TY - JOUR
T1 - Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties
AU - Zhang, Meijuan
AU - Yan, Qingyou
AU - Guan, Yajuan
AU - Ni, Da
AU - Agundis Tinajero, Gibran David
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/7/1
Y1 - 2024/7/1
N2 - Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.
AB - Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.
KW - Capacity configuration optimization
KW - Demand response
KW - Electric vehicle charging station
KW - Uncertainties
UR - http://www.scopus.com/inward/record.url?scp=85190958973&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2024.131370
DO - 10.1016/j.energy.2024.131370
M3 - Journal article
AN - SCOPUS:85190958973
SN - 0360-5442
VL - 298
JO - Energy
JF - Energy
M1 - 131370
ER -