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The integration of stochastic and intermittent distributed PVs brings great challenges for power system operation. Precise net load forecasting performs a critical factor in dependable operation and dispensing. An approach to probabilistic net load prediction is introduced for sparse variant Gaussian process based algorithms. The forecasting of the net load is transferred to a regression problem and solved by the sparse variational Gaussian process (SVPG) method to provide uncertainty quantification results. The proposed method can capture the uncertainties caused by the customer and PVs and provide effective inductive reasoning. The results obtained using real-world data show that the proposed method outperforms other best-of-breed algorithms. [ABSTRACT FROM AUTHOR]

With the growing concern for the environment, sustainable development centred on a low-carbon economy has become a unifying pursuit for the energy industry. Integrated energy systems (IES) that combine multiple energy sources such as electricity, heat and gas are essential to facilitate the consumption of renewable energy and the reduction of carbon emission. In this paper, gas turbine (GT), carbon capture and storage (CCS) and power-to-gas (P2G) device are introduced to construct a new carbon capture coupling device model, GT-CCS-P2G (GCCP), which is applied to the integrated electrical and gas system (IEGS). Multi-agent soft actor critic (MASAC) applies historical trajectory representations, parameter spatial techniques and deep densification frameworks to reinforcement learning for reducing the detrimental effects of time-series data on the decisional procedure. The energy scheduling problem of IEGS is redefined as a Markov game, which is addressed by adopting a low carbon economic control framework based on MASAC with minimum operating cost and minimum carbon emission as the optimization objectives. To validate the rationality and effectiveness of the proposed low-carbon economy scheduling model of IEGS based on MASAC, this paper simulates and analyses in integrated PJM-5 node system and seven nodes natural gas system. [ABSTRACT FROM AUTHOR]

An efficient 3D porous bulk electrocatalyst is a feasible way to improve the conductivity. Herein, the preparation of a novel self-supporting 3D porous bulk FeCoNiMgB catalyst exhibits excellent electrocatalytic activity and stability properties towards OER. The self-supporting 3D porous framework structure catalyst possesses multiple advantages including large surface area, excellent conductivity, leading to the fast mass and charge transfers, low electrochemical impedance and fast water splitting reaction kinetics. As a result, the 3D porous bulk FeCoNiMgB catalyst requires substantially lower overpotential (234 mV) with a small Tafel slope (36.1 mV dec−1) as compared to pure FeCoNiMgB powders and RuO 2 electrodes, as well as stable catalytic properties for over 72 h with no obvious evidence of degradation. Comparative structural characterization and DFT calculations reveal that the needle-shaped nanowires with Fe rich are formed from many open pores during the OER process, which helps to reduce the rate determining step energy barrier (O∗→OOH∗) for OER, and thus has excellent long-term electrochemical stability for continuous oxygen production under alkaline conditions. This work provides a new feasible and promising protocol to realize a novel self-supporting 3D porous bulk high entropy alloy catalyst as an inexpensive catalyst for efficient water splitting. • The excellent catalytic ability was acquired in 3D porous FeCoNiMgB electrocatalys. • The OER performance of 3D/FeCoNiMgB system was confirmed via theoretical calculation. • The ∗O to ∗OOH step played a dominant role in 3D/FeCoNiMgB model. • The formed needle-shaped nanowires reduced the adsorption energy and energy barrier. [ABSTRACT FROM AUTHOR]

In this work, Fe64Nb3B17Si6Cr6Ni4 and Fe60Nb3B17Si6Cr6Ni4Mo4 (at. %) coatings were prepared with a high-velocity air fuel spraying method, and the effects of minor Mo addition on the microstructure, glass formation, and corrosion resistance of the coating were studied. It was found that the Mo addition improves the glass-forming ability of the alloy and a fully amorphous structure with a higher compactness was obtained in the Mo-containing coating. The thermal stability of the coating is enhanced by Mo addition and the onset crystallization temperature was increased by 20 K. In addition, the Mo-containing amorphous coating exhibited higher corrosion resistance than the Mo-free coating. The superior corrosion resistance can be attributed to the increased proportion of protective, stable Cr, Nb, and Mo oxides in the passive film and fewer defects of the Mo-containing coating. [ABSTRACT FROM AUTHOR]

Graphene is widely used for various applications, especially after nitrogen doping and incorporation with metal nanoparticles. Herein, a simultaneous approach to reducing, nitrogen doping and noble metals coating of graphene oxide (GO) is reported using an advanced active-screen plasma (ASP) technique. With a noble metal plate added as an extra lid of active screen cage, the corresponding noble metal, mainly or fully in pure metal state, depending on the noble metal type, as well as a minority of Fe and Cr, is deposited on GO with simultaneous reduction and nitrogen doping. The ASP treated GO exhibits varying levels of improvement in electrical property depending on the type of noble metal nanoparticles hybridized with. Specifically, ASP treated GO incorporated with Pt or Au revealed 2â€"4 orders of magnitude of improvement in electrical property. [ABSTRACT FROM AUTHOR]