Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads

Conference · Wed Jun 19 04:00:00 EDT 2019

DOI:https://doi.org/10.1109/ITEC.2019.8790617· OSTI ID:1556922

Duoba, Michael; Fernandez Canosa, Alejandro

This paper presents a mesoscopic model to populate a set of sub-fleets of battery electric vehicles (BEVs) that simulates how grid power demand changes with varied mixes of different charging strategies. The proposed model considers real driving activities of vehicles based on the National Household Travel Survey (NHTS) and various charging strategies while parked (at home and in other locations). To be easily scaled to any fleet size, a mesoscopic model is implemented, where BEVs with the same physical properties and driving schedules and clustered together into sub-fleets. The degree of granularity is explored for stable results. Monte Carlo simulations were run to demonstrate the potential of this model's use in grid loads under various charging strategies. Furthermore, an example cost function was optimized to demonstrate finding optimal allocations of charging strategies.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Science
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1556922
• Country of Publication: United States
• Language: English

Related Subjects

Electric Vehicles
Grid Integration
Monte Carlo Simulations