Graphical Models and Belief Propagation-hierarchy for Optimal Physics-Constrained Network Flows
For researchers in network optimization, this work introduces a physics-constrained graphical model framework, but it is a review of first results rather than a breakthrough.
The paper reviews the application of Graphical Models and Belief Propagation to optimize network flows with physics constraints, demonstrating the approach on power and gas systems at continental and district scales.
In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems.