TY - JOUR
T1 - Optimal use of vehicle-to-grid technology to modify the load profile of the distribution system
AU - Ahmadi, Sajjad
AU - Arabani, Hamoun Pourroshanfekr
AU - Haghighi, Donya Ashtiani
AU - Guerrero, Josep M.
AU - Ashgevari, Yazdan
AU - Akbarimajd, Adel
PY - 2020/10
Y1 - 2020/10
N2 - Managing the use of electric vehicles (EVs) and power injections from their batteries pose the issue of controlling the charge and discharge of EVs as an attractive research field. Charging a large number of EVs’ batteries will, if not controlled, hurt the power distribution system. By adopting optimal planning for the use of EVs, their parking stations can role as either load or energy source. In this paper, the effect of charging and discharging scheduling of EVs on load characteristic enhancement is investigated. On the other hand, the behavior of EVs’ owners is probabilistic. Therefore, in the first step, the probabilistic model using Monte Carlo is developed for estimation of uncertain variables including: EVs arrival and departure time, the duration of the EVs’ presence in parking lots, the battery capacity of each EV. Afterward, the scheduling of EVs’ charging and discharging is determined by JAYA algorithm so that the daily load variance is reduced and the network load characteristic becomes smooth. The performance of proposed approach is investigated on the IEEE-69-bus system and simulation results show the advantages of the suggested approach.
AB - Managing the use of electric vehicles (EVs) and power injections from their batteries pose the issue of controlling the charge and discharge of EVs as an attractive research field. Charging a large number of EVs’ batteries will, if not controlled, hurt the power distribution system. By adopting optimal planning for the use of EVs, their parking stations can role as either load or energy source. In this paper, the effect of charging and discharging scheduling of EVs on load characteristic enhancement is investigated. On the other hand, the behavior of EVs’ owners is probabilistic. Therefore, in the first step, the probabilistic model using Monte Carlo is developed for estimation of uncertain variables including: EVs arrival and departure time, the duration of the EVs’ presence in parking lots, the battery capacity of each EV. Afterward, the scheduling of EVs’ charging and discharging is determined by JAYA algorithm so that the daily load variance is reduced and the network load characteristic becomes smooth. The performance of proposed approach is investigated on the IEEE-69-bus system and simulation results show the advantages of the suggested approach.
KW - Electric vehicle
KW - Load variance
KW - Metaheuristic JAYA Algorithm
KW - Monte Carlo Simulation
UR - http://www.scopus.com/inward/record.url?scp=85088631965&partnerID=8YFLogxK
U2 - 10.1016/j.est.2020.101627
DO - 10.1016/j.est.2020.101627
M3 - Journal article
AN - SCOPUS:85088631965
SN - 2352-152X
VL - 31
JO - Journal of Energy Storage
JF - Journal of Energy Storage
M1 - 101627
ER -