New energy power generation scheduling optimization system

Abstract

The invention, which relates to the technical field of new energy power generation, discloses a new energy power generation scheduling optimization system comprising a multi-dimensional evaluation module, a dynamic weight adjustment module, a priority calculation and determination module, and a scheduling decision module. By establishing a multi-dimensional evaluation grade and a dynamic weight adjustment mechanism, the method can comprehensively and accurately evaluate the factors of the emergency degree of the energy demand, the power generation capacity of the power generation equipment and the operation state of the equipment, dynamically adjust the priority of each power generation equipment according to the real-time monitored data in different time periods, and ensure that the power generation capacity of the power generation equipment is greatly improved in the peak time period of the energy demand. Equipment which is high in power generation capacity, stable in operation and capable of responding quickly is scheduled preferentially, emergency energy requirements are met, power outage accidents are effectively avoided, new energy power generation can better adapt to complex and changeable energy requirements and power generation conditions due to the intelligent and accurate scheduling capacity, and more reliable energy supply is provided for users.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation, and in particular to a renewable energy power generation scheduling optimization system. Background Art

[0002] As the global demand for clean energy continues to grow, the position of renewable energy power generation in the energy field is becoming increasingly important. Renewable energy power generation has the advantages of sustainability and environmental protection, but it also faces many challenges, such as the volatility of energy demand, the diversity of power generation equipment and the requirements of grid stability. In order to better utilize renewable energy power generation and improve the reliability and efficiency of energy supply, the renewable energy power generation scheduling optimization system came into being.

[0003] However, the existing renewable energy power generation dispatching systems mostly use static and relatively single standards when determining the priority of power generation equipment, that is, they simply sort according to the rated power or current output power of the power generation equipment, which cannot fully reflect the complexity and diversity of energy demand in different time periods and situations. In addition, the existing systems generally lack a dynamic adjustment mechanism and cannot flexibly adjust the priority of each power generation equipment according to real-time energy demand and power generation conditions. This limitation leads to the inability to timely dispatch equipment with strong power generation capacity to meet emergency needs during peak energy demand periods, making it difficult to respond quickly and effectively to ensure the stability and security of energy supply.

[0004] To sum up, the current renewable energy power generation dispatching system has many shortcomings in dealing with complex and changeable energy demand and power generation conditions, especially the lack of effective mechanism in dynamic priority dispatching, which not only affects the utilization rate and operation efficiency of renewable energy power generation equipment, but also restricts the sustainable and healthy development of the new energy industry. Therefore, it is of great practical significance and application value to develop a renewable energy power generation dispatching optimization system that can comprehensively consider multiple dimensions such as the urgency of energy demand, the power generation capacity of power generation equipment, and the operating status of equipment, and realize intelligent and efficient dispatching by dynamically adjusting the weights of factors, so as to realize the intelligent and refined dispatching of renewable energy power generation and improve the overall stability and economic benefits of the system. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a new energy power generation scheduling optimization system. It can introduce multi-dimensional evaluation, comprehensively consider the urgency of energy demand, the power generation capacity of power generation equipment, and the operating status of equipment, so that the scheduling system can more comprehensively evaluate the applicability and priority of power generation equipment, and utilize the dynamic weight adjustment mechanism to intelligently adjust the weight of each factor according to the real-time monitored energy demand and power generation status, thereby ensuring priority scheduling of those equipment that can respond quickly, have strong power generation capacity and stable operation to meet urgent energy needs.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a new energy power generation scheduling optimization system, the components of which include: a multi-dimensional evaluation module, a dynamic weight adjustment module, a priority calculation and determination module, and a scheduling decision module;

[0007] The multi-dimensional assessment module comprehensively considers factors such as the urgency of energy demand, the power generation capacity of power generation equipment, and the operating status of the equipment by establishing a multi-dimensional assessment level;

[0008] The dynamic weight adjustment module monitors the energy demand and power generation status in different time periods in real time, and calculates the real-time weight of each factor according to the energy demand and power generation status in the current time period and the weight factor;

