Day-Ahead Scheduling of Electric Vehicles for Overloading Management in Active Distribution System via Web-Based Application

Abstract

Electric vehicles (EVs) if become a major presence on the worlds’ road can have an adverse impact, i.e., line overloading, fuse blowouts, transformer failures, and at worst case blackouts in active distribution system. To handle such impact, the existing literature suggested a day-ahead scheduling of EVs in active distribution system providing a tentative prediction of energy requirements for EVs. However, this paper proposes a novel web-based registration system that gathers the actual upcoming energy requirements from EV owners well in advance. Thereby, bridging the gap between the grid and end users resulting in more reliable EV's information, such as initial and expected state of charge (SoC) and arrival and departure time. These input data are further processed using a proposed social welfare maximization algorithm that determines the price responsive schedule for EVs and calculates the distribution locational marginal price (DLMP) signal. This DLMP signal is high in the period of overloading and low during the time with less load. DLMP signal is finally shared with the end users via a web application that facilitates them to choose most economic charging slots. The proposed scheme is validated on a referenced active distribution system. The simulation and results show the effectiveness of the proposed scheme in preventing overloading of lines, satisfying EVs energy requirements, and evaluating their cost incurred during charging hours. Additionally, the results validate the resiliency of the proposed algorithm against potential damages, such as change of line flow limits, change of expected SoC, stay hours of EV, and the change in total number of EVs.