TY - JOUR
T1 - Transactive Energy Management of V2G-Capable Electric Vehicles in Residential Buildings: An MILP Approach
AU - Saber, Hossein
AU - Ranjbar, Hossein
AU - Fattaheian-Dehkordi, Sajjad
AU - Moeini-Aghtaie, Moein
AU - Ehsan, Mehdi
AU - Shahidehpour, Mohammad
N1 - Publisher Copyright:
IEEE
PY - 2022/7
Y1 - 2022/7
N2 - This paper proposes a new energy management model for residential buildings to handle the uncertainties of demand and on-site PV generation. For this purpose, the building energy management system (BEMS) organizes a transactive energy (TE) market among plug-in electric vehicles (PEVs) to determine their charge/discharge scheduling. According to the proposed TE framework, the PEV owners get reimbursed by the BEMS for the flexibility they offer. In this regard, the PEV owners submit their response curves for reimbursement upon arrival. Then, the BEMS solves an optimization problem to maximize its own profit and determine the real-time TE market-clearing price. Afterward, based on the clearing price, the real-time scheduling of PEV batteries and the reimbursements to the PEV owners for their responses are determined. Additionally, the original mixed-integer non-linear optimization problem is reformulated as a mixed-integer linear programming one using a set of linearization techniques. Finally, the proposed model is applied to a residential building with 50 PEV charging piles, and the simulation results show that the proposed model decreases the actual charging payment of PEV owners by 17.6% and 52.3%, and the total cost of BEMS by 5.1% and 10.8% compared to demand response concept-based and uncontrolled charging models, respectively.
AB - This paper proposes a new energy management model for residential buildings to handle the uncertainties of demand and on-site PV generation. For this purpose, the building energy management system (BEMS) organizes a transactive energy (TE) market among plug-in electric vehicles (PEVs) to determine their charge/discharge scheduling. According to the proposed TE framework, the PEV owners get reimbursed by the BEMS for the flexibility they offer. In this regard, the PEV owners submit their response curves for reimbursement upon arrival. Then, the BEMS solves an optimization problem to maximize its own profit and determine the real-time TE market-clearing price. Afterward, based on the clearing price, the real-time scheduling of PEV batteries and the reimbursements to the PEV owners for their responses are determined. Additionally, the original mixed-integer non-linear optimization problem is reformulated as a mixed-integer linear programming one using a set of linearization techniques. Finally, the proposed model is applied to a residential building with 50 PEV charging piles, and the simulation results show that the proposed model decreases the actual charging payment of PEV owners by 17.6% and 52.3%, and the total cost of BEMS by 5.1% and 10.8% compared to demand response concept-based and uncontrolled charging models, respectively.
KW - Batteries
KW - Buildings
KW - Costs
KW - Optimal scheduling
KW - Real-time systems
KW - State of charge
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85132504836&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2022.3173943
DO - 10.1109/TSTE.2022.3173943
M3 - Article
AN - SCOPUS:85132504836
SN - 1949-3029
VL - 13
SP - 1734
EP - 1743
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 3
ER -