Distributed photovoltaic power generation output characteristic analysis method for rail transit infrastructure
By building simulation models in distributed photovoltaic power generation systems, using clustering and meteorological data analysis, representative typical sunrise force scenarios are generated, which solves the problems of inaccurate simulation and high computational complexity in traditional analysis methods, and improves the analysis Accuracy and efficiency provide scientific analysis results to support power scheduling and system optimization.
Patent Information
- Application Number
- CN202411406102.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-10
- Publication Date
- 2025-01-10
AI Technical Summary
The traditional distributed photovoltaic power output characteristics analysis method fails to fully consider the complex meteorological factors and other variables in the actual application environment, resulting in inaccurate simulation of photovoltaic power output, high computational complexity, slow processing speed, and lack of quantitative indicator support, making it difficult to Directly applied to power scheduling and system optimization.
By building a distributed photovoltaic power generation system consistent with the actual application environment, using simulation to obtain annual photovoltaic output power information, determining the number of typical scenes based on the clustering effectiveness index DBI, calculating the similarity between scenes for classification, and generating typical sunrise force scenes. Qualitative and quantitative analysis were carried out, typical scenarios were generated using Euclidean distance measurement standards, and output characteristics were evaluated in combination with meteorological data.
The accuracy of the distributed photovoltaic power generation model and the efficiency of large-scale photovoltaic output data processing are improved, the typical scenarios generated are more representative, providing scientific basis for system operation and maintenance work, and optimizing energy planning and power scheduling capabilities.
Smart Images
Figure CN119298010A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of distributed photovoltaic power generation, and in particular relates to a method for analyzing output characteristics of distributed photovoltaic power generation of rail transit infrastructure. Background Art
[0002] Distributed photovoltaic power generation, as an important form of renewable energy, has the characteristics of wide sources, environmental protection and pollution-free, and has become an important way to solve energy and environmental problems. There are many underutilized spaces in rail transit infrastructure, such as roadbed slopes, roofs of vehicle depots, roofs of production and living houses along the line, awnings, sound barriers, bridges, station ramp pavement, etc. Distributed photovoltaic power generation systems are installed in these spaces to provide power supply for rail transit systems, including but not limited to lighting systems along the railway. This not only improves the self-sufficiency of the rail transit power supply system and reduces the system's dependence on traditional energy, but also provides a clean and sustainable energy source for the rail transit system, promoting rail transit to develop in a cleaner, more efficient and sustainable direction.
[0003] However, as an important part of the rail transit power supply system, the power supply quality and stability of the distributed photovoltaic power generation system are crucial to the reliable operation of the rail transit power supply system. However, affected by various factors such as weather, time, and environment, the distributed photovoltaic output has the characteristics of randomness, intermittency, and volatility. Therefore, the output characteristics analysis of the distributed photovoltaic power generation system of rail transit infrastructure is helpful to understand the output fluctuation and periodic changes of the distributed photovoltaic system and ensure the reliability of the power supply of the distributed photovoltaic power generation system. However, the traditional distributed photovoltaic power generation output characteristics analysis method often fails to fully consider the complex meteorological factors and other variables in the actual application environment, resulting in inaccurate simulation of photovoltaic power generation output. At the same time, due to the lack of efficient data clustering and classification methods, the traditional analysis method has the problems of high computational complexity and slow processing speed when processing large-scale photovoltaic output data. In addition, when determining the typical output scene of distributed photovoltaics, the traditional analysis method usually relies on simple statistical methods or subjective judgment to select typical days, which may lead to too many or too few typical scenes and cannot truly reflect the system output characteristics. Finally, most current studies focus on qualitatively describing the output characteristics of photovoltaic systems, lacking quantitative indicators to support, making it difficult to directly apply the analysis results to power dispatching and system optimization design. Summary of the invention
[0004] In order to solve the above problems, the present invention proposes a method for analyzing the output characteristics of distributed photovoltaic power generation of rail transit infrastructure, which improves the accuracy of the distributed photovoltaic power generation model of rail transit infrastructure and the efficiency of large-scale photovoltaic output data processing, while ensuring that the generated distributed photovoltaic typical scenes are more representative, and provides a scientific basis for system operation and maintenance by conducting qualitative and quantitative analysis of the distributed photovoltaic typical daily output scenes.
[0005] To achieve the above object, the technical solution adopted by the present invention is: a method for analyzing the output characteristics of distributed photovoltaic power generation in rail transit infrastructure, comprising the steps of:
[0006] S100, builds a distributed photovoltaic power generation system for rail transit infrastructure that is consistent with the actual application environment, obtains annual photovoltaic output power information through simulation, and provides data basis for subsequent typical scenario generation and output characteristic analysis;
[0007] S200, dividing the distributed photovoltaic power output into daily power output scenarios, and determining the optimal typical scenario number of the distributed photovoltaic typical daily power output scenario based on the clustering effectiveness index DBI;
[0008] S300, calculating the similarity between the daily working scenes, classifying the scenes according to the similarity between the scenes, and selecting the initial typical scene to prevent the random selection of the initial typical scene from causing the typical scene generation result to fall into a local optimal solution;
[0009] S400, generating a distributed photovoltaic typical daily power generation scenario based on the above-mentioned optimal typical scenario number and the initial typical scenario, calculating the Euclidean distance between the distributed photovoltaic daily power generation scenarios, and generating a typical daily power generation scenario using the Euclidean distance as a measurement standard;
[0010] S500 analyzes the output characteristics of typical daily output scenarios of distributed photovoltaics, grasps the law of distributed photovoltaic output through qualitative analysis, and quantitatively analyzes the output characteristics of distributed photovoltaics through output peak-to-valley difference and fluctuation output characteristic indicators, guiding the energy planning and scheduling of rail transit power supply systems.
[0011] Furthermore, in step S100, a distributed photovoltaic power generation system is simulated and modeled, and a rail transit infrastructure distributed photovoltaic power generation system consistent with the actual application environment is built based on PVsyst software, and the annual photovoltaic output power information is obtained using the analysis tool of PVsyst software.
[0012] Furthermore, in step S200, the optimal number of typical scenarios is determined based on the DBI index, and on the basis of dividing the distributed photovoltaic output into daily output scenarios, the optimal number of typical scenarios for generating distributed photovoltaic typical scenarios is determined using the DBI index, which is the ratio of intra-class data density to inter-class data density.
[0013] Furthermore, in step S300, initial typical scenes are selected based on the similarity classification between scenes, the similarity between data points is calculated by the Ward variance minimization method, and the data points are grouped and then the initial typical scenes are selected, thereby improving the defect that the random selection of initial typical scenes causes the typical scene generation results to fall into a local optimal solution.
[0014] Furthermore, in the step S400, a typical daily power generation scenario of distributed photovoltaics is generated. Based on the above-mentioned number of optimal typical scenarios and the establishment of the initial typical scenarios, the Euclidean distance between the distributed photovoltaic daily power generation scenarios is calculated. The Euclidean distance is used as the measurement standard, and all data are clustered with the initial typical scenarios as different categories. The sum of the distances within each cluster is calculated, the typical scenarios within each category are updated, and then the clusters are re-divided with the new typical scenarios as categories. The above process is repeated until the typical scenarios are no longer updated to complete the generation of the typical daily power generation scenario of distributed photovoltaics.
[0015] Furthermore, in step S500, the output characteristics of the typical daily power generation scenario of distributed photovoltaics are qualitatively and quantitatively analyzed, and the output rules and output characteristic changes of distributed photovoltaics in different time periods, different weather conditions and different seasonal changes are understood through qualitative analysis; a day is divided into multiple time periods, and the output characteristic changes of the photovoltaic system in different time periods are observed; combined with meteorological data, the output characteristics of the photovoltaic system under different weather conditions are observed; considering the influence of sunshine time and solar altitude angle factors in different seasons, the output characteristics of the photovoltaic system in different seasons are observed; by drawing the output curve of the photovoltaic power generation system in the typical daily power generation scenario, the distributed photovoltaic output characteristics are quantitatively analyzed with the help of output peak-to-valley difference and fluctuation output characteristic indicators, the stability of the distributed photovoltaic system and the power generation capacity of the distributed photovoltaic system are evaluated, and based on this, the design and operation of the distributed photovoltaic power generation system and the rail transit power supply system are optimized, the system operation and maintenance work is guided, and the system energy management and power dispatching capabilities are improved.
[0016] The beneficial effects of adopting this technical solution are:
[0017] The present invention builds a distributed photovoltaic power generation system for rail transit infrastructure that is consistent with the actual application environment, generates a typical daily output scenario of distributed photovoltaic power generation, and performs output characteristic analysis, providing a more accurate modeling and analysis method for the distributed photovoltaic power generation system for rail transit infrastructure, which helps to better understand and grasp the power generation capacity of the photovoltaic system and provide guidance for optimizing system design and operation.
[0018] The present invention takes into account the optimal number of typical scenarios and the initial typical scenario selection method for generating typical scenarios of distributed photovoltaic power generation systems, provides a more representative scenario generation method for distributed photovoltaic output, and improves the efficiency of photovoltaic output data processing and analysis.
[0019] The present invention is easy to implement, further optimizes the system modeling and analysis methods, requires less human resources, and has strong economic value and use value. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 A schematic diagram of a method for analyzing output characteristics of distributed photovoltaic power generation in rail transit infrastructure according to the present invention;
[0021] Figure 2 A schematic diagram of a distributed photovoltaic power generation system for rail transit infrastructure consistent with an actual application environment is established in an embodiment of the present invention;
[0022] Figure 3 The figure is a schematic diagram of a typical scenario generation process of a distributed photovoltaic power generation system established based on the optimal typical scenario number and the initial typical scenario in an embodiment of the present invention. DETAILED DESCRIPTION
[0023] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.
[0024] In this embodiment, see Figure 1 As shown, the present invention proposes a method for analyzing the output characteristics of distributed photovoltaic power generation in rail transit infrastructure, including the steps of
[0025] S100, builds a distributed photovoltaic power generation system for rail transit infrastructure that is consistent with the actual application environment, obtains annual photovoltaic output power information through simulation, and provides data basis for subsequent typical scenario generation and output characteristic analysis;
[0026] S200, dividing the distributed photovoltaic power output into daily power output scenarios, and determining the optimal typical scenario number of the distributed photovoltaic typical daily power output scenario based on the clustering effectiveness index DBI;
[0027] S300, calculating the similarity between the daily working scenes, classifying the scenes according to the similarity between the scenes, and selecting the initial typical scene to prevent the random selection of the initial typical scene from causing the typical scene generation result to fall into a local optimal solution;
[0028] S400, generating a distributed photovoltaic typical daily power generation scenario based on the above-mentioned optimal typical scenario number and the initial typical scenario, calculating the Euclidean distance between the distributed photovoltaic daily power generation scenarios, and generating a typical daily power generation scenario using the Euclidean distance as a measurement standard;
[0029] S500 analyzes the output characteristics of typical daily output scenarios of distributed photovoltaics, grasps the law of distributed photovoltaic output through qualitative analysis, and quantitatively analyzes the output characteristics of distributed photovoltaics through output peak-to-valley difference and fluctuation output characteristic indicators, guiding the energy planning and scheduling of rail transit power supply systems.
[0030] As an optimization solution for the above embodiment, in step S100, a distributed photovoltaic power generation system is simulated and modeled, and a distributed photovoltaic power generation system for rail transit infrastructure consistent with the actual application environment is built based on PVsyst software, and the analysis tool of PVsyst software is used to obtain annual photovoltaic output power information.
[0031] Combined with PVsyst software, a distributed photovoltaic power generation system consistent with the actual rail transit infrastructure environment is built, taking into account factors such as photovoltaic module type and parameters, photovoltaic module array design, installation inclination and installation orientation, weather and light. Then, with the help of PVsyst software analysis tools, the power generation performance of the built distributed photovoltaic system is evaluated and optimized, the system energy output is predicted, and economic analysis is performed.
[0032] In this embodiment, see Figure 2 The figure shows a schematic diagram of a distributed photovoltaic power generation system for rail transit infrastructure, which consists of a photovoltaic array, an inverter, and a rail transit power supply system. The photovoltaic array takes into account the type and parameters of photovoltaic modules, the design of the photovoltaic module array (including the number of series modules and parallel strings, etc.), the installation inclination and orientation of photovoltaic modules (considering factors such as the sunshine time and solar altitude angle of the installation site), and the inverter design takes into account the model and parameters of the inverter.
[0033] As an optimization scheme of the above embodiment, in step S200, the optimal number of typical scenarios is determined based on the DBI index. On the basis of dividing the distributed photovoltaic output into daily output scenarios, the DBI index, which is the ratio of intra-class data density to inter-class data density, is used to determine the optimal number of typical scenarios for generating distributed photovoltaic typical scenarios.
[0034] In order to reduce the complexity of the processing and analysis of distributed photovoltaic output data of rail transit infrastructure, the present invention divides the distributed photovoltaic output data into scenes, generates typical output scenes that can represent the characteristics of most scene data, and then focuses on the output characteristics of typical output scenes. If the number of typical scenes is set too small, scenes with different data characteristics will be classified into the same category, and the representativeness of the scene cannot be reflected. If the number of typical scenes is set too large, the same type of data will be divided into different categories, and the differences between scenes cannot be reflected, which will also reduce the efficiency of data processing and analysis. Therefore, the number of typical scenes is crucial to the effect of the final typical scene.
[0035] The present invention determines the optimal number of typical scenarios based on the DBI index. The DBI index represents the ratio of the intra-class data density to the inter-class data density after all output scenarios are classified according to the number of typical scenarios. The intra-class data density is the sum of the number of intra-class data whose distance from the class center point is less than the class radius within all classes, representing the concentration of the intra-class data. The inter-class data density is the sum of the number of scenarios within any two classes whose distance from the inter-class median point is less than the class radius, representing the inter-class dispersion. Therefore, the higher the DBI index, the better the representativeness of the number of typical scenarios.
[0036] As an optimization scheme for the above embodiment, in step S300, the initial typical scene is selected based on the similarity classification between scenes, the similarity between data points is calculated by the Ward variance minimization method, and the initial typical scene is grouped and then selected, thereby improving the defect that the typical scene generation result falls into a local optimal solution due to the random selection of the initial typical scene.
[0037] Randomly selecting the initial typical scene may cause the final typical scene to fall into a local optimal solution, so the present invention proposes to improve the selection method of the initial typical scene based on the similarity between scenes. The similarity between scenes can be calculated by various distance metrics, such as minimum and maximum distances, average or weighted distances, etc.
[0038] The present invention adopts the Ward variance minimization method to measure the distance and similarity between scenes. The Ward variance minimization method divides the data points into different scene groups, and optimizes the initial typical scene generation results by minimizing the variance within the group. The criterion for the Ward variance minimization method to achieve effective data grouping is to minimize the variance increase of the merged group. Specifically, for two groups A and B to be merged, the variance increase of the merged group AB is calculated, that is, A, B and AB are respectively regarded as the overall calculated variance, and then the original sum of variances is subtracted, and the merge operation with the smallest variance increase is selected until the preset number of groups is reached, and the initial typical scene is selected.
[0039] As an optimization scheme for the above embodiment, in step S400, a distributed photovoltaic typical daily power generation scenario is generated. Based on the above-mentioned number of optimal typical scenarios and the initial typical scenarios, the Euclidean distance between the distributed photovoltaic daily power generation scenarios is calculated, and the distributed photovoltaic typical daily power generation scenario is generated using the Euclidean distance as a measurement standard.
[0040] See also Figure 3The figure shows a schematic diagram of the typical scenario generation process of a distributed photovoltaic power generation system based on the best typical scenario number and the initial typical scenario. Based on the above-mentioned best typical scenario number and the initial typical scenario, the Euclidean distance between distributed photovoltaic daily power generation scenarios is calculated, and the Euclidean distance is used as the measurement standard. All data are clustered with the initial typical scenarios as different categories, and the sum of the distances within each cluster is calculated. The typical scenarios within each category are updated, and then the clusters are re-divided with the new typical scenarios as categories. The above process is repeated until the typical scenario is no longer updated to complete the generation of the distributed photovoltaic typical daily power generation scenario.
[0041] As an optimization scheme for the above-mentioned embodiment, in step S500, the output characteristics of the typical daily output scenario of distributed photovoltaic are analyzed, the law of distributed photovoltaic output is grasped through qualitative analysis, and the output characteristics of distributed photovoltaic are quantitatively analyzed through output characteristic indicators such as output peak-to-valley difference and fluctuation amount, so as to guide the energy planning and scheduling of the rail transit power supply system.
[0042] Qualitative and quantitative analysis of the output characteristics of typical daily power generation scenarios of distributed photovoltaics can provide a comprehensive understanding of the power generation capacity and effect of photovoltaic systems in different time periods. Through qualitative analysis, the output law and output characteristics of distributed photovoltaics in different time periods, different weather conditions and different seasonal changes can be mastered. Divide a day into multiple time periods (such as early morning, morning, afternoon, evening, etc.), observe the output characteristics of photovoltaic systems in different time periods; combine meteorological data to observe the output characteristics of photovoltaic systems under different weather conditions such as sunny, cloudy and cloudy; consider the influence of factors such as sunshine time and solar altitude angle in different seasons, and observe the output characteristics of photovoltaic systems in different seasons (spring, summer, autumn and winter). By drawing the output curve of the typical daily power generation scenario of the photovoltaic power generation system, the output characteristics of distributed photovoltaics are quantitatively analyzed with the help of output characteristic indicators such as output peak-to-valley difference and fluctuation, evaluate the stability of the distributed photovoltaic system and the power generation capacity of the distributed photovoltaic system, and optimize the design and operation of the distributed photovoltaic power generation system and the rail transit power supply system based on this, guide the system operation and maintenance work, and improve the system energy management and power dispatching capabilities.
[0043] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.
Claims
1. A method for analyzing the output characteristics of distributed photovoltaic power generation in rail transit infrastructure, characterized in that: Included steps S100, builds a distributed photovoltaic power generation system for rail transit infrastructure that is consistent with the actual application environment, obtains annual photovoltaic output power information through simulation, and provides data basis for subsequent typical scenario generation and output characteristic analysis; S200, dividing the distributed photovoltaic power output into daily power output scenarios, and determining the optimal typical scenario number of the distributed photovoltaic typical daily power output scenario based on the clustering effectiveness index DBI; S300, calculating the similarity between the daily working scenes, classifying the scenes according to the similarity between the scenes, and selecting the initial typical scene to prevent the random selection of the initial typical scene from causing the typical scene generation result to fall into a local optimal solution; S400, generating a distributed photovoltaic typical daily power generation scenario based on the above-mentioned optimal typical scenario number and the initial typical scenario, calculating the Euclidean distance between the distributed photovoltaic daily power generation scenarios, and generating a typical daily power generation scenario using the Euclidean distance as a measurement standard; S500 analyzes the output characteristics of typical daily output scenarios of distributed photovoltaics, grasps the law of distributed photovoltaic output through qualitative analysis, and quantitatively analyzes the output characteristics of distributed photovoltaics through output peak-to-valley difference and fluctuation output characteristic indicators, guiding the energy planning and scheduling of rail transit power supply systems.
2. A method for analyzing output characteristics of distributed photovoltaic power generation of rail transit infrastructure according to claim 1, characterized in that: In the step S100, a distributed photovoltaic power generation system is simulated and modeled, a rail transit infrastructure distributed photovoltaic power generation system consistent with the actual application environment is built based on PVsyst software, and the annual photovoltaic output power information is obtained using the analysis tool of PVsyst software.
3. A method for analyzing output characteristics of distributed photovoltaic power generation of rail transit infrastructure according to claim 1, characterized in that: In step S200, the optimal number of typical scenarios is determined based on the DBI index. On the basis of dividing the distributed photovoltaic output into daily output scenarios, the optimal number of typical scenarios for generating distributed photovoltaic typical scenarios is determined using the DBI index, which is the ratio of intra-class data density to inter-class data density.
4. The method for analyzing output characteristics of distributed photovoltaic power generation of rail transit infrastructure according to claim 1 is characterized in that: In step S300, initial typical scenes are selected based on the similarity between scenes, the similarity between data points is calculated using the Ward variance minimization method, and the data points are grouped and then the initial typical scenes are selected, thereby improving the defect that the random selection of initial typical scenes causes the typical scene generation results to fall into a local optimal solution.
5. The method for analyzing output characteristics of distributed photovoltaic power generation of rail transit infrastructure according to claim 1, characterized in that: In the step S400, a typical daily power generation scenario of distributed photovoltaics is generated. Based on the above-mentioned number of optimal typical scenarios and the establishment of the initial typical scenarios, the Euclidean distance between the distributed photovoltaic daily power generation scenarios is calculated. Taking the Euclidean distance as the measurement standard, all data are clustered with the initial typical scenarios as different categories, the sum of the distances within each cluster is calculated, the typical scenarios within each category are updated, and then the clusters are re-divided with the new typical scenarios as categories. The above process is repeated until the typical scenarios are no longer updated to complete the generation of the typical daily power generation scenario of distributed photovoltaics.
6. A method for analyzing output characteristics of distributed photovoltaic power generation of rail transit infrastructure according to claim 1, characterized in that: In the step S500, the output characteristics of the typical daily power generation scenario of the distributed photovoltaic system are qualitatively and quantitatively analyzed, and the output rules and output characteristic changes of the distributed photovoltaic system under different time periods, different weather conditions and different seasonal changes are understood through qualitative analysis; a day is divided into multiple time periods, and the output characteristic changes of the photovoltaic system in different time periods are observed; combined with meteorological data, the output characteristic differences of the photovoltaic system under different weather conditions are observed; considering the influence of sunshine time and solar altitude angle factors in different seasons, the output characteristic changes of the photovoltaic system in different seasons are observed; by drawing the output curve of the photovoltaic power generation system under the typical daily power generation scenario, the distributed photovoltaic output characteristics are quantitatively analyzed with the help of the output peak-to-valley difference and the fluctuation output characteristic indicators, the stability of the distributed photovoltaic system and the power generation capacity of the distributed photovoltaic system are evaluated, and based on this, the design and operation of the distributed photovoltaic power generation system and the rail transit power supply system are optimized, the system operation and maintenance work is guided, and the system energy management and power dispatching capabilities are improved.