Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm
Gonggui Chen1, 2, *, Zhengmei Lu1, 2, Zhizhong Zhang3, Zhi Sun4
1 Key Laboratory of Network control & Intelligent Instrument (Chongqing University of Posts and Telecommunications), Ministry of Education, Chongqing, China
2 Research Center on Complex Power System Analysis and Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3 Key Laboratory of Communication Network and Testing Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4 Guodian Enshi Hydropower Development, Enshi, 445000, China
Abstract
Objective:
In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem.
Method:
In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions.
Results:
The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.
Keywords: Power system, Optimal power flow, Improved hybrid differential evolution algorithm, Particle swarm optimization, Learning mechanism, Replacement mechanism.
Article Information
Article History:
Received Date: 09/08/2017
Revision Received Date: 18/08/2017
Acceptance Date: 20/08/2017
Electronic publication date: 31/10/2017
Collection year: 2017
© 2017 Chen et al.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at:
https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
* Address correspondence to this author at the Research Center on Complex Power System Analysis and Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R. China; Tel: +86-15696106539; Fax: +86-23-62461535; E-mails: chenggpower@163.com, chenggpower@126.com
Open Peer Review Details |
Manuscript submitted on 09-08-2017 |
Original Manuscript |
Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm |