TY - GEN
T1 - Multi-Objective Optimized Configuration of Electric Vehicle Fast Charging Station Combined with PV Generation and Energy Storage
AU - Liu, Jing
AU - Gao, Congzhe
AU - Cao, Yecong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - To meet the growing demand for electric vehicle charging, large-scale fast charging stations need to be built. However, due to the randomness and impact characteristics of fast charging load, the construction of electric vehicle charging stations is a huge challenge for current distribution networks with limited power capacitance. Building a fast charging station with a photovoltaic generation system and energy storage system (FCS-PVS&ESS) is a promising solution to this problem. This paper proposes a multi-objective optimization method for the configuration of FCS-PVS&ESS based on dynamic adjustment strategy. Compared with conventional optimization methods, this method considers not only system costs but also power fluctuations and renewable energy shares. Better economic and stable performance was achieved by dynamic adjustment strategy, which changes the ESS upper power limit determined by load characteristics and real-time electricity prices. Non-dominant sort genetic algorithm (NSGA-II) is used to obtain the frontier of Pareto optimal solutions. According to the simulation results, the flexible configuration of PVS and ESS can increase annual income by more than 13%, reduce grid power fluctuation by more than 21%, and the proportion of green renewable energy sources exceeds 6%.
AB - To meet the growing demand for electric vehicle charging, large-scale fast charging stations need to be built. However, due to the randomness and impact characteristics of fast charging load, the construction of electric vehicle charging stations is a huge challenge for current distribution networks with limited power capacitance. Building a fast charging station with a photovoltaic generation system and energy storage system (FCS-PVS&ESS) is a promising solution to this problem. This paper proposes a multi-objective optimization method for the configuration of FCS-PVS&ESS based on dynamic adjustment strategy. Compared with conventional optimization methods, this method considers not only system costs but also power fluctuations and renewable energy shares. Better economic and stable performance was achieved by dynamic adjustment strategy, which changes the ESS upper power limit determined by load characteristics and real-time electricity prices. Non-dominant sort genetic algorithm (NSGA-II) is used to obtain the frontier of Pareto optimal solutions. According to the simulation results, the flexible configuration of PVS and ESS can increase annual income by more than 13%, reduce grid power fluctuation by more than 21%, and the proportion of green renewable energy sources exceeds 6%.
KW - dynamic control
KW - fast charging station
KW - load characteristics
KW - multi-objective optimized configuration
UR - http://www.scopus.com/inward/record.url?scp=85087493870&partnerID=8YFLogxK
U2 - 10.1109/ICET49382.2020.9119613
DO - 10.1109/ICET49382.2020.9119613
M3 - Conference contribution
AN - SCOPUS:85087493870
T3 - 2020 IEEE 3rd International Conference on Electronics Technology, ICET 2020
SP - 467
EP - 473
BT - 2020 IEEE 3rd International Conference on Electronics Technology, ICET 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Electronics Technology, ICET 2020
Y2 - 8 May 2020 through 12 May 2020
ER -
