Power Systems Data Fusion based on Belief Propagation
This addresses the need for unified system views in power grids with distributed resources, though it appears incremental as it builds on existing probabilistic methods.
The paper tackles the challenge of integrating diverse data sources in complex power grids by proposing a probabilistic graphical model framework for data fusion, which is demonstrated through semi-synthetic simulations on an IEEE 14-bus test case to quantify distributed solar energy.
The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires 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 semi-synthetic simulations of the standard IEEE 14-bus test case.