Here in this article, we propose a framework for running optimal control-estimation synthesis in distribution networks. Our approach combines a primal-dual gradient-based optimal power flow solver with a state estimation 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 certain online state measurements and noisy "pseudomeasurements." We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least-squares 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 pseudomeasurement variability and inherent sensor measurement noise.
Guo, Yi, et al. "Optimal Power Flow With State Estimation in the Loop for Distribution Networks." IEEE Systems Journal, vol. 17, no. 3, Mar. 2023. https://doi.org/10.1109/jsyst.2023.3253966
Guo, Yi, Zhou, Xinyang, Zhao, Changhong, Chen, Lijun, & Summers, Tyler Holt (2023). Optimal Power Flow With State Estimation in the Loop for Distribution Networks. IEEE Systems Journal, 17(3). https://doi.org/10.1109/jsyst.2023.3253966
Guo, Yi, Zhou, Xinyang, Zhao, Changhong, et al., "Optimal Power Flow With State Estimation in the Loop for Distribution Networks," IEEE Systems Journal 17, no. 3 (2023), https://doi.org/10.1109/jsyst.2023.3253966
@article{osti_1971902,
author = {Guo, Yi and Zhou, Xinyang and Zhao, Changhong and Chen, Lijun and Summers, Tyler Holt},
title = {Optimal Power Flow With State Estimation in the Loop for Distribution Networks},
annote = {Here in this article, we propose a framework for running optimal control-estimation synthesis in distribution networks. Our approach combines a primal-dual gradient-based optimal power flow solver with a state estimation 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 certain online state measurements and noisy "pseudomeasurements." We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least-squares 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 pseudomeasurement variability and inherent sensor measurement noise.},
doi = {10.1109/jsyst.2023.3253966},
url = {https://www.osti.gov/biblio/1971902},
journal = {IEEE Systems Journal},
issn = {ISSN 1932-8184},
number = {3},
volume = {17},
place = {United States},
publisher = {IEEE},
year = {2023},
month = {03}}
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; ETH Zürich Postdoctoral Fellowship; Hong Kong RGC Early Career; National Science Foundation (NSF)
2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton), 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)https://doi.org/10.1109/allerton.2015.7447032
2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton), 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)https://doi.org/10.1109/ALLERTON.2015.7447006
