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The increasing demand for energy and commodities has led to escalating greenhouse gas emissions, the chief of which is represented by carbon dioxide (CO2). Blue hydrogen (H2), a low-carbon hydrogen produced from natural gas with carbon capture technologies applied, has been suggested as a possible alternative to fossil fuels in processes with hard-to-abate emission sources, including refining, chemical, petrochemical and transport sectors. Due to the recent international directives aimed to combat climate change, even existing hydrogen plants should be retrofitted with carbon capture units. To optimize the process economics of such retrofit, it has been proposed to remove CO2 from the pressure swing adsorption (PSA) tail gas to exploit the relatively high CO2 concentration. This study aimed to design and numerically investigate a vacuum pressure swing adsorption (VPSA) process capable of capturing CO2 from the PSA tail gas of an industrial steam methane reforming (SMR)-based hydrogen plant using NaX zeolite adsorbent. The effect of operating conditions, such as purge-to-feed ratio and desorption pressure, were evaluated in relation to CO2 purity, CO2 recovery, bed productivity and specific energy consumption. We found that conventional cycle configurations, namely a 2-bed, 4-step Skarstrom cycle and a 2-bed, 6-step modified Skarstrom cycle with pressure equalization, were able to concentrate CO2 to a purity greater than 95% with a CO2 recovery of around 77% and 90%, respectively. Therefore, the latter configuration could serve as an efficient process to decarbonize existing hydrogen plants and produce blue H2.

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

Countries’ emission reduction commitments under the Paris Agreement have significant implications for lifestyles. National planning to meet emission targets is based on modelling and analysis specific to individual countries, whereas global integrated assessment models provide scenario projections in a consistent framework but with less granular output. We contribute a novel methodology for translating global scenarios into lifestyle implications at the national and household levels, which is generalisable to any service or country and versatile to work with any model or scenario. Our 5Ds method post-processes Integrated Assessment Model projections of sectoral energy demand for the global region to derive energy-service-specific lifestyle change at the household level. We illustrate the methodology for two energy services (mobility, heating) in two countries (UK, Sweden), showing how effort to reach zero carbon targets varies between countries and households. Our method creates an analytical bridge between global model output and information that can be used at national and local levels, making clear the lifestyle implications of climate targets.

The temperature prediction for floating PV (FPV) must account for the effect of humidity. In this work, PV temperature prediction for steady-state Tpv and transient conditions Tpv(t) incorporates the effect of humidity and cooling due to seawater (s.w.) splashing and evaporation on PV modules. The proposed formulas take as main inputs the in-plane solar irradiance, wind speed, ambient temperature, relative humidity (RH), and s.w. temperature. The transient effects of s.w. splashing and the evaporation of the s.w. layer from the module are theoretically described considering the layer’s thickness using Navier–Stokes equations. Tpv and Tpv(t) measurements were taken before and after s.w. splashing on c-Si modules at the seashore and inland. PV temperature predictions compared to measured values showed very good agreement. The 55% RH at the seashore versus 45% inland caused the Tpv to decrease by 18%. The Tpv(t) at the end of the s.w. flow of 50–75 mL/s/m on the module at the seashore was 35–51% lower than the Tpv inland. This Tpv(t) profile depends on the s.w. splashing, lasts for about 1 min, and is attributed to higher convection, water cooling, and evaporation on the modules. The PV efficiency at FPV conditions was estimated to be 4–11.5% higher compared to inland.

The lattice Boltzmann method (LBM) has recently been used to simulate wave propagation, one of the challenging aspects of wind turbine modeling and simulation. However, standard LB methods suffer from the instability that occurs at low viscosities and from its characteristic lattice uniformity, which results in issues of accuracy and computational efficiency following mesh refinement. The local radial point interpolation cumulant lattice Boltzmann method (LRPIC-LBM) is proposed in this paper to overcome these shortcomings. The LB equation is divided into collision and streaming steps. The collision step is modeled by the cumulant method, one of the stable LB methods at low viscosities. In addition, the streaming step, which is naturally a pure advection equation, is discretized in time and space using the Lax–Wendroff scheme and the local radial point interpolation method (RPIM), a mesh free method. We describe the propagation of planar acoustic waves, including the temporal decay of a standing plane wave and the spatial decay of a planar acoustic pulse. The analysis of these specific benchmark problems has yielded qualitative and quantitative data on acoustic dispersion and dissipation, and their deviation from analytical results demonstrates the accuracy of the method. We found that the LRPIC-LBM replicates the analytical results for different viscosities, and the errors of the fundamental acoustic properties are negligible, even for quite low resolutions. Thus, this method may constitute a useful platform for effectively predicting complex engineering problems such as wind turbine simulations, without parameter dependencies such as the number of points per wavelength Nppw and resolution σ or the detrimental effect caused by the use of coarse grids found in other accurate and stable LB models.

This paper presents an in-depth investigation into the avalanche breakdown robustness of commercial state-of-the-art silicon carbide (SiC) power MOSFETs comprising of functional as well as structural characterization and the corresponding underlying physical mechanisms responsible for device failure. One aspect of robustness for power MOSFETs is determined by its ability to withstand energy during avalanche breakdown. Avalanche energy (EAV) is an important figure of merit for all applications requiring load dumping and/or to benefit from snubber-less converter design. 2D TCAD electro-thermal simulations were performed to get important insight into the failure mechanism of SiC power MOSFETs during avalanche breakdown.

Depletion of fossil fuel deposits is the main current issue related to the world’s power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development.

The electricity market and environmental concerns, with wide utilization of renewable sources, have improved the diffusion of distributed generation units changing the operations of distribution grids from passive networks to microgrids. A microgrid includes a cluster of electrical loads, energy storage devices and microsources, which provide both power and heat to their local area. A microgrid has usually one connection point to the utility grid through power electronic converters placed at customers’ sites. This paper analyses a Reconfigurable Integrated Converter (RIC) used for a domestic microgrid with inputs from the AC mains and photovoltaic arrays, and two DC outputs at different voltage levels. A RIC as a dual-boost DC-DC converter is proposed, modelled and analysed in the paper. The advantages of such a topology in comparison with traditional boost converters are outlined. Reported simulations results give evidence on the controllability of this converter and the capability of achieving the desired voltage outputs with reduced ripple.