EVSE Effectiveness in Multi-Unit Residential Buildings Using Composite Optimization and Heuristic Search

Abstract

The effectiveness of electric vehicle supply equipment (EVSE) is a very important factor in multi-unit residential buildings (MRBs) when planning to invest in an electric vehicle (EV). However, the expected benefits depend on not only technology but also need many non-functional requirements such as building facilities, electrical infrastructure, operating costs, and energy demand management of EVSE. We discuss the functional and non-functional constraints in utilizing EVSEs in MRBs and addresses them by constructing a composite optimization problem with a heuristic boundary selection. The proposed model urges the use of electrical safety codes –to mitigate the hazard risk– and promotes deploying a flexible and scalable energy management system (EMS) to schedule, reserve, and tune the charging sessions. This article provides an effective EMS for mitigating the energy demand growth caused by EV charging on MRBs, by finding the equilibrium number of EVSEs and their energy usage through a heuristic search. The proposed EMS is reinforced by embedding machine learning tools such as k-means, silhouette scoring, heuristic search, and autoregressive model to adjust the optimal operational state of EVSEs. We delineated the proposed framework and the outcomes using two case studies with real data. The results showed that more EVSEs can be utilized in MRBs and 100% EVs can be fully charged with a lower capital cost and no additional energy demand cost.