Synchronization-Aware and Algorithm-Efficient Chance Constrained Optimal Power Flow

TL;DR

This paper introduces convex and chance-constrained formulations for optimal power flow that incorporate synchronization constraints and wind variability, improving the reliability and efficiency of power system dispatch decisions.

Contribution

It develops the first convex formulations for nonlinear phase-difference OPF and a tractable probabilistic chance-constrained OPF considering stability and overload risks.

Findings

Convex formulation of deterministic phase-difference OPF.

Probabilistic chance-constrained OPF for stability and overloads.

Enhanced computational tractability for complex power system constraints.

Abstract

One of the most common control decisions faced by power system operators is the question of how to dispatch generation to meet demand for power. This is a complex optimization problem that includes many nonlinear, non convex constraints as well as inherent uncertainties about future demand for power and available generation. In this paper we develop convex formulations to appropriately model crucial classes of nonlinearities and stochastic effects. We focus on solving a nonlinear optimal power flow (OPF) problem that includes loss of synchrony constraints and models wind-farm caused fluctuations. In particular, we develop (a) a convex formulation of the deterministic phase-difference nonlinear Optimum Power Flow (OPF) problem; and (b) a probabilistic chance constrained OPF for angular stability, thermal overloads and generation limits that is computationally tractable.