2013/06/12 by Russell Bent, Daniel Bienstock, Bent, Russell +3
Decision Sciences · Engineering · #Electric Power System Optimization #FOS: Electrical engineering #FOS: Mathematics #FOS: Physical sciences #Optimal Power Flow Distribution #Optimization and Control (math.OC) #Physics and Society (physics.soc-ph) #Power System Optimization and Stability #Probabilistic and Robust Engineering Design #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1306.2972
openalex publication_date 2013/06/12 · openalex created_date 2022/10/02 · openalex updated_date 2026/08/01
One of the most common control decisions faced by power system operators is\nthe question of how to dispatch generation to meet demand for power. This is a\ncomplex optimization problem that includes many nonlinear, non convex\nconstraints as well as inherent uncertainties about future demand for power and\navailable generation. In this paper we develop convex formulations to\nappropriately model crucial classes of nonlinearities and stochastic effects.\nWe focus on solving a nonlinear optimal power flow (OPF) problem that includes\nloss of synchrony constraints and models wind-farm caused fluctuations. In\nparticular, we develop (a) a convex formulation of the deterministic\nphase-difference nonlinear Optimum Power Flow (OPF) problem; and (b) a\nprobabilistic chance constrained OPF for angular stability, thermal overloads\nand generation limits that is computationally tractable.\n