Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times
arXiv:2606.19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on reinforcement learning (RL) -- can alleviate these issues by learning temporal and contextual patterns from historical data. Yet, in real-world scenarios, key features, such as departure time, often are unavailable. This, in turn, makes it harder for an RL agent to learn and execute an effective charging policy. To mitigate this uncertainty, a trained forecaster can a
The increasing adoption of Electric Vehicles (EVs) is pushing power grids to their limits, necessitating smarter and more adaptive charging solutions leveraging advanced AI.
This development addresses a critical challenge in scaling EV infrastructure by enabling more efficient grid management and reducing peak demand, directly impacting energy stability and adoption rates.
The ability of AI to manage EV charging intelligently, even with incomplete data like departure times, improves grid resilience and opens pathways for more widespread, seamless EV integration.
- · EV manufacturers
- · Smart grid technology providers
- · Utilities and power grid operators
- · AI/ML developers
- · Legacy power generation methods (untouched by smart grid tech)
- · Inefficient energy management systems
Improved grid stability and reduced strain on existing power infrastructure due to optimized EV charging.
Accelerated EV adoption rates as charging becomes more reliable, convenient, and less disruptive to the grid.
Potential for new business models around dynamic energy pricing and vehicle-to-grid (V2G) services based on advanced AI forecasting.
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Read at arXiv cs.AI