Learning-Augmented Scheduling for Solar-Powered Electric Vehicle
  Charging
Tongxin Li
We tackle the complex challenge of scheduling the charging of electric
vehicles (EVs) equipped with solar panels and batteries, particularly under
out-of-distribution (OOD) conditions. Traditional scheduling approaches, such
as reinforcement learning (RL) and model predictive control (MPC), often fail
to provide satisfactory results when faced with OOD data, struggling to balance
robustness (worst-case performance) and consistency (near-optimal average
performance). To address this gap, we introduce a novel learning-augmented
policy. This policy employs a dynamic robustness budget, which is adapted in
real-time based on the reinforcement learning policy's performance.
Specifically, it leverages the temporal difference (TD) error, a measure of the
learning policy's prediction accuracy, to assess the trustworthiness of the
machine-learned policy. This method allows for a more effective balance between
consistency and robustness in EV charging schedules, significantly enhancing
adaptability and efficiency in real-world, unpredictable environments. Our
results demonstrate that this approach markedly improves scheduling
effectiveness and reliability, particularly in OOD contexts, paving the way for
more resilient and adaptive EV charging systems.