skip to main content

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

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.
; ;
Publication Date:
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Presented at the 2016 North American Power Symposium (NAPS), 18-20 September 2016, Denver, Colorado
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
NREL Laboratory Directed Research and Development (LDRD)
Country of Publication:
United States
24 POWER TRANSMISSION AND DISTRIBUTION distribution systems; optimal power flow; chance constraints; renewable integration; voltage regulation