Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale

Yuqing Yang, Weige Zhang, Liyong Niu and Jiuchun Jiang
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Yuqing Yang: National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
Weige Zhang: National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
Liyong Niu: National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
Jiuchun Jiang: National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China

Energies, 2015, vol. 8, issue 2, 1-17

Abstract: Currently, electric taxis have been deployed in many cities of China. However, the charging unbalance in both temporal and spatial scale has become a rising problem, which leads to low charging efficiency or charging congestion in different stations or time periods. This paper presents a multi-objective coordinated charging strategy for electric taxis in the temporal and spatial scale. That is, the objectives are maximizing the utilization efficiency of charging facilities, minimizing the load unbalance of the regional power system and minimizing the customers’ cost. Besides, the basic configuration of a charging station and operation rules of electric taxis would be the constraints. To tackle this multi-objective optimizing problems, a fuzzy mathematical method has been utilized to transfer the multi-objective optimization to a single optimization issue, and furthermore, the Improved Particle Swarm Optimization (IPSO) Algorithm has been used to solve the optimization problem. Moreover, simulation cases are carried out, Case 1 is the original charging procedure, and Cases 2 and 3 are the temporal and spatial scale optimized separately, followed with Case 4, the combined coordinated charging. The simulation shows the significant improvement in charging facilities efficiency and users’ benefits, as well as the better dispatching of electric taxis’ charging loads.

Keywords: electric taxis; temporal scale; spatial scale; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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