Energy internet operation state evaluation method and evaluation system

Abstract

The invention discloses an energy internet operation state evaluation method and system, and relates to the technical field of energy internet, and the method comprises the following specific steps: building a micrometeorological model: combing the geographic position information of various energy facilities distributed in the energy internet, according to the method, a regional differentiation comprehensive evaluation mechanism considering ecological benefits and energy utilization efficiency is established by fusing dynamically quantified ecological environment related indexes and energy operation state indexes obtained based on micrometeorological differences, so that real-time influences of the energy internet on the ecological environment in different regions are comprehensively and accurately mastered; according to the method and the system, comprehensive evaluation results such as the interference degree of a wind power plant on a bird migration path, the ecological interference degree of a solar power station on land vegetation and the influence range and intensity of electromagnetic radiation on surrounding ecology are evaluated, and energy internet optimization operation strategies and ecological protection measures which are more practical and more targeted are formulated based on the comprehensive evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of energy Internet, and particularly to a method and a system for evaluating the operating state of an energy Internet. Background Art

[0002] With the transformation of the global energy structure and the rapid development of technology, the energy Internet, as an important form of the new generation of energy systems, is gradually becoming a key platform for connecting various energy facilities and achieving efficient energy utilization and optimal allocation. The energy Internet integrates multiple elements such as traditional power grids, distributed energy, energy storage systems, electric vehicles, and intelligent control, forming a complex and large-scale energy ecosystem. The operating efficiency and performance of various energy facilities, especially distributed energy such as solar energy and wind energy, are often significantly affected by meteorological conditions. Meteorological elements not only directly affect the energy conversion efficiency but also indirectly affect the overall performance of the energy system by influencing the operating environment of the equipment. Therefore, accurately grasping and predicting the changes in these meteorological elements is of great significance for improving the operating efficiency and stability of the energy Internet.

[0003] However, the existing methods for evaluating the operating state of the energy Internet have many deficiencies when dealing with complex and changeable actual operations. Traditional evaluation methods usually fail to fully consider the spatial subtle differences of micro-meteorological elements in small-scale areas and the direct impact of these differences on the operating state of energy facilities. For example, for wind farms and solar power plants, their power generation efficiency depends to a large extent on meteorological conditions such as wind speed, wind direction, and light intensity, and these conditions may vary significantly in different small-scale areas. In addition, traditional evaluation methods also fail to fully evaluate the impact of the energy Internet on the ecological environment and lack a comprehensive evaluation mechanism that combines micro-meteorological conditions with ecological environment impacts, resulting in insufficient accuracy and comprehensiveness in evaluating the operating state of the energy Internet and making it difficult to formulate optimization strategies and protection measures that are practical and take into account both ecological benefits and energy utilization efficiency.

[0004] In view of the above problems, it is necessary to optimize the existing methods and systems for evaluating the operating state of the energy Internet. By collecting and analyzing detailed meteorological data in small-scale areas and using data analysis and modeling techniques, a high-precision and real-time updated micro-meteorological model is established to achieve accurate differential evaluation of the operating state of various energy facilities within the energy Internet. Therefore, it is of great significance to develop a method and a system for evaluating the operating state of an energy Internet that can comprehensively achieve the above characteristics. Summary of the Invention

[0005] The objective of the present invention is to make up for the deficiencies of the prior art and provide a method and a system for evaluating the operation status of an energy Internet. It can construct a highly accurate and real-time updated micro-meteorological model to capture the subtle spatial differences of meteorological elements in a small-scale area, and based on these differences, differentially evaluate the operation status of energy subsystems in different regions. At the same time, fully considering the close connection between micro-meteorological conditions and the ecological environment and their synergistic impact on the operation of the energy Internet, by dynamically quantifying and evaluating the specific impact of distributed energy facilities on the ecological environment in different regions and at different times, a comprehensive and accurate evaluation of the operation status of the energy Internet is realized. On this basis, a regional differential comprehensive evaluation mechanism that takes into account both ecological benefits and energy utilization efficiency is further constructed, providing a strong basis for formulating more scientific and reasonable energy Internet optimized operation strategies and ecological protection measures.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a method for evaluating the operation status of an energy Internet, the method includes the following specific steps:

[0007] Establishment of a micro-meteorological model: Sort out the geographical location information of various energy facilities distributed in the energy Internet, demarcate the areas where distributed energy is significantly affected by meteorological conditions, deploy meteorological monitoring equipment in the determined target areas, integrate the existing historical meteorological data records in the areas to form a meteorological data set, and at the same time formulate a data collection plan to clarify the data collection frequency and time nodes of each monitoring device, and perform preprocessing operations on the collected meteorological data. Combining the geographical spatial information of the target area, including topographical and geomorphological features and geographical coordinate data, determine the spatial resolution and area division method of the model, and construct a highly accurate and real-time updated micro-meteorological model;

[0008] Differential evaluation of energy operation status based on micro-meteorology: From the established micro-meteorological model, extract the real-time data and historical statistical data of each meteorological element corresponding to different regions according to the requirements of area division and time interval. The data specifically includes detailed information such as wind speed, wind direction, light intensity, light duration, temperature and humidity. For different types of energy subsystems in the energy Internet, namely wind farms, solar power plants and conventional energy facilities, respectively construct exclusive operation status evaluation index systems. Based on the extracted meteorological data and the evaluation index systems, analyze and evaluate the operation status of energy subsystems in different regions, and obtain the specific numerical values of the operation status indexes corresponding to each energy subsystem in each region at different times, and then summarize and analyze to obtain the relevant data on the energy supply capacity and operation efficiency of each local area of the entire energy Internet;

