Open AccessOptimal Operation Strategy of Energy Storage System in PV-integrated EV Charging Station Based on improved NSGA-II
Author(s) -
Shenxing Shi,
Yu Zhang,
Zhangjie Fu,
Qi Xu,
Qixing Yang,
Chen Fang,
Shuntian Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012035
Subject(s) - energy storage , charging station , genetic algorithm , mathematical optimization , grid , transformer , computer science , pareto principle , multi objective optimization , fuzzy logic , power (physics) , automotive engineering , engineering , electrical engineering , electric vehicle , voltage , mathematics , physics , geometry , quantum mechanics , artificial intelligence
In order to realize the economic operation of PV-integrated EV charging station and reduce the additional construction and transformation brought by the charging station to the power grid, an optimal operation strategy of energy storage system in PV-integrated EV charging station based on the improved NSGA-II is proposed. Firstly, with the power of the energy storage system and the capacity of the transformer as constraints, the optimization operation model of energy storage is built with the minimum variance of side loads of the power grid and the minimum purchase cost from the power grid as objective functions. Then, aiming at the low efficiency of the traditional NSGA-II gene recombination operator, the improved NSGA-II based on the adaptive recombination operator is proposed to solve the model, and the optimal operation strategy is obtained from the final Pareto solution set by using fuzzy clustering method. Finally, the effectiveness of the proposed algorithm is verified by example simulation, indicating that the improved NSGA-II can further improve the operation economy of charging stations and the load level of the power grid.