A multi-objective optimization model for fast electric vehicle charging stations with wind, PV power and energy storage

The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model's effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. A multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions, which is solved by a hybrid optimization algorithm combining MOPSO and TOPSIS. The proposed method shows faster computation speed and higher solution quality compared with simulated annealing and genetic algorithm.