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Stochastic Dual Dynamic Programming for Optimal Power Flow Problems under Uncertainty

Adriana Kiszka (adriana.kiszka***at***tum.de)
David Wozabal (david.wozabal***at***tum.de)

Abstract: We propose the first computationally tractable framework to solve multi-stage stochastic optimal power flow (OPF) problems in alternating current (AC) power systems. To this end, we use recent results on dual convex semi-definite programming (SDP) relaxations of OPF problems in order to adapt the stochastic dual dynamic programming (SDDP) algorithm for problems with a Markovian structure, employing scenario lattices to discretize the underlying randomness. We show that the usual SDDP lower bound remains valid and that the algorithm converges to a globally optimal solution of the stochastic AC-OPF problem as long as the SDP relaxations are tight. To test the practical viability of our approach, we set up an extensive case study of a storage sitting, sizing, and operations problem under uncertainty about demand and renewable generation using the IEEE RTS-GMLC network. We show that the convex SDP relaxation of the stochastic problem is usually tight and discuss ways to obtain near-optimal physically feasible solutions when this is not the case. Using these results, we demonstrate that the algorithm finds a physically feasible policy with a small optimality gap to the original non-convex problem and yields a significant added value of 27\% over a rolling deterministic planning policy.

Keywords: Optimal power flow, semi-definite programming, stochastic dual dynamic programming, electricity storage

Category 1: Stochastic Programming

Category 2: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Category 3: Applications -- Science and Engineering (Smart Grids )


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Entry Submitted: 11/17/2021
Entry Accepted: 11/17/2021
Entry Last Modified: 11/24/2021

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