[0009] Dynamic quantitative evaluation of ecological environment impact: Obtain real-time meteorological element data for different regions from a micrometeorological model. At the same time, collect corresponding ecological environment monitoring data through ecological environment monitoring equipment deployed in each region. Clearly define that the data types to be collected cover data related to bird activities, land vegetation status data, and electromagnetic environment-related data. For different ecological environment impact factors, construct ecological impact quantitative calculation models. For the impact on bird migration, construct a quantitative model for the degree of interference that combines wind speed, wind direction, and the location of bird activities. For the impact on land vegetation, construct a quantitative model for the degree of ecological interference based on light, temperature, precipitation, and vegetation cover changes. For the impact of electromagnetic radiation, construct a quantitative model for the impact range and intensity that considers the propagation characteristics of electromagnetic radiation under micrometeorological conditions. Input the collected real-time data into the corresponding quantitative calculation models, and according to the calculation logic and algorithm rules, dynamically calculate the specific quantitative values of the impacts of distributed energy facilities on all aspects of the ecological environment at different times and in different regions;

[0010] Construction of comprehensive evaluation mechanism: Organize and normalize the data of the energy operation status indicators for each region obtained based on micrometeorological differences and the dynamically quantified ecological environment-related indicators, so that the data of different indicators are within the same dimension and numerical range. Determine the weight coefficients of each indicator in the comprehensive evaluation system through intelligent algorithms, fully considering the differences in the importance of different indicators for the overall operation status of the energy Internet and the ecological environment impact, and use the correlation analysis method to explore the internal connections and interaction relationships between the energy operation status indicators and the ecological environment impact indicators, construct an association model between the two. Based on the determined weight coefficients and the association model, deeply integrate the energy operation status indicators and the ecological environment impact indicators, and establish a regional differentiated comprehensive evaluation mechanism that takes into account both ecological benefits and energy utilization efficiency;

[0011] Formulation of optimization strategies: Based on the comprehensive evaluation results of each region output by the comprehensive evaluation mechanism, set different optimization goals, and prioritize the optimization goals according to the actual situation of each region,

[0012] At the same time, construct an optimization strategy library, match the comprehensive evaluation results of each region with the strategies in the optimization strategy library, and screen out the specific energy Internet optimized operation strategies and ecological protection measures that meet the actual situation of each region and can effectively achieve the optimization goals, forming a customized strategy plan to guide the subsequent operation and management of each region of the energy Internet.

[0013] Further, in the step of differential evaluation of the energy operation status based on micrometeorology, for different types of energy subsystems within the energy Internet, namely wind farms, solar power plants, and other conventional energy facilities, exclusive operation status evaluation index systems are constructed respectively according to their energy conversion principles and operation characteristic factors. For a wind farm, its power generation efficiency is calculated, and the calculation formula is: Among them, η wind represents the power generation efficiency of the wind farm, P actual is the actual output electric power of the wind farm within a specific time period, P theoretical represents the theoretical power generation calculated based on the wind speed and air density data provided by the regional micrometeorological model and combined with the power curve of the wind turbine. The calculation formula is: Among them, ρ is the air density, A is the swept area of a single blade of the wind turbine, v is the average wind speed at the hub height, C p (λ, β) is the power coefficient of the wind turbine. For a solar power plant, its photoelectric conversion efficiency is calculated, and the calculation formula is: Among them, η solar represents the photoelectric conversion efficiency of the solar power plant, P elec is the actual output electric power of the solar power plant within a specific time period, P in represents the incident light power received on the surface of the solar panel. The calculation formula is: P in = I × S. Among them, I is the light intensity provided by the regional micrometeorological model, and S is the effective receiving area of the solar panel. Similarly, the operation status evaluation index of the conventional energy facility can be calculated.

[0014] Furthermore, in the step of differential evaluation of the energy operation status based on micrometeorology, according to the specific numerical values of the operation status indicators corresponding to each energy subsystem in each region at different time periods, relevant data on the energy supply capacity and operation efficiency of each local region of the entire energy Internet are summarized and analyzed. The calculation formula is: Among them, U supply (i) represents the comprehensive evaluation value of the energy supply capacity of the i-th local region, b is the number of different types of energy subsystems included in this region, λ u is the weight coefficient of the u-th energy subsystem in the evaluation of the energy supply capacity, V u (i) is the energy supply capacity utility value of the u-th energy subsystem in the i-th region.

[0015] Furthermore, in the step of dynamic quantitative evaluation of the ecological environment impact, for the impact on bird migration, a quantitative model of the interference degree combining wind speed, wind direction, and bird activity position factors is constructed. The model formula is: Among them, D birdRepresents the quantified value of the interference degree of the wind farm on the bird migration path. m is the number of divided sections of the bird migration path area, and ω i is the weight coefficient of the i-th section, and d i is the distance that the bird is affected by the wind farm within the i-th section. D toal is the total length of the entire bird migration path, and P encounter (v i , θ i ) is the probability function of the bird encountering adverse impact factors of the wind farm within the i-th section.

[0016] Furthermore, in the step of dynamically quantifying and evaluating the ecological environment impact, for the impact on land vegetation, a quantification model of the ecological interference degree based on the changes in light, temperature, precipitation, and vegetation cover is constructed. The model formula is: Among them, E veg represents the quantified value of the ecological interference degree of the solar power station on land vegetation. ΔV veg is the change amount of the ecological indicators related to land vegetation after a certain period of construction and operation of the solar power station. V veg0 is the initial ecological indicator value of the land vegetation before the construction of the power station. k is the number of main ecological factors affecting vegetation growth, and α j is the importance weight coefficient of the j-th ecological factor in the process of vegetation growth. f j (T j , I j , P j ) is the functional relationship of the j-th ecological factor affecting vegetation growth.

