Battery Design and Management, Energies 2023, 16(9), 3899, DOI:10.3390/EN16093899

Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

Simin Peng1, Liyang Zhu1, Zhenlan Dou2, Dandan Liu1, Ruixin Yang3, and Michael G. Pecht4
1School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
2State Grid Shanghai Integrated Energy Service Co., Ltd., Shanghai 200023, China
3School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
4Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, USA

For more information about this article and related research, please contact Prof. Michael G. Pecht


The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

This article is available online here.

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