IEEE Access (Jan 2024)
Demand Estimation for Electric Vehicle Charging Infrastructure: An Extensive Approach Method
Abstract
Urban planning must coordinate expanding public electric vehicle charging infrastructure (EV-CI) with energy distribution companies to satisfy growing demand. To this end, measurements and charger load curves are indispensable to execute appropriate operational studies and expansion planning. However, the necessary databases are small in cities with low EV penetration, and may not adequately characterize future consumption patterns. This article proposes an “Extensive Method for Demand Estimation in Charging Infrastructures” (X-Modeci) to fill this gap, which is especially beneficial for large metropolitan areas. The method employs statistical regressions to model the different phases of EV adoption, using traffic simulations to incorporate new travel dynamics that may arise due to increased charging and new EV battery range values. The proposal result is a spatial database that shows EV-CI load curves for typical urban traffic days in locations that would be more appropriate for installing new chargers. This results class can help energy companies identify the best connection locations and necessary reinforcements in the distribution grid to meet electromobility demand. The method was applied in the most significant capital in the Northeast of Brazil, showing that the estimated load curves tend to reach their maximum point between the end of the afternoon and the beginning of the night, following the high rate of vehicle circulation during this period. The case study clarified that the characterization of EV-CI use provided a 10% gain in the EV-CI usage rate about the national average, respecting regulated levels. In this way, the results of the proposed method can help the agents involved better understand the energy demand within the scope of electromobility plans with high spatial resolution at the location of the main avenues in urban areas.
Keywords
