Research on the EV charging load estimation and mode optimization methods
With the increasing number of electric vehicles (EVs), the disordered charging of a large number of EVs will have a large influence on the power grid. The problems of charging and discharging optimization management for EVs are studied in this paper. The distribution of characteristic quantities of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2019-12-01
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| Series: | Archives of Electrical Engineering |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/114126/PDF/09_AEE-2019-4_INTERNET.pdf |
| _version_ | 1811315702463725568 |
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| author | Zhiyan Zhang Kailang Dong Xiaochen Pang Hongfei Zhao Aifang Wang |
| author_facet | Zhiyan Zhang Kailang Dong Xiaochen Pang Hongfei Zhao Aifang Wang |
| author_sort | Zhiyan Zhang |
| collection | DOAJ |
| description | With the increasing number of electric vehicles (EVs), the disordered charging of a large number of EVs will have a large influence on the power grid. The problems of charging and discharging optimization management for EVs are studied in this paper. The distribution of characteristic quantities of charging behaviour such as the starting time and charging duration are analysed. The results show that charging distribution is in line with a logarithmic normal distribution. An EV charging behaviour model is established, and error calibration is carried out. The result shows that the error is within its permitted scope. The daily EV charge load is obtained by using the Latin hypercube Monte Carlo statistical method. Genetic particle swarm optimization (PSO) is proposed to optimize the proportion of AC 1, AC 2 and DC charging equipment, and the optimal solution can not only meet the needs of users but also reduce equipment investment and the EV peak valley difference, so the effectiveness of the method is verified. |
| first_indexed | 2024-04-13T11:36:02Z |
| format | Article |
| id | doaj.art-33e79771f5024cb2837a8ebbafdf1154 |
| institution | Directory Open Access Journal |
| issn | 2300-2506 |
| language | English |
| last_indexed | 2024-04-13T11:36:02Z |
| publishDate | 2019-12-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Electrical Engineering |
| spelling | doaj.art-33e79771f5024cb2837a8ebbafdf11542022-12-22T02:48:27ZengPolish Academy of SciencesArchives of Electrical Engineering2300-25062019-12-01vol. 68No 4831842https://doi.org/10.24425/aee.2019.130686Research on the EV charging load estimation and mode optimization methodsZhiyan ZhangKailang DongXiaochen PangHongfei ZhaoAifang WangWith the increasing number of electric vehicles (EVs), the disordered charging of a large number of EVs will have a large influence on the power grid. The problems of charging and discharging optimization management for EVs are studied in this paper. The distribution of characteristic quantities of charging behaviour such as the starting time and charging duration are analysed. The results show that charging distribution is in line with a logarithmic normal distribution. An EV charging behaviour model is established, and error calibration is carried out. The result shows that the error is within its permitted scope. The daily EV charge load is obtained by using the Latin hypercube Monte Carlo statistical method. Genetic particle swarm optimization (PSO) is proposed to optimize the proportion of AC 1, AC 2 and DC charging equipment, and the optimal solution can not only meet the needs of users but also reduce equipment investment and the EV peak valley difference, so the effectiveness of the method is verified.https://journals.pan.pl/Content/114126/PDF/09_AEE-2019-4_INTERNET.pdfevsgap optimizationlatin hypercube samplingmonte carlo simulation |
| spellingShingle | Zhiyan Zhang Kailang Dong Xiaochen Pang Hongfei Zhao Aifang Wang Research on the EV charging load estimation and mode optimization methods Archives of Electrical Engineering evs gap optimization latin hypercube sampling monte carlo simulation |
| title | Research on the EV charging load estimation and mode optimization methods |
| title_full | Research on the EV charging load estimation and mode optimization methods |
| title_fullStr | Research on the EV charging load estimation and mode optimization methods |
| title_full_unstemmed | Research on the EV charging load estimation and mode optimization methods |
| title_short | Research on the EV charging load estimation and mode optimization methods |
| title_sort | research on the ev charging load estimation and mode optimization methods |
| topic | evs gap optimization latin hypercube sampling monte carlo simulation |
| url | https://journals.pan.pl/Content/114126/PDF/09_AEE-2019-4_INTERNET.pdf |
| work_keys_str_mv | AT zhiyanzhang researchontheevchargingloadestimationandmodeoptimizationmethods AT kailangdong researchontheevchargingloadestimationandmodeoptimizationmethods AT xiaochenpang researchontheevchargingloadestimationandmodeoptimizationmethods AT hongfeizhao researchontheevchargingloadestimationandmodeoptimizationmethods AT aifangwang researchontheevchargingloadestimationandmodeoptimizationmethods |