4.7 Article

A comprehensive planning framework for electric vehicles fast charging station assisted by solar and battery based on Queueing theory and non-dominated sorting genetic algorithm-II in a co-ordinated transportation and power network

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JOURNAL OF ENERGY STORAGE
卷 49, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.est.2022.104180

关键词

Electric vehicle charging station; Solar PV; Battery electric storage system; Optimization technique

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This paper proposes a two-stage sustainable framework for joint allocation of fast charging EVCS, solar PV, and BESS. The framework optimizes the location and sizing of PV integrated EVCS in the first stage, and calculates the size of BESS and additional PV capacity in the second stage. Numerical results demonstrate multiple benefits of the proposed framework.
The success of the electric vehicles (EVs) sector hinges on the deployment of fast charging electric vehicle charging station (EVCS). The inclusion of clean energy into EV charging stations poses both risks and oppor-tunities. A viable and adequate capacity setup with appropriate planning of EVCS is favourable and crucial. This paper proposes a two-stage sustainable framework for joint allocation of fast charging EVCS, solar photo voltaic (PV) and battery energy storage system (BESS) with dynamic charging and discharging under coupled distri-bution and transportation network. In the first stage, modified Queuing theory (M1/M2/N) is used in conjunction with the gravity interaction approach to model the stochastic charging demand of EVs with multiple batteries at each charging station, taking into account the spatial -temporal distribution of EVs and wait time limit. Further, non-dominated sorting genetic algorithm (NSGA-II) and fuzzy satisfaction-based hybrid optimization are used to optimise the location and sizing of PV integrated EVCS across multiple objectives such as power loss, voltage deviation, served EV flow, and investment, as well as the operation and maintenance costs of the EVCS and PV system and then estimate the relevant factor such as; number of charging ports, sizing of solar PV system at EVCS and EV served flow. In the second stage, size of the BESS and the additional PV capacity needed to charge the BESS at each EVCS is computed using the Bi-section method while considering solar irradiance of all seasons (summer, spring rainy, winter). The proposed scheme is validated on an IEEE 123 bus unbalanced distribution system coupled to a 25-node transportation network under a variety of seasonal scenarios over a planning year. Numerical results reveal the multiple benefits of proposed framework such as reduction in active power losses, power drawn from system and voltage deviation at point of common coupling (PCC) that may occur due to the increased EVs charging demand.

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