[0009] The priority calculation and determination module calculates the comprehensive priority score of each power generation equipment according to the multi-dimensional evaluation level and the dynamic weight adjustment result. The equipment with a high priority score will be dispatched first to meet the energy demand;

[0010] The scheduling decision module builds an intelligent scheduling decision engine based on multi-dimensional evaluation levels, dynamic weight adjustment mechanism and priority scoring. The engine can analyze the comprehensive score of each power generation equipment in real time and determine the optimal scheduling plan based on this, including the start and stop sequence of the power generation equipment and the power distribution.

[0011] Furthermore, the multi-dimensional evaluation module specifically includes:

[0012] Energy demand urgency assessment submodule: Determine the urgency level of energy demand based on the power load curves, user needs and emergency factors in different regions;

[0013] Power generation equipment power generation capacity assessment submodule: comprehensively consider the rated power, actual output power, power generation efficiency and aging degree of the power generation equipment to assess its power generation capacity;

[0014] Equipment operation status assessment submodule: Determine the equipment's operation status by real-time monitoring of the equipment's temperature, vibration, current, and voltage parameters.

[0015] Furthermore, the energy demand urgency assessment submodule determines the electric load curve by analyzing historical electricity consumption data, assigns different weights according to the classification of users and their demand characteristics, considers the impact of emergencies on electricity demand, and adjusts the urgency level specifically including:

[0016] Data collection: Collect electricity load curve data in different regions, including historical electricity consumption data, real-time electricity consumption data and predicted future electricity consumption data. At the same time, collect users' specific demand information and emergency reports;

[0017] Analysis of emergency factors: Analyze the collected data to identify key factors that affect the urgency of energy demand, including peak power load, load growth rate, proportion of important users, type of emergency and scope of impact;

[0018] Urgency level classification: Based on the analysis results, the urgency of energy demand is divided into four levels, namely, very high, high, medium and low;

[0019] Calculation and evaluation: Combine real-time data with historical data to calculate the urgency value of current energy demand and map it to the corresponding urgency level. The algorithm considers the weight of different factors;

[0020] Result output: The calculated urgency level is used as output for subsequent use by the multi-dimensional evaluation module and the dynamic weight adjustment module to influence the priority sorting and scheduling decisions of power generation equipment.

[0021] Furthermore, the energy demand urgency assessment submodule collects real-time power load data, historical power load data, user-specific demand information, and emergency reports of each region, cleans and preprocesses the data, and introduces a quantitative index of the urgency factor, namely, the power load growth rate L grouth 、Proportion of important users I ratio 、Effect coefficient of emergency factor , combined with quantitative factors, and introduced weight coefficients w1, w2, w3 to adjust the relative importance of each factor in the urgency assessment. The calculation formula is: It means rounding down, N is the total number of preset urgency levels, namely, very high, high, medium, and low. The weight coefficients w1, w2, and w3 are adjusted according to the needs to make the evaluation results reasonable and accurate. level value, and maps it to the preset urgency level, that is, N = 4, then E level =1 means "very low" urgency, E level =4 indicates “extremely high” urgency.

[0022] Furthermore, the power generation equipment power generation capacity assessment submodule collects the rated power, real-time output power, historical output power data, power generation efficiency records and equipment aging assessment report of the power generation equipment, and comprehensively considers the collected data, including the rated power P rated : The maximum output power designed for the equipment, the actual output power P actual : The actual output power of the equipment at present, power generation efficiency η: the ratio of actual output power to rated power, that is, Degree of aging D: Based on the service life, maintenance record and performance degradation of the equipment, the weight coefficients w1, w2, w3 and w4 are the weight coefficients of rated power, actual output power, power generation efficiency and equipment aging coefficient respectively. The formula for calculating the power generation capacity score of the power generation equipment is: C generation It represents the power generation capacity level, M is the total number of preset power generation capacity levels, ∑(w1,w2,w3,w4) represents the sum of weight coefficients, +1 is to avoid the calculation result being 0 and ensure that the minimum power generation capacity level is 1, α is the equipment aging coefficient, which reflects the degree of equipment aging. The smaller the value, the higher the degree of equipment aging. The power generation capacity level is dynamically calculated according to the different parameters of the power generation equipment, thereby guiding the scheduling decision of the new energy power generation scheduling optimization system.

