Optimal Power Flow with State Estimation In the Loop for Distribution Networks

Yi Guo; Xinyang Zhou; Changhong Zhao; Lijun Chen; Tyler H. Summers

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arXiv · 2005.00345

Optimal Power Flow with State Estimation In the Loop for Distribution Networks

Yi Guo·Xinyang Zhou·Changhong Zhao·Lijun Chen·Tyler H. Summers

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Abstract

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.

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BibTeX RIS

Yi Guo, Xinyang Zhou, Changhong Zhao, Lijun Chen, Tyler H. Summers. 2020-04-29. Optimal Power Flow with State Estimation In the Loop for Distribution Networks. https://arxiv.org/abs/2005.00345

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