Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather
In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty probl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/15/1/11 |
| _version_ | 1797339309171277824 |
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| author | Xiaofei Li Chi Li Chen Jia |
| author_facet | Xiaofei Li Chi Li Chen Jia |
| author_sort | Xiaofei Li |
| collection | DOAJ |
| description | In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning. |
| first_indexed | 2024-03-08T09:45:05Z |
| format | Article |
| id | doaj.art-dd80b3b65ea6409085f88d6e999753e6 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-08T09:45:05Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-dd80b3b65ea6409085f88d6e999753e62024-01-29T14:26:22ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-01-011511110.3390/wevj15010011Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature WeatherXiaofei Li0Chi Li1Chen Jia2National Key Laboratory of Renewable Energy Grid-Integration (China Electric Power Research Institute), Haidian District, Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Integration (China Electric Power Research Institute), Haidian District, Beijing 100192, ChinaState Grid Liaoning Electric Power Research Institute, Shenyang 110006, ChinaIn recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning.https://www.mdpi.com/2032-6653/15/1/11copula functionscenario generationkernel density estimation methodK-means clustering methodscenario reductionhigh-temperature weather |
| spellingShingle | Xiaofei Li Chi Li Chen Jia Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather World Electric Vehicle Journal copula function scenario generation kernel density estimation method K-means clustering method scenario reduction high-temperature weather |
| title | Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather |
| title_full | Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather |
| title_fullStr | Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather |
| title_full_unstemmed | Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather |
| title_short | Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather |
| title_sort | electric vehicle and photovoltaic power scenario generation under extreme high temperature weather |
| topic | copula function scenario generation kernel density estimation method K-means clustering method scenario reduction high-temperature weather |
| url | https://www.mdpi.com/2032-6653/15/1/11 |
| work_keys_str_mv | AT xiaofeili electricvehicleandphotovoltaicpowerscenariogenerationunderextremehightemperatureweather AT chili electricvehicleandphotovoltaicpowerscenariogenerationunderextremehightemperatureweather AT chenjia electricvehicleandphotovoltaicpowerscenariogenerationunderextremehightemperatureweather |