A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric VehiclesReport as inadecuate




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1

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

2

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4067, Australia

3

State Grid Zhejiang Electric Power Company, Hangzhou 310007, China





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Author to whom correspondence should be addressed.



Academic Editor: Michael Gerard Pecht

Abstract Coordinated dispatch of plug-in electric vehicles PEVs with renewable energies has been proposed in recent years. However, it is difficult to achieve effective PEV dispatch with a win-win result, which not only optimizes power system operation, but also satisfies the requirements of PEV owners. In this paper, a multi-period PEV dispatch framework, combining day-ahead dispatch with real-time dispatch, is proposed. On the one hand, the day-ahead dispatch is used to make full use of wind power and minimize the fluctuation of total power in the distribution system, and schedule the charging-discharging power of PEV stations for each period. On the other hand, the real-time dispatch arranges individual PEVs to meet the charging-discharging power demands of PEV stations given by the day-ahead dispatch. To reduce the dimensions of the resulting large-scale, non-convex problem, PEVs are clustered according to their travel information. An interval optimization model is introduced to obtain the problem solution of the day-ahead dispatch. For the real-time dispatch, a priority-ordering method is developed to satisfy the requirements of PEV owners with fast response. Numerical studies demonstrate the effectiveness of the presented framework. View Full-Text

Keywords: plug-in electric vehicles PEVs; day-ahead dispatch; real-time dispatch; interval optimization; PEV-clustered model; priority-ordering method plug-in electric vehicles PEVs; day-ahead dispatch; real-time dispatch; interval optimization; PEV-clustered model; priority-ordering method





Author: Yinuo Huang 1, Chuangxin Guo 1,* , Yi Ding 1, Licheng Wang 2, Bingquan Zhu 3 and Lizhong Xu 3

Source: http://mdpi.com/



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