Self-consistent optimization method for clean energy of expressway

Abstract

The invention relates to the technical field of highway construction, and discloses an expressway clean energy self-consistent optimization method, which accurately predicts the clean energy generating capacity of a key area through illumination intensity, wind speed and geographic data, and performs optimization in combination with a terrain factor and an environment adaptation factor, thereby remarkably improving the generating efficiency of clean energy. The optimal deployment area is determined by adopting a comprehensive scoring and sorting mode, and a systematic solution is provided for clean energy utilization of the expressway. The method has high universality, can be applied to highway clean energy planning in different regions, and has good expandability.

Description

Technical Field

[0001] The invention relates to the technical field of highway construction, and in particular to a self-consistent optimization method for clean energy of highways. Background Art

[0002] With the global emphasis on sustainable development, the application of clean energy in the transportation field is becoming more and more extensive. Especially in highway construction, the use of renewable resources such as wind energy and solar energy to power service areas, lighting systems and other facilities has become an important means to achieve low-carbon and environmental protection goals. However, in the actual application process, due to the complexity of the geographical environment and the diversity of natural conditions, how to scientifically and rationally plan and deploy clean energy equipment to maximize its power generation efficiency and meet the needs of highway service facilities is still a technical problem that needs to be solved urgently.

[0003] In recent years, the application of clean energy in the field of transportation has attracted much attention. We strongly support the development of green transportation. In order to promote the green and low-carbon transformation of transportation infrastructure, we encourage the deployment of clean energy facilities in highway service areas, toll stations and other areas. According to statistics, the transportation industry is one of the important sources of carbon emissions in my country, accounting for about 10% of the country's total emissions, and the task of energy conservation and emission reduction is particularly arduous. Therefore, reducing carbon emissions in the transportation field through the application of clean energy technologies has become a key path to resolve the contradiction between energy and environment. Plateau areas have unique wind and solar resource conditions due to their high altitude, thin air, long sunshine time, and high wind speed. According to relevant statistical data, the annual average sunshine hours in the Qinghai-Tibet Plateau can reach more than 3,000 hours, and the annual average wind speed in some areas exceeds 6 meters per second, making it an ideal area for the development of clean energy. However, the terrain in the plateau area is complex and the climate is changeable. It is difficult for traditional energy development methods to make full use of these advantageous resources. Specifically, in plateaus or complex terrain areas, the site selection of highway service facilities such as service areas is mainly based on terrain and engineering needs, which is not necessarily the best area for wind and solar resources, and there is relatively little wind energy in highway scenic areas. Due to the uneven distribution of natural conditions such as light intensity and wind speed, traditional clean energy deployment methods often fail to fully utilize resources, resulting in low power generation efficiency, which in turn leads to the inability to achieve optimal energy deployment. In view of the technical difficulties in the use of clean energy on highways in plateaus or complex terrain areas, a self-consistent optimization method for clean energy on highways is proposed, which can effectively solve the problems of resource waste and low efficiency in traditional deployment methods. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a self-consistent optimization method for clean energy of highways, comprising: Step 1, obtaining light intensity, wind speed, and geographic data of key areas of the highway; Step 2: construct terrain factors based on geographic data; construct a prediction model based on light intensity, wind speed, and terrain factors to predict clean energy power generation in key areas; Step 3: Obtain the environmental adaptation factors of key areas, and construct priority indicators based on the environmental adaptation factors and clean energy power generation to calculate the priority of each type of clean energy. The clean energy with the highest priority is taken as the preferred energy to optimize the clean energy. Step 4, presetting a terrain threshold for the terrain factor, presetting a power generation threshold for the clean energy power generation, and taking the key areas where the clean energy power generation is greater than the power generation threshold and the terrain factor is less than or equal to the terrain threshold as candidate areas; Step 5: Calculate the comprehensive score for each candidate area, and arrange the comprehensive scores from high to low to form a scoring sequence. Take the candidate areas corresponding to the first N comprehensive scores in the scoring sequence as deployment areas, N=2 / 3L, L is the total number of comprehensive scores in the scoring sequence; and deploy the preferred energy in the deployment area to form the optimal operation mode of the clean energy supply side.

[0005] Furthermore, constructing terrain factors based on geographic data includes the following steps: Step 21, obtaining the altitude of each geographical location in the key area; Step 22, obtaining the slope of each geographical location in the key area; Step 23, obtaining the surface roughness of each geographical location in the key area; Step 24, obtaining the wind speed gradient at each geographical location within the key area; Step 25, construct terrain factors for each geographic location based on altitude, slope, surface roughness, and wind speed gradient.

[0006] Furthermore, the prediction model is a linear regression model: ; Where P represents the predicted clean energy power generation, P Representative geographical location Forecasted clean energy generation, V Representative geographical location Wind speed, I Representative geographical location The light intensity, T represents the terrain factor, T Representative geographical location The terrain factor, b is a constant greater than 0, , , , , They represent the model parameters of the linear regression model respectively.

[0007] Furthermore, the environmental adaptation factors of key areas are obtained, including: Step 31, obtaining the historical temperatures of the temperature sensors deployed in the key areas, and the operating efficiency of the temperature sensors at each historical temperature, and calculating the climate adaptability score of the key areas based on the historical temperature data and the operating efficiency; Step 32, obtaining the area of ​​the key region, and presetting an ideal terrain factor, and calculating the terrain adaptability score based on the area of ​​the key region, the terrain factor, and the ideal terrain factor; Step 33, obtaining the historical wind speed of the key area and screening out the historical maximum wind speed, and calculating the resource score according to the historical maximum wind speed and clean energy power generation; Step 34, calculating the environmental adaptability factor according to the climate adaptability score, the terrain adaptability score, and the resource score.

[0008] Furthermore, the calculation formula of the priority index is: Q=P+E, where Q represents the priority index, P represents the predicted clean energy power generation, and E represents the environmental adaptability factor.

[0009] Furthermore, the climate adaptability score is: ; In the formula, C1 represents the climate adaptability score, F i represents the operating efficiency of the temperature sensor at the i-th historical temperature, and n represents the total number of historical temperatures.

[0010] Furthermore, the terrain adaptability score is: ; In the formula, A represents the key area, represents the ideal terrain factor, represents the terrain adaptability score, and S represents the area of ​​the key area.

[0011] Furthermore, the resource score is: ; In the formula, C3 represents the resource score, P represents the predicted clean energy power generation, V max Represents the highest wind speed in history.

[0012] Furthermore, based on each candidate region, a comprehensive score is calculated, which specifically includes the following steps: Step 51, obtaining the slope of the key area and obtaining the maximum slope; Step 52, calculating a comprehensive score based on the slope, maximum slope, clean energy power generation, and terrain factors.

[0013] Furthermore, the comprehensive score is: ; In the formula, represents the comprehensive score of the jth candidate position, represents the clean energy generation of the jth candidate location, represents pi, represents the slope of the jth candidate position, represents the maximum slope of the jth candidate position, represents the terrain factor of the jth candidate location, , , v is the weight coefficient.

[0014] The embodiments of the present invention have the following technical effects: The present invention is suitable for plateaus or areas with complex terrains, and can make full use of the abundant local wind and solar resources to significantly improve the power generation efficiency of clean energy. Through the accurate prediction and optimization of light intensity, wind speed, and geographical data, it provides a systematic solution for the utilization of clean energy on highways.

[0015] The present invention accurately predicts the clean energy generation in key areas through light intensity, wind speed, and geographic data, and optimizes it in combination with terrain factors and environmental adaptability factors, significantly improving the power generation efficiency of clean energy. It uses a comprehensive scoring and sorting method to determine the optimal deployment area, providing a systematic solution for the clean energy utilization of highways. It has strong universality and can be applied to clean energy planning for highways in different regions, with good scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a highway clean energy self-consistent optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0019] Figure 1 Flowchart of a self-consistent optimization method for clean energy of highway provided by an embodiment of the present invention. Figure 1 , specifically including: Step 1: Obtain light intensity, wind speed, and geographic data of key areas of the highway through satellite remote sensing images.

[0020] Among them, geographic data includes at least information such as geographic location, altitude, slope, slope aspect, surface roughness, soil type, and geological stability.

[0021] Key areas refer to specific functional areas along highways where clean energy deployment is required, including the following two categories: Core areas of energy demand: areas where facilities such as service areas, toll booths, and tunnel lighting areas are located. These areas themselves have high electricity demand (such as lighting, heating, charging piles, etc.) and are the main targets of clean energy supply. Road sections with high requirements for continuous power supply, such as areas with dense monitoring equipment, lighting systems in long tunnels or bridges, etc. Resource potential assessment areas: In highway planning or existing sections, areas rich in wind and solar resources are screened out through geographical data (such as slope, altitude, surface roughness) and meteorological data (light, wind speed). These areas may not be selected by traditional planning, but are predicted to have high power generation potential through models.

[0022] Step 2: construct terrain factors based on geographic data; construct a prediction model based on light intensity, wind speed, and terrain factors to predict clean energy power generation in key areas.

[0023] Construct terrain factors based on geographic data, including: Step 21, obtaining the altitude of each geographical location in the key area.

[0024] First, take the current location as the origin and the set of all points within a circular range with a radius of x. For example, x=500m, then the geographic location includes all points within a radius of 500 meters with the current location as the center. The altitude can be obtained through step 1.

[0025] Step 22, obtaining the slope of each geographical location in the key area.

[0026] The slope can be obtained through step 1.

[0027] Step 23: Obtain the surface roughness of each geographical location in the key area.

[0028] The surface roughness can be obtained through step 1.

[0029] Step 24, obtaining the wind speed gradient at each geographical location within the key area.

[0030] The wind speed gradient describes the law of wind speed change with height, and in this embodiment, it is represented by the wind speed change rate in the vertical direction.

[0031] Step 25, construct terrain factors for each geographic location based on altitude, slope, surface roughness, and wind speed gradient.

[0032] ; Where, T Representative geographical location The terrain factor is h, which represents the altitude, s represents the slope, r represents the surface roughness, and g represents the wind speed gradient. , , , is the weight coefficient, which is obtained by analyzing different data samples. represents the altitude influence factor, represents the slope influence factor, Represents the influencing factor of surface roughness, which is obtained by regression analysis of different data samples.

[0033] The prediction model is a linear regression model: ; Where P represents the predicted clean energy power generation, P Representative geographical location Forecasted clean energy generation, V Representative geographical location Wind speed, I Representative geographical location The light intensity, T represents the terrain factor, T Representative geographical location The terrain factor, b is a constant greater than 0, , , , , They represent the model parameters of the linear regression model respectively.

[0034] Step 3: Obtain the environmental adaptation factors of key areas, and construct priority indicators based on the environmental adaptation factors and clean energy power generation to calculate the priority of each type of clean energy. The clean energy with the highest priority is taken as the preferred energy to optimize the clean energy. Obtain environmental adaptation factors for key areas, including: Step 31, obtaining the historical temperatures of the temperature sensors deployed in the key areas, and the operating efficiency of the temperature sensors at each historical temperature, and calculating the climate adaptability score of the key areas based on the historical temperature data and the operating efficiency; ; In the formula, C1 represents the climate adaptability score, F i represents the operating efficiency of the temperature sensor at the i-th historical temperature, and n represents the total number of historical temperatures.

[0035] Step 32, obtaining the area of ​​the key region, and presetting an ideal terrain factor, and calculating the terrain adaptability score based on the area of ​​the key region, the terrain factor, and the ideal terrain factor; ; In the formula, A represents the key area, represents the ideal terrain factor, represents the terrain adaptability score, and S represents the area of ​​the key area.

[0036] Based on the above terrain factor formula, the historical terrain factors are calculated for the historical altitude, slope, surface roughness, and wind speed gradient of the key areas, and the median value of the historical terrain factors is taken as the ideal terrain factor.

[0037] Step 33, obtaining the historical wind speed of the key area and screening out the historical maximum wind speed, and calculating the resource score according to the historical maximum wind speed and clean energy power generation; ; In the formula, C3 represents the resource score, P represents the predicted clean energy power generation, V max Represents the highest wind speed in history.

