Deterministic dual dynamic programming applied to optimal power system restoration

In this paper, we apply a single-scenario version of the Stochastic Dual Dynamic Integer Programming (SDDiP) Algorithm to solve the optimal power system restoration (OPSR) problem. This deterministic variant of SDDiP is implemented as a first step towards solving a version of the OPSR problem that incorporates multi-stage uncertainty. We test the impact of adding various strengthening cuts and grouping time periods on the solution time and number of iterations until convergence for the deterministic variant of the algorithm. The algorithm is tested on IEEE 39- and 118-bus systems, with promising results, especially when bundling time periods into single-stage subproblems. We conclude the paper by discussing these results and outlining future extensions of the current work.