An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing

Bin Wang, Zhongping Yang, Fei Lin and Wei Zhao
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Bin Wang: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Beijing 100044, China
Zhongping Yang: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Beijing 100044, China
Fei Lin: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Beijing 100044, China
Wei Zhao: Beijing Metro R&D Center, Beijing 100044, China

Energies, 2014, vol. 7, issue 10, 1-25

Abstract: The application of a stationary ultra-capacitor energy storage system (ESS) in urban rail transit allows for the recuperation of vehicle braking energy for increasing energy savings as well as for a better vehicle voltage profile. This paper aims to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. This paper firstly raises the optimization objective functions from the perspectives of energy savings, regenerative braking cancellation and installation cost, respectively. Then, proper mathematical models of the DC (direct current) traction power supply system are established to simulate the electrical load-flow of the traction supply network, and the optimization objections are evaluated in the example of a Chinese metro line. Ultimately, a methodology for optimal ultra-capacitor energy storage system locating and sizing is put forward based on the improved genetic algorithm. The optimized result shows that certain preferable and compromised schemes of ESSs’ location and size can be obtained, acting as a compromise between satisfying better energy savings, voltage profile and lower installation cost.

Keywords: energy storage system; energy saving rate; voltage profile; installation cost; artificial neural network; improved genetic algorithm (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: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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