Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-I: Day-ahead and Numerical Validation

TL;DR

This work addresses dispatching an active distribution network with high EVCS penetration under uncertainties in EV demand, load, and PV generation. It introduces a two-stage approach: a day-ahead dispatch plan (DP) obtained via a scenario-based stochastic OPF using EVCS demand forecasts modeled with Gaussian Mixture Models, PV forecasts from SoDa data, and day-type load predictions, all constrained by a linearized OPF with power-flow sensitivity coefficients. Tracking the DP in real time is handled by an MPC that leverages EVCS and BESS flexibility, with EVCS SoC dynamics and linearized constraints to maintain tractability; the framework also includes a virtual-resistance technique to model converter losses without adding decision variables. Numerical validation on an EPFL ADN demonstrates substantial reductions in energy error, tracking error, and peak deviations when using EVCS and BESS together, illustrating practical viability; Part II extends the work to real-time experimental validation.

Abstract

This paper proposes a grid-aware scheduling and control framework for Electric Vehicle Charging Stations (EVCSs) for dispatching the operation of an active power distribution network. The framework consists of two stages. In the first stage, we determine an optimal day-ahead power schedule at the grid connection point (GCP), referred to as the dispatch plan. Then, in the second stage, a real-time model predictive control is proposed to track the day-ahead dispatch plan using flexibility from EVCSs. The dispatch plan accounts for the uncertainties of vehicles connected to the EVCS along with other uncontrollable power injections, by day-ahead predicted scenarios. We propose using a Gaussian-Mixture-Model (GMM) for the forecasting of EVCS demand using the historical dataset on arrival, departure times, EV battery capacity, State-of-Charge (SoC) targets, etc. The framework ensures that the grid is operated within its voltage and branches power-flow operational bounds, modeled by a linearized optimal power-flow model, maintaining the tractability of the problem formulation. The scheme is numerically and experimentally validated on a real-life distribution network at the EPFL connected to two EVCSs, two batteries, three photovoltaic plants, and multiple heterogeneous loads. The day-ahead and real-time stages are described in Part-I and Part-II papers respectively.

Figures (13)

• Figure 1: Schematic overview of the proposed two-stage ADN dispatch.
• Figure 2: EV forecasting tool-chain.
• Figure 3: PV forecasting tool-chain.
• Figure 4: Load forecasting tool-chain.
• Figure 5: Schematic representation of the experimental infrastructure of the ELL building, EPFL.