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The adoption of electric vehicles (EVs), including electric taxis and buses,as a mode of transportation, is rapidly increasing in cities. In addition toproviding economic and environmental benefits, these fleets can potentiallyparticipate in the energy arbitrage market by leveraging their mobile energystorage capabilities. This presents an opportunity for EV owners to contributeto a more sustainable and efficient energy system while also reducing theiroperational costs. The present study introduces deterministic and single-stagestochastic optimization frameworks that aim to maximize revenue by optimizingthe charging, discharging, and travel of a fleet of electric vehicles in thecontext of uncertainty surrounding both spatial and temporal energy prices. Thesimulations are performed on a fleet of electric delivery trucks, which have tomake deliveries to certain locations on specific dates.  The findings indicate the promising potential of bidirectional electricvehicle charging as a mobile grid asset. However, it is important to note thatsignificant revenue is only realized in scenarios where there is substantialvariation in prices between different areas, and when these price variationscan be accurately forecasted with a high level of confidence.

Spatial Arbitrage Through Bidirectional Electric Vehicle Charging with Delivery Fleets