Optimal Sizing and Placement of Electrical Energy Storage System in Distribution Network via MMOMA

Battery energy storage system (BESS) has fast power regulation and flexible energy management capabilities. Based on this, this paper focuses on the optimal configuration of BESS in the distribution network. First, a multi-objective optimization model of optimal location and capacity of BESSs in dis...

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Bibliographic Details
Published in:2022 4th International Conference on Power and Energy Technology (ICPET) pp. 1075 - 1080
Main Authors: Cai, Wantong, Yang, Zhengang, Zhuo, Yingjun, Zhou, Baorong
Format: Conference Proceeding
Language:English
Published: IEEE 28.07.2022
Subjects:
Costs
distribution network BESSS model
Distribution networks
energy storage systems
Fluctuations
modified multi-objective mayfly algorithm
Optimization
Pareto optimization
Solid modeling
Voltage fluctuations
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Summary:Battery energy storage system (BESS) has fast power regulation and flexible energy management capabilities. Based on this, this paper focuses on the optimal configuration of BESS in the distribution network. First, a multi-objective optimization model of optimal location and capacity of BESSs in distribution network is established with the goal of minimizing the average daily comprehensive cost, voltage fluctuation and load fluctuation of BESSs. In order to obtain the Pareto optimal solution set of decision variables such as BESSs, a modified multi-objective mayfly algorithm (MMOMA) was designed to solve the problem. In order to achieve the best trade-off between the three objectives, the improved ideal-point based decision (IIPBD) method is used to make a compromise decision on the Pareto optimal solution set. Finally, based on the IEEE-33 node standard test system, the BESS configuration model built is solved. The simulation results show that the Pareto frontier distribution obtained by the proposed MMOMA is wider and more uniform, and the voltage quality and load level of the distribution network are effectively improved, which verifies the feasibility and superiority of the model and method proposed in this paper.
DOI:10.1109/ICPET55165.2022.9918482