OPTIMIZATION AND STRATEGIC PLANNING OF ELECTRIC VEHICLE CHARGING STATION LOCATIONS IN QATAR UNDER MULTIPLE THRESHOLD SCENARIOS

Abstract

This thesis presents a framework for optimizing the placement of Electric Vehicle Charging Stations (EVCS) in Qatar, strategically using the Set Covering Problem (SCP). The SCP model determines the optimal number and arrangement of EVCS required to achieve full accessibility across Qatar’s eight municipalities, while minimizing the total installation costs. The study focuses on two charging technologies, which are Medium DC Fast (MDC) (≈50 kW) and High-Power DC (HPDC) (≈350 kW), to represent varying cost and coverage aspects. The SCP model developed is implemented in IBM ILOG CPLEX Studio using location coordinates, population data, and travel-time data obtained from Google Maps and Haversine calculations. Six scenario threshold combinations are tested for both charging technologies, ranging from the most restrictive (10-25 minutes) to the most wide-ranging (25-30 minutes), under a planning horizon for 2025 and 2030. The results indicate that smaller thresholds require opening more EVCS, while bigger thresholds reduce infrastructure cost with the trade-off of limited accessibility. Sensitivity analyses show that coverage constraints are binding and proportional cost variations have a limited impact on the network configuration. The sequential 2030 model considers population growth and the continuity of stations opened in 2025 for all six scenario thresholds, which provides a realistic expansion that aligns with Qatar National Vision 2030 (QNV 2030). The proposed SCP framework helps policymakers, such as the government, in designing an efficient EVCS infrastructure with a focus on the future, since Electric Vehicles in Qatar are accelerating.

Date of Award	2025
Original language	American English
Awarding Institution	HBKU College of Science and Engineering