Sustainable Urban Planning: Criteria for Efficient Photovoltaic Electric Vehicle Charging Stations Deployment in Malaysia

Abstract

In Malaysia, the electric vehicle (EV) market is experiencing significant growth, necessitating the installation of charging stations (CSs) along routes to alleviate range anxiety among EV users. Integrating EVs with renewable energy sources (RESs), such as solar energy, is imperative to support the United Nations' Sustainable Development Goals (SDGs). Given the limited availability of photovoltaic electric vehicle charging stations (PEVCS) in Malaysia, their installation becomes imperative, requiring careful consideration of several key criteria for ideal allocation. This paper presents a comprehensive study focused on establishing criteria for strategically siting PEVCS in Malaysia. The objective of the study is to finalize the criteria for determining the ideal PEVCS placement, taking into consideration the unique geographical and infrastructural context of Malaysia. The methodology involved an extensive literature review, a rigorous needs analysis, and the validation of the selection criteria through expert feedback. In the initial phase, comprising 41 sub-criteria and six main criteria, the study meticulously narrowed down the selection to 12 sub-criteria and retained the six main criteria. This selection is made based on the valuable insights provided by experts, ensuring a refined and relevant set of criteria for the subsequent phases of validation and instrument development. The discussion section of the article details the rationale behind the chosen criteria and their alignment with the overarching goal of efficient PEVCS deployment. The findings of this study contribute to the ongoing efforts in sustainable urban planning and transportation infrastructure development, specifically in the domain of PEVCS in Malaysia. The paper concludes by recommending the application of the Fuzzy Delphi Method (FDM) to determine the final criteria for the ideal placement of PEVCS in Malaysia. This method is proposed as a robust method to handle the uncertainties and complexities associated with the dynamic nature of the decision-making process in this context.