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Studying the thermal performance of Double Skin Facades (DSFs) with vertical layers has dominated the literature,however, there is still a lack of in-depth research on the performance of DSFs with atypical geometries suchas folded cases which can be applied to Building Integrated Photovoltaic (BIPV) systems to improve their performance.To this end, the study evaluates the influence of the fold geometry on heat transfer, flow structure, andairflow rate in the Folded DSF cavities under a hot climate in Iran using an efficient method titled “patching”; themethod integrates Soltrace3 with a 2D steady-state CFD model by ANSYS-Fluent. The results show that the foldposition and its depth can alter the DSFs performance significantly; the higher the fold depth the more distortionof the flow field inside the cavity; from a practical perspective, the fold position in the upper part of the cavity issuitable for BIPVs application since it can capture 250% higher amount of solar radiation compared to a conventionalvertical-layer DSF as the Base Case; the net heