A distributed charging strategy based on day ahead price model for PV-powered electric vehicle charging station |
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| Affiliation: | 1. Faculty of Engineering and Environment, Department of Maths, Physics and Electrical Engineering, Northumbria University Newcastle, Newcastle upon Tyne NE1 8ST, UK;2. School of Electrical and Electronic Engineering, University of Adelaide, Australia;3. Center of research excellence in renewable energy and power systems, King Abdulaziz University, Jeddah, Saudi Arabia;1. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 30072, China;2. Institute of Energy, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK;3. Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China;1. Department of Electrical Engineering, Tsinghua University, Beijing, People’s Republic of China;2. Electric Power Research Institute, China Southern Grid, Guangzhou, People’s Republic of China |
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| Abstract: | This paper studies a distributed charging model based on day-ahead optimal internal price for PV-powered Electric Vehicle (EV) Charging Station (PVCS). Considering the feed-in-tariff of PV energy, the price of utility grid and the forecast model of PV based on back-propagation neural network (BPNN), a system operation model of PVCS is introduced, which consists of the profit model of PVCS operator (PO) and the cost model of EV users. The model proposed in this paper can be designed as a Stackelberg game model, where the PO acts as the leader and all EV users participated are regarded as the followers. An optimization strategy based on heuristic algorithm and nonlinear constrained programming are adopted by the PO and each EV user, respectively. Moreover, a real-time billing strategy is proposed to deal with the errors from the forecasted PV energy and the expected charging arrangements. Finally, through a practical case, the validity of the model is verified in terms of increasing operation profit and reducing charging cost. |
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| Keywords: | Electric vehicle Charging strategy Day-ahead price Stackelberg game Neural network |
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