[0017] Furthermore, in the step of dynamically quantifying and evaluating the ecological environment impact, for the impact of electromagnetic radiation, a quantification model of the influence range and intensity considering the propagation characteristics of electromagnetic radiation under micro-meteorological conditions is constructed. The calculation formula of the model is: Among them, I rad (r, θ, h) represents the electromagnetic radiation intensity at a position with a horizontal distance of r from the energy transmission line, an angle of θ with the line, and a vertical height of h. P trans is the electric power transmitted on the energy transmission line. r is the horizontal distance between the observation point and the energy transmission line, θ is the angle between the observation point and the energy transmission line, h is the vertical height of the observation point relative to the energy transmission line, μ(ρ, v) is the attenuation coefficient of electromagnetic radiation in the atmosphere, which is a function of air density ρ and wind speed v, and G(θ, h) is the direction gain function of the energy transmission line, reflecting the radiation directivity characteristics of electromagnetic radiation in different directions θ and vertical heights h.

[0018] Further, in the step of constructing the comprehensive evaluation mechanism, the weight coefficients of each index in the comprehensive evaluation system are determined by an intelligent algorithm. Specifically, there are m regions and n evaluation indexes, and the original data matrix is X = (x ij ) m×n . The data is standardized as follows: i = 1, 2, …, m; j = 1, 2, …, n, and the standardized data matrix Y = (y ij ) m×n is obtained. Calculate the proportion p ij of each region in the total value of the j-th index: i = 1, 2, …, m; j = 1, 2, …, n. Calculate the information entropy e j : j = 1, 2, …, n. The value range of the information entropy e j is between 0 and 1, which reflects the degree of dispersion of the data of the j-th index. Calculate the weight coefficient w j : j = 1, 2, …, n. Determine the weight based on the information entropy calculation result, so that the index with a large degree of data dispersion can be assigned a relatively larger weight, thereby reflecting the importance difference of each index in the comprehensive evaluation.

[0019] Further, in the step of constructing the comprehensive evaluation mechanism, the correlation analysis method is used to mine the internal connection and interaction relationship between the energy operation status index and the ecological environment impact index, and establish a correlation model between the two. Specifically, there are n samples, the energy operation status index data sequence is X = {x1, x2, …, x n}, and the ecological environment impact index data sequence is Y = {y1, y2, …, x n}. Calculate the mean values of the two index sequences: Calculate the Pearson correlation coefficient r xy according to the mean values. The formula is: Among them, x i represents the specific value of the energy operation status index corresponding to the i-th sample, y i represents the specific value of the ecological environment impact index corresponding to the i-th sample, and the value range of r xy is [-1, 1]. When r xy = 1, it means that there is a completely positive correlation between the energy operation status index and the ecological environment impact index, that is, the change trend of the energy operation status is completely consistent with the change trend of the ecological environment impact. When r xy = -1, it represents a completely negative correlation, meaning that the change of the energy operation status is completely opposite to the change trend of the ecological environment impact. When r xyWhen it is 0, it indicates that there is no linear correlation between the two indicators. Based on the results of the correlation analysis, it is initially judged whether there is a linear association between the energy operation status indicator and the ecological environment impact indicator and the degree of closeness of the association.

[0020] On the other hand, an energy Internet operation status evaluation system, which includes the following components: a data collection module, a micro-meteorological model construction module, an energy operation status evaluation module, an ecological environment impact evaluation module, and a comprehensive evaluation module;

[0021] The data collection module is used to collect meteorological data related to energy facilities in different regions within the energy Internet and ecological environment monitoring data, and preprocess the data;

[0022] The micro-meteorological model construction module receives the meteorological data from the data collection module, uses data analysis and modeling algorithms to construct a high-precision and real-time updated micro-meteorological model, so as to accurately present the subtle differences in meteorological elements in space within a small-scale area, and output the corresponding meteorological element values for each region;

[0023] The energy operation status evaluation module obtains the meteorological data of different regions, and according to the preset energy operation status evaluation algorithm, evaluates the operation status of the energy subsystems in different regions, and calculates the key operation indicators of each region;

[0024] The ecological environment impact evaluation module receives the ecological environment monitoring data from the data collection module and the meteorological data output by the micro-meteorological model construction module, and through the ecological impact quantification algorithm, calculates the impact of distributed energy facilities on the ecological environment in different regions and different time periods, including the quantification values of the impact on bird migration, land vegetation, and electromagnetic environment;

[0025] The comprehensive evaluation module receives the energy operation status indicator and the ecological environment impact indicator, and according to the set weight distribution and correlation analysis rules, deeply integrates the two, constructs a regional differential comprehensive evaluation mechanism, and outputs the comprehensive evaluation result to reflect the overall operation status and ecological impact of each region of the energy Internet;

[0026] The strategy formulation module formulates optimized operation strategies and ecological protection measures that conform to the actual situation of each region of the energy Internet based on the results output by the comprehensive evaluation module.

[0027] Compared with the prior art, the energy Internet operation status evaluation method and evaluation system of the present invention have the following beneficial effects:

[0028] I. By integrating the dynamically quantified ecological environment - related indicators with the energy operation status indicators derived from micro - meteorological differences, the present invention establishes a regional - differentiated comprehensive evaluation mechanism that takes into account both ecological benefits and energy utilization efficiency, thereby comprehensively and accurately grasping the real - time impact of the energy Internet on the ecological environment in different regions. For example, the degree of interference of a wind farm on the bird migration path, the degree of ecological interference of a solar power station on land vegetation, and the scope and intensity of the impact of electromagnetic radiation on the surrounding ecology. Based on the comprehensive evaluation results, more practical and targeted energy Internet optimized operation strategies and ecological protection measures are formulated to achieve the coordinated development of the energy Internet ecology and energy utilization.