[0023] Furthermore, the equipment operation status assessment submodule calculates the operation status value of the current equipment by real-time monitoring of the key parameters of the equipment, such as temperature, vibration, current, and voltage, and combines the real-time monitoring data to map it to the corresponding operation status level. The algorithm considers the weights of different factors and uses the calculated operation status level as output for subsequent use by the multi-dimensional assessment module to influence the priority sorting and scheduling decisions of the power generation equipment. The collected real-time temperature T, vibration V, current I, and voltage U of the power generation equipment are combined together and the weight coefficient w is introduced. T 、w V 、w I and w U To adjust the relative importance of each factor in the operation status evaluation, the equipment operation status calculation formula is: S operation Indicates the equipment operating status level, T max 、V max ,I max , U max They represent the maximum allowable values ​​of temperature, vibration, current and voltage during normal operation of the equipment respectively, and O is the total number of preset operating status levels.

[0024] Furthermore, the dynamic weight adjustment module monitors the energy demand and power generation status in different time periods in real time, analyzes the collected data, identifies the key factors affecting the weights of various factors, including the urgency of energy demand, the power generation capacity of power generation equipment, and the equipment operating status, and dynamically adjusts the weights of various factors according to the energy demand and power generation status of the current period, namely, the urgency of energy demand E, the power generation capacity of power generation equipment C, and the equipment operating status S, and uses the adjusted weight values ​​as output for use by the multi-dimensional evaluation module to influence the priority sorting and scheduling decisions of power generation equipment. The weight adjustment calculation formula is: W adjustIt represents the adjusted weight value. According to the real-time changes of energy demand urgency, power generation equipment status and market price factors, the weights of various dimensions are intelligently adjusted. During the peak period of energy demand, the weights of energy demand urgency and power generation equipment power generation capacity are increased. When the equipment failure rate increases, the weight of equipment operation status is increased.

[0025] Furthermore, the priority calculation and determination module receives the energy demand urgency assessment value, the power generation capacity assessment value, and the equipment operation status assessment value output by the multi-dimensional assessment module, and the adjusted weights of each factor output by the dynamic weight adjustment module, and calculates the comprehensive priority score of each power generation equipment, that is, P = α × E × W E +β×C×W C +γ×S×W S , P represents the comprehensive priority score, α, β, γ are adjustment coefficients, and the calculated comprehensive priority score is output to the scheduling decision module to provide a basis for it to formulate a scheduling plan.

[0026] Furthermore, the scheduling decision module receives the comprehensive priority score of each power generation equipment output by the priority calculation and determination module, as well as the relevant information of the energy demand urgency assessment value, the power generation capacity assessment value of the power generation equipment, and the equipment operation status assessment value output by the multi-dimensional assessment module, performs real-time analysis on the received data, considers the stability and security constraints of the power grid, and formulates a power generation equipment scheduling plan based on the analysis results, introducing the decision variable x i Indicates the start and stop status of the i-th power generation equipment, as well as the output power adjustment amount p i , the comprehensive score calculation formula is: Score = ∑ i (P i × i +δ i ×p i ), where P i is the comprehensive priority score of the i-th power generation equipment, δ i To adjust the coefficient, the optimal scheduling plan is formulated based on the calculated scheduling decision results.

