Repository logo
 

Dynamic knowledge graph approach for modelling the decarbonisation of power systems

Accepted version
Peer-reviewed

Change log

Authors

Xie, Wanni 
Farazi, Feroz 
Atherton, John 
Bai, Jiaru 
Mosbach, Sebastian 

Abstract

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.

Description

Keywords

Journal Title

Energy and AI

Conference Name

Journal ISSN

2666-5468
2666-5468

Volume Title

Publisher

Elsevier

Publisher DOI

Publisher URL

Sponsorship
This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Part of this work was also supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1. The authors would further like to thank and acknowledge the financial support provided by the Cambridge Trust. Markus Kraft gratefully acknowledges the support of the Alexander von Humboldt Foundation.