Nowadays, running out of fossil fuels and more attention to reduce environmental pollutions are essential factors for the growing use of electric vehicles (EVs). Owing to these factors, it is important to present a method, which schedules charging or discharging of EVs and simultaneously considers economic and environmental aspects of the problem. This study proposes a multi‐objective optimisation programme for charging or discharging of EVs in a smart distribution system by taking advantage of advanced metering infrastructure and uses a ɛ‐constraint method for minimising operational costs and (CO2) emissions. Simulating stochastic patterns of EV owner driving behaviour as well as considering different models and types of EVs with the help of trip planning algorithm are the main advantages of this study. Investigating the multi‐objective problem in two cases shows the effectiveness and flexibility of this algorithm in real cases. Besides, vehicle‐to‐grid capability of EVs was also considered. This method was tested on a 33‐bus distribution test system for over 24 h. As the results show, the total amount of scheduled power, the peak‐to‐valley difference of daily load, transmission power loss, CO2 emission, and the total operational cost are reduced by the trip planning programme and EV owner revenue is increased.
Method for EV charging in stochastic smart microgrid operation with fuel cell and renewable energy source (RES) units
IET Electrical Systems in Transportation ; 10 , 3 ; 249-258
2020-09-01
10 pages
Article (Journal)
Electronic Resource
English
smart distribution system , fuel cell , fossil fuels , total operational cost , charging discharging , EV charging , multiobjective problem , power distribution economics , power generation scheduling , economic aspects , time 24.0 hour , optimisation , CO2 , environmental pollutions , multiobjective optimisation programme , smart power grids , trip planning programme , environmental aspects , constraint method , electric vehicle charging , air pollution , fuel cell vehicles , 33‐bus distribution test system , distributed power generation , EV owner driving behaviour , stochastic smart microgrid operation , battery powered vehicles
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