TY - GEN
T1 - Research on scheduling strategy of electric vehicle fast charging station combined with photovoltaic generation and energy storage
AU - Cao, Yecong
AU - Gao, Congzhe
AU - Liu, Zihan
AU - Liu, Jing
AU - Liu, Mingshi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - The random fluctuation of photovoltaic(PV) generation and the random charging load of electric vehicles(EVs) will have a great impact on the power grid. It is an effective scheme to equip the fast charging station with photovoltaic and Energy Storage System(ESS), which has the advantage of suppressing the fluctuation of the power grid and absorbing the renewable energy. To solve the problem of energy management in Fast Charging Stations(FCS), a power prediction model based on EV charging behavior and PV data is studied, and a multi-objective optimization mathematical model is established considering the economic benefit of charging stations and the smooth load fluctuation, a dynamic ESS charging and discharging power constraint strategy considering both charging load and real-time electricity price is proposed, which is solved by Multi-Objective Particle Swarm Optimization(MOPSO) algorithm with mutation links based on grid sorting. The simulation results show that compared with the conventional charging station scheme, the scheme can improve the economic performance, improve the use of new efficient energy, and effectively reduce the impact of the original charging load on the peak load of distribution network, to reduce grid fluctuations.
AB - The random fluctuation of photovoltaic(PV) generation and the random charging load of electric vehicles(EVs) will have a great impact on the power grid. It is an effective scheme to equip the fast charging station with photovoltaic and Energy Storage System(ESS), which has the advantage of suppressing the fluctuation of the power grid and absorbing the renewable energy. To solve the problem of energy management in Fast Charging Stations(FCS), a power prediction model based on EV charging behavior and PV data is studied, and a multi-objective optimization mathematical model is established considering the economic benefit of charging stations and the smooth load fluctuation, a dynamic ESS charging and discharging power constraint strategy considering both charging load and real-time electricity price is proposed, which is solved by Multi-Objective Particle Swarm Optimization(MOPSO) algorithm with mutation links based on grid sorting. The simulation results show that compared with the conventional charging station scheme, the scheme can improve the economic performance, improve the use of new efficient energy, and effectively reduce the impact of the original charging load on the peak load of distribution network, to reduce grid fluctuations.
KW - Charging and discharging
KW - Electric vehicle
KW - MOPSO
KW - Photovoltaic generation
KW - Scheduling strategy
UR - http://www.scopus.com/inward/record.url?scp=85114014087&partnerID=8YFLogxK
U2 - 10.1109/AEMCSE51986.2021.00058
DO - 10.1109/AEMCSE51986.2021.00058
M3 - Conference contribution
AN - SCOPUS:85114014087
T3 - Proceedings - 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021
SP - 241
EP - 248
BT - Proceedings - 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021
Y2 - 26 March 2021 through 28 March 2021
ER -
