TY - GEN
T1 - Mixed Incentive-based and Direct Control Framework for EV Demand Response
AU - Diaz-Londono, Cesar
AU - Cordoba, Andres
AU - Vuelvas, José
AU - Ruiz, Fredy
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Electric Vehicles (EVs) are becoming an important part of the demand in electricity distribution networks. Extensive works have proposed strategies for direct load control of EV charging stations, where the connection times of the vehicle are handled as uncertain parameters or random variables. However, those strategies do not exploit all the flexibility of the charging process, due to the requirement to guarantee a safe operation of the network. This paper presents a model that combines an incentive-based mechanism to regulate the dwell time of EVs with a direct load control to define the charging profile. First, the aggregator offers a contract to the user to agree on the dwell time, making appropriate use of the EVs' flexibility in a successive step. The model is solved by backward induction and it shows that the strategy generates a win-win situation for both, the aggregator and the user, who obtain non-negative profits. A sensitivity analysis verifies that the user can save up to 10% of the charging cost and the aggregator can increase the income by 25%, on the scenarios considered. Moreover, taking advantage of the high price of the flexibility, the user can be induced to increase the dwell time by a 30%-50% of the minimum charging time offering up to 40% discount.
AB - Electric Vehicles (EVs) are becoming an important part of the demand in electricity distribution networks. Extensive works have proposed strategies for direct load control of EV charging stations, where the connection times of the vehicle are handled as uncertain parameters or random variables. However, those strategies do not exploit all the flexibility of the charging process, due to the requirement to guarantee a safe operation of the network. This paper presents a model that combines an incentive-based mechanism to regulate the dwell time of EVs with a direct load control to define the charging profile. First, the aggregator offers a contract to the user to agree on the dwell time, making appropriate use of the EVs' flexibility in a successive step. The model is solved by backward induction and it shows that the strategy generates a win-win situation for both, the aggregator and the user, who obtain non-negative profits. A sensitivity analysis verifies that the user can save up to 10% of the charging cost and the aggregator can increase the income by 25%, on the scenarios considered. Moreover, taking advantage of the high price of the flexibility, the user can be induced to increase the dwell time by a 30%-50% of the minimum charging time offering up to 40% discount.
KW - Electric vehicles
KW - demand response
KW - direct load control
KW - flexible loads
KW - mechanism design
UR - http://www.scopus.com/inward/record.url?scp=85185346891&partnerID=8YFLogxK
U2 - 10.1109/VPPC60535.2023.10403387
DO - 10.1109/VPPC60535.2023.10403387
M3 - Conference contribution
AN - SCOPUS:85185346891
T3 - 2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings
BT - 2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Vehicle Power and Propulsion Conference, VPPC 2023
Y2 - 24 October 2023 through 27 October 2023
ER -