Open AccessMethod for EV charging in stochastic smart microgrid operation with fuel cell and renewable energy source (RES) units
Author(s) -
Tashviri Mehdi Hatef,
Ghaffarzadeh Navid
Publication year - 2020
Publication title -
iet electrical systems in transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 26
eISSN - 2042-9746
pISSN - 2042-9738
DOI - 10.1049/iet-est.2019.0013
Subject(s) - microgrid , flexibility (engineering) , smart grid , automotive engineering , renewable energy , energy storage , metering mode , engineering , revenue , computer science , reliability engineering , simulation , power (physics) , electrical engineering , business , mechanical engineering , statistics , physics , mathematics , accounting , quantum mechanics
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 (CO 2 ) 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, CO 2 emission, and the total operational cost are reduced by the trip planning programme and EV owner revenue is increased.