Real-time risk analysis with optimization proxies

The increasing penetration of renewable generation and distributed energy resources requires new operating practices for power systems, wherein risk is explicitly quantified and managed. However, traditional risk-assessment frameworks are not fast enough for real-time operations, because they require numerous simulations, each of which requires solving multiple economic dispatch problems sequentially. The paper addresses this computational challenge by proposing proxy-based risk assessment, wherein optimization proxies are trained to learn the input-to-output mapping of an economic dispatch optimization solver. Once trained, the proxies make predictions in milliseconds, thereby enabling real-time risk assessment. The paper leverages self-supervised learning and end-to-end-feasible architecture to achieve high-quality sequential predictions. Numerical experiments on large systems demonstrate the scalability and accuracy of the proposed approach. •Rising renewables and distributed resources need new power system risk assessments.•A new risk assessment framework utilizing fast optimization proxies is proposed.•The framework uses an end-to-end feasible proxy for scalable and accurate training.•Proposed risk assessment is highly accurate and 30x faster than optimization methods.