Exact Mixed-Integer Programming Approach for Chance-Constrained Multi-Area Reserve Sizing

An exact algorithm is developed for the chance-constrained multi-area reserve sizing problem in presence of transmission network constraints. The can be cast as a two-stage stochastic mixed integer linear program using sample approximation. Due to complicated structure problem, existing methods attempt find feasible solution based on heuristics. Existing mixed-integer algorithms that applied directly only address small-scale problems are not practical. We have found minimal description projection our onto space first-stage variables. This enables us apply more general Integer Programming techniques mixing sets, arise problems. Combining advantages and strengthening reformulation from IP techniques, method tackle real-world specifically consider case study 10-zone Nordic with 100,000 scenarios where optimal approximately 5 minutes.