Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback

TL;DR

This paper introduces a novel approach to solving optimal power flow problems in distribution networks by integrating state estimation feedback, enhancing observability and robustness against measurement noise and pseudo measurement variability.

Contribution

It proposes a new OPF solution method that incorporates state estimation feedback to handle partial observability in distribution networks.

Findings

The approach improves robustness to sensor noise.

It enhances system observability with partial measurements.

The method converges analytically under certain conditions.

Abstract

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.