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• W2552532244 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." @default.
• W2552532244 created "2016-11-30" @default.
• W2552532244 creator A5051772147 @default.
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• W2552532244 date "2016-09-01" @default.
• W2552532244 modified "2023-10-16" @default.
• W2552532244 title "Distribution-agnostic stochastic optimal power flow for distribution grids" @default.
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• W2552532244 doi "https://doi.org/10.1109/naps.2016.7747962" @default.
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