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Title: Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids

Abstract

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flow equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.

Authors:
; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), NREL Laboratory Directed Research and Development (LDRD)
OSTI Identifier:
1342088
Report Number(s):
NREL/CP-5D00-67824
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2016 North American Power Symposium (NAPS), 18-20 September 2016, Denver, Colorado
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; distribution systems; optimal power flow; chance constraints; renewable integration; voltage regulation

Citation Formats

Baker, Kyri, Dall'Anese, Emiliano, and Summers, Tyler. Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids. United States: N. p., 2016. Web. doi:10.1109/NAPS.2016.7747962.
Baker, Kyri, Dall'Anese, Emiliano, & Summers, Tyler. Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids. United States. https://doi.org/10.1109/NAPS.2016.7747962
Baker, Kyri, Dall'Anese, Emiliano, and Summers, Tyler. 2016. "Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids". United States. https://doi.org/10.1109/NAPS.2016.7747962.
@article{osti_1342088,
title = {Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids},
author = {Baker, Kyri and Dall'Anese, Emiliano and Summers, Tyler},
abstractNote = {This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flow equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.},
doi = {10.1109/NAPS.2016.7747962},
url = {https://www.osti.gov/biblio/1342088}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Nov 21 00:00:00 EST 2016},
month = {Mon Nov 21 00:00:00 EST 2016}
}

Conference:
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