A robust approach to chance constrained optimal power flow with renewable generation

• Published in: 2017 IEEE Power & Energy Society General Meeting p. 1
• Main Authors: Lubin, Miles, Dvorkin, Yury, Backhaus, Scott
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Computational modeling; Generators; Load flow; Probability distribution; Robustness; Uncertainty
• ISSN: 1944-9933
• DOI: 10.1109/PESGM.2017.8274507

Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a Robust Chance Constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. Deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.