Forecasting Long-term Electric Vehicle Energy Demand in a Specific Geographic Region

• Published in: 2020 IEEE Power & Energy Society General Meeting (PESGM) pp. 1 - 5
• Main Authors: McBee, K. D., Bukofzer, D., Chong, J., Bhullar, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.08.2020
• Subjects: Electric vehicle; forecasting; generation capacity; system planning
• ISSN: 1944-9933
• DOI: 10.1109/PESGM41954.2020.9282045

The increased penetration rate of EVs can significantly impact the grid's energy generation capacity requirement. Level 2 chargers demand up to 7.7 kW during peak. As such, most EV forecasting approaches focus on short planning by identifying next hour, day, or week energy requirements. These approaches are ideal for areas with high penetration rates. Utilities supporting these areas have utilized early growth indicators to predict generation capacity requirements. However, areas with low penetration rates are just now beginning to develop long-term plans to identify when and "if" to initiate large capacity investments that require years to build. The goal of the research described within this report was to develop a long-term forecasting approach by combining all attributes required to predict energy demand in a specific geographic location based upon its time dependent evolution of EV penetration rate, which is a function of newly purchased EVs, previously purchased EVs, salvage rate of EVs, customer habits and choices, EV charging types, and EV battery capacity. The approach applies customer feedback and a Poisson pdf analysis to evaluate the uncertainty of customer charging habits. The approach is utilized to analyze the 25 year energy demand for 1,136, 949 registered vehicles (gasoline and EV).