Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

TL;DR

This paper addresses the challenge of optimizing power flow in energy networks with uncertain, non-Gaussian renewable sources by applying an inner-outer approximation method to chance constrained problems, demonstrated on wind power data.

Contribution

It introduces an innovative application of inner-outer approximation to solve chance constrained OPF with non-Gaussian uncertainties, enhancing solution feasibility and accuracy.

Findings

Effective handling of non-Gaussian uncertainties in OPF

Demonstrated approach improves solution feasibility

Applicable to renewable energy integration scenarios

Abstract

In recent years, there has been a huge trend to penetrate renewable energy sources into energy networks. However, these sources introduce uncertain power generation depending on environmental conditions. Therefore, finding 'optimal' and 'feasible' operation strategies is still a big challenge for network operators and thus, an appropriate optimization approach is of utmost importance. In this paper, we formulate the optimal power flow (OPF) with uncertainties as a chance constrained optimization problem. Since uncertainties in the network are usually 'non-Gaussian' distributed random variables, the chance constraints cannot be directly converted to deterministic constraints. Therefore, in this paper we use the recently-developed approach of inner-outer approximation to approximately solve the chance constrained OPF. The effectiveness of the approach is shown using DC OPF incorporating uncertain non-Gaussian distributed wind power.