  Production cost minimization (PCM) simulation is commonly employed for
assessing the operational efficiency, economic viability, and reliability,
providing valuable insights for power system planning and operations. However,
solving a PCM problem is time-consuming, consisting of numerous binary
variables for simulation horizon extending over months and years. This hinders
rapid assessment of modern energy systems with diverse planning requirements.
Existing methods for accelerating PCM tend to sacrifice accuracy for speed. In
this paper, we propose a stable relay learning optimization (s-RLO) approach
within the Branch and Bound (B&B) algorithm. The proposed approach offers rapid
and stable performance, and ensures optimal solutions. The two-stage s-RLO
involves an imitation learning (IL) phase for accurate policy initialization
and a reinforcement learning (RL) phase for time-efficient fine-tuning. When
implemented on the popular SCIP solver, s-RLO returns the optimal solution up
to 2 times faster than the default relpscost rule and 1.4 times faster than IL,
or exhibits a smaller gap at the predefined time limit. The proposed approach
shows stable performance, reducing fluctuations by approximately 50% compared
with IL. The efficacy of the proposed s-RLO approach is supported by numerical
results.