[0029] II. By constructing a high - precision and real - time updated micro - meteorological model, the present invention can meticulously capture the subtle spatial differences of meteorological elements in a small - scale area, making the evaluation of the operation status of various energy facilities distributed in different geographical locations, especially distributed energy significantly affected by meteorological conditions, more accurate. Based on the meteorological data output by the micro - meteorological model, a differentiated evaluation can be carried out for the energy subsystems in different regions, accurately analyzing the operation indicators of each region. This not only improves the accuracy of the energy Internet operation status evaluation but also provides a scientific basis for formulating practical and efficient energy optimization strategies.

[0030] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is the flow operation diagram of the evaluation method of the present invention;

[0032] Figure 2 is the flow chart of the evaluation system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following combines the drawings and preferred embodiments to detail the specific implementation manner, structure, features, and their effects of the present invention as follows.

[0034] Embodiment 1

[0035] This embodiment details the specific application of an energy Internet operation status evaluation method and evaluation system in a hybrid energy Internet area of a mountain wind farm and a photovoltaic power station. Through the present invention, it can provide a strong guarantee for accurately evaluating the operation status and reasonably formulating optimization strategies, and help the mountain energy Internet achieve sustainable development.

[0036] Meteorological monitoring equipment, including anemometers, thermometers, hygrometers, and light sensors, are deployed at certain spatial intervals (for example, one monitoring station is set up every square kilometer) at the locations of various wind farms and solar photovoltaic power stations in mountainous areas and their surrounding areas to collect meteorological data for one consecutive year. At the same time, the existing historical meteorological data of the local meteorological department is integrated to cover data from different seasons and different time periods. The massive amount of meteorological data collected is pre-processed to eliminate outliers caused by equipment failures and other reasons, and a small amount of missing data is supplemented by linear interpolation. The data quality is verified by statistical methods to ensure the accuracy of the data. In order to ensure the integrity and completeness of the mountain area, a micro-meteorological model is constructed based on the topographic information of the mountainous area (data such as the direction of the mountain range, the location of the valley, and the altitude are obtained through the Geographic Information System (GIS)). The model divides the entire mountain energy Internet area into square grids with a side length of 500 meters. It can accurately simulate the spatial distribution of meteorological elements such as temperature, humidity, wind speed, and light intensity in each grid in different seasons and weather conditions. For example, the wind speed in the valley area is relatively small, while the wind speed in the mountain top area is relatively large, and the duration and intensity of light are affected by the terrain and are different from those in the valley. These situations can be reflected in the model.

[0037] Extract meteorological data corresponding to the grids in the areas where each wind farm and solar photovoltaic power station are located from the micro-meteorological model. For example, for wind farms, obtain data such as the average wind speed, wind direction, and air density (calculated based on temperature and air pressure) at the wheel load height. For solar photovoltaic power stations, obtain data such as the light intensity, light duration, and temperature of the area where the panels are installed. For wind farms, based on the extracted meteorological data, use the wind farm power generation efficiency calculation formula (where P actual is the actual output power, P theoretical according to Calculate the power generation efficiency of each wind farm. For solar photovoltaic power plants, substitute the acquired light intensity, light duration and temperature data into the photoelectric conversion efficiency calculation formula. (where P elec is the actual output power, P in Based on the light intensity and the effective receiving area of ​​the solar panels, the key operating indicators such as the photovoltaic conversion efficiency of each power station are calculated, and then the energy supply capacity and operating efficiency of power stations in different regions are analyzed.

[0038] In the mountainous area energy Internet region, install bird tracking devices (such as satellite positioning tags, radar monitoring systems, etc.) to monitor information such as the migration routes and stopover times of birds. At the same time, use means such as drone remote sensing and ground vegetation quadrat surveys to obtain data such as land vegetation coverage, vegetation types, and vegetation growth status. And set up electromagnetic radiation monitors around the energy transmission lines to collect electromagnetic environment-related data. Combine with the meteorological data output by the micro-meteorological model. For the impact of bird migration, through the quantification formula of the interference degree of the wind farm on the bird migration route Calculate the quantification value of the interference degree of each wind farm on the bird migration route; for the impact on land vegetation, based on the quantification formula of the ecological interference degree of the solar power station on land vegetation Measure the change of the ecological interference degree of each solar photovoltaic power station on the surrounding land vegetation over time; in the energy transmission link, with the help of the quantification formula of the influence range and intensity of electromagnetic radiation on the surrounding ecology Accurately evaluate the influence range and intensity of electromagnetic radiation on the surrounding ecology.

[0039] Sort out the energy operation status indicators and ecological environment impact quantification indicators of the above-mentioned wind farms and solar photovoltaic power stations obtained. Use the range standardization method to normalize the data so that all indicator data is between 0 and 1, which is convenient for unified comparison and fusion analysis. Use intelligent algorithms to determine the weight coefficients of each indicator in the comprehensive evaluation system. Invite experts in the energy field, ecological environment experts, and local staff who have been engaged in energy management for a long time. Make pairwise comparisons according to the importance of each indicator to the overall operation and ecological impact of the mountainous area energy Internet, construct a judgment matrix, and after passing the consistency test, calculate the weights of each indicator such as power generation efficiency and the interference degree of bird migration. Specifically, assume there are m regions and n evaluation indicators, and the original data matrix is X=(x ij ) m×n . Standardize the data: i = 1, 2, …, m; j = 1, 2, …, n, and obtain the standardized data matrix Y=(y ij ) m×n . Calculate the proportion p ij of each region in the total value of the jth indicator: i = 1, 2, …, m; j = 1, 2, …, n, calculate the information entropy e j : j = 1, 2, …, n, the value range of the information entropy e j is between 0 and 1, which reflects the dispersion degree of the jth indicator data. Calculate the weight coefficient w j : j = 1, 2, …, n. Determine the weights through the calculation results of information entropy, so that the indicators with a greater degree of data dispersion can be assigned relatively greater weights, thereby reflecting the importance differences of each indicator in the comprehensive evaluation. Through the correlation analysis method, explore the internal relationship between the energy operation status indicators and the ecological environment impact indicators, and construct a correlation model. Specifically, there are n samples, and the energy operation status indicator data sequence is X = {x1, x2, …, x n}, and the ecological environment impact indicator data sequence is Y = {y1, y2, …, x n}. Calculate the means of the two indicator sequences: Calculate the Pearson correlation coefficient r xy using the formula: where, x i represents the specific value of the energy operation status indicator corresponding to the i-th sample, y i represents the specific value of the ecological environment impact indicator corresponding to the i-th sample, and the value range of r xy is [-1, 1]. When r xy = 1, it means that there is a completely positive correlation between the energy operation status indicator and the ecological environment impact indicator, that is, the change trend of the energy operation status is completely consistent with the change trend of the ecological environment impact. When r xy = -1, it represents a completely negative correlation, meaning that the change of the energy operation status is completely opposite to the change trend of the ecological environment impact. When r xy = 0, it indicates that there is no linear correlation between the two indicators. Based on the results of the correlation analysis, preliminarily judge whether there is a linear correlation and the degree of tightness of the correlation between the energy operation status indicator and the ecological environment impact indicator, and establish a regional differentiated comprehensive evaluation mechanism that takes into account both ecological benefits and energy utilization efficiency.

