Constraint-driven deep learning for N-k security constrained optimal power flow

The transition to green energy is reshaping the energy landscape, marked by increased integration of renewables, distributed resources, and the electrification of other energy sectors. These changes challenge grid security, particularly regarding the N-1 security criterion, a crucial factor in preventing blackouts. This necessitates studying the security constrained optimal power flow (SCOPF) problem with multiple line outages (N-k). Conventional methods exhibit poor scalability as k increases. This paper proposes a constraint-driven machine learning (ML) approach using line outage distribution factors (LODF). The method shows promise in its ability to scale effectively to N-k contingencies. Key contributions include a deterministic approach for N-k security and a probabilistic security assessment. Case studies on the IEEE 39-bus and the IEEE-118 bus systems show the approach’s effectiveness in identifying violating post-contingency cases, with up to 173x speedups and close to optimal dispatch costs.