1. Real-time risk analysis with optimization proxies.
- Author
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Chen, Wenbo, Tanneau, Mathieu, and Van Hentenryck, Pascal
- Subjects
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POWER resources , *RISK assessment , *SCALABILITY , *FORECASTING - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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