[0040] Based on the comprehensive evaluation results of each region obtained from the comprehensive evaluation mechanism, set optimization goals, such as improving the overall supply stability of energy in mountainous areas and reducing the impact on bird habitats, etc. And according to the actual layout of wind farms and solar photovoltaic power stations in each region, micro-meteorological characteristics, and the current status of the ecological environment, prioritize the optimization goals, and construct a strategy library containing various strategies, such as adjusting the layout and installation height of wind turbines to optimize wind energy utilization while reducing the impact on birds, adjusting the tilt angle and orientation of the battery panels of solar photovoltaic power stations according to the lighting conditions in different regions to improve power generation efficiency and reduce land vegetation interference, taking ecological restoration measures in ecologically sensitive areas, etc. And record in detail the applicable conditions, expected effects, and implementation steps of each strategy, etc. Match the comprehensive evaluation results of each region with the strategies in the strategy library. For example, set the rule "If the degree of interference of a wind farm in a certain region on bird migration is greater than 0.5 and the power generation efficiency is lower than 0.6, then consider adjusting the layout of the wind farm", screen out specific energy Internet optimization operation strategies and ecological protection measures that meet the actual situation of each region and can effectively achieve the optimization goals, and form a customized strategy plan to guide the subsequent operation and management of the energy Internet in this mountainous area, and realize the coordinated development of energy utilization and ecological protection.

[0041] Embodiment 2

[0042] This embodiment details the specific application of an energy Internet operation status evaluation method and evaluation system in the integrated wind-solar-storage energy Internet region of the grassland area. Through the present invention, customized strategies and ecological protection measures that meet the actual situation and can effectively achieve the optimization goals are screened out for each region, which are used to guide the subsequent operation and management of the grassland energy Internet, and are committed to realizing the harmonious coexistence and sustainable development of energy development and utilization and ecological environment protection in the grassland area.

[0043] Meteorological monitoring stations are set up around various wind farms, solar photovoltaic power stations, and energy storage facilities in grassland areas at reasonable intervals (for example, one monitoring point is set every 5 kilometers). Each monitoring station is equipped with high-precision equipment such as wind speed sensors, wind vanes, thermometers, light sensors, and hygrometers to collect meteorological data for one and a half years. At the same time, historical meteorological records of the local meteorological department over the years are collected to cover data under different seasons and climate conditions. Data cleaning and preprocessing techniques are used to remove abnormal data caused by factors such as accidental equipment failures and extreme weather. Combining with the topographic information of the grassland area (obtaining data such as altitude, slope, and aspect through a geographic information system, as well as the distribution of grassland landform features such as flat areas, gentle slope areas, and low-lying areas), a micro-meteorological model is constructed using numerical simulation methods. The model divides the entire grassland energy Internet area into square grids with a side length of 1 kilometer. Through learning and simulation of a large amount of historical meteorological data and real-time monitoring data, it can accurately present the variation laws of wind speed and wind direction in each grid in different seasons and different time periods, as well as the spatial differences in meteorological elements such as light, temperature, and humidity affected by factors such as terrain and vegetation coverage. For example, the wind speed is relatively large and the wind direction is more stable in areas with higher terrain, while in low-lying areas, the temperature is relatively low and the humidity is relatively high in the early morning and evening.

[0044] Extract the meteorological data corresponding to the grid areas where each wind farm, solar photovoltaic power station, and energy storage facility is located from the micro-meteorological model. For wind farms, focus on obtaining data such as the average wind speed, wind direction, and air density (calculated by combining information such as temperature and pressure) at the hub height; for solar photovoltaic power stations, obtain data such as the light intensity, light duration, and temperature change at the installation position of the solar panels; for energy storage facilities, obtain data such as ambient temperature and humidity that affect their charge-discharge efficiency and energy storage performance. For wind farms, calculate the power generation efficiency of each wind farm according to the wind farm power generation efficiency calculation formula (where P actual is the actual output electric power, P theoretical According to Calculate) and analyze the numerical values of the operation status indicators of each wind farm under different seasons and weather conditions, and then evaluate its energy supply capacity. For solar photovoltaic power stations, according to the photovoltaic conversion efficiency calculation formula (where P elec is the actual output electric power, P inBased on the calculation of light intensity and the effective receiving area of the battery panels), key indicators such as the photoelectric conversion efficiency of each power station are calculated. Combining the analysis of the influence of light duration and temperature on the power generation power, the operating efficiency of power stations in different regions is comprehensively evaluated. For energy storage facilities, according to their own performance models (different types of energy storage facilities have different efficiency and environmental factor relationship models. For example, the specific functional relationship between the charge-discharge efficiency of a lithium battery energy storage station and the environmental temperature), combined with the obtained micro-meteorological data such as environmental temperature and humidity, the operating state indicators such as the charge-discharge efficiency and energy storage capacity retention rate of energy storage facilities in different regions and different time periods are analyzed, and its guarantee role in the energy storage and regulation functions of the entire energy Internet is considered.

