DC Optimal Power Flow With Joint Chance Constraints

Managing uncertainty and variability in power injections has become a major concern for system operators due to increasing levels of fluctuating renewable energy connected the grid. This work addresses this via joint chance-constrained formulation DC optimal flow (OPF) problem, which satisfies all constraints jointly with pre-determined probability. The few existing approaches solving OPF problems are typically either computationally intractable large-scale or give overly conservative solutions that satisfy far more often than required, resulting excessively costly operation. paper proposes an algorithm by adopting S$\ell _1$QP-type trust-region algorithm. uses sample-based approach avoids making strong assumptions on distribution uncertainties, scales favorably large problems, can be tuned obtain less results. We illustrate performance our method using several IEEE test cases. results demonstrate proposed algorithm's advantages computational times limited conservativeness relative other algorithms.