TY - GEN
T1 - Understanding Model Predictive Control for Electric Vehicle Charging Dispatch
AU - Diaz, Cesar
AU - Mazza, Andrea
AU - Ruiz, Fredy
AU - Patino, Diego
AU - Chicco, Gianfranco
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/20
Y1 - 2018/11/20
N2 - This paper illustrates the principles of Model Predictive Control (MPC) applied to control the dispatch of power to Electric Vehicle (EV) chargers in a charging station. The MPC strategy aims to determine a control signal by following a day-ahead scheduling and minimizing an economic objective function. The strategy works in closed-loop architecture. The MPC calculates an optimal charging sequence at each time step of the prediction horizon, but it applies the control signal only for the first step of the sequence, following a receding horizon strategy. The results of the MPC strategy lead to track a dayahead scheduling by considering uncertainties on the EV arrival state of charge, and generation disturbances. The MPC strategy outcomes are compared with an open-loop strategy, with the target to apply the scheduled power.
AB - This paper illustrates the principles of Model Predictive Control (MPC) applied to control the dispatch of power to Electric Vehicle (EV) chargers in a charging station. The MPC strategy aims to determine a control signal by following a day-ahead scheduling and minimizing an economic objective function. The strategy works in closed-loop architecture. The MPC calculates an optimal charging sequence at each time step of the prediction horizon, but it applies the control signal only for the first step of the sequence, following a receding horizon strategy. The results of the MPC strategy lead to track a dayahead scheduling by considering uncertainties on the EV arrival state of charge, and generation disturbances. The MPC strategy outcomes are compared with an open-loop strategy, with the target to apply the scheduled power.
KW - Arrival SoC uncertainty
KW - Economic dispatch
KW - Education
KW - Electric vehicle chargers
KW - Model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85059962492&partnerID=8YFLogxK
U2 - 10.1109/UPEC.2018.8542050
DO - 10.1109/UPEC.2018.8542050
M3 - Conference contribution
AN - SCOPUS:85059962492
T3 - Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018
BT - Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 53rd International Universities Power Engineering Conference, UPEC 2018
Y2 - 4 September 2018 through 7 September 2018
ER -
