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For high penetration of distributed energy resources (DER) like solar, electric power grid operators and planners must be able to incorporate large datasets from photovoltaic (PV) sources, local and line mounted precision instruments, customer load data from smart meters, and EV charging data into their analyses. This project will design and implement a platform for the visualization and analytics of distribution systems with high penetrations of distributed energy resources (VADER). VADER is a unified data analytics platform that will enable the integration of massive and varied data streams for real-time monitoring with analytics, visualization, and control of DERs in distribution networks.
The project team will build a set of tools using machine learning to integrate and model large numbers of disparate sensor sources that will integrate distribution system planning and control. They will also verify these tools using data from the solar industry and utility partners. The tools will be validated in a pilot project that combines hardware-in-the-loop simulations with real-time data from hardware deployed in the field.
Future distribution systems will produce large amounts of unstructured data. The VADER system will make this data to be available to distribution network operators and planners in a consistent and user-friendly manner. Using advanced statistical and power systems analysis techniques, VADER will provide more accurate modeling of the new network components. VADER will enable researchers and software developers to develop more accurate state estimation techniques. Integration of the new data-driven models of load and distributed energy resources will enhance the capability and accuracy of new power flow algorithms for analyzing sensitivities of key network variables with different DER penetration levels.