Abstract
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.
Highlights
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
Impact Statement The methodology developed in this paper allows simulation of complex systems that consist of many interdependent parts, such as an industrial park, as well as variations thereof, referred to as parallel worlds
A need to allow for parallel existence of relevant entities within the knowledge graph forming a semantic representation of a “parallel world.”
Summary
Andreas Eibeck1, Arkadiusz Chadzynski1, Mei Qi Lim1, Kevin Aditya2, Laura Ong1, Aravind Devanand3, Gourab Karmakar1, Sebastian Mosbach4 , Raymond Lau2, Iftekhar A. Karimi3, Eddy Y. S. Foo1,5 and Markus Kraft1,2,4 Cambridge Centre for Advanced Research and Education in Singapore (CARES), Singapore, Singapore School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
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