OPTIMIZATION OF CURRENT AND PROPOSED EV CHARGING INFRASTRUCTURE IN SHAH ALAM USING MAXIMUM FLOW ANALYSIS AND MILP

Abstract

The rapid growth of electric vehicles (EVs) in Malaysia has intensified the need for effective and spatially efficient planning of charging infrastructure. This study proposes two approaches to evaluate existing electric vehicle charging stations (EVCS) and to identify optimal expansion strategies within Shah Alam, Selangor, focusing on Sections 1 to 24. In the first phase, a maximum flow-based model is employed to assess how much charging demand can be satisfied by the current network of fast charging stations (CS)s. The results reveal a substantial imbalance between demand and available capacity, with only 1.42% of total charging demand being satisfied, thereby exposing significant spatial coverage gaps across the study area. Building upon these findings, the second phase applies a mixed-integer linear programming (MILP) model to determine optimal locations and capacity allocations for new EVCS under realistic budget constraints, with the objective of maximizing satisfied demand. The optimization outcomes provide a targeted expansion strategy that improves demand coverage, station utilization, and cost efficiency. The resulting station configuration is also examined alongside the locations proposed through national planning initiatives to illustrate differences in spatial emphasis and coverage outcomes. Overall, the study demonstrates that integrating maximum flow analysis with MILP offers a systematic and data-driven approach for EVCS planning. The optimization models provide practical insights for urban planners and policymakers seeking to support sustainable EV adoption through efficient and demand-responsive charging infrastructure deployment.