Projektdetaljer
Beskrivelse
Abstract:
The global green transition hinges on developing affordable and efficient sustainable technologies. Power-to-X (P2X) systems, which convert renewable energy into green fuels, hold immense potential for decarbonizing sectors like industries, aviation and shipping. However, current P2X technology remains in its early stages, limited by high costs and lack of accessibility. Scientists are focusing on enhancing the efficiency of P2X systems by discovering new, more active materials that can reduce energy losses and improve overall performance. However, most research on such materials is carried out in small-scale, controlled environments, which limits the understanding of their impact on the entire P2X system, such as how they influence the behaviour of large power systems. To address these limitations, computational modelling, offers a promising solution. The current project aims to develop a data driven chemistry-aware digital twin for power systems applying P2X capacity. A Digital Twin is a virtual representation of a physical system, which mirrors its performance and behaviour in real-time. By leveraging machine learning and real-time data from P2X facilities, these digital twins can simulate the overall system and assess crucial factors such as temperature, degradation, and power conversion. This comprehensive modelling approach allows for better control analysis, system sizing, and energy management. It also provides insights into how design variables and manufacturing processes affect system performance, helping optimize both materials and operational strategies. Ultimately, the deployment of digital twin has the potential to significantly improve P2X system efficiency, affordability, and scalability, accelerating the transition to sustainable energy solutions worldwide.
Funding:
The global green transition hinges on developing affordable and efficient sustainable technologies. Power-to-X (P2X) systems, which convert renewable energy into green fuels, hold immense potential for decarbonizing sectors like industries, aviation and shipping. However, current P2X technology remains in its early stages, limited by high costs and lack of accessibility. Scientists are focusing on enhancing the efficiency of P2X systems by discovering new, more active materials that can reduce energy losses and improve overall performance. However, most research on such materials is carried out in small-scale, controlled environments, which limits the understanding of their impact on the entire P2X system, such as how they influence the behaviour of large power systems. To address these limitations, computational modelling, offers a promising solution. The current project aims to develop a data driven chemistry-aware digital twin for power systems applying P2X capacity. A Digital Twin is a virtual representation of a physical system, which mirrors its performance and behaviour in real-time. By leveraging machine learning and real-time data from P2X facilities, these digital twins can simulate the overall system and assess crucial factors such as temperature, degradation, and power conversion. This comprehensive modelling approach allows for better control analysis, system sizing, and energy management. It also provides insights into how design variables and manufacturing processes affect system performance, helping optimize both materials and operational strategies. Ultimately, the deployment of digital twin has the potential to significantly improve P2X system efficiency, affordability, and scalability, accelerating the transition to sustainable energy solutions worldwide.
Funding:
The PhD project is financially supported by the CAPeX ‐ Pionercenter for Accelerating P2X Materials Discovery project running from 2023 to 2036.
| Status | Igangværende |
|---|---|
| Effektiv start/slut dato | 01/07/2024 → 30/06/2027 |
Samarbejdspartnere
- Technical University of Denmark
- University of Copenhagen
- Aarhus University
- University of Southern Denmark
- University of Toronto
- Stanford University
- University of Twente
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