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Open AccessArticle

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

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College of Automation, Harbin Engineering University, Harbin 150001, China
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Department of Electrical Engineering, Chung Yuan Christian University, Chung Li District 320, Taoyuan City 32023, Taiwan
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Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Josep M. Guerrero and Amjad Anvari-Moghaddam
Appl. Sci. 2017, 7(2), 155; https://doi.org/10.3390/app7020155
Received: 4 November 2016 / Revised: 24 January 2017 / Accepted: 25 January 2017 / Published: 14 February 2017
Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method. View Full-Text
Keywords: renewable energy; energy storage system; hybrid multi-objective particle swarm optimization; back-propagation neural network; power-voltage sensitivity analysis; three-point estimation method renewable energy; energy storage system; hybrid multi-objective particle swarm optimization; back-propagation neural network; power-voltage sensitivity analysis; three-point estimation method
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Lan, H.; Yin, H.; Wen, S.; Hong, Y.-Y.; Yu, D.C.; Zhang, L. Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System. Appl. Sci. 2017, 7, 155.

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