Optimization Approaches for Fast Charging Stations Allocation and Sizing: A Review

Abstract

The widespread use of fossil fuels in transportation has resulted in significant carbon dioxide emissions and increased reliance on non-renewable energy sources. To address these environmental challenges, the electrification of transportation systems through Electric Vehicles (EVs) has emerged as a promising solution. However, the successful deployment of EVs hinges on the availability of a robust charging infrastructure capable of meeting the charging demands and extending the driving range of EVs. Nonetheless, the large-scale deployment of EV charging infrastructure presents several challenges, including the ability of the electric grid to supply the required energy to accommodate the charging demand. Moreover, determining the optimal locations for fast-charging stations (FCS) in the traffic network to ensure accessibility, convenience, and efficient resource utilization poses a significant challenge. As a result, numerous studies have investigated the optimal allocation and sizing of EV charging infrastructure. The primary objective of this research is to conduct a comprehensive literature review to examine how this optimization problem has been addressed in the literature over the past decade. The review aims to identify the key factors considered in the problem formulation and the optimization techniques for allocating and sizing the charging stations. To achieve this goal, a systematic literature review was conducted following the PRISMA methodology for a comprehensive and unbiased approach. This review contributes to the existing literature by highlighting critical gaps and proposing a framework that can possibly bridge these gaps. The review identifies several critical gaps in current research, including: 1) Transportation-focused studies largely ignore electrical grid constraints; 2) Electrical-focused research often relies on statistically modeled EV charging demand, which may include some geographical assumptions; 3) Multidisciplinary approaches integrating both transportation and electrical networks are still in early stage; 4) Dynamic traffic flow of EVs is rarely considered; 5) Exact optimization methods largely rely on linearized or approximated models; 6) Dominance of approximate methods in transportation and electrical network modeling, primarily relying on evolutionary algorithms; and 7) Hybrid approaches are mainly utilized to solve a specific part of the problem rather than enhancing the quality of the solution. In response to these gaps, the research proposes a novel framework to integrate the transportation network and the electrical grid planning process, offering a holistic and practical solution in FCS infrastructure development.