A computational model of mid-term outage scheduling for long-term system studies

Abstract

Abstract-This paper presents a computational model of the mid-term outage scheduling process of electric power transmission assets, to be used in long-term studies such as maintenance policy assessments and system development studies, while accounting for the impact of outage schedules on short-term system operation. We propose a greedy algorithm that schedules the outages one by one according to their impact on system operation estimated via Monte-Carlo simulations. The model is implemented in JULIA and applied to the IEEE RTS-96.

Chance-Constrained Outage Scheduling Using a Machine Learning Proxy

“…168-hour-long scenario minimized UC scheduling costs under changing network topology. In more recent work [9], a greedy outage scheduling algorithm used Monte-Carlo simulations to assess the impact of outages on system operation. By mimicking experts heuristics for mid-term outage-scheduling, it enables long-term assessment of system development and maintenance policies.…”