Related Projects

Probabilistic and Risk-based Methods in Operations

This is focused on identifying where significant uncertainty puts operations at risk and developing and test methodologies to manage that risk-based. It will continually build on a roadmap previously developed. The work includes detailing the required data and conditional information such as operational outage rates and how to apply them in the methodologies. Some methodologies and processes are and will be developed in separate projects but monitored and communicated through this project as well.

Optimal Outage Scheduling

This is focused on the need to improve the coordination of scheduled outages to minimize reliability issues and costly repositioning by implementing more proactive rather than reactive processes. It looks at optimization and AI methods to improve the scheduling of outages to be more optimal and improved coordination between asset owners and schedulers while performing efficient, automated studies. Current first come first serve methods will be compared to optimal methods in case studies and prototype software.

Case Selection and Building

This is focused on developing processes to identify scenarios that are feasible and pose the most risk to the system that need to be studied in operations planning – including outage scheduling. To study these scenarios efficiently, it also focuses on developing automated processes to build the cases to perform the studies and allows engineers to spend more time on studying results.

Operational Performance Analysis

Currently there is rarely a study of inadequacies in operational cases unless there is a postmortem on an event. This workstream focuses on developing metrics and systematic methods to evaluate performance of operational cases to predict and encompass the risk realized in real-time operations. Metrics for evaluation are being developed and how to calculate them and then use them to drive changes to improve the cases are explored.

Operations Models

This is focused on the operational needs to improve device models (e.g., load models) used in operations studies to improve accuracy. Current models used in operations environment are evaluated and potential improvements identified with new data and processes to implement them. Then case studies are performed to demonstrate improvements.