A Self-Optimizing Scheduling Model for Large-Scale EV Fleets in Microgrids

Abstract

The increasing number of electric vehicles (EVs) demands management solutions to deal with the impacts of EV charging on the efficiency of distribution grids. Many suggested methods are derived from analysis on laboratory-scale systems with declared data, which cannot be implemented for real networks. In this article, a two-step scheduling model is developed that effectively guides a large-scale EV fleet in microgrids without demanding a dynamic monetary scheme. The first step corresponds to prediction-based day-ahead optimal scheduling for large scale EVs, which minimizes the costs of electricity supply and EVs' battery degradation. To avoid dimensional problems in calculations, an improved K-means clustering algorithm is presented to divide vehicles into different clusters. In the second step, online coordination is deployed based on an effective scoring system to encourage drivers to follow the first-step provided model. The proposed model is analyzed on a grid-connected microgrid with photovoltaic system integration. The problem (real) data are derived based on an estimate of the development process on the Ontario energy network over the next ten years. Results show that the introduced model can guarantee the accurate deployment of optimal charging/discharging schedules in large-scale systems.