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 paper introduces a grid-aware EV charging scheduling framework that optimizes day-ahead plans and employs real-time control to ensure grid stability, validated on a real distribution network with diverse energy sources.

Contribution

It presents a novel two-stage scheduling and control framework incorporating Gaussian-Mixture-Model demand forecasting for EVCSs, ensuring grid constraints are met.

Findings

Effective day-ahead dispatch planning with uncertainty handling.

Real-time control successfully tracks the dispatch plan.

Validated on a real distribution network with diverse energy sources.

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.