Dr. Stefano Mazzoni - Research Fellow
Born in Rome in 1985, Stefano Mazzoni begins his studies of Mechanical Engineer at Roma Tre University in 2004. He graduates 110/110 cum laude in 2010, working on the Steam Section of a CHP power plant. Since 2010 he starts the research activities in the energy conversion fields. Since 2011 to 2013 Stefano performs PhD working on the European Project H2-IGCC developing component models for 500MW IGCC Power Island. After finishing the PhD, he has been employed as Research Fellow at Roma Tre University since 2014 to June 2016, working on techno/economic optimization of Concentrated Solar Power Plants under the OMSoP European Project. At the moment he’s Research Fellow at NTU in the Energy Research Institute at NTU (ERI@N).
Energy conversion systems
Modelling and simulation
Optimal dispatch and master planning
Optimal Planning Tool for Smart Multi-Energy System (SMES) in Energy Research Institute @NTU
The proposed Smart Multi-Energy Systems (SMES) project hence aims to develop and demonstrate an intelligent multi-energy management & information system at a commercial industrial site through seamless integration of energy generation, storage and demand management facilities across the electric, thermal, and gas networks. This system will also have an enhanced information and communication technology (ICT) platform for supply/demand management and real-time energy market interactions.Formulation of this project resulted from two sets of data-points: 1) Theoretical studies and simulations on multi-energy systems have shown the potential of greater than 30% cost reduction1. 2) JTC’s effort in addressing integrated estate management at Biopolis and Fusionopolis resulted in 15-18% cost savings. This effort at One-North did not extend beyond building automation and air-conditioning systems. Thus a full systems-integration and optimization approach that includes a montage of energy sources, load management, and market interaction clearly holds the potential to exceed both the One-North effort and the theoretical predictions of at least 30% cost reduction.
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