Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks Part-II: Intra-day and Experimental Validation
<|reference_start|>Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks Part-II: Intra-day and Experimental Validation: In Part-I, we presented an optimal day-ahead scheduling scheme for dispatching active distribution networks accounting for the flexibility provided by electric vehicle charging stations (EVCSs) and other controllable resources such as battery energy storage systems (BESSs). Part-II presents the intra-day control layer for tracking the dispatch plan computed from the day-ahead scheduling stage. The control problem is formulated as model predictive control (MPC) with an objective to track the dispatch plan setpoint every 5 minutes, while actuated every 30 seconds. MPC accounts for the uncertainty of the power injections from stochastic resources (such as demand and generation from photovoltaic - PV plants) by short-term forecasts. MPC also accounts for the grid's operational constraints (i.e., the limits on the nodal voltages and the line power-flows) by a linearized optimal power flow (LOPF) model based on the power-flow sensitivity coefficients, and for the operational constraints of the controllable resources (i.e., BESSs and EVCSs). The proposed framework is experimentally validated on a real-life ADN at the EPFL's Distributed Electrical Systems Laboratory and is composed of a medium voltage (MV) bus connected to three low voltage distribution networks. It hosts two controllable EVCSs (172 kWp and 32 F~kWp), multiple PV plants (aggregated generation of 42~kWp), uncontrollable demand from office buildings (20 kWp), and two controllable BESSs (150kW/300kWh and 25kW/25kWh).<|reference_end|>
@article{gupta2024grid-aware,
    title={Grid-aware Scheduling and Control of Electric Vehicle Charging Stations
  for Dispatching Active Distribution Networks. Part-II: Intra-day and
  Experimental Validation},
    author={Rahul K. Gupta, Sherif Fahmy, Max Chevron, Enea Figini and Mario
  Paolone},
    journal={arXiv preprint arXiv:2404.12870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12870},
    primaryClass={eess.SY cs.SY}
}