[0027] Furthermore, the scheduling scheme specifically includes: startup decision, output power adjustment decision, and shutdown decision:

[0028] The startup decision determines the order of starting the power generation equipment according to the comprehensive priority scores of the power generation equipment, the current energy demand and the power generation capacity of the power generation equipment, and determines the number of power generation equipment that needs to be started;

[0029] The output power adjustment decision determines the magnitude of the power increase for the power generation equipment that needs to increase the output power according to the grid load status and the power generation capacity of the power generation equipment, and determines the magnitude of the power reduction for the power generation equipment that needs to reduce the output power according to the grid load status and the power generation capacity of the power generation equipment;

[0030] The shutdown decision determines the order of shutting down the power generation equipment according to the comprehensive priority scores of the power generation equipment and the current grid load status, and determines the number of power generation equipment that needs to be shut down by using the changes in energy demand and the power generation capacity of the power generation equipment.

[0031] Compared with the existing technology, this new energy power generation dispatch optimization system has the following beneficial effects:

[0032] 1. The present invention can comprehensively and accurately evaluate the factors of the urgency of energy demand, the power generation capacity of power generation equipment, and the operating status of equipment by establishing a multi-dimensional evaluation level and a dynamic weight adjustment mechanism. At different time periods, the priority of each power generation equipment is dynamically adjusted according to the real-time monitoring data to ensure that during the peak period of energy demand, equipment with strong power generation capacity, stable operation and rapid response is preferentially dispatched to meet emergency energy needs and effectively avoid the occurrence of power outages. This intelligent and precise scheduling capability enables new energy power generation to better adapt to complex and changeable energy demands and power generation conditions, and provide users with a more reliable energy supply.

[0033] 2. This system rationally dispatches power generation equipment by considering various factors of the equipment. During periods of low energy demand, it reduces the weight of the urgency of energy demand and increases the weight of equipment maintenance energy saving and energy market price. It performs maintenance on power generation equipment with a long operating time to reduce its operating power, thereby reducing equipment wear and energy waste and operating costs. By utilizing optimized scheduling, it extends the service life of equipment, reduces resource consumption, and promotes the sustainable development of the new energy industry.

[0034] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0036] Figure 1It is an operation flow chart of a new energy power generation dispatch optimization system;

[0037] Figure 2 Schematic diagram of a multi-dimensional evaluation module for a renewable energy power generation scheduling optimization system. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1

[0040] This embodiment introduces in detail the specific implementation process of a renewable energy power generation scheduling optimization system. The system achieves efficient and intelligent scheduling of renewable energy power generation through multi-dimensional evaluation, dynamic weight adjustment, priority calculation and determination, and intelligent scheduling decision-making, improves the reliability and efficiency of energy supply, meets complex and changeable energy needs, and ensures the stability and security of the power grid.

[0041] In the specific implementation, first, the multi-dimensional assessment module collects and analyzes the electricity load curves, user-specific demand information and emergency reports of different regions through the energy demand urgency assessment sub-module, and uses quantitative indicators and weight coefficients to determine the urgency level of energy demand. The power generation equipment power generation capacity assessment sub-module collects various data of the power generation equipment, including rated power, actual output power, power generation efficiency and aging degree, and obtains the power generation capacity level of the power generation equipment through comprehensive calculation. The equipment operation status assessment sub-module monitors the key parameters of equipment temperature, vibration, current and voltage in real time, and calculates the equipment operation status level. These evaluation results provide an important basis for subsequent dynamic weight adjustment and priority calculation.

[0042] Then, the dynamic weight adjustment module dynamically adjusts the weight of each factor according to the real-time monitored energy demand and power generation status. The module first analyzes the urgency of energy demand, the power generation capacity of power generation equipment and the equipment operating status in the current period, identifies the key factors affecting the weight of each factor, and calculates the real-time weight of each factor through an intelligent algorithm. The weight is output to the multi-dimensional evaluation module. During peak energy demand periods, the system will increase the weight of the urgency of energy demand and the power generation capacity of power generation equipment accordingly; when the equipment failure rate increases, the weight of the equipment operating status will be increased. This dynamic adjustment mechanism ensures that the system can flexibly respond to various complex situations and achieve the optimal scheduling decision.

