Out-of-Distribution-Aware Electric Vehicle Charging
This addresses the challenge of balancing near-optimal performance with worst-case guarantees in EV charging for real-world applications, though it appears incremental as it builds on existing scheduling methods.
The paper tackled the problem of learning to charge Electric Vehicles (EVs) with Out-of-Distribution (OOD) data, introducing an OOD-aware scheduling algorithm that improves scheduling reward reliably under real OOD scenarios, such as those caused by COVID-19 in the Caltech ACN-Data.
We tackle the challenge of learning to charge Electric Vehicles (EVs) with Out-of-Distribution (OOD) data. Traditional scheduling algorithms typically fail to balance near-optimal average performance with worst-case guarantees, particularly with OOD data. Model Predictive Control (MPC) is often too conservative and data-independent, whereas Reinforcement Learning (RL) tends to be overly aggressive and fully trusts the data, hindering their ability to consistently achieve the best-of-both-worlds. To bridge this gap, we introduce a novel OOD-aware scheduling algorithm, denoted OOD-Charging. This algorithm employs a dynamic "awareness radius", which updates in real-time based on the Temporal Difference (TD)-error that reflects the severity of OOD. The OOD-Charging algorithm allows for a more effective balance between consistency and robustness in EV charging schedules, thereby significantly enhancing adaptability and efficiency in real-world charging environments. Our results demonstrate that this approach improves the scheduling reward reliably under real OOD scenarios with remarkable shifts of EV charging behaviors caused by COVID-19 in the Caltech ACN-Data.