TY - GEN
T1 - A Real-Time Simulation to Evaluate the Peak Shaving Energy Management System for Vanadium Redox Flow Battery-based Electric Vehicle Charging Station
AU - Nguyen, Ngoc Duc
AU - Lee, Changdae
AU - Lee, Young Il
N1 - Publisher Copyright:
© 2024 ICROS.
PY - 2024
Y1 - 2024
N2 - This paper presents a peak-shaving energy management system (EMS) for the electric vehicle charging station (EVCS) with vanadium redox flow battery (VRFB). The mathematical model of VRFB and circuit models of EV chargers, photovoltaic (PV) system, hybrid power conversion system and EVs, are first simulated in the hardware-in-the-loop (HIL) devices. An online EMS for the real-time peak-shaving operation of EVCS is composed of the receding large-step and small-step optimization using 1-hour and 10-seconds time intervals, respectively. The large-step optimization is designed to maintain the grid power within the desired limit in the 24-hours ahead time horizon based on the long-term forecasting data of PV generation and EV load. The small-step optimization is for tracking the grid-power reference from the high-step optimization. Both of the optimization problems can be directly solved by QP solver. The optimized charge/discharge power reference computed by the small-step controller is applied to the VRFB system in the HIL devices which is connected to the same communication network as the EMS program. The proposed EMS for the EVCS with VRFB is validated for the peak shaving capability of 50% reduction of the net load using the established software-in-the-loop real-time simulator.
AB - This paper presents a peak-shaving energy management system (EMS) for the electric vehicle charging station (EVCS) with vanadium redox flow battery (VRFB). The mathematical model of VRFB and circuit models of EV chargers, photovoltaic (PV) system, hybrid power conversion system and EVs, are first simulated in the hardware-in-the-loop (HIL) devices. An online EMS for the real-time peak-shaving operation of EVCS is composed of the receding large-step and small-step optimization using 1-hour and 10-seconds time intervals, respectively. The large-step optimization is designed to maintain the grid power within the desired limit in the 24-hours ahead time horizon based on the long-term forecasting data of PV generation and EV load. The small-step optimization is for tracking the grid-power reference from the high-step optimization. Both of the optimization problems can be directly solved by QP solver. The optimized charge/discharge power reference computed by the small-step controller is applied to the VRFB system in the HIL devices which is connected to the same communication network as the EMS program. The proposed EMS for the EVCS with VRFB is validated for the peak shaving capability of 50% reduction of the net load using the established software-in-the-loop real-time simulator.
KW - and peak shaving
KW - electric vehicle charging station
KW - energy management system
KW - flow battery
UR - http://www.scopus.com/inward/record.url?scp=85214422029&partnerID=8YFLogxK
U2 - 10.23919/ICCAS63016.2024.10773145
DO - 10.23919/ICCAS63016.2024.10773145
M3 - Conference contribution
AN - SCOPUS:85214422029
T3 - International Conference on Control, Automation and Systems
SP - 365
EP - 370
BT - 2024 24th International Conference on Control, Automation and Systems, ICCAS 2024
PB - IEEE Computer Society
T2 - 24th International Conference on Control, Automation and Systems, ICCAS 2024
Y2 - 29 October 2024 through 1 November 2024
ER -