NDLI: An efficient multi-objective memetic algorithm for uncertainties in distribution network expansion planning

Content Provider	IEEE Xplore Digital Library
Author	Mori, H. Yoshida, T.
Copyright Year	2009
Description	Author affiliation: Department of Electronics and Bioinformatics, Meiji University, Kawasaki 214-857, Japan (Mori, H.; Yoshida, T.)
Abstract	This paper proposes an efficient Multi-objective Memetic Algorithm for distribution network expansion planning (DNEP). It may be expressed as a complex multi-objective optimization problem as well as combinatorial optimization one. Recently, deregulated and competitive power markets brought about the uncertainty of distribution systems. There are correlations between the nodal specific values. A Monte-Carlo-Simulation-based method is proposed for handling the uncertainty and correlation. Also, this paper focuses of multi-objective meta-heuristics (MOMH). It obtains many final solution candidates at the same time. Also, MOMH has specific strategies in terms of high accuracy and diversity of the set. This paper makes use of the improved Strength Pareto Evolutionary Algorithm (SPEA2) and the Controlled fast elitist Non-dominated Sorting Genetic Algorithm (CNSGA2) that are well-known for more effective methods in MOMH. As Memetic Algorithm (MA) that integrates meta-heuristic with local search (LS). This paper combines SPEA2 and CNSGA2 with Random-Multi Start Local Search (RMSLS) for improving the solution set. This is called Memetic Algorithm (MA). RMSLS plays a key role to provide many solution candidates. The proposed method has advantage to keep high accuracy and diversity of solution sets in MOMH. The proposed method is successfully applied to a sample system.
Starting Page	1
Ending Page	6
File Size	517919
Page Count	6
File Format	PDF
ISBN	9781424442416
ISSN	19449925
DOI	10.1109/PES.2009.5275469
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Sorting Cost function Evolutionary computation Genetic algorithms Wind power generation Power system planning Power markets Consumer electronics Bioinformatics Monte-Carlo simulation DNEP Load uncertainty Multi-objective meta-heuristics SPEA2 Controlled-NSGA2 Memetic Algorithm (MA)
Content Type	Text
Resource Type	Article

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