Descriptive, diagnostic, predictive, and preventive data analytics applications developed on a big-data platform for distribution system operators. An Automated Distribution Grid Operation Assistance Tool using Data-driven Solutions on a Big-data Platform.
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Power distribution systems have been changing drastically with the introduction of distributed and intermittent energy production resources, such as private solar panels, as well as the emergence of new energy consumers, such as electric mobility charging stations. Thus, Distribution System Operators (DSOs) need to adapt the grid operation process to integrate these novel resources into the medium and low grids without jeopardizing grid security and the quality of service.

Additionally, installed grid measurement devices, though they provide a huge amount of data, give only partial monitoring of low voltage grids. Consequently, interpretation of these data is hard, and decision even harder.

The Innovative part of this project is to valorize available data to DSOs through a data-driven and automated process. It aims at providing to the DSOs all the necessary information and analysis for a secure and optimal operation of the distribution grids. This is achieved by developing a big-data platform with descriptive, diagnostic, predictive, and preventive data analytics applications dedicated to distribution grid operation.

To realize this joint academic and industrial project, various competencies from different engineering fields, including computer science, machine learning, embedded software, and power systems are brought together. DEPsys, as the main implementation partner, will commercialize the results of the project.


Figure – Grid data digger system representation.



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