We propose a framework for integrating optimal power flow (OPF) with state estimation (SE) in the loop for distribution networks. Our approach combines a primal-dual gradient-based OPF solver with a SE feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on a few appropriate online state measurements and noisy "pseudo-measurements". We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least squares (WLS) estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudo measurement variability and inherent sensor measurement noise.
@article{arxiv.2005.00345,
title = {Optimal Power Flow with State Estimation In the Loop for Distribution Networks},
author = {Yi Guo and Xinyang Zhou and Changhong Zhao and Lijun Chen and Tyler H. Summers},
journal= {arXiv preprint arXiv:2005.00345},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:1909.12763