Power Systems Data Fusion based on Belief Propagation

Abstract--The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices requires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on probabilistic graphical models, capable of combining heterogeneous data sources with classical state estimation nodes and other customised computational nodes, is proposed. The framework allows flexible extension of the notion of grid state beyond the view of flows and injection in bus-branch models, and an efficient, naturally distributed inference algorithm can be derived. An application of the data fusion model to the quantification of distributed solar energy is proposed through numerical examples based on semisynthetic simulations of the standard IEEE 14-bus test case. The electrical grid is going through a significant transformation towards a more distributed architecture for demand-supply balancing, due to a higher penetration of distributed sources of renewable generation, storage and demand flexibility. Internet-of-Things (IOT) technologies are an integral part of the transformation, with energy utilities availing of more and more highly-distributed intelligent devices which produce an ever-increasing amount of heterogeneous data significantly different in terms of format, resolution and quality [1], [2].