Second-order cone programming based methods for two variants of optimal power flow

Official URL: https://risc01.sabanciuniv.edu/record=b2486367 _(Table of contents)

Optimal Power Flow (OPF) is a fundamental optimization problem in power system operations. In this thesis, we focus on two variants of the OPF problem: Reactive Optimal Power Flow (ROPF) and Multi-Period Optimal Power Flow (MOPF). In Chapter 2, we provide an overview of the classical OPF formulations. In Chapter 3, we present an alternative mixed-integer non-linear programming formulation of the ROPF problem. We utilize a mixed-integer second-order cone programming (MISOCP) based approach to find globally optimal solutions of the proposed ROPF problem formulation. We strengthen the MISOCP relaxation via the addition of convex envelopes and cutting planes. Computational experiments on challenging test cases show that the MISOCP-based approach yields promising results with small optimality gaps compared to a semidefinite programming based approach from the literature. In Chapter 4, we focus on the MOPF problem with electric vehicles (EV) under emission considerations. Our model integrates three different real data sets: household electricity consumption, marginal emission factors, and EV driving profiles. We present a systematic solution approach based on SOCP to find globally optimal solutions. Our computational experiments on instances with up to 2000 buses demonstrate that our solution approach leads to globally optimal solutions with very small optimality gaps, in addition to significant emission savings and reductions in cost with the coordination of EV charging