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Multi Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Distribution Networks

Name: Multi Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Distribution Networks
Creator: M Laxmidevi Ramanaiah
Keywords: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Distributed Generators, Multi-objective Grey Wolf Optimization, Real Power Loss, Savings, Voltage deviation.

descriptionPublicationkeyboard_double_arrow_right Article 30 Sep 2020Publisher:Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESPJournal:International Journal of Recent Technology and Engineering (IJRTE), volume 9, pages 664-670 (eissn: 2277-3878,

Authors: M Laxmidevi Ramanaiah;

doi: 10.35940/ijrte.e5807.099320

Multi Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Distribution Networks

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Abstract

The power loss in the radial distribution network is appreciable as compared to transmission network. To reduce the power loss in distribution network which is radial in nature, the solution methodology adopted in this paper is optimal placement of distributed generators (DG). The optimization incorporated is Multi-objective Grey Wolf Optimization (MOGWO). The optimization is accomplished for three different cases. In each case two objective functions are simultaneously optimized to obtain non-dominated solutions using Multi-objective Grey Wolf Optimization. Case (1): To minimize the real power loss and maximize the savings obtained due to DG installation. Case (2): To minimize real power loss and maximum voltage deviation in the network. Case (3): To minimize real power loss and rating of DG installed. MOGWO method maintains an archive which contains pareto-optimal solutions. The archive mimics the behaviour of grey wolves. MOGWO method is verified on radial distribution networks. The effectiveness of the optimization method is proven by comparing the results with other optimization methods available in the literature.

Related Organizations

Shenyang Aerospace University
China (People's Republic of)

Keywords

Distributed Generators, Multi-objective Grey Wolf Optimization, Real Power Loss, Savings, Voltage deviation.

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