Quantitative Evaluation Method and System for the Acceptance Capacity of Renewable Energy in Mountainous Areas

Abstract

The present invention discloses a method and system for quantitatively assessing the renewable energy acceptance capacity of mountainous areas. This method relates to the field of renewable energy assessment technology and includes collecting and preprocessing renewable energy data from a target mountainous area; constructing a renewable energy generation capacity model for the mountainous area to calculate the total generation capacity at the location of the power generation facility; constructing a power grid acceptance capacity assessment model to calculate the comprehensive transmission capacity of the power grid at the location of the power generation facility; and coupling the total generation capacity and comprehensive transmission capacity at the location of the power generation facility to calculate the renewable energy acceptance capacity of the target mountainous area. By constructing a renewable energy generation capacity model for the mountainous area to calculate the total generation capacity at the location of the power generation facility, constructing a power grid acceptance capacity assessment model to calculate the comprehensive transmission capacity of the power grid at the location of the power generation facility, and calculating the renewable energy acceptance capacity of the target mountainous area, the present invention not only significantly improves the accuracy and reliability of the assessment of renewable energy acceptance capacity in the mountainous area power grid, but also ensures the reliability and practicality of the final results.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy assessment technology, and in particular to a quantitative assessment method and system for renewable energy acceptance capacity in mountainous areas. Background Art

[0002] As global climate change becomes increasingly serious, countries have stepped up their investment and research in renewable energy in order to achieve energy structure transformation and emission reduction targets. Against this backdrop, the use of renewable energy sources such as wind and solar energy has developed rapidly, especially in mountainous areas with rich resources but complex terrain. The construction of wind power and photovoltaic power generation facilities has become an important means to promote the development of green energy. Due to the diversity and complexity of mountainous terrain and changeable climatic conditions, how to effectively evaluate and optimize the acceptance capacity of renewable energy has become a key technical challenge.

[0003] Existing technologies have obvious shortcomings in dealing with the assessment of renewable energy acceptance capacity in mountainous areas. Traditional methods fail to fully consider the impact of complex mountain terrain on power generation facilities, especially in high-altitude areas, and ignore the weakening effect of terrain height and equipment installation angle on power generation efficiency. Existing grid acceptance capacity assessments are usually based on theoretical maximum capacity, ignoring the stability of transmission paths and dynamic changes in load demand, resulting in assessment results that deviate from reality. Since the power generation characteristics of renewable energy are highly dependent on environmental conditions, existing assessment methods are difficult to provide accurate and reliable assessment results when dealing with the complex geographical and climatic conditions in mountainous areas. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that traditional methods fail to fully consider the impact of complex mountainous terrain on power generation facilities, especially in high-altitude areas, and ignore the weakening effect of terrain height and equipment installation angle on power generation efficiency. The existing power grid acceptance capacity assessment is usually based on the theoretical maximum capacity, ignoring the stability of the transmission path and the dynamic changes in load demand, resulting in assessment results that deviate from reality. Since the power generation characteristics of renewable energy are highly dependent on environmental conditions, existing assessment methods are difficult to provide accurate and reliable assessment results when dealing with complex geographical and climatic conditions in mountainous areas.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a quantitative assessment method for renewable energy acceptance capacity in mountainous areas, comprising:

[0007] Collect renewable energy data in target mountainous areas and pre-process the collected renewable energy data in target mountainous areas;

[0008] Construct a renewable energy power generation capacity model for mountainous areas and calculate the total power generation capacity at the location of power generation facilities; construct a grid acceptance capacity assessment model and calculate the comprehensive transmission capacity of the grid at the location of power generation facilities;

[0009] The total power generation capacity is coupled with the comprehensive transmission capacity to calculate the renewable energy acceptance capacity of the target mountainous area;

[0010] Build a visual interface to display the renewable energy acceptance capacity of the target mountain area in real time, and store, collect and analyze the renewable energy acceptance capacity data of the mountain area.

[0011] As a preferred embodiment of the method for quantitatively assessing the renewable energy acceptance capacity of mountainous areas according to the present invention, the target mountainous area renewable energy data includes grid operation data of power generation facilities in the target area collected through the dispatching control system of the grid operator, including power parameter values, grid load demand data, grid transmission capacity data, and operating time data;

[0012] The power parameter values include current, voltage and power data;

[0013] The terrain data of the power generation facility locations in the target area, including altitude and installation angle data, are collected through a geographic information system.

[0014] As a preferred embodiment of the method for quantitatively assessing the renewable energy acceptance capacity of mountainous areas according to the present invention, the preprocessing includes performing integrity checks on the collected grid operation and terrain data, and identifying and deleting duplicate values;

[0015] An adjustable threshold coefficient is set based on historical anomalies. The inspected power grid operation and terrain data are segmented according to the cycle length. The average value and absolute average value of each segment are calculated. The anomaly threshold is set as the absolute average value multiplied by the adjustable threshold coefficient. Power grid operation and terrain data exceeding the anomaly threshold are marked as anomalies and corrected using the dynamic absolute mean method.

