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