A Robust Approach to Chance Constrained Optimal Power Flow with Renewable Generation

TL;DR

This paper introduces a robust chance constrained optimal power flow method that accounts for uncertainty in renewable generation distributions, improving reliability and scalability in large power systems.

Contribution

It develops a robust chance constrained OPF formulation that handles distributional uncertainty and proposes a scalable cutting-plane algorithm for large systems.

Findings

RCC OPF reduces transmission overloads compared to deterministic and chance constrained methods.

The approach scales efficiently to large power networks with thousands of buses.

RCC OPF achieves cost-effective and reliable power dispatch under uncertain renewable generation.

Abstract

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.