Location and Capacity Optimization of Distributed Energy Storage System in Peak-Shaving

Ruiyang Jin, Jie Song, Jie Liu, Wei Li and Chao Lu
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Ruiyang Jin: College of Engineering, Peking University, Beijing 100871, China
Jie Song: College of Engineering, Peking University, Beijing 100871, China
Jie Liu: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Wei Li: Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China
Chao Lu: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Energies, 2020, vol. 13, issue 3, 1-15

Abstract: The peak-valley characteristic of electrical load brings high cost in power supply coming from the adjustment of generation to maintain the balance between production and demand. Distributed energy storage system (DESS) technology can deal with the challenge very well. However, the number of devices for DESS is much larger than central energy storage system (CESS), which brings challenges for solving the problem of location selection and capacity allocation with large scale. We formulate the charging/discharging model of DESS and economic analysis. Then, we propose a simulation optimization method to determine the locations to equip with DESSs and the storage capacity of each location. The greedy algorithm with Monte Carlo simulation is applied to solve the location and capacity optimization problem of DESS over a large scale. Compared with the global optimal genetic algorithm, the case study conducted on the load data of a district in Beijing validates the efficiency and superiority of our method.

Keywords: distributed energy storage system; location selection; capacity allocation; peak-shaving (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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