Vehicle-to-Vehicle Charging: Model, Complexity, and Heuristics
This addresses the problem of managing peak electricity demand from EVs for grid operators and decision-makers, but it is incremental as it builds on existing V2VC adoption.
The paper tackles the challenge of optimizing Vehicle-to-Vehicle Charging (V2VC) for electric vehicles, showing the problem is NP-Complete and proposing a heuristic called R-V2VC that achieves near-optimal solutions with linear time growth for realistic problem sizes.
The rapid adoption of Electric Vehicles (EVs) poses challenges for electricity grids to accommodate or mitigate peak demand. Vehicle-to-Vehicle Charging (V2VC) has been recently adopted by popular EVs, posing new opportunities and challenges to the management and operation of EVs. We present a novel V2VC model that allows decision-makers to take V2VC into account when optimizing their EV operations. We show that optimizing V2VC is NP-Complete and find that even small problem instances are computationally challenging. We propose R-V2VC, a heuristic that takes advantage of the resulting totally unimodular constraint matrix to efficiently solve problems of realistic sizes. Our results demonstrate that R-V2VC presents a linear growth in the solution time as the problem size increases, while achieving solutions of optimal or near-optimal quality. R-V2VC can be used for real-world operations and to study what-if scenarios when evaluating the costs and benefits of V2VC.