Research on Coordinated Scheduling of Electric Vehicle Charging/Discharging and Renewable Energy Power Generation

DOI

10.2991/eia-17.2017.12How to use a DOI?

Keywords

component; electric vehicle; fuzzy optimization; PSO; active power losses; peak-to-valley deference ratio

Abstract

With the dramatic increase of plug-in electric vehicles (EVs) grid penetration, the random characteristics of EVs will influence the normal operation of the power system. Given this background, a multi-objective optimization model is proposed in this paper to mitigate the peak-to-valley deference of equivalent load and reduce the active power losses of the distributed grid for a regional electrical power system, taking the storage capacity of EVs, the charging/discharging power, the distributed power flow, and the driving characteristics of EVs into consideration. Defining each objective membership function, multi-objective optimization problem is reformulated into a nonlinear single-objective programming problem by means of fuzzy satisfaction-maximizing method, and this nonlinear single-objective programming problem is solved by using modified particle swarm optimization algorithm based on hybrid mechanism. Simulation results indicate that the proposed model and algorithm can flat the curve of equivalent load, reduce the reserved capacity in adjusting the peak, optimize the active power losses and provide the voltage support for the system.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Weisheng LI
AU  - Guangxu ZHOU
AU  - Pinglai WANG
AU  - Guangqing BAO
PY  - 2017/07
DA  - 2017/07
TI  - Research on Coordinated Scheduling of Electric Vehicle Charging/Discharging and Renewable Energy Power Generation
BT  - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
PB  - Atlantis Press
SP  - 54
EP  - 59
SN  - 1951-6851
UR  - https://doi.org/10.2991/eia-17.2017.12
DO  - 10.2991/eia-17.2017.12
ID  - LI2017/07
ER  -