Financially inspired methodologies for risk management in electricity markets
Abstract
We present a framework to determine the risk of assets in electricity systems. We train machine learning and estimation models with optimal outputs from simulating Security Constrained Economic Dispatch (SCED) and Security Constraint Unit Commitment (SCUC). The operational score we present provides a quantitative measure of the likelihood that a generating unit will cause imbalances in the grid due to variability and uncertainty. We illustrate our methodology using real world data from the New York Independent System Operator (NYISO).
Fields of Study & Subjects
Electric Power System Optimization
Risk and Portfolio Optimization
Power System Reliability and Maintenance
Publication details
- Journal / source
- Renewable Energy
- Publisher
- Elsevier BV
- Volume
- 260
- Pages
- 125016–125016
- Publication date
- 3 January 2026
- Language
- EN