Management and Optimal Control Algorithm for Electric Vehicle Charging in Random Environment

doi:10.16183/j.cnki.jsjtu.2021.499

Abstract

Abstract:

With the increasing scale of electric vehicles (EVs), the adaptive management of its charging behavior becomes an urgent problem to be solved. From the point of view of charging service provider, an online management algorithm for EV charging is proposed based on the Lyapunov optimization theory under the random environment in this paper, considering renewable sources energy, storage equipment, time-varying electricity price, and the tolerable delay of EV, with an aim of maximizing the benefits of charging service providers (i.e., minimizing the cost of electricity purchased). The performance of the proposed algorithm is analyzed to verify that it can achieve near-optimal optimization results without any a priori statistical information about the system inputs (renewable energy generation, charging demand, and time-varying electricity price). The simulation results show that the proposed algorithm can effectively reduce the economic cost by 27.3% compared with the benchmark algorithm.

Key words: electric vehicles (EVs), charging schedule, smart grid, random environment, renewable energy source

CLC Number:

TM734

LIU Didi, YANG Yifei, YANG Yuhui, ZOU Yanli, WANG Xiaohua, LI Xin. Management and Optimal Control Algorithm for Electric Vehicle Charging in Random Environment[J]. Journal of Shanghai Jiao Tong University, 2023, 57(1): 1-9.

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参数	取值
时隙间隔/min	10
总时隙数	1440
电价/元	0.5~2.0
b_max/kJ	1000
能量收集	泊松分布
平均每个时隙能量收集/kJ	5000
储能设备容量/kJ	4500
V	1400

队列	t/个
队列	本文算法	“purchase-at-deadline”算法
一	1.8264	6.2139
二	1.2889	6.2243
三	1.0014	5.9993

队列	时延
队列	t=0	t=1	t=2	t=3	t=4
一	0	422	846	172	0
二	545	0	829	66	0
三	719	0	721	0	0

队列	时延
队列	t=1	t=2	t=3	t=4	t=5	t=6	t=7	t=8
一	4	4	6	31	226	609	470	90
二	4	2	6	10	155	744	494	25
三	4	4	7	10	206	927	282	0