TY - GEN
T1 - Smart Charge of an Electric Vehicles Station
T2 - 2nd IEEE Conference on Control Technology and Applications, CCTA 2018
AU - Diaz, Cesar
AU - Ruiz, Fredy
AU - Patino, Diego
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - The increasing use of Electric Vehicles (EVs) connected to the power grid generates challenges in the EV charging coordination and operation cost management. An EV Charging Station (EVCS), with time-variant prices and customers who have different charging time preferences, presents challenges for scheduling all requests. In this article, an aggregator based on a Model Predictive Control (MPC) strategy is proposed. It reduces the operating costs in the EVCS through managing EVs as flexible loads, i.e., the power delivered to each EV and its charging time can be modified. The MPC approach is analyzed by two scenarios. First, with full information, such as, EVs arrival State of Charge (SoC), arrival and departure times. Second, with uncertainty in the arrival SoC. Results show possible cost savings about 21.5% with full information and 21.0% with uncertainty in the arrival SoC. This MPC strategy might provide a new tool for reducing the EVCS operation costs fulfilling EV owners requirements.
AB - The increasing use of Electric Vehicles (EVs) connected to the power grid generates challenges in the EV charging coordination and operation cost management. An EV Charging Station (EVCS), with time-variant prices and customers who have different charging time preferences, presents challenges for scheduling all requests. In this article, an aggregator based on a Model Predictive Control (MPC) strategy is proposed. It reduces the operating costs in the EVCS through managing EVs as flexible loads, i.e., the power delivered to each EV and its charging time can be modified. The MPC approach is analyzed by two scenarios. First, with full information, such as, EVs arrival State of Charge (SoC), arrival and departure times. Second, with uncertainty in the arrival SoC. Results show possible cost savings about 21.5% with full information and 21.0% with uncertainty in the arrival SoC. This MPC strategy might provide a new tool for reducing the EVCS operation costs fulfilling EV owners requirements.
KW - Aggregator
KW - Electric Vehicle Charging Station
KW - Flexible Load
KW - Model Predictive Control
UR - https://www.scopus.com/pages/publications/85056848512
U2 - 10.1109/CCTA.2018.8511498
DO - 10.1109/CCTA.2018.8511498
M3 - Conference contribution
AN - SCOPUS:85056848512
T3 - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
SP - 54
EP - 59
BT - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 August 2018 through 24 August 2018
ER -