TY - GEN
T1 - Optimized electric vehicles charging in an urban village network considering transformer aging
AU - Srithapon, Chitchai
AU - Ghosh, Prasanta
AU - Siritaratiwat, Anirat
AU - Chatthaworn, Rongrit
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Electric vehicle (EV) replacing the internal combustion engine may be the solution to the PM2.5 pollution issues. However, uncontrolled increase of EVs would challenge the power-distribution-system operation, which includes the reduction of distribution transformer lifetime. Therefore, it is necessary to implement some level of control over EV charging procedure, especially in the residential network. In this paper, we present an optimization method for EV charging considering a transformer aging factor in an urban village environment. The optimized strategy focuses on the reduction of the charging cost, power loss and peak load power. The optimization problem is solved using the Genetic Algorithm (GA) in MATLAB. As a case study, we have used data from the village in Udon Thani, Thailand to demonstrate the applicability of the proposed method. Simulation results show a reduction in the charging cost, power loss cost and peak demand power. In addition, the application of the proposed method prolongs the transformer lifetime, which can benefit both EV owner and distribution system operator (DSO).
AB - Electric vehicle (EV) replacing the internal combustion engine may be the solution to the PM2.5 pollution issues. However, uncontrolled increase of EVs would challenge the power-distribution-system operation, which includes the reduction of distribution transformer lifetime. Therefore, it is necessary to implement some level of control over EV charging procedure, especially in the residential network. In this paper, we present an optimization method for EV charging considering a transformer aging factor in an urban village environment. The optimized strategy focuses on the reduction of the charging cost, power loss and peak load power. The optimization problem is solved using the Genetic Algorithm (GA) in MATLAB. As a case study, we have used data from the village in Udon Thani, Thailand to demonstrate the applicability of the proposed method. Simulation results show a reduction in the charging cost, power loss cost and peak demand power. In addition, the application of the proposed method prolongs the transformer lifetime, which can benefit both EV owner and distribution system operator (DSO).
KW - electric mobility
KW - genetic algorithm
KW - optimization
KW - residential network
KW - transformer loss of life
UR - http://www.scopus.com/inward/record.url?scp=85084635013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084635013&partnerID=8YFLogxK
U2 - 10.1109/EPEC47565.2019.9074820
DO - 10.1109/EPEC47565.2019.9074820
M3 - Conference contribution
AN - SCOPUS:85084635013
T3 - 2019 IEEE Electrical Power and Energy Conference, EPEC 2019
BT - 2019 IEEE Electrical Power and Energy Conference, EPEC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Electrical Power and Energy Conference, EPEC 2019
Y2 - 16 October 2019 through 18 October 2019
ER -