DOI:10.35833/MPCE.2020.000093 |
| |
| |
Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II |
| |
|
| |
Page view: 107
Net amount: 466 |
| |
Author:
Hongqian Wei1,Jun Liang2,Chuanyue Li2,Youtong Zhang1
|
Author Affiliation:
1.Low Emission Vehicle (Beijing Key Lab) Research Laboratory, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;2.School of Engineering, Cardiff University, Cardiff, CF24 3AA, U.K
|
Foundation: |
This work was supported by Chinese Scholarship Council (No. 201906030036) and in part by National Academician Workstation Project (No. 20180525YX002). |
|
|
Abstract: |
As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time of EVs can reduce the users’ expense. Thus, the schedule of the EV load requires multi-objective optimization. A diversity-maximization non-dominated sorting genetic algorithm (DM-NSGA)-II is developed to perform multi-objective optimization by considering the power load profile, the users’charging cost, and battery degradation. Furthermore, a real-time locally optimal schedule is adopted by utilizing a flexible time scale. The case study illustrates that the proposed DM-NSGA-II can prevent being trapped in a relatively limited region so as to diversify the optimal results and provide trade-off solutions to decision makers. The simulation analysis shows that the variable time scale can continuously involve the present EVs in the real-time optimization rather than rely on the forecasting data. The schedule of the EV load is more practical without the loss of accuracy. |
Keywords: |
Electric vehicle (EV) ; locally optimal schedule ; multi-objective optimization ; diversity maximization ; genetic algorithm |
| |
Received:February 20, 2020
Online Time:2021/08/04 |
| |
|
|
View Full Text
Download reader
|