Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization

Hui Wang, Tengxin Wang, Xiaohan Xie, Zhixiang Ling, Guoliang Gao and Xu Dong
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Hui Wang: School of Electrical Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, China
Tengxin Wang: School of Electrical Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, China
Xiaohan Xie: State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China
Zhixiang Ling: State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China
Guoliang Gao: State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China
Xu Dong: State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China

Energies, 2018, vol. 11, issue 2, 1-14

Abstract: The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this paper. A quantum-behaved particle swarm optimization (QPSO) algorithm that optimizes the HESS capacity is used. Based on the respective power compensation capabilities of ultracapacitors and batteries, a rational energy scheduling strategy is proposed using the principle of a low-pass filter and can help to avoid frequent batteries charging and discharging. Considering the rated power of each energy storage type, the respective compensation power is corrected. By determining whether the charging state reaches the limit, the value is corrected again. Additionally, a mathematical model that minimizes the daily cost of the HESS is derived. This paper takes an isolated micrgrid in north China as an example to verify the effectiveness of this method. The comparison between QPSO and a traditional particle swarm algorithm shows that QPSO can find the optimal solution faster and the HESS has lower daily cost. Simulation results for an isolated microgrid verified the effectiveness of the HESS optimal capacity configuration method.

Keywords: capacity configuration; hybrid energy storage; energy scheduling; quantum-behaved particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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