Dynamic Scheduling for Charging Electric Vehicles: A Priority Rule

TL;DR

This paper develops a dynamic scheduling policy for charging electric vehicles with deadline constraints, minimizing costs by prioritizing vehicles with less laxity and longer remaining processing times, outperforming traditional policies.

Contribution

It introduces the LLLP principle, a novel scheduling rule that improves charging efficiency and cost-effectiveness for PHEVs under uncertainty, based on a dynamic programming approach.

Findings

LLLP principle improves scheduling performance over traditional policies.

Heuristic policies violating LLLP can cause significant cost increases.

Numerical results validate the effectiveness of the proposed scheduling rule.

Abstract

We consider the scheduling of multiple tasks with pre-determined deadlines under random processing cost. This problem is motivated by the potential of large scale adoption of plug-in (hybrid) electric vehicles (PHEVs) in the near future. The charging requests of PHEVs usually have deadline constraints, and the electricity cost associated with PHEV charging is usually random due to the uncertainty in both system load and renewable generation. We seek to properly schedule the battery charging of multiple PHEVs so as to minimize the overall cost, which is derived from the total charging cost and the penalty for not completing charging before requested deadlines. Through a dynamic programming formulation, we establish the Less Laxity and Longer remaining Processing time (LLLP) principle that improves any charging policy on a sample-path basis, when the non-completion penalty is a convex function of the additional time needed to fulfill the uncompleted request. Specifically, the LLLP principle states that priority should be given to vehicles that have less laxity and longer remaining processing times. Numerical results demonstrate that heuristic policies that violate the LLLP principle, for example, the earliest deadline first (EDF) policy, can result in significant performance loss.