Spatial Arbitrage Through Bidirectional Electric Vehicle Charging with Delivery Fleets

TL;DR

This paper develops optimization models for electric delivery fleets to participate in energy arbitrage through bidirectional charging, highlighting the potential revenue gains and the importance of price variability and forecasting accuracy.

Contribution

It introduces deterministic and stochastic optimization frameworks for EV fleet management in energy arbitrage, considering spatial and temporal price uncertainties.

Findings

Bidirectional EV charging can be a valuable mobile grid asset.

Significant revenue depends on price variation and accurate forecasting.

Potential for revenue increase in scenarios with high price differences.

Abstract

The adoption of electric vehicles (EVs), including electric taxis and buses, as a mode of transportation, is rapidly increasing in cities. In addition to providing economic and environmental benefits, these fleets can potentially participate in the energy arbitrage market by leveraging their mobile energy storage capabilities. This presents an opportunity for EV owners to contribute to a more sustainable and efficient energy system while also reducing their operational costs. The present study introduces deterministic and single-stage stochastic optimization frameworks that aim to maximize revenue by optimizing the charging, discharging, and travel of a fleet of electric vehicles in the context of uncertainty surrounding both spatial and temporal energy prices. The simulations are performed on a fleet of electric delivery trucks, which have to make deliveries to certain locations on specific dates. The findings indicate the promising potential of bidirectional electric vehicle charging as a mobile grid asset. However, it is important to note that significant revenue is only realized in scenarios where there is substantial variation in prices between different areas, and when these price variations can be accurately forecasted with a high level of confidence.