Periodic Reporting for period 1 - MoCEGS (Molecules to Circular Energy Generation at Scale)
Berichtszeitraum: 2022-10-01 bis 2025-02-28
The MoCEGS project uses a data-driven approach in providing a more circular and reliable multi-level energy planning framework for EU. In today's integrated global energy landscape, the connection between domestic energy planning and international energy planning and trading is essential. This highlights the essentially of macro-level considerations within the region. Furthermore, meso-level integration of energy systems with national energy planning is a crucial task that determines the future of a nation's energy landscape in terms of economic and environmental sustainability. Exploitation of knowledge at meso-level allows for more rapid and efficient reconciliation actions in energy systems. The micro-level considerations of how chemical fundamental impact energy systems also connects the complexity between molecules and systems. The needs to connect insights of different modelling level (micro to macro) is crucial in understanding EU's energy infrastructure, and the potential that it brings. the MoCEGS project capitalize chemometrics and process network analysis in tackling this interlinked energy challenge.
The MoCEGS project has successfully performed research in various parts of energy circularity. On the macro-scale, the MoCEGS project has provided a large-scale data reconciliation algorithm, domain adaptation zero-shot learning in sequence (DAZLS) to allow high resolution energy data for energy planning. On the meso-scale, various retrofit algorithms were developed such as graph theory based retrofit algorithm, automation of P-graph framework, multi-period multi-objective graph theory planning, Shapley-Shubik based superstructure modelling etc. These fundamental development allows for advanced planning for the circular economy. Furthermore, a digital twin based on such fundamental understanding and the combination of graph theory development and high resolution energy data within EU27 (considering neighbouring countries was developed). This digital twin may assist policy making in high time resolution for energy and circularity in Europe. Furthermore, the MoCEGS project also explores some promising technologies at the micro-scale, this is a part of wider collaborative research efforts which includes co-pyrolysis technologies, waste oil technologies, single atom catalysts, etc.
In the MoCEGS project, large-scale data reconciliation for energy data was proposed via domain adaptation with zero-shot learning, which is a new development of methodology which was demonstrated for real-world substations. Furthermore, the project also promotes further research of graph theory (P-graph) for large-scale circularity planning. Via such development, a digital twin for Europe's energy planning is develop to optimize regional energy flow in high temporal resolution, and being updatable in short-time scale.