Smart Charging of Electric Vehicle Fleets: Modeling, Algorithm Development, and Grid Impact Analysis, with Emphasis on Fleets of Transit and Heavy-Duty Freight Vehicles

Christian Viteri, David Taylor and Michael Leamy

Institute of Transportation Studies, Working Paper Series from Institute of Transportation Studies, UC Davis

Abstract: High charging loads associated with fleets of commercial electric vehicles (EVs) are expected to significantly stress electric power distribution networks, leading to high costs seen by fleet operators. To address these challenges, this report presents a highly flexible smart charging (SC) algorithm for managing EV fleets that arrive and depart from a common depot on a schedule. The algorithm features (i) primary consideration of multiple fleet operator preferences (e.g. minimizing cost, using carbon-free energy), (ii) secondary consideration of grid impact that leverages the existence of multiple optimal (or near-optimal) ways to satisfy fleet operator preferences, and (iii) automatic detection and handling of infeasibility due to large energy demands (characteristic of fleet charging). Provided in this document are two numerical impact assessment studies in which the SC algorithm is shown to be superior to conventional rapid charging, and conventional ‘smart’ charging solutions on the market. These studies utilize a set of synthetic, but realistic fleet charging requirements, a physics-based model of a real feeder and one year of real, hourly load data for that feeder. The first numerical study shows that the proposed SC algorithm can lead to significant (up to 44%, but scenario-dependent) reductions in a fleet operator’s annual electricity bill. The second numerical study shows that significant transformer overloading and voltage drop issues can be associated with conventional fleet charging methods, and that the proposed SC algorithm eliminates these issues, thereby enabling higher EV penetration levels and offsetting infrastructure upgrades. View the NCST Project Webpage

Keywords: Engineering; Physical Sciences and Mathematics; Algorithms; Commercial vehicles; Electric utilities; Electric vehicle charging; Energy consumption; Heavy duty vehicles; Scheduling; Vehicle fleets (search for similar items in EconPapers)
Date: 2024-09-01
New Economics Papers: this item is included in nep-ene and nep-tre
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