Abstract
Reliability is an overriding concern for power systems that involve different types of uncertainty including contingencies and intermittent renewables. Contingency-constrained unit commitment (CCUC) satisfying the “N – 1 rule” is extremely complex, and the complexity is now compounded by the drastic increase in renewables. This paper develops a novel interval optimization approach for CCUC with N – 1 transmission contingencies and renewable generation. A large number of transmission contingencies are innovatively described by treating corresponding generation shift factors (GSFs) as uncertain parameters varying within intervals. To ensure solution robustness, bounds of GSFs and renewables in different types of constraints are captured based on interval optimization. The resulting model is a mixed-integer linear programming problem. To alleviate its conservativeness and to further reduce the problem size, ranges of GSFs are shrunk through identifying and removing redundant transmission constraints. To solve large-scale problems, Surrogate Lagrangian Relaxation and branch-and-cut (B&C) are used to simultaneously exploit separability and linearity. Numerical results demonstrate that the new approach is effective in terms of computational efficiency, solution robustness, and simulation costs.
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