[0045] In the grassland energy Internet region, by installing wildlife trackers (for key protected animals such as ungulates and migratory birds on the grassland), setting up vegetation monitoring plots (regularly measuring indicators such as the height, coverage, and species diversity of grassland vegetation in the field), deploying drones for remote sensing monitoring regularly (obtaining image information on large-area grassland vegetation and ecological environment changes), and setting up ecological environment monitoring stations around energy facilities (monitoring soil quality, water body changes, electromagnetic radiation intensity, etc.), a comprehensive set of ecological environment data is collected. Combining the meteorological data output by the micro-meteorological model, for the impact of wind farms on wildlife habitats, a quantitative model based on animal behavior and ecological principles is constructed (for example, considering the influence of wind speed and direction on animal migration paths and foraging ranges, and the quantification model of the interference degree of factors such as noise and light and shadow changes generated by the operation of wind turbines on animal activities). The quantitative value of the interference degree of each wind farm on wildlife activities in different regions is calculated. For the impact of solar photovoltaic power stations on grassland vegetation, based on the relationship model between vegetation growth and factors such as light, temperature, and precipitation, combined with the construction and operation of the power station and micro-meteorological data, the change of the ecological interference degree on the surrounding grassland vegetation over time is measured. In the energy transmission and energy storage links, considering factors such as electromagnetic radiation and the possible leakage of chemical substances from energy storage facilities on soil and water body ecology, through constructing corresponding quantitative models, the impact range and intensity on the surrounding ecological environment are accurately evaluated.

[0046] Sort out and summarize the energy operation status indicators and the quantified indicators of ecological environment impact of the above-mentioned wind farms, solar photovoltaic power stations, and energy storage facilities. Use the range standardization method to normalize all indicator data, and uniformly convert the values of each indicator to the interval of 0-1, which is convenient for subsequent comprehensive comparison and fusion analysis. Use the entropy weight method to determine the weight coefficients of each indicator in the comprehensive evaluation system, and allocate weights according to the amount of information contained in the data of each indicator (measured by the size of information entropy), so that indicators with a large degree of dispersion (small information entropy) are given relatively larger weights, objectively reflecting the importance differences of each indicator. Through the correlation analysis algorithm, explore the internal relationship between the energy operation status indicators and the ecological environment impact indicators. For example, it is found that there is a certain negative correlation between the power generation stability of the wind farm and the degree of interference with wildlife habitats (the more stable the power generation, the less interference with animals). Based on this, establish a regional differential comprehensive evaluation mechanism that takes into account both ecological benefits and energy utilization efficiency for the energy operation status indicators and the ecological environment impact indicators of each region, and realize a comprehensive and comprehensive evaluation of each region of the entire grassland energy Internet.

[0047] According to the comprehensive evaluation results of each region output by the comprehensive evaluation mechanism, combined with the ecological protection needs and energy supply goals in the grassland area, set optimization goals, such as reducing the fragmentation and interference with wildlife habitats while improving energy utilization efficiency, enhancing the ecological restoration ability of grassland vegetation, and ensuring the environmental friendliness of energy storage facilities. And based on factors such as the distribution characteristics of energy facilities, micro-meteorological conditions, and ecological vulnerability in each region, prioritize the optimization goals, and build a strategy library containing various specific strategies, such as adjusting the layout and turbine spacing of the wind farm to avoid wildlife migration channels and habitats, and at the same time optimizing the turbine type selection to improve the power generation efficiency under specific wind conditions in the grassland. For solar photovoltaic power stations, take measures such as regularly cleaning the grassland vegetation under the panels, optimizing the installation height and angle of the panels to reduce the shading effect on vegetation to improve power generation efficiency and protect grassland vegetation. For energy storage facilities, strengthen the construction of protective facilities to prevent chemical substance leakage, optimize the temperature control system of the energy storage system to adapt to the temperature changes in the grassland, and reduce the impact on the surrounding soil and water ecology. And record in detail information such as the applicable conditions, expected effects, and implementation steps of each strategy.

[0048] Match the comprehensive evaluation results of each region with the strategies in the strategy library, set a suitable probability threshold (such as 0.6), and screen out specific energy Internet optimized operation strategies and ecological protection measures that meet the actual conditions of each region and can effectively achieve the optimization goals, and form a customized strategy plan to guide the subsequent operation and management of the energy Internet in this grassland area, and promote the coordinated and sustainable development of grassland energy utilization and ecological protection.

