supply plans corresponding to the expected energy-saving power supply characteristic matching values ​​of each optimized power grid system.

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