TY - JOUR
T1 - Optimization of electric vehicle charging scheduling in urban village networks considering energy arbitrage and distribution cost
AU - Srithapon, Chitchai
AU - Ghosh, Prasanta
AU - Siritaratiwat, Apirat
AU - Chatthaworn, Rongrit
N1 - Funding Information:
Funding: This research was funded by the Young Researcher Development project of Khon Kaen University and the Faculty of Engineering, Khon Kaen University under grant number: Ph.D.Ee -1/2562.
Funding Information:
This research was funded by the Young Researcher Development project of Khon Kaen University and the Faculty of Engineering, Khon Kaen University under grant number: Ph.D.Ee -1/2562. Authors would like to acknowledge the Provincial Electricity Authority (PEA) to support all data used in this work.
Publisher Copyright:
© 2020 by the authors.
PY - 2020
Y1 - 2020
N2 - Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners’ energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator.
AB - Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners’ energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator.
KW - Electric vehicle
KW - Energy arbitrage
KW - Optimization
KW - Power loss
KW - Residential network
KW - Transformer aging
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U2 - 10.3390/en13020349
DO - 10.3390/en13020349
M3 - Article
AN - SCOPUS:85077801916
SN - 1996-1073
VL - 13
JO - Energies
JF - Energies
IS - 2
M1 - 349
ER -