(445h) Global Renewable Energy and Negative Emission Potential Observatory - in a Knowledge Graph Context

Under the common vision of humanity to reduce carbon emissions, renewable energy and negative emission technologies are promising technologies in the blueprint of low-carbon transition of energy systems. However, hybrid renewable energy systems combined with negative emission technologies, which have a synthetic effect and promising potential in building low-carbon energy systems, are still under-explored. In our recent study, we found that sustainable energy systems combining renewable energy and negative emission technologies offer considerable environmental benefits compared to conventional energy systems and are economically competitive in areas with abundant renewable resources. The proposed decision support tools can be readily applied by end-users, operators, and governments, and the findings from the regional-scale analysis will help countries refine their emissions reduction targets, agree on global emissions reduction commitments, and design sustainable energy systems that benefit the world as a whole.

However, practically, the selection, design, and placement of appropriate renewable energy and negative emission technologies within a given place is a complex, multi-domain, and multi-objective problem that necessitates an efficient knowledge management framework. One possible approach to utilize real-time and multi-domain data for this is The World Avatar (TWA) project. TWA is a dynamic knowledge graph (dKG) based on the Semantic Web and its associated technologies, with intelligent agents operating on it. The agents act autonomously to constantly update and extend TWA, thus making it evolves in time. The purpose of this paper is to illustrate how digital twinning (of the energy system, renewable and negative emission resources, and the technology and market conditions) in the context of a dKG can support and augment our previous work to develop a decision-support system that determining the optimal placement of technologies that is economic and carbon-reducing for the given context. The system consists of a bi-objective robust optimization model – to minimize both the cost of deploying the technologies and maximize the carbon reduction potential that the technologies would bring to the area. A case study considering the climate mitigation of renewable energy and negative emission technologies in China and the UK is introduced to demonstrate the application of this approach.