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Energies 2017, 10(5), 625; doi:10.3390/en10050625

Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms

1
College of Automation, Harbin Engineering University, Harbin 150001, China
2
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
3
Department of Electrical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 32023, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Adolf Acquaye
Received: 19 January 2017 / Revised: 13 April 2017 / Accepted: 27 April 2017 / Published: 4 May 2017
(This article belongs to the Special Issue Sustainable and Renewable Energy Systems)
View Full-Text   |   Download PDF [868 KB, uploaded 4 May 2017]   |  

Abstract

With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms. View Full-Text
Keywords: multi-correlated wind distribution; Clayton-Copula method; point estimation method (PEM); energy storage system (ESS); multi-objective particle swarm optimization (MOPSO) multi-correlated wind distribution; Clayton-Copula method; point estimation method (PEM); energy storage system (ESS); multi-objective particle swarm optimization (MOPSO)
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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).

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Wen, S.; Lan, H.; Fu, Q.; Yu, D.C.; Hong, Y.-Y.; Cheng, P. Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms. Energies 2017, 10, 625.

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