[0038] Step 34, calculate the environmental adaptability factor according to the climate adaptability score, the terrain adaptability score, and the resource score: E=C1+C2+C3, where E represents the environmental adaptability factor.

[0039] The environmental adaptability factor comprehensively considers multiple key dimensions such as climate adaptability, terrain adaptability and resource score, and can comprehensively evaluate whether the candidate area is suitable for deploying a specific type of clean energy equipment. Through quantitative analysis, the environmental adaptability factor not only helps decision makers screen out the optimal deployment area, but also provides a scientific basis for selecting the appropriate energy type. For example, in plateau areas, the environmental adaptability factor can highlight the advantages of long sunshine hours and high wind speeds, thereby giving priority to recommending solar or wind power equipment. In addition, it can effectively avoid potential risks caused by complex geographical conditions or extreme climates, and ensure the efficient operation and long-term stability of clean energy facilities. Therefore, the environmental adaptability factor is a core tool for achieving scientific planning and optimized deployment of clean energy, and is of great significance to promoting green and low-carbon transformation in the transportation sector.

[0040] The calculation formula for the priority index is: Q=P+E, where Q represents the priority index, P represents the predicted clean energy power generation, and E represents the environmental adaptability factor.

[0041] Step 4, presetting a terrain threshold for the terrain factor, presetting a power generation threshold for the clean energy power generation, and taking the key areas where the clean energy power generation is greater than the power generation threshold and the terrain factor is less than or equal to the terrain threshold as candidate areas; The historical terrain factor is calculated based on the historical data of the key areas through the above terrain factor formula, the mean of the historical terrain factor is used as the terrain threshold, and the median value of the historical clean energy power generation in the key areas is used as the power generation threshold.

[0042] Among them, only those areas whose clean energy power generation exceeds the preset power generation threshold and whose terrain factor is less than or equal to the terrain threshold will be included in the candidate range. The setting of this standard fully considers the impact of terrain complexity on equipment installation and operation, while ensuring that the selected area has sufficient resource endowment to support the efficient output of clean energy. Such a selection mechanism not only improves resource utilization efficiency, but also effectively reduces the potential risks caused by terrain restrictions or insufficient resources, thus providing a reliable basis for subsequent comprehensive scoring and final deployment.

[0043] Step 5: Calculate the comprehensive score for each candidate area, and arrange the comprehensive scores from high to low to form a scoring sequence. Take the candidate areas corresponding to the first N comprehensive scores in the scoring sequence as deployment areas, N=2 / 3L, L is the total number of comprehensive scores in the scoring sequence; and deploy the preferred energy in the deployment area to form the optimal operation mode of the clean energy supply side.

[0044] ; In the formula, represents the comprehensive score of the jth candidate position, represents the clean energy generation of the jth candidate location, represents pi, represents the slope of the jth candidate position, represents the maximum slope of the jth candidate position, represents the terrain factor of the jth candidate location, , , v is the weight coefficient.

[0045] The slope is normalized to the maximum slope and a sine function is applied to assess the impact of terrain on the deployment of clean energy facilities. The sine function ensures that changes in slope within a certain range (usually 0 to 90 degrees) are smoothly reflected in the score. The slope is converted into a value between 0 and 1 to adjust the weight of power generation. When the slope is close to the maximum value, the value is close to 1; when the slope is close to 0, the value is close to 0. By comprehensively considering power generation, slope and terrain factors, the clean energy deployment potential of different candidate locations can be fully evaluated, thereby helping decision makers select the optimal clean energy deployment area.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A self-consistent optimization method for clean energy of highways, characterized in that: include: Step 1, obtaining light intensity, wind speed, and geographic data of key areas of the highway; Step 2, construct terrain factors based on geographic data; Build a prediction model based on light intensity, wind speed, and terrain factors to predict clean energy power generation in key areas; Step 3: Obtain the environmental adaptation factors of key areas, and construct priority indicators based on the environmental adaptation factors and clean energy power generation to calculate the priority of each type of clean energy. The clean energy with the highest priority is taken as the preferred energy to optimize the clean energy. Step 4, presetting a terrain threshold for the terrain factor, presetting a power generation threshold for the clean energy power generation, and taking the key areas where the clean energy power generation is greater than the power generation threshold and the terrain factor is less than or equal to the terrain threshold as candidate areas; Step 5: Calculate the comprehensive score for each candidate area, and arrange the comprehensive scores from high to low to form a scoring sequence. Take the candidate areas corresponding to the first N comprehensive scores in the scoring sequence as deployment areas, N=2 / 3L, L is the total number of comprehensive scores in the scoring sequence; and deploy the preferred energy in the deployment area to form the optimal operation mode of the clean energy supply side.