[0049] The above are only the preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above in the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the above-disclosed technical content without departing from the technical solution of the present invention. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the operation status of energy internet, characterized in that: The method comprises the following specific steps: Establishment of micro-meteorological model: sort out the geographical location information of various energy facilities distributed in the energy internet, identify the areas where distributed energy is located that are significantly affected by meteorological conditions, deploy meteorological monitoring equipment in the determined target areas, integrate the existing historical meteorological data records in the area, form a meteorological data set, and formulate a data collection plan, clarify the data collection frequency and time nodes of each monitoring device, and pre-process the collected meteorological data. Combined with the geographic spatial information of the target area, including topographic features and geographic coordinate data, determine the spatial resolution and regional division method of the model, and build a high-precision and real-time updateable micro-meteorological model; Differentiated evaluation of energy operation status based on micro-meteorology: From the established micro-meteorological model, according to the regional division and time interval requirements, the real-time data and historical statistical data of various meteorological elements corresponding to different regions are extracted. The data specifically includes detailed information on wind speed, wind direction, light intensity, light duration, temperature and humidity. For different types of energy subsystems in the energy Internet, namely wind farms, solar power stations and conventional energy facilities, according to their energy conversion principles and operating characteristics, a dedicated operation status evaluation index system is constructed respectively. Based on the extracted meteorological data and the evaluation index system, the operation status of energy subsystems in different regions is analyzed and evaluated, and the specific values ​​of the operation status indicators corresponding to each energy subsystem in each region at different time periods are obtained, and then the energy supply capacity and operation efficiency related data of each local area of ​​the entire energy Internet are summarized and analyzed; Dynamic quantitative evaluation of ecological and environmental impacts: obtain real-time meteorological data of different regions from micro-meteorological models, and collect corresponding ecological and environmental monitoring data through ecological and environmental monitoring equipment deployed in various regions. It is clear that the types of data to be collected include bird activity-related data, land vegetation status data and electromagnetic environment-related data. According to different ecological and environmental influencing factors, a quantitative calculation model of ecological impact is constructed. For the impact on bird migration, a quantitative model of interference degree combining wind speed, wind direction and bird activity location factors is constructed. For the impact on land vegetation, a quantitative model of ecological interference degree based on changes in light, temperature, precipitation and vegetation coverage is constructed. For the impact of electromagnetic radiation, a quantitative model of the influence range and intensity considering the propagation characteristics of electromagnetic radiation under micro-meteorological conditions is constructed. The collected real-time data is input into the corresponding quantitative calculation model. According to the calculation logic and algorithm rules, the specific quantitative values ​​of the impact of distributed energy facilities on various aspects of the ecological environment in different time periods and different regions are dynamically calculated; Construction of comprehensive evaluation mechanism: Data collation and normalization of energy operation status indicators of each region obtained based on micro-meteorological differences and dynamically quantified ecological environment-related indicators are carried out to make the data of different indicators in the same dimension and numerical range. The weight coefficient of each indicator in the comprehensive evaluation system is determined by intelligent algorithm, and the importance of different indicators to the overall energy Internet operation status and ecological environment is fully considered. The internal connection and interaction relationship between energy operation status indicators and ecological environment impact indicators are explored by using correlation analysis methods, and the correlation model between the two is constructed. Based on the determined weight coefficient and correlation model, the energy operation status indicators and ecological environment impact indicators are deeply integrated to establish a regional differentiated comprehensive evaluation mechanism that takes into account both ecological benefits and energy utilization efficiency. Optimization strategy formulation: According to the comprehensive evaluation results of each region output by the comprehensive evaluation mechanism, different optimization goals are set, and the optimization goals are prioritized according to the actual situation of each region. At the same time, an optimization strategy library is built to match the comprehensive evaluation results of each region with the strategies in the optimization strategy library, screen out specific energy Internet optimization operation strategies and ecological protection measures that meet the actual conditions of each region and can effectively achieve the optimization goals, and form a customized strategy plan to guide the subsequent operation and management of each region of the Energy Internet.

2. According to claim 1, a method for evaluating the operation status of energy internet is characterized in that: In the step of differentiated evaluation of energy operation status based on micro-meteorology, for different types of energy subsystems in the energy Internet, namely wind farms, solar power stations and other conventional energy facilities, exclusive operation status evaluation index systems are constructed according to their energy conversion principles and operation characteristic factors. For wind farms, their power generation efficiency is calculated, and the calculation formula is: Among them, η wind Represents the power generation efficiency of the wind farm, P actual is the actual output power of the wind farm in a specific period of time, P theoretical It represents the theoretical power generation calculated based on the wind speed and air density data provided by the micro-meteorological model of the area and the power curve of the wind turbine. The calculation formula is: Where ρ is the air density, A is the swept area of ​​a single blade of the wind turbine, v is the average wind speed at the hub height, C p (λ, β) is the power coefficient of the wind turbine. For the solar power station, the photoelectric conversion efficiency is calculated as follows: Among them, η solar Represents the photoelectric conversion efficiency of the solar power station, P elec is the actual output power of the solar power station in a specific period of time, P in It represents the incident light power received by the surface of the solar panel, and its calculation formula is: P in =I×S, where I is the light intensity provided by the regional micro-meteorological model, and S is the effective receiving area of ​​the solar panel. Similarly, the operating status evaluation indicators of conventional energy facilities can be calculated.

3. The method for evaluating the operation status of energy internet according to claim 1, characterized in that: In the step of differentiated evaluation of energy operation status based on micro-meteorology, according to the specific values ​​of the operation status indicators corresponding to each energy subsystem in each region at different time periods, the energy supply capacity and operation efficiency related data of each local area of ​​the entire energy Internet are summarized and analyzed, and the calculation formula is: Among them, U supply (i) represents the comprehensive evaluation value of the energy supply capacity of the i-th local area, b is the number of different types of energy subsystems contained in the area, and λ u is the weight coefficient of the u-th energy subsystem in the energy supply capacity evaluation, V u (i) is the utility value of the energy supply capacity of the u-th energy subsystem in the i-th region.

4. The method for evaluating the operation status of energy internet according to claim 1, characterized in that: In the step of dynamic quantitative evaluation of ecological environmental impact, for the impact on bird migration, a quantitative model of interference degree combining wind speed, wind direction and bird activity location factors is constructed, and the model formula is: Among them, D bird represents the quantitative value of the interference degree of wind farms on bird migration routes, m is the number of regional segments of bird migration routes, ω i is the weight coefficient of the ith region segment, d i is the number of birds affected by wind farms in the i-th area, D total is the total length of the bird’s entire migration path, P encounter (v i ,θ i ) is the probability function of birds encountering adverse factors of wind farms in the i-th area segment.