[0043] Subsequently, the priority calculation and determination module receives the output results of the multi-dimensional evaluation module and the dynamic weight adjustment module, and calculates the comprehensive priority score of each power generation equipment. This module adopts an advanced comprehensive scoring algorithm, comprehensively considering factors such as the urgency of energy demand, the power generation capacity of the power generation equipment and the operating status of the equipment, and introduces an adjustment coefficient for fine-tuning. Through the calculated comprehensive priority score, the system can accurately determine the priority order of each power generation equipment and provide strong support for subsequent scheduling decisions.

[0044] Finally, the dispatch decision module builds an intelligent dispatch decision engine based on multi-dimensional evaluation levels, dynamic weight adjustment mechanism and priority scoring results. The engine can analyze the comprehensive scores of each power generation equipment and grid status information in real time and formulate the optimal dispatch plan. The plan specifically includes startup decision, output power adjustment decision and shutdown decision. In the startup decision, the system determines the order and quantity of starting power generation equipment according to the comprehensive priority score of the power generation equipment and the current energy demand. In the output power adjustment decision, the system adjusts the output power of the power generation equipment according to the grid load status and the power generation capacity of the power generation equipment. In the shutdown decision, the system determines the order and quantity of shutting down the power generation equipment according to the comprehensive priority score of the power generation equipment and the grid load status. Through this series of refined dispatch decisions, the system can ensure the stable operation of the power grid and the efficient use of energy.

[0045] This embodiment elaborates on the specific implementation process of a new energy power generation scheduling optimization system. Through core modules such as multi-dimensional evaluation, dynamic weight adjustment, priority calculation and determination, and intelligent scheduling decision-making, it realizes efficient and intelligent scheduling of new energy power generation, comprehensively and accurately evaluates energy demand and power generation equipment status, formulates the optimal scheduling plan, and meets complex and changeable energy demand. The system can also reduce operating costs, improve energy utilization efficiency, extend equipment service life and promote the sustainable development of the new energy industry.

[0046] Embodiment 2

[0047] This embodiment provides a new energy power generation scheduling optimization system. During peak electricity consumption periods, in the face of sharply increasing energy demand, the new energy power generation scheduling optimization system gives full play to its advantages of multi-dimensional evaluation and dynamic weight adjustment to ensure the stability and reliability of energy supply. Through real-time data analysis and intelligent scheduling, the system gives priority to starting generators and power generation equipment with strong power generation capacity and good operating status, effectively responding to the energy demand challenges during peak periods.

[0048] In the specific implementation, the system first starts the multi-dimensional evaluation module, which consists of three sub-modules: energy demand urgency evaluation sub-module, power generation equipment power generation capacity evaluation sub-module and equipment operation status evaluation sub-module. In the energy demand urgency evaluation sub-module, the system collects historical electricity consumption data, real-time electricity consumption data and predicted future electricity consumption data, and takes into account factors such as emergency reports. By analyzing these data, the system calculates the growth rate of electricity load, the proportion of important users and the impact coefficient of emergencies, and uses the quantitative indicators of urgency for evaluation. According to the formula It means rounding down, N is the total number of preset urgency levels, namely, very high, high, medium, and low. The weight coefficients w1, w2, and w3 are adjusted according to the needs to make the evaluation results reasonable and accurate. level The system maps the value to the preset emergency level and concludes that the current energy demand emergency level is "extremely high", i.e., N=4. This level is used as the evaluation result. At the same time, the power generation equipment power generation capacity evaluation submodule conducts a comprehensive evaluation of the wind turbines and solar power generation equipment in the area, comprehensively considering the rated power, actual output power, power generation efficiency and aging degree of the equipment, and determines the power generation capacity level of each device. The equipment operation status evaluation submodule ensures that the selected equipment is in good operating condition by real-time monitoring of key parameters such as temperature, vibration, current, and voltage of the equipment.

