  Unit commitment (UC) is a fundamental problem in the day-ahead electricity
market, and it is critical to solve UC problems efficiently. Mathematical
optimization techniques like dynamic programming, Lagrangian relaxation, and
mixed-integer quadratic programming (MIQP) are commonly adopted for UC
problems. However, the calculation time of these methods increases at an
exponential rate with the amount of generators and energy resources, which is
still the main bottleneck in industry. Recent advances in artificial
intelligence have demonstrated the capability of reinforcement learning (RL) to
solve UC problems. Unfortunately, the existing research on solving UC problems
with RL suffers from the curse of dimensionality when the size of UC problems
grows. To deal with these problems, we propose an optimization method-assisted
ensemble deep reinforcement learning algorithm, where UC problems are
formulated as a Markov Decision Process (MDP) and solved by multi-step deep
Q-learning in an ensemble framework. The proposed algorithm establishes a
candidate action set by solving tailored optimization problems to ensure a
relatively high performance and the satisfaction of operational constraints.
Numerical studies on IEEE 118 and 300-bus systems show that our algorithm
outperforms the baseline RL algorithm and MIQP. Furthermore, the proposed
algorithm shows strong generalization capacity under unforeseen operational
conditions.