5. The method for evaluating the operation status of energy internet according to claim 1, characterized in that: In the step of dynamic quantitative evaluation of ecological environmental impact, for the impact on land vegetation, a quantitative model of ecological disturbance degree based on changes in light, temperature, precipitation and vegetation coverage is constructed, and the model formula is: Among them, E veg It represents the quantitative value of the degree of ecological disturbance of solar power stations on land vegetation, ΔV veg V is the change in the ecological indicators of land vegetation after a certain period of time of construction and operation of the solar power station. veg0 is the initial ecological index value of land vegetation before the construction of the power station, k is the number of main ecological factors affecting vegetation growth, a j is the importance weight coefficient of the jth ecological factor in the vegetation growth process, f j (T j , I j , P j ) is the functional relationship of the jth ecological factor affecting vegetation growth.

6. The method for evaluating the operation status of energy internet according to claim 1, characterized in that: In the step of dynamic quantitative evaluation of ecological environmental impact, for the impact of electromagnetic radiation, a quantitative model of the impact range and intensity considering the electromagnetic radiation propagation characteristics under micro-meteorological conditions is constructed, and the calculation formula of the model is: Among them, I rad (r, θ, h) represents the electromagnetic radiation intensity at a position with a horizontal distance of r from the energy transmission line, an angle of θ with the line, and a vertical height of h. trans is the electric power transmitted on the energy transmission line, r is the horizontal distance between the observation point and the energy transmission line, θ is the angle between the observation point and the energy transmission line, h is the vertical height of the observation point relative to the energy transmission line, μ(ρ, v) is the attenuation coefficient of electromagnetic radiation in the atmosphere, which is a function of air density ρ and wind speed v, and G(θ, h) is the directional gain function of the energy transmission line, which reflects the radiation directional characteristics of electromagnetic radiation in different directions θ and vertical heights h.

7. The method for evaluating the operation status of energy internet according to claim 1, characterized in that: In the step of constructing the comprehensive evaluation mechanism, the weight coefficient of each indicator in the comprehensive evaluation system is determined by an intelligent algorithm. Specifically, there are m regions, n evaluation indicators, and the original data matrix is ​​X=(x ij ) m×n , standardize the data: i = 1, 2, ..., m; j = 1, 2, ..., n, and the standardized data matrix Y = (y ij ) m×n , calculate the proportion p of each region in the total value of the indicator under the jth indicator ij : i=1,2,…,m;j=1,2,…,n,calculate information entropy e j : j = 1, 2, ..., n, information entropy e j The value range is between 0 and 1, reflecting the discrete degree of the j-th indicator data, and calculating the weight coefficient w j : The weights are determined by the information entropy calculation results, so that indicators with large data dispersion can be given relatively larger weights, thereby reflecting the differences in the importance of each indicator in the comprehensive evaluation.

8. The method for evaluating the operation status of energy internet according to claim 1, characterized in that: In the step of constructing the comprehensive evaluation mechanism, the correlation analysis method is used to explore the internal connection and interaction relationship between the energy operation status indicators and the ecological environment impact indicators, and to construct a correlation model between the two. Specifically, there are n samples, and the energy operation status indicator data sequence is X={x1, x2, ..., x n }, the ecological environment impact index data sequence is Y = {y1, y2, ..., x n }, calculate the mean of the two indicator series: Calculate Pearson's correlation coefficient r based on the mean xy The formula is: Among them, x i represents the specific value of the energy operation status indicator corresponding to the i-th sample, y i represents the specific value of the ecological environment impact index corresponding to the i-th sample, r xy The value range is [-1, 1]. xy =1, it means that there is a completely positive correlation between the energy operation status index and the ecological environment impact index, that is, the change trend of the energy operation status is completely consistent with the change trend of the ecological environment impact. xy = -1, which represents a completely negative correlation, meaning that the change in energy operation status is completely opposite to the change trend of the ecological environment impact. xy =0, it means that there is no linear correlation between the two indicators. Based on the correlation analysis results, it is preliminarily judged whether there is a linear correlation between the energy operation status indicator and the ecological environment impact indicator, as well as the degree of correlation.

9. An energy internet operation status evaluation system, the method being applicable to an energy internet operation status evaluation method as claimed in any one of claims 1 to 8, characterized in that: The system comprises the following components: a data acquisition module, a micro-meteorological model building module, an energy operation status evaluation module, an ecological environment impact evaluation module and a comprehensive evaluation module; The data acquisition module is used to collect meteorological data and ecological environment monitoring data related to energy facilities in different areas of the energy internet, and pre-process the data; The micro-meteorological model building module receives the meteorological data from the data acquisition module, and uses data analysis and modeling algorithms to build a high-precision and real-time updated micro-meteorological model, so as to accurately present the subtle spatial differences of meteorological elements in small-scale areas, and output the meteorological element values ​​corresponding to each area; The energy operation status evaluation module obtains meteorological data of different regions, evaluates the operation status of energy subsystems in different regions according to a preset energy operation status evaluation algorithm, and calculates key operation indicators of each region; The ecological environment impact assessment module receives the ecological environment monitoring data from the data acquisition module and the meteorological data output by the micro-meteorological model construction module, and calculates the impact of distributed energy facilities on the ecological environment in different regions and at different times through an ecological impact quantification algorithm, including quantitative values ​​of the degree of impact on bird migration, land vegetation and electromagnetic environment; The comprehensive evaluation module receives energy operation status indicators and ecological environment impact indicators, deeply integrates the two according to the set weight distribution and correlation analysis rules, builds a regional differentiated comprehensive evaluation mechanism, and outputs comprehensive evaluation results to reflect the overall operation status and ecological impact of each region of the energy Internet; The strategy formulation module formulates optimized operation strategies and ecological protection measures that meet the actual conditions of each region of the energy Internet based on the results output by the comprehensive evaluation module.

Images