Deep Reinforcement Learning Based Unit Commitment Scheduling under Load and Wind Power Uncertainty

The intermittent nature of renewable energy sources and fluctuating electricity demand induce significant uncertainty that needs to be tackled with computationally efficient solution techniques to provide reliable and cost-effective generation schedules of power systems. In this work, we present a deep reinforcement learning (DRL) based approach for the day-ahead scheduling of generation resources under demand and wind power uncertainties. The proposed approach yields a causal policy relying only on historical uncertainty realizations and forecast data that is trained with an actor-critic-based reinforcement learning algorithm. Through safe exploration, the DRL-based approach guarantees a feasible commitment schedule without any operational constraint violations. We conduct computational experiments on the IEEE 39-bus and 118-bus test cases to demonstrate the effectiveness of the proposed solution strategy and improvement over existing approaches, including a deterministic approach with point forecasts and the stochastic dual dynamic integer programming method. The results show that the proposed approach enjoys superior performance in terms of computational efficiency and incurred operational costs, with significant reduction in penalty costs caused by insufficient net load supply than the deterministic approach.

Automated summary

This work proposes a deep reinforcement learning (DRL) based approach to address the uncertainties in renewable energy and fluctuating electricity demand, and provide reliable and cost-effective generation schedules of power systems. The approach relies on historical uncertainty realizations and forecast data, and guarantees a feasible commitment schedule without operational constraint violations through safe exploration. Computational experiments on IEEE 39-bus and 118-bus test cases show that the proposed approach outperforms existing methods in terms of computational efficiency and incurred operational costs.