[0049] Then, the dynamic weight adjustment module intelligently adjusts the weights of each factor according to the urgency of the current energy demand and the real-time status of the power generation equipment. The weight adjustment calculation formula is: W adjust It represents the adjusted weight value. According to the real-time changes of the urgency of energy demand, the status of power generation equipment and market price factors, during peak hours, the system greatly increases the weights of the urgency of energy demand and the power generation capacity of power generation equipment, making these two factors dominant in the priority calculation. At the same time, it reduces the weight of energy market price to ensure that in an emergency, equipment with strong power generation capacity can be quickly dispatched without being disturbed by market price fluctuations.

[0050] The priority calculation and determination module receives the output results of the multi-dimensional evaluation module and the dynamic weight adjustment module, and uses the formula P = α × E × W E +β×C×W C +γ×S×W S, P represents the comprehensive priority score, α, β, γ are adjustment coefficients. By calculating the comprehensive priority score, the system can accurately determine the priority order of each power generation equipment, provide strong support for subsequent scheduling decisions, and output the calculated comprehensive priority score to the scheduling decision module.

[0051] The dispatch decision module analyzes the received data in real time based on the output results of the priority calculation and determination module, considers the stability and security constraints of the power grid, and formulates a dispatch plan for power generation equipment based on the analysis results, introducing the decision variable x i Indicates the start and stop status of the i-th power generation equipment, as well as the output power adjustment amount p i , the comprehensive score calculation formula is: Score = ∑ i (P i × i +δ i ×p i ), where P i is the comprehensive priority score of the i-th power generation equipment, δ i In order to adjust the coefficient, the weight of the urgency of energy demand and the power generation capacity of power generation equipment is greatly increased based on the comprehensive score calculation results, and the weight of energy market price is reduced. The scheduling decision module quickly starts these high-priority equipment and adjusts their output power to the maximum to meet the energy demand during peak hours. The solution gives priority to starting wind turbines and solar power generation equipment with high comprehensive priority scores, and adjusts the output power of these equipment to the maximum according to the load status of the power grid. The system also monitors the operation status of the power grid in real time to ensure stability and safety during the scheduling process.

[0052] In summary, by implementing this new energy power generation scheduling optimization system, the sharp increase in energy demand during peak electricity consumption hours has been successfully responded to, and the power supply stability has been significantly improved. The system ensures the rational scheduling and efficient utilization of power generation equipment through multi-dimensional evaluation and dynamic weight adjustment mechanism, effectively avoiding the occurrence of power outages, and providing local residents and enterprises with a stable and reliable energy supply.

[0053] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A new energy power generation dispatch optimization system, characterized in that: The components of the power generation dispatch optimization system include: multi-dimensional evaluation module, dynamic weight adjustment module, priority calculation and determination module, and dispatch decision module; The multi-dimensional assessment module comprehensively considers factors such as the urgency of energy demand, the power generation capacity of power generation equipment, and the operating status of the equipment by establishing a multi-dimensional assessment level; The dynamic weight adjustment module monitors the energy demand and power generation status in different time periods in real time, and calculates the real-time weight of each factor according to the energy demand and power generation status in the current time period and the weight factor; The priority calculation and determination module calculates the comprehensive priority score of each power generation equipment according to the multi-dimensional evaluation level and the dynamic weight adjustment result. The equipment with a high priority score will be dispatched first to meet the energy demand; The scheduling decision module builds an intelligent scheduling decision engine based on multi-dimensional evaluation levels, dynamic weight adjustment mechanism and priority scoring. The engine can analyze the comprehensive score of each power generation equipment in real time and determine the optimal scheduling plan based on this, including the start and stop sequence of the power generation equipment and the power generation distribution.

2. A new energy power generation dispatch optimization system according to claim 1, characterized in that: The multi-dimensional evaluation module specifically includes: Energy demand urgency assessment submodule: Determine the urgency level of energy demand based on the power load curves, user needs and emergency factors in different regions; Power generation equipment power generation capacity assessment submodule: comprehensively consider the rated power, actual output power, power generation efficiency and aging degree of the power generation equipment to assess its power generation capacity; Equipment operation status assessment submodule: Determine the equipment's operation status by real-time monitoring of the equipment's temperature, vibration, current, and voltage parameters.

