Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-II: Intra-day and Experimental Validation

TL;DR

This work addresses intra-day dispatch tracking in active distribution networks by leveraging the flexibility of electric vehicle charging stations (EVCS) and battery energy storage systems (BESS) to follow a day-ahead dispatch plan. It introduces a real-time model predictive control (RT-MPC) layer that solves every 30 seconds to track a 5-minute horizon, incorporating short-term forecasts of uncertain demand and PV generation and using a linearized optimal power flow model to enforce grid constraints. EVCS and BESS are modeled with SoC dynamics, wear considerations, and fairness, and the entire RT-MPC problem is linear, enabling fast LP solutions; the approach is experimentally validated on a live ADN at EPFL. Results over multiple days show substantial reductions in dispatch-tracking errors while maintaining SoC targets and battery health, demonstrating practical viability for dispatch support in real distribution grids.

Abstract

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).

Figures (14)

• Figure 1: Overview of different processes during the real-time operation. On the top left, the measurement system (providing weather and grid measurements) feeds the RTSE, the short-term forecaster, and the RT-MPC layer. The RT-MPC layer sends power setpoints to the actuation layer.
• Figure 2: Schematic representation of the measured quantities and optimized variables in the RT-MPC. The implemented power at the GCP shown in light green color and the dispatch setpoint in grey color. The anticipated power at the GCP with RT-MPC is shown in red.
• Figure 3: Schematic representation of the experimental infrastructure of the ELL building, EPFL.
• Figure 4: Two battery storage installations used for the real-time experiments. BESS 1 and BESS 2 are connected to B04 and B10 respectively.
• Figure 5: (a) Level-3 Gofast EVCS with 5 plugs and (b) Level-2 EVCS with 3 plugs.