Technical Report 317, c4e-Preprint Series, Cambridge

Authors: Wanni Xie, Feroz Farazi, John Atherton, Jiaru Bai, Sebastian Mosbach, Jethro Akroyd, and Markus Kraft*

Reference: Technical Report 317, c4e-Preprint Series, Cambridge, 2024

Associated Themes:
 

• Dynamic knowledge graph approach for modelling power systems.
• Developed domain ontologies to instantiate data relevant to power systems.
• Designed computational agents for automated data processing and simulation.
• Case study investigates decarbonisation trajectories for UK power system.

This paper presents a dynamic knowledge graph approach that offers a reusable, interoperable, and extensible framework for modelling power systems. Domain ontologies have been developed to support a linked data representation of infrastructure data, socio-demographic data, areal attributes like demand, and models describing power systems. The knowledge graph links the data with a hierarchical representation of administrative regions, supporting geospatial queries to retrieve information about the population within the vicinity of a power plant, the number of power plants, total generation capacity, and demand within specific areas. Computational agents were developed to operate on the knowledge graph. The agents performed tasks including data uploading, updating, retrieval, processing, model construction and scenario analysis. A derived information framework was used to track the provenance of information calculated by agents involved in each scenario. The knowledge graph was populated with data describing the UK power system. Two alternative models of the transmission grid with different levels of structural resolution were instantiated, providing the foundation for the power system simulation and optimisation tasks performed by the agents. The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

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