The carbon nanotube (CNT) is a promising reinforcement material for manufacturing
metal-or ceramic-based composites. However, CNTs are prone to interact with the matrix
in a reactive atmosphere that often alters the structure and properties of CNTs and
depresses their reinforcing effect. To overcome this problem, a protective silicon layer
has been deposited on multi-walled carbon nanotubes (MWNTs) using cycled
vacuum-feeding chemical vapour deposition by the in situ decomposition of gaseous
SiH4. The silicon coating is well covered and continuous with a cubic-phase structure. It
effectively improves the thermal stability of MWNTs by acting as a protective film,
which inhibits and delays the onset of oxidation. Thermogravimetric analysis
(TGA) reveals that the oxidation of Si-coated MWNTs occurs at a temperature of
676.3 °C, which
is 105.1 °C
higher than that of uncoated MWNTs, and the weight loss decreases with the increasing
thickness of silicon coating.
In order to improve the photovoltaic penetration of the power system, an optimal scheduling model of pumped storage system with large-scale photovoltaic based on carbon trading is proposed in this paper. Firstly, based on the analysis of low-carbon economy, the stepped carbon emission trading mechanism is introduced into the economic dispatch of power system. And in order to improve the calculating efficiency, an improved K-means clustering algorithm based on maximum and minimum distance criterion is proposed to cluster the photovoltaic power generation scenarios. Then the optimal scheduling model is established. Take the lowest comprehensive operation costs of the system as the objective function, and the operation economy and low carbon of the system are considered. The pumped storage is used as energy storage for peak load shaving in the generation system. At last, an improved IEEE RTS-96 system is taken to finish the case study, and the simulation results show that the model proposed in this paper is reasonable and effective.
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