Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm

doi:10.12028/j.issn.2095-4239.2018.0240

Abstract

Abstract: This paper proposes a method for economic evaluation of energy storage system capacity using an improved quantum genetic algorithm. First, a mathematical model was established for the peak-filling valley of the energy storage system with the capacity allocation of the energy storage system carried out under the constraints of charge and discharge, state of charge and power balance. On this basis, an internal point method was used to transform the constrained the problem into a solvable unconstrained one. Then the improved quantum genetic algorithm was used to optimize the energy storage system capacity configuration for shortening the calculation time, improving the algorithm calculation efficiency and global optimization ability, meeting the technical requirements and engineering indexes of the energy storage system, and minimizing the cost of the energy storage system. Finally, an example analysis was performed to validate that the proposed method, enabling the energy storage system to realize its optimal economic cost capacity configuration while satisfying the peak load and valley filling of the daily load.

Key words: energy storage technology, peak clipping and valley filling, genetic algorithm, capacity onfiguration, economic cost

CLC Number:

TK912.9

XIA Xinmao, GUAN Honghao, DING Pengfei, MENG Gaojun. Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm[J]. Energy Storage Science and Technology, 2019, 8(3): 551-558.

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