Press Start to Charge: Videogaming the Online Centralized Charging Scheduling Problem

TL;DR

This paper introduces a gamified learning approach for real-time EV charging scheduling that improves load balancing and reduces costs, outperforming traditional heuristics and supervised methods.

Contribution

It presents a novel gamification-based modeling and learning framework for online EV charging, with theoretical benefits and practical cost savings demonstrated in a real-world case study.

Findings

Gamification reduces model complexity and improves generalization.

The DAgger-trained image-to-movement model outperforms baselines.

Operational savings amount to tens of millions annually in a real case.

Abstract

We study the online centralized charging scheduling problem (OCCSP). In this problem, a central authority must decide, in real time, when to charge dynamically arriving electric vehicles (EVs), subject to capacity limits, with the objective of balancing load across a finite planning horizon. To solve the problem, we first gamify it; that is, we model it as a game where charging blocks are placed within temporal and capacity constraints on a grid. We design heuristic policies, train learning agents with expert demonstrations, and improve them using Dataset Aggregation (DAgger). From a theoretical standpoint, we show that gamification reduces model complexity and yields tighter generalization bounds than vector-based formulations. Experiments across multiple EV arrival patterns confirm that gamified learning enhances load balancing. In particular, the image-to-movement model trained with DAgger consistently outperforms heuristic baselines, vector-based approaches, and supervised learning agents, while also demonstrating robustness in sensitivity analyses. These operational gains translate into tangible economic value. In a real-world case study for the Greater Montr\'eal Area (Qu\'ebec, Canada) using utility cost data, the proposed methods lower system costs by tens of millions of dollars per year over the prevailing practice and show clear potential to delay costly grid upgrades.