Fast and Certified Bounding of Security-Constrained DCOPF via Interval Bound Propagation
For transmission system operators, this provides a fast and certified bounding method for SC DCOPF that scales to large systems, addressing a computational bottleneck.
The paper tackles the scalability problem of Security-Constrained DC Optimal Power Flow (SC DCOPF) for large power systems with many contingencies. Using Interval Bound Propagation (IBP), they achieve certified bounds with mean optimal solution gaps below 3.98% on small cases and scale to 8,316 bus systems with thousands of contingencies.
Security-Constrained DC Optimal Power Flow (SC DCOPF) is an important tool for transmission system operators, enabling economically efficient and physically secure dispatch decisions. Although CPU-based commercial solvers (e.g., Gurobi) can efficiently solve SC-DCOPF problems with a reasonable number of security constraints, their performance degrades rapidly as both system size and the number of contingencies grow into thousands. In this paper, we design a computational graph representation of the SC-DCOPF-based market-clearing problem, inspired by the third ARPA-E Grid Optimization Competition. Using a tool from the neural network verification community known as Interval Bound Propagation (IBP), we quickly compute bounds on the optimal objective across the full set of N-1 contingencies. Our results demonstrate that IBP can compute certified bounds with mean optimal solution gaps below 3.98% on small cases, and it can efficiently scale up to 8,316 bus systems with thousands of contingencies.