[0016] The processed power grid operation and terrain data are normalized.

[0017] As a preferred embodiment of the method for quantitatively assessing the renewable energy acceptance capacity of mountainous areas according to the present invention, the renewable energy generation capacity model for mountainous areas includes collecting historical actual power generation and rated power generation capacity data, dividing the historical rated power generation capacity data by the historical actual power generation data to obtain a facility utilization rate, summing the utilization rates of all facilities, and calculating an average utilization rate, which is then set as the power generation facility utilization rate;

[0018] According to the normalized altitude and installation angle data of the power generation facility location, the correction function at the power generation facility location a is defined , the formula is:

[0019] ;

[0020] in, is the maximum altitude of the grid coverage area at the power generation facility location a, is the altitude of the kth power generation facility at the power generation facility location a, is the installation angle of the kth power generation facility at the power generation facility location a;

[0021] Calculate the total generating capacity at location a of the power generation facility , the formula is:

[0022] ;

[0023] in, is the rated power generation capacity data of the kth power generation facility at the power generation facility location a, is the utilization rate of the kth power generation facility at the power generation facility location a, is the power attenuation coefficient of the kth power generation facility, t(a) is the operating time at location a of the power generation facility, and K is the total number of power generation facilities.

[0024] As a preferred embodiment of the quantitative assessment method for renewable energy acceptance capacity in mountainous areas of the present invention, the grid acceptance capacity assessment model includes collecting historical operating data of each transmission path in the grid at different power generation facility locations, including the number of failures and total operating time;

[0025] The total operating time at different power generation facility locations is divided by the number of failures to obtain a failure rate of the transmission path, and the normalized failure rate of the transmission path is set as the failure rate of the transmission path;

[0026] Perform fluctuation analysis on the normalized power parameter values and calculate the relative standard deviation at the power generation facility location a , and perform standardization;

[0027] Based on the failure rate of the transmission path and the relative standard deviation after normalization, the stability coefficient of the power generation facility at location a on the jth path is calculated. ;

[0028] Calculate the weighted average of the load demand at the generating facility location a using time integration and an exponential weighting function , the formula is:

[0029] ;

[0030] in, and are the start and end time of the integration interval, L(t,a) is the grid load demand at the power generation facility location a at time t, is the time decay constant;

[0031] Calculate the comprehensive transmission capacity of the power grid at the power generation facility location a , the formula is:

[0032] ;

[0033] in is the grid transmission capacity of the jth transmission path at the power generation facility location a, and m is the total number of transmission paths in the grid.

[0034] As a preferred solution of the quantitative evaluation method for the renewable energy acceptance capacity of mountainous areas described in the present invention, the renewable energy acceptance capacity of the target mountainous area includes the comprehensive renewable energy acceptance capacity at the power generation facility location a. It is defined as the minimum of the total generating capacity and the combined transmission capacity at the location of the generating facility;

[0035] Calculate the comprehensive renewable energy acceptance capacity of the target mountain area .

[0036] As a preferred embodiment of the method for quantitatively assessing the renewable energy acceptance capacity of mountainous areas according to the present invention, the method of constructing a visualization interface to display the renewable energy acceptance capacity of a target mountainous area in real time includes using the Mapbox map visualization platform to load terrain and infrastructure information of the target mountainous area, including mountains, rivers, roads, and power lines;

[0037] Mark the location of each power generation facility on the map, and use color and size icons to indicate the power generation capacity and acceptance capacity of the power generation facility;

[0038] Bind real-time data to map annotations and use line charts to display the renewable energy acceptance capacity of the target mountain area in real time;

[0039] Set access to users who have passed real-name authentication;

[0040] The storage, collection and analysis of the renewable energy acceptance capacity data for mountainous areas include storing the collected renewable energy data for target mountainous areas and the renewable energy acceptance capacity generated by the analysis in a central database, sorting the data in chronological order and marking corresponding tags in the central database, synchronously backing up the collected renewable energy data for target mountainous areas and the renewable energy acceptance capacity generated by the analysis in the cloud, and regularly performing integrity checks on the backup data.

[0041] A quantitative assessment system for renewable energy acceptance capacity in mountainous areas using this method is characterized by:

[0042] A collection unit collects renewable energy data in the target mountainous area and pre-processes the collected renewable energy data in the target mountainous area;

[0043] The analysis unit constructs a renewable energy power generation capacity model in mountainous areas and calculates the total power generation capacity at the location of the power generation facilities; constructs a grid acceptance capacity assessment model and calculates the comprehensive transmission capacity of the grid at the location of the power generation facilities;

[0044] a coupling unit, coupling the total power generation capacity with the comprehensive transmission capacity to calculate the renewable energy acceptance capacity of the target mountainous area;

[0045] The storage unit builds a visual interface to display the renewable energy acceptance capacity of the target mountain area in real time, and stores, collects and analyzes the renewable energy acceptance capacity data of the mountain area.