2. A highway clean energy self-consistent optimization method according to claim 1, characterized in that: Constructing terrain factors based on geographic data includes the following steps: Step 21, obtaining the altitude of each geographical location in the key area; Step 22, obtaining the slope of each geographical location in the key area; Step 23, obtaining the surface roughness of each geographical location in the key area; Step 24, obtaining the wind speed gradient at each geographical location within the key area; Step 25, construct terrain factors for each geographic location based on altitude, slope, surface roughness, and wind speed gradient.

3. A highway clean energy self-consistent optimization method according to claim 1, characterized in that: The prediction model is a linear regression model: ； Where P represents the predicted clean energy power generation, P Representative geographical location Forecasted clean energy generation, V Representative geographical location Wind speed, I Representative geographical location The light intensity, T represents the terrain factor, T Representative geographical location The terrain factor, b is a constant greater than 0, , , , , They represent the model parameters of the linear regression model respectively.

4. A highway clean energy self-consistent optimization method according to claim 3, characterized in that: Obtain environmental adaptation factors for key areas, including: Step 31, obtaining the historical temperatures of the temperature sensors deployed in the key areas, and the operating efficiency of the temperature sensors at each historical temperature, and calculating the climate adaptability score of the key areas based on the historical temperature data and the operating efficiency; Step 32, obtaining the area of ​​the key region, and presetting an ideal terrain factor, and calculating the terrain adaptability score based on the area of ​​the key region, the terrain factor, and the ideal terrain factor; Step 33, obtaining the historical wind speed of the key area and screening out the historical maximum wind speed, and calculating the resource score according to the historical maximum wind speed and clean energy power generation; Step 34, calculating the environmental adaptability factor according to the climate adaptability score, the terrain adaptability score, and the resource score.

5. A highway clean energy self-consistent optimization method according to claim 4, characterized in that: The calculation formula of the priority index is: Q=P+E, where Q represents the priority index, P represents the predicted clean energy power generation, and E represents the environmental adaptability factor.

6. A highway clean energy self-consistent optimization method according to claim 4, characterized in that: Climate adaptability scores are: ； In the formula, C1 represents the climate adaptability score, F i represents the operating efficiency of the temperature sensor at the i-th historical temperature, and n represents the total number of historical temperatures.

7. A highway clean energy self-consistent optimization method according to claim 5, characterized in that: Terrain suitability scores are: ； In the formula, A represents the key area, represents the ideal terrain factor, represents the terrain adaptability score, and S represents the area of ​​the key area.

8. A highway clean energy self-consistent optimization method according to claim 4, characterized in that: The resource scores are: ； In the formula, C3 represents the resource score, P represents the predicted clean energy power generation, V max Represents the highest wind speed in history.

9. A highway clean energy self-consistent optimization method according to claim 3, characterized in that: Based on each candidate region, a comprehensive score is calculated, which includes the following steps: Step 51, obtaining the slope of the key area and obtaining the maximum slope; Step 52, calculating a comprehensive score based on the slope, maximum slope, clean energy power generation, and terrain factors.

10. A highway clean energy self-consistent optimization method according to claim 9, characterized in that: The formula for calculating the comprehensive score is: ； In the formula, represents the comprehensive score of the jth candidate position, represents the clean energy generation of the jth candidate location, represents pi, represents the slope of the jth candidate position, represents the maximum slope of the jth candidate position, represents the terrain factor of the jth candidate location, , , v is the weight coefficient.

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