Stochastic Co-Optimization of Midterm and Short-Term Maintenance Outage Scheduling Considering Covariates in Power Systems

This paper proposes an integrated framework based on covariates, which coordinates short-term generation and transmission maintenance scheduling with midterm maintenance decisions by considering the effects of short-term security-constrained unit commitment (SCUC). A recursive sampling method is introduced in the proposed Monte Carlo-based framework for generating scenarios, in which the effects of component aging and covariates on the outage process are quantified by the proportional hazard model (PHM). For each sampled scenario, an iterative dynamic scenario updating approach is introduced to consider interactions among covariate conditions, random component outages, and maintenance outage scheduling. The co-optimization problem is decoupled into three separate optimization subproblems by Lagrangian relaxation (LR), which include generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC problems. Each scenario is dynamically updated based on the optimal maintenance outage and SCUC solutions, and maintenance and SCUC solutions are re-optimized using the updated scenario. The iterative procedure stops when neither the optimal schedule nor the dynamic scenario changes any further. The overall convergence of the proposed Monte Carlo-based framework is checked by the coefficient of variation (CV) of costs over multiple scenarios. Case studies on the 6-bus system and the IEEE 118-bus system are used to exhibit the effectiveness of proposed framework.