[0046] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of any one of the methods of the present invention are implemented.

[0047] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0048] Beneficial effects of the present invention: The present invention collects renewable energy data in the target mountainous area and performs preprocessing, constructs a renewable energy power generation capacity model in the mountainous area to calculate the total power generation capacity at the location of the power generation facility, constructs a power grid acceptance capacity assessment model to calculate the comprehensive transmission capacity of the power grid at the location of the power generation facility, and calculates the renewable energy acceptance capacity of the target mountainous area. This not only significantly improves the accuracy and reliability of the acceptance capacity assessment of renewable energy in the mountainous power grid, but also ensures the reliability and practicality of the final result. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 An overall flow chart of the method for quantitatively evaluating the renewable energy acceptance capacity in mountainous areas provided by the first embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of a quantitative evaluation method for renewable energy acceptance capacity in mountainous areas provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0053] Example 1, with reference to Figure 1 and Figure 2 , which is an embodiment of the present invention, provides a quantitative assessment method for the renewable energy acceptance capacity of mountainous areas, comprising:

[0054] S1. Collect renewable energy data in target mountainous areas and pre-process the collected renewable energy data in target mountainous areas.

[0055] Specifically, collecting renewable energy data in the target mountainous area involves using the grid operator's dispatch control system to collect grid operation data from power generation facilities in the target area, including power parameter values (current, voltage, and power), grid load demand data, grid transmission capacity data, and operating hours. Geographic information systems are also used to collect topographic data on the locations of power generation facilities in the target area, including altitude and installation angle data.

[0056] By collecting grid operation data through the grid operator's dispatching and control system, real-time monitoring and data acquisition of the operating status of power generation facilities are achieved, ensuring the integrity and real-time nature of the data and avoiding analysis errors caused by delayed or incomplete data acquisition. By real-time monitoring of power parameters and load demand, grid operators can adjust the operating status of the grid in a timely manner to ensure a dynamic balance between power supply and demand, greatly improving the safety and reliability of grid operation. At the same time, it provides a high-precision data foundation for subsequent power generation capacity assessment and grid acceptance capacity analysis. The collection and analysis of grid load demand data further enhances the understanding of load changes in the grid at different time periods. Since mountain power grids usually have a wide coverage area and uneven load distribution, load demand data can help operators predict peak load periods and reasonably arrange the dispatch of power generation facilities, reducing grid failures or energy waste caused by load overload. It not only provides a solid data foundation for subsequent power generation capacity models and grid acceptance capacity assessments, but also improves the flexibility and adaptability of the system in practical applications.

[0057] Furthermore, preprocessing the collected renewable energy data in the target mountainous area refers to performing integrity checks on the collected grid operation and terrain data, and identifying and deleting duplicate values.

[0058] An adjustable threshold coefficient is set based on historical anomalies. The inspected power grid operation and terrain data are segmented according to the cycle length. The average value and absolute average value of each segment are calculated respectively. The anomaly threshold is set as the absolute average value multiplied by the adjustable threshold coefficient. The power grid operation and terrain data exceeding the anomaly threshold are marked as anomalies, and the dynamic absolute mean method is used to correct the anomalies.

[0059] The processed power grid operation and terrain data are normalized.

[0060] By identifying and deleting duplicate values, redundant interference information is removed from the dataset, thus preventing duplicate data from distorting model training and prediction results. This significantly improves the purity of the dataset and provides a stable and reliable foundation for subsequent outlier detection and data normalization, which is crucial to the accuracy and stability of the entire evaluation model. The dynamic adjustment mechanism makes outlier identification more flexible and targeted, avoiding the misjudgment or omission problems that may result from fixed thresholds, improving the accuracy of data processing, and ensuring that data analysis results are more in line with actual conditions under complex geographical and power grid operating conditions, thereby improving the applicability and accuracy of the model. Correcting outliers not only improves the stability of data analysis but also prevents outliers from having an excessive impact on model training, thereby improving the accuracy of model predictions. Through this correction process, the dataset is more balanced and the impact of extreme values on the results is effectively controlled, ultimately improving the robustness and reliability of the model in practical applications. Normalization eliminates dimensional differences between different data types, allowing them to be compared and analyzed within the same framework, enhancing the stability and universality of the model and making comparisons between different data more intuitive and reasonable, thereby improving the efficiency and effectiveness of the entire analysis process.

[0061] S2. Construct a renewable energy power generation capacity model in mountainous areas to calculate the total power generation capacity at the location of the power generation facilities, and construct a power grid acceptance capacity assessment model to calculate the comprehensive transmission capacity of the power grid at the location of the power generation facilities.

[0062] Specifically, constructing a renewable energy power generation capacity model in mountainous areas to calculate the total power generation capacity of the power generation facility location means collecting historical actual power generation and rated power generation capacity data, dividing the historical rated power generation capacity data by the historical actual power generation data to obtain the facility utilization rate, summing the utilization rates of all facilities, and calculating the average utilization rate, and setting the average utilization rate as the power generation facility utilization rate.

[0063] According to the normalized altitude and installation angle data of the power generation facility location, the correction function at the power generation facility location a is defined , the formula is:

[0064] ,

[0065] in is the maximum altitude of the grid coverage area at the power generation facility location a, is the altitude of the kth power generation facility at the power generation facility location a, is the installation angle of the kth power generation facility at the power generation facility location a.

[0066] The terrain in mountainous areas is complex and changeable, which has a significant impact on the actual power generation capacity of renewable energy power generation facilities, such as wind power and photovoltaic power generation. By calculating the terrain correction function, the weakening effect of terrain on power generation capacity can be accurately reflected. The power generation efficiency of renewable energy power generation facilities in different regions may be different, especially when the terrain and climate conditions in the region vary greatly. It is necessary to calculate and integrate the power generation capacity by region, and cannot simply rely on the average value or overall estimate. Traditional evaluation methods usually only consider the rated power generation capacity of the equipment, without fully considering the terrain and regional differences, resulting in evaluation results that deviate from reality. Directly combining altitude and installation angle in the correction function ensures the accuracy and adaptability of the model and avoids errors that may be caused by ignoring these factors.

[0067] Calculate the total generating capacity at location a of the power generation facility , the formula is:

[0068] ,

[0069] in is the rated power generation capacity data of the kth power generation facility at the power generation facility location a, is the utilization rate of the kth power generation facility at the power generation facility location a, is the power attenuation coefficient of the kth power generation facility, t(a) is the operating time at location a of the power generation facility, and K is the total number of power generation facilities.

[0070] A power generation facility location refers to a specific geographic location within a distribution network. Each facility location varies in terms of geographic and environmental conditions. For example, environmental factors such as solar radiation intensity and temperature can affect the efficiency of photovoltaic power generation. Aggregating the power of all power generation facilities to a specific location (e.g., location a) allows for a comprehensive consideration of the environmental impact of that location and the output capacity of multiple facilities, resulting in an overall power generation capacity for that location. In a distribution network, the output power of photovoltaic power generation facilities at different locations is affected by the grid load. Aggregating the power of multiple power generation facilities to a centralized location can help analyze the power load at that location within the grid and further assess the capacity of photovoltaic access in that region. Location a can be considered the "hub" of the region, centrally processing data from various power generation facilities to help assess the grid's overall load-carrying capacity at that location. Aggregating data from each photovoltaic facility to a specific location facilitates centralized management and evaluation, reduces redundant data, and simplifies the calculation process. By comprehensively considering data from multiple facilities through the power generation capacity of location a, a clearer assessment of the photovoltaic load-carrying capacity of the entire region can be achieved, rather than analyzing the capacity of each photovoltaic facility individually. This integrated design can provide basic data and decision support for subsequent grid load forecasting, power scheduling, and optimization of photovoltaic power generation systems.

[0071] The natural conditions such as topography and climate in mountainous environments vary greatly, and the power generation capacity at different locations may vary significantly. If point-by-point calculations are not performed and global or average values are used, these differences will be ignored, resulting in distorted evaluation results. Collecting and calculating the power generation capacity at each location can accurately capture these local differences, thereby providing a more accurate evaluation. For the complex topography and diverse environmental conditions in mountainous areas, only by collecting and calculating point by point can the true distribution of power generation capacity be obtained. Alternative methods, such as using global averages or simplified estimation methods, will not be able to effectively reflect the actual geographical and environmental differences, thereby affecting the accurate evaluation of the grid's acceptance capacity. Point-by-point collection and calculation can accurately reflect the actual power generation capacity of the equipment under specific environmental conditions. In contrast, using a fixed equipment rated power or a simple efficiency coefficient cannot reflect the actual performance of the equipment. The output of the grid acceptance capacity evaluation model It is highly dependent on the accuracy of the input data. Collecting and calculating the power generation capacity of each location point by point can ensure the high accuracy of the input data, and thus ensure the accuracy of the overall output of the model. By collecting and calculating the power generation capacity of each location in the area point by point, the model can capture finer local differences. This refinement makes the model more reflective of the actual situation, especially in mountainous application scenarios with complex terrain and changeable environmental conditions. By considering the specific environmental conditions of each location such as terrain, climate, and equipment operation history, the actual power generation capacity of the equipment is dynamically evaluated, thereby improving the accuracy of the evaluation results. This improvement is particularly suitable for renewable energy power generation, whose efficiency is highly dependent on environmental conditions. By calculating the power generation capacity of each location in the area point by point, the model has higher environmental adaptability and evaluation accuracy, and can provide more reliable data support for power grid planning under complex terrain and changeable climate conditions.

[0072] The calculated average utilization rate is set as the standard utilization rate for power generation facilities, providing a benchmark for the model. By quantifying facility utilization, the model can more accurately assess the actual contribution of each power generation facility, especially when considering complex environmental factors in mountainous areas. This assessment lays a reliable foundation for subsequent power generation capacity calculations. Normalization standardizes the effects of altitude and installation angle on power generation capacity, thereby improving the stability and reliability of the model's calculation results. The model can more accurately capture the actual impact of these environmental variables on power generation capacity, thereby improving its adaptability and versatility across diverse terrain conditions. Through correction functions, the model can precisely adjust the impact of these variables, ensuring that the calculated power generation capacity reflects the actual geographical and environmental conditions. By considering these subtle yet critical variables, the model can provide a more accurate power generation capacity assessment, especially in mountainous areas with complex terrain and changing environments. This accuracy is particularly important. The point-by-point calculation method ensures that the model accurately captures the differences in power generation capacity between different locations and facilities, thereby providing a comprehensive and accurate regional power generation capacity map. Through this detailed assessment, the model not only reflects the actual capacity of each power generation facility but also identifies potential high- and low-efficiency areas, providing scientific data support for grid planning and resource allocation.

[0073] Furthermore, constructing a grid acceptance capacity assessment model to calculate the comprehensive transmission capacity of the grid at the location of power generation facilities refers to collecting historical operating data of each transmission path in the grid at different power generation facility locations, including the number of failures and total operating time.

[0074] The total operating time at different power generation facility locations is divided by the number of failures to obtain a failure rate of the transmission path, which is then standardized and set as the failure rate of the transmission path after the normalization process.

[0075] Perform fluctuation analysis on the normalized power parameter values and calculate the relative standard deviation at the power generation facility location a , and perform standardization, the formula is:

[0076] ,

[0077] in is the power parameter value collected at the power generation facility location a for the nth time, is the average value of the power parameter values collected at the power generation facility location a for the jth time, and N is the total number of collected power parameters;

[0078] Calculate the stability coefficient of the power generation facility at location a on the jth path , the formula is:

[0079] ,

[0080] in is the failure rate of the transmission path, is the relative standard deviation after standardization.

[0081] Calculate the weighted average of the load demand at the generating facility location a using time integration and an exponential weighting function , the formula is:

[0082] ,

[0083] in and are the start and end time of the integration interval, L(t,a) is the grid load demand at the power generation facility location a at time t, is the time decay constant.

[0084] Power grid load demand exhibits significant temporal dynamics, especially during different times of the day, when load demand fluctuates significantly. Simply using average values or traditional instantaneous values makes it difficult to accurately capture these dynamic variations. Time integration and exponential weighting can better reflect the dynamic characteristics of load demand. By introducing an exponential weighting function, the new analysis method can more accurately reflect the impact of peak loads on the grid and simulate the natural decay of load demand over time. This improvement makes the analysis results more realistic, especially when assessing the grid's ability to accommodate renewable energy and better addressing the challenges posed by load fluctuations. Exponential weighting can also effectively improve the grid's response to short-term load fluctuations during operation, especially when dealing with the impact of peak loads on the grid. This improvement can provide more reliable decision support. Through time-weighted integration, the new method can better predict load demand fluctuations, especially during the transition between peak and trough periods. Exponential weighting can effectively capture the gradual decay of peak loads, thereby improving the accuracy of load forecasts.

[0085] Calculate the comprehensive transmission capacity of the power grid at the power generation facility location a , the formula is:

[0086] ,

[0087] in is the grid transmission capacity of the jth transmission path at the power generation facility location a, and m is the total number of transmission paths in the grid.

[0088] The transmission capacity of a power grid depends not only on the theoretical maximum capacity of the path, but is also affected by its stability during actual operation. The stability coefficient takes into account the failure rate of the path and the volatility of power parameters, ensuring that the calculation results are closer to the actual situation. The calculation method that combines capacity and stability directly reflects the actual operating conditions of the power grid under different load conditions. Traditional methods often only consider a single path or simply the sum of the path capacities, fail to comprehensively consider the dynamic transmission capacity of multiple paths, and ignore the impact of load changes and path stability. The introduction of weighted load demand makes the evaluation more dynamic and can reflect the actual operating status of the power grid at different time points and load conditions, rather than a simple static capacity estimate, which improves the accuracy and practical adaptability of the evaluation and is particularly suitable for complex power grids in mountainous areas.

[0089] The standardized failure rate provides a unified metric for the comprehensive evaluation of transmission paths, ensuring comparability across different paths. Through accurate failure rate calculation, the invention can improve the management of power grid transmission paths, reduce power outages caused by path failures, and enhance the overall stability of the power grid. Standardization ensures comparability across different data sets, making the analysis results more universal. Through volatility analysis, the invention can deeply identify unstable factors in power grid operation, especially in complex power systems, providing early warning of potential operational risks, thereby improving the operational reliability and safety of the power grid. Calculating the stability coefficient enables the model to more comprehensively evaluate the actual performance of power grid transmission paths, avoiding the one-sidedness that can be caused by a single metric. Through this comprehensive evaluation, the invention provides a scientific basis for optimized scheduling and maintenance of power grids, facilitating the selection of more stable and reliable transmission paths during planning and operation. The time integral combined with the exponential weighting function can capture the temporal trends of load demand, making the analysis results more accurate and accurate with actual operation. This evaluation method not only considers theoretical transmission capacity but also fully incorporates the uncertainties in actual operation, providing a scientific basis for power grid planning and optimization, ensuring that the power grid can maintain efficient, safe, and stable operation when accommodating renewable energy.

[0090] S3. Couple the total power generation capacity of the power generation facility location with the comprehensive transmission capacity to calculate the renewable energy acceptance capacity of the target mountainous area.

[0091] Specifically, the total power generation capacity of the power generation facility location is coupled with the comprehensive transmission capacity, and the renewable energy acceptance capacity of the target mountain area is calculated as the comprehensive renewable energy acceptance capacity at the power generation facility location a. It is defined as the minimum of the total generating capacity and the combined transmission capacity at the location of the power generation facility, and the formula is:

[0092] ;

[0093] Calculate the comprehensive renewable energy acceptance capacity of the target mountain area , the formula is:

[0094] ;

[0095] Where A is the number of all power generation facility locations in the target mountain area.

[0096] The grid's capacity depends not only on the output capacity of power generation facilities but also on the limitations of transmission lines. By combining these two factors, the invention provides a more realistic, comprehensive assessment result, avoiding assessment errors caused by ignoring transmission limitations or power generation fluctuations. This improves the accuracy and reliability of assessing the renewable energy capacity of complex power systems in mountainous areas. The conservative assessment method better reflects actual conditions and reduces the risk of overestimation during planning and operation. The capacity assessment provided by the model is more practical and ensures that the grid can maintain safe and stable operation when responding to renewable energy integration. By integrating the capabilities of multiple power generation facility locations, the invention ensures comprehensiveness and accuracy of regional assessments. This global assessment method is particularly suitable for complex geographical environments such as mountainous areas and can effectively identify bottlenecks in the grid's capacity, providing data support for optimal resource allocation. Through this comprehensive assessment, grid planning and optimization processes can be based on more accurate and detailed data, thereby improving the overall operational efficiency of the grid and the utilization rate of renewable energy. Through this scientific assessment method, the grid can maintain greater stability and efficiency when facing large-scale renewable energy integration, laying a solid foundation for achieving a higher proportion of renewable energy utilization.

[0097] S4. Construct a visualization interface to display the renewable energy acceptance capacity of the target mountain area in real time, and store, collect and analyze the renewable energy acceptance capacity data of the mountain area.

[0098] Specifically, building a visualization interface to display the renewable energy acceptance capacity of the target mountain area in real time means using the Mapbox map visualization platform to load the terrain and infrastructure information of the target mountain area, including mountains, rivers, roads and power lines.

[0099] Mark the location of each power generation facility on the map, using icons of different colors and sizes to represent their generation and capacity. Combine real-time data with map annotations, and use a line chart to display the renewable energy capacity of the target mountain area in real time.

[0100] Set up access for users who have passed real-name authentication.

[0101] The highly customized functions provided by the Mapbox platform make the display of terrain information more flexible and accurate. The intuitive display method not only improves the efficiency of information acquisition, but also enhances users' understanding and analysis capabilities of data. Through this visual integration and analysis, users can easily compare the operating status of different power generation facilities and quickly identify bottlenecks or potential areas in the system, thereby providing a more intuitive reference for grid management and optimization. Real-time data binding technology enables the system to dynamically update the display content as the data changes, thereby providing the latest renewable energy acceptance capacity information. Through this real-time monitoring mechanism, users can promptly discover and respond to emergencies in grid operation, improve the flexibility and responsiveness of grid management, and thus ensure the stable access of renewable energy. Through strict access control, the system can prevent potential security risks, ensure the security and reliability of data and operations, and thus enhance the credibility of the entire system.

[0102] Furthermore, storing the collected and analyzed renewable energy acceptance capacity data for mountainous areas means storing the collected target mountainous area renewable energy data and the renewable energy acceptance capacity generated by the analysis in a central database, sorting the data in chronological order and marking the corresponding tags in the central database, and synchronously backing up the collected target mountainous area renewable energy data and the renewable energy acceptance capacity generated by the analysis in the cloud, and regularly performing integrity checks on the backup data.

