ELECTRIC VEHICLE (EV) DEMAND PROFILING FOR ASSESSING RESIDENTIAL LOAD PATTERNS IN QATAR

Electric vehicle charging demand forecasting is crucial for power grid planning in Qatar, as studies predict a significant growth in EVs over the next 8 years. This paper proposes a survey-based methodology to develop hourly probability EV demand profiles to be used in forecasting residential demand. A survey was conducted in Qatar, targeting both fuel-based and electric vehicle drivers, to capture average daily driving distances, at-home durations, and other behavioral factors. The responses from 180 participants were analyzed to develop weekday and weekend demand profiles for summer and winter, employing robust data cleansing, state-of-charge (SoC) calculations, realistic battery specifications, and an assessment of the ambient temperature effect on EV energy consumption. The methodology can be adopted regionally and internationally. Results show high evening peaks and low early-morning demand with seasonal influences on overall demand probabilities. This work contributes to Qatar’s goal of a smooth and reliable transition to widespread EV adoption by demonstrating the feasibility of this transition and delivering a realistic probability EV demand profiles specific to Qatar.