A Parallel World Framework for scenario analysis in knowledge graphs

Authors: Andreas Eibeck, Arkadiusz Chadzynski, Mei Qi Lim, Leonardus Kevin Aditya, Laura Ong, Aravind Devanand, Gourab Karmakar, Sebastian Mosbach, Raymond Lau, Iftekhar A. Karimi, Eddy Y.S. Foo, and Markus Kraft*

Reference: Data-Centric Engineering 1, 6, (2020)

  • Architecture of the J-Park Simulator and its Knowledge Graph
  • Parallel World Framework based on Semantic Technologies
  • Scenario-based Power Grid modelling and optimisation for carbon tax within the J-Park Simulator using the Parallel World Framework

Graphical abstract This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture.

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