Next Article in Journal
Forces and Moments on Flat Plates of Small Aspect Ratio with Application to PV Wind Loads and Small Wind Turbine Blades
Previous Article in Journal
Spatial Downscaling of 2-Meter Air Temperature Using Operational Forecast Data
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(4), 2412-2437; doi:10.3390/en8042412

An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

National Active Distribution Network Technology Research Center (NANTEC), Beijing JiaoTong University, Beijing 100044, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 13 November 2014 / Revised: 14 March 2015 / Accepted: 17 March 2015 / Published: 26 March 2015
View Full-Text   |   Download PDF [989 KB, uploaded 26 March 2015]   |  

Abstract

Optimal power flow (OPF) objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC) algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable. View Full-Text
Keywords: optimal power flow; fuzzy satisfaction-maximizing method; artificial bee colony algorithm; differential evolution algorithm; tent chaos mapping optimal power flow; fuzzy satisfaction-maximizing method; artificial bee colony algorithm; differential evolution algorithm; tent chaos mapping
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

He, X.; Wang, W.; Jiang, J.; Xu, L. An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow. Energies 2015, 8, 2412-2437.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top