3. A new energy power generation dispatch optimization system according to claim 2, characterized in that: The energy demand urgency assessment submodule determines the electric load curve by analyzing historical electricity consumption data, assigns different weights according to the user classification and demand characteristics, and considers the impact of emergencies on electricity demand. The adjustment of the urgency level specifically includes: Data collection: Collect electricity load curve data in different regions, including historical electricity consumption data, real-time electricity consumption data and predicted future electricity consumption data. At the same time, collect users' specific demand information and emergency reports; Analysis of emergency factors: Analyze the collected data to identify key factors that affect the urgency of energy demand, including peak power load, load growth rate, proportion of important users, type of emergency and scope of impact; Urgency level classification: Based on the analysis results, the urgency of energy demand is divided into four levels, namely, very high, high, medium and low; Calculation and evaluation: Combine real-time data with historical data to calculate the urgency value of current energy demand and map it to the corresponding urgency level. The algorithm considers the weight of different factors; Result output: The calculated urgency level is used as output for subsequent use by the multi-dimensional evaluation module and the dynamic weight adjustment module to influence the priority sorting and scheduling decisions of power generation equipment.

4. A new energy power generation dispatch optimization system according to claim 3, characterized in that: The energy demand urgency assessment submodule collects real-time power load data, historical power load data, user-specific demand information, and emergency reports of each region, cleans and preprocesses the data, and introduces a quantitative index of the urgency factor, namely, the power load growth rate L grouth 、Proportion of important users I ratio 、Effect coefficient of emergency factor , combined with quantitative factors, and introduced weight coefficients w1, w2, w3 to adjust the relative importance of each factor in the urgency assessment. The calculation formula is: It means rounding down, N is the total number of preset urgency levels, namely, very high, high, medium, and low. The weight coefficients w1, w2, and w3 are adjusted according to the needs to make the evaluation results reasonable and accurate. level value, and maps it to the preset urgency level, that is, N = 4, then E level =1 means "very low" urgency, E level =4 indicates "extremely high" urgency.

5. A new energy power generation dispatch optimization system according to claim 2, characterized in that: The power generation equipment power generation capacity assessment submodule collects the rated power, real-time output power, historical output power data, power generation efficiency records and equipment aging assessment report of the power generation equipment, and comprehensively considers the collected data, including the rated power P rated : The maximum output power designed for the equipment, the actual output power P actual : The actual output power of the equipment at present, power generation efficiency η: the ratio of actual output power to rated power, that is, Degree of aging D: Based on the service life, maintenance record and performance degradation of the equipment, the weight coefficients w1, w2, w3 and w4 are the weight coefficients of rated power, actual output power, power generation efficiency and equipment aging coefficient respectively. The formula for calculating the power generation capacity score of the power generation equipment is: C generation It represents the power generation capacity level, M is the total number of preset power generation capacity levels, ∑(w1,w2,w3,w4) represents the sum of weight coefficients, +1 is to avoid the calculation result being 0 and ensure that the minimum power generation capacity level is 1, α is the equipment aging coefficient, which reflects the degree of equipment aging. The smaller the value, the higher the degree of equipment aging. The power generation capacity level is dynamically calculated according to the different parameters of the power generation equipment, thereby guiding the scheduling decision of the new energy power generation scheduling optimization system.

6. A new energy power generation dispatch optimization system according to claim 2, characterized in that: The equipment operation status assessment submodule calculates the current equipment operation status value by real-time monitoring of the equipment's temperature, vibration, current, and voltage key parameters, and combines the real-time monitoring data to map it to the corresponding operation status level. The algorithm considers the weights of different factors and uses the calculated operation status level as output for subsequent use by the multi-dimensional assessment module to influence the priority sorting and scheduling decisions of power generation equipment. The collected real-time temperature T, vibration V, current I, and voltage U of the power generation equipment are combined together and the weight coefficient w is introduced. T 、w V 、w I and w U To adjust the relative importance of each factor in the operation status evaluation, the equipment operation status calculation formula is: S operation Indicates the equipment operating status level, T max 、V max ,I max , U max They represent the maximum allowable values ​​of temperature, vibration, current and voltage during normal operation of the equipment respectively, and O is the total number of preset operating status levels.

7. A new energy power generation dispatch optimization system according to claim 1, characterized in that: The dynamic weight adjustment module monitors the energy demand and power generation status in different time periods in real time, analyzes the collected data, identifies the key factors affecting the weight of each factor, including the urgency of energy demand, the power generation capacity of power generation equipment, and the equipment operation status, and dynamically adjusts the weight of each factor according to the energy demand and power generation status of the current period, that is, the urgency of energy demand E, the power generation capacity of power generation equipment C, and the equipment operation status S. The adjusted weight value is used as output for use by the multi-dimensional evaluation module to influence the priority sorting and scheduling decision of power generation equipment. The weight adjustment calculation formula is: W adjust It represents the adjusted weight value. According to the real-time changes of energy demand urgency, power generation equipment status and market price factors, the weights of various dimensions are intelligently adjusted. During the peak period of energy demand, the weights of energy demand urgency and power generation equipment power generation capacity are increased. When the equipment failure rate increases, the weight of equipment operation status is increased.

8. A new energy power generation dispatch optimization system according to claim 1, characterized in that: The priority calculation and determination module receives the energy demand urgency assessment value, the power generation capacity assessment value, the equipment operation status assessment value output by the multi-dimensional assessment module, and the adjusted weights of each factor output by the dynamic weight adjustment module, and calculates the comprehensive priority score of each power generation equipment, that is, P = α × E × W E +β×C×W C +γ×S×W S , P represents the comprehensive priority score, α, β, γ are adjustment coefficients, and the calculated comprehensive priority score is output to the scheduling decision module to provide a basis for it to formulate a scheduling plan.

9. A new energy power generation dispatch optimization system according to claim 8, characterized in that: The scheduling decision module receives the comprehensive priority score of each power generation equipment output by the priority calculation and determination module, as well as the relevant information of the energy demand urgency assessment value, the power generation capacity assessment value of the power generation equipment, and the equipment operation status assessment value output by the multi-dimensional assessment module, and performs real-time analysis on the received data, taking into account the stability and security constraints of the power grid, and formulates a power generation equipment scheduling plan based on the analysis results, introducing the decision variable x i Indicates the start and stop status of the i-th power generation equipment, as well as the output power adjustment amount p i , the comprehensive score calculation formula is: Score = ∑ i (P i × i +δ i ×p i ), where P i is the comprehensive priority score of the i-th power generation equipment, δ i To adjust the coefficient, the optimal scheduling plan is formulated based on the calculated scheduling decision results.

10. A new energy power generation dispatch optimization system according to claim 9, characterized in that: The scheduling scheme specifically includes: startup decision, output power adjustment decision, and shutdown decision: The startup decision determines the order of starting the power generation equipment according to the comprehensive priority scores of the power generation equipment, the current energy demand and the power generation capacity of the power generation equipment, and determines the number of power generation equipment that needs to be started; The output power adjustment decision determines the magnitude of the power increase for the power generation equipment that needs to increase the output power according to the grid load status and the power generation capacity of the power generation equipment, and determines the magnitude of the power reduction for the power generation equipment that needs to reduce the output power according to the grid load status and the power generation capacity of the power generation equipment; The shutdown decision determines the order of shutting down the power generation equipment according to the comprehensive priority scores of the power generation equipment and the current grid load status, and determines the number of power generation equipment that needs to be shut down by using the changes in energy demand and the power generation capacity of the power generation equipment.

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