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Distributed Sequential Optimal Power Flow under Uncertainty in Power Distribution Systems: A Data-driven Approach

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posted on 2022-10-13, 13:13 authored by Georgios TsaousoglouGeorgios Tsaousoglou, Petros Ellinas, Juan Giraldo, Emmanouel Varvarigos

Modern distribution systems with high penetration of distributed energy resources face multiple sources of uncertainty. This transforms the traditional Optimal Power Flow problem into a problem of sequential decision-making under uncertainty. In this framework, the solution concept takes the form of a \textit{policy}, i.e., a method of making dispatch decisions when presented with a real-time system state. Reasoning over the future uncertainty realization and the optimal online dispatch decisions is especially challenging when the number of resources increases and only a small dataset is available for the system's random variables. In this paper, we present a data-driven distributed policy for making dispatch decisions online and under uncertainty. The proposed policy is guaranteed to satisfy the system's constraints while experimentally shown to achieve a performance close to the optimal-in-hindsight solution, significantly outperforming state-of-the-art policies based on stochastic programming and plain machine-learning approaches.


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Technical University of Denmark

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  • Denmark