Electric Vehicle Cluster Scheduling Model for Distribution Systems Considering Reactive-Power Compensation of Charging Piles

Author:

Huang Liping1,Li Haisheng2,Lai Chun Sing23ORCID,Zobaa Ahmed F.3ORCID,Zhong Bang4,Zhao Zhuoli2,Lai Loi Lei5ORCID

Affiliation:

1. School of Electronic and Electrical Engineering, Zhaoqing University, Zhaoqing 526061, China

2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China

3. Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK

4. Zhaoqing Power Supply Bureau, Guangdong Power Grid Company, China Southern Power Grid, Zhaoqing 526020, China

5. DRPT International Inc., Perth, WA 6009, Australia

Abstract

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

Funder

Key Fields Special Project of Guangdong Provincial Colleges and Universities

National Natural Science Foundation of China

Publisher

MDPI AG

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