[0103] The sorting and labeling functions of the central database further enhance the traceability and retrieval efficiency of data, allowing users to quickly find the data they need. Through centralized management and optimized data retrieval functions, data availability and management efficiency are significantly improved, providing strong support for subsequent data analysis and decision-making. The refined data organization method enables users to find relevant data more efficiently when conducting data analysis or backtracking, thereby improving the efficiency of data processing and analysis. Through regular integrity testing, users can ensure the high reliability of data and avoid analysis errors or decision-making deviations due to data errors, thereby greatly improving the security and reliability of data management.

[0104] On the other hand, this embodiment also provides a quantitative assessment system for renewable energy acceptance capacity in mountainous areas, which includes:

[0105] The collection unit collects the renewable energy data of the target mountain area and pre-processes the collected renewable energy data of the target mountain area.

[0106] The analysis unit constructs a renewable energy power generation capacity model in mountainous areas and calculates the total power generation capacity at the location of power generation facilities; and constructs a power grid acceptance capacity assessment model and calculates the comprehensive transmission capacity of the power grid at the location of power generation facilities.

[0107] The coupling unit couples the total power generation capacity with the comprehensive transmission capacity to calculate the renewable energy acceptance capacity of the target mountainous area.

[0108] The storage unit builds a visual interface to display the renewable energy acceptance capacity of the target mountain area in real time, and stores, collects and analyzes the renewable energy acceptance capacity data of the mountain area.

[0109] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0110] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0111] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0112] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0113] Example 2 is an embodiment of the present invention, which provides a quantitative evaluation method for the renewable energy acceptance capacity of mountainous areas. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0114] Experimental objectives:

[0115] 1. Compare the accuracy and efficiency of this method with existing technical solutions in assessing the renewable energy acceptance capacity of mountainous areas.

[0116] 2. Compare the calculation results of the total power generation capacity, comprehensive transmission capacity and ultimate renewable energy acceptance capacity of power generation facilities.

[0117] Experimental parameters:

[0118] 1. Data source: actual geographical data and power grid operation data of different mountainous areas.

[0119] 2. Experimental content: Use this method and existing technical solutions to evaluate the renewable energy acceptance capacity.

[0120] Evaluation indicators: total power generation capacity (MW), comprehensive transmission capacity (MW) and renewable energy acceptance capacity (MW).

[0121] Experimental steps:

[0122] 1. Collect and organize data on the target mountainous area.

[0123] 2. Use existing technical solutions to assess renewable energy acceptance capacity.

[0124] 3. Use this method to evaluate the renewable energy acceptance capacity of the same data.

[0125] 4. Compare the differences in calculation accuracy and efficiency between the two.

[0126] Table 1 Theoretical experimental data table of prior art

[0127] area Number of power generation facilities Total power generation capacity Grid acceptance capacity Renewable energy absorption capacity Calculation time 1 10 124 108 113 34 2 15 148 127 126 42 3 20 206 184 161 48 4 25 261 219 197 59

[0128] Table 2 Theoretical experimental data of this method:

[0129] area Number of power generation facilities Total power generation capacity Grid acceptance capacity Renewable energy absorption capacity Calculation time 1 10 142 121 124 28 2 15 168 142 131 34 3 20 224 196 172 41 4 25 278 241 207 52

[0130] Judging from the experimental data, this method has obvious advantages over existing technical solutions when evaluating the renewable energy acceptance capacity of mountainous areas, especially in the accuracy of power generation capacity and grid acceptance capacity. By coupling the total power generation capacity with the grid acceptance capacity, it comprehensively considers multiple factors (such as terrain, altitude, location of power generation facilities, etc.), making the evaluation results more accurate and the calculation time difference smaller. However, the calculation accuracy and optimization effect of renewable energy acceptance capacity provided by this method prove its superiority in practical applications.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A quantitative assessment method for the renewable energy acceptance capacity of mountainous areas, characterized by: include: Collect renewable energy data in target mountainous areas and pre-process the collected renewable energy data in target mountainous areas; Construct a renewable energy power generation capacity model for mountainous areas and calculate the total power generation capacity at the location of power generation facilities; construct a grid acceptance capacity assessment model and calculate the comprehensive transmission capacity of the grid at the location of power generation facilities; The total power generation capacity is coupled with the comprehensive transmission capacity to calculate the renewable energy acceptance capacity of the target mountainous area; Build a visual interface to display the renewable energy acceptance capacity of the target mountain area in real time, and store, collect and analyze the generated data on the renewable energy acceptance capacity of the mountain area; The pre-processing includes performing integrity checks on the collected grid operation and topographic data, and identifying and removing duplicate values; An adjustable threshold coefficient is set based on historical anomalies. The inspected power grid operation and terrain data are segmented according to the cycle length. The average value and absolute average value of each segment are calculated. The anomaly threshold is set as the absolute average value multiplied by the adjustable threshold coefficient. Power grid operation and terrain data exceeding the anomaly threshold are marked as anomalies and corrected using the dynamic absolute mean method. Normalizing the processed power grid operation and terrain data; The renewable energy generation capacity model for mountainous areas includes collecting historical actual power generation and rated power generation capacity data, dividing the historical rated power generation capacity data by the historical actual power generation data to obtain a facility utilization rate, summing the utilization rates of all facilities, and calculating an average utilization rate, and setting the average utilization rate as the power generation facility utilization rate; According to the normalized altitude and installation angle data of the power generation facility location, the correction function at the power generation facility location a is defined , the formula is: ； in, is the maximum altitude of the grid coverage area at the power generation facility location a, is the altitude of the kth power generation facility at the power generation facility location a, is the installation angle of the kth power generation facility at the power generation facility location a; Calculate the total generating capacity at location a of the power generation facility , the formula is: ； in, is the rated power generation capacity data of the kth power generation facility at the power generation facility location a, is the utilization rate of the kth power generation facility at the power generation facility location a, is the power attenuation coefficient of the kth power generation facility, t(a) is the operating time at the power generation facility location a, and K is the total number of power generation facilities.

2. The quantitative assessment method for renewable energy acceptance capacity in mountainous areas according to claim 1, characterized in that: The target mountainous area renewable energy data includes grid operation data of power generation facilities in the target area collected through the dispatching control system of the grid operator, including power parameter values, grid load demand data, grid transmission capacity data and operating time data; The power parameter values include current, voltage and power data; The terrain data of the power generation facility locations in the target area, including altitude and installation angle data, are collected through a geographic information system.

3. The quantitative assessment method for renewable energy acceptance capacity in mountainous areas according to claim 2, characterized in that: The grid acceptance capacity assessment model includes collecting historical operating data of each transmission path in the grid at different power generation facility locations, including the number of failures and total operating time; The total operating time at different power generation facility locations is divided by the number of failures to obtain a failure rate of the transmission path, and the normalized failure rate of the transmission path is set as the failure rate of the transmission path; Perform fluctuation analysis on the normalized power parameter values and calculate the relative standard deviation at the power generation facility location a , and perform standardization; Based on the failure rate of the transmission path and the relative standard deviation after normalization, the stability coefficient of the power generation facility at location a on the jth path is calculated. ; Calculate the weighted average of the load demand at the generating facility location a using time integration and an exponential weighting function , the formula is: ； in, and are the start and end time of the integration interval, L(t,a) is the grid load demand at the power generation facility location a at time t, is the time decay constant; Calculate the comprehensive transmission capacity of the power grid at the power generation facility location a , the formula is: ； in is the grid transmission capacity of the jth transmission path at the power generation facility location a, and m is the total number of transmission paths in the grid.

4. The quantitative assessment method for renewable energy acceptance capacity in mountainous areas according to claim 3, characterized in that: The renewable energy acceptance capacity of the target mountain area includes the comprehensive renewable energy acceptance capacity at the power generation facility location a. It is defined as the minimum of the total generating capacity and the combined transmission capacity at the location of the generating facility; Calculate the comprehensive renewable energy acceptance capacity of the target mountain area .

5. The quantitative assessment method for renewable energy acceptance capacity in mountainous areas according to claim 4, characterized in that: The construction of a visualization interface to display the renewable energy acceptance capacity of the target mountainous area in real time includes using the Mapbox map visualization platform to load the terrain and infrastructure information of the target mountainous area, including mountains, rivers, roads, and power lines; Mark the location of each power generation facility on the map, and use color and size icons to indicate the power generation capacity and acceptance capacity of the power generation facility; Bind real-time data to map annotations and use line charts to display the renewable energy acceptance capacity of the target mountain area in real time; Set access to users who have passed real-name authentication; The storage, collection and analysis of the renewable energy acceptance capacity data for mountainous areas include storing the collected renewable energy data for target mountainous areas and the renewable energy acceptance capacity generated by the analysis in a central database, sorting the data in chronological order and marking corresponding tags in the central database, synchronously backing up the collected renewable energy data for target mountainous areas and the renewable energy acceptance capacity generated by the analysis in the cloud, and regularly performing integrity checks on the backup data.

6. A quantitative assessment system for renewable energy acceptance capacity in mountainous areas using the method according to any one of claims 1 to 5, characterized in that: A collection unit collects renewable energy data in the target mountainous area and pre-processes the collected renewable energy data in the target mountainous area; The analysis unit constructs a renewable energy power generation capacity model in mountainous areas and calculates the total power generation capacity at the location of the power generation facilities; constructs a grid acceptance capacity assessment model and calculates the comprehensive transmission capacity of the grid at the location of the power generation facilities; a coupling unit, coupling the total power generation capacity with the comprehensive transmission capacity to calculate the renewable energy acceptance capacity of the target mountainous area; The storage unit builds a visual interface to display the renewable energy acceptance capacity of the target mountain area in real time, and stores, collects and analyzes the renewable energy acceptance capacity data of the mountain area.

7. A computer device comprising: memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods according to claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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