An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic
Renewable energy, such as wind and photovoltaic (PV), produces intermittent and variable power output. When superimposed on the load curve, it transforms the load curve into a ‘load belt’, i.e. a range. Furthermore, the large scale development of electric vehicle (EV) will also...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2015-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9018450/ |
| _version_ | 1818734191338061824 |
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| author | Hong Liu Pingliang Zeng Jianyi Guo Huiyu Wu Shaoyun Ge |
| author_facet | Hong Liu Pingliang Zeng Jianyi Guo Huiyu Wu Shaoyun Ge |
| author_sort | Hong Liu |
| collection | DOAJ |
| description | Renewable energy, such as wind and photovoltaic (PV), produces intermittent and variable power output. When superimposed on the load curve, it transforms the load curve into a ‘load belt’, i.e. a range. Furthermore, the large scale development of electric vehicle (EV) will also have a significant impact on power grid in general and load characteristics in particular. This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs. The probabilistic model of wind and PV power outputs is developed. Based on the probabilistic model, the method of assessing the peak-valley difference of the stochastic load curve is put forward, and a two-stage peak-valley price model is built for controlled EV charging. On this basis, an optimization model is built, in which genetic algorithms are used to determine the start and end time of the valley price, as well as the peak-valley price. Finally, the effectiveness and rationality of the method are proved by the calculation result of the example. |
| first_indexed | 2024-12-18T00:01:26Z |
| format | Article |
| id | doaj.art-eb929a091eb84eb3909e99dba00a9e29 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-18T00:01:26Z |
| publishDate | 2015-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-eb929a091eb84eb3909e99dba00a9e292022-12-21T21:27:56ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202015-01-013223223910.1007/s40565-015-0117-z9018450An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaicHong Liu0Pingliang Zeng1Jianyi Guo2Huiyu Wu3Shaoyun Ge4Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China,300072Electric Power Research Institute of China,Beijing,China,100192Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China,300072University of Wisconsin Madison,Madison,WI,USA,53706Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China,300072Renewable energy, such as wind and photovoltaic (PV), produces intermittent and variable power output. When superimposed on the load curve, it transforms the load curve into a ‘load belt’, i.e. a range. Furthermore, the large scale development of electric vehicle (EV) will also have a significant impact on power grid in general and load characteristics in particular. This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs. The probabilistic model of wind and PV power outputs is developed. Based on the probabilistic model, the method of assessing the peak-valley difference of the stochastic load curve is put forward, and a two-stage peak-valley price model is built for controlled EV charging. On this basis, an optimization model is built, in which genetic algorithms are used to determine the start and end time of the valley price, as well as the peak-valley price. Finally, the effectiveness and rationality of the method are proved by the calculation result of the example.https://ieeexplore.ieee.org/document/9018450/Renewable energyElectric vehicleControlled electric vehicle (EV) chargingDemand side responsePeak-valley price |
| spellingShingle | Hong Liu Pingliang Zeng Jianyi Guo Huiyu Wu Shaoyun Ge An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic Journal of Modern Power Systems and Clean Energy Renewable energy Electric vehicle Controlled electric vehicle (EV) charging Demand side response Peak-valley price |
| title | An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic |
| title_full | An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic |
| title_fullStr | An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic |
| title_full_unstemmed | An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic |
| title_short | An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic |
| title_sort | optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic |
| topic | Renewable energy Electric vehicle Controlled electric vehicle (EV) charging Demand side response Peak-valley price |
| url | https://ieeexplore.ieee.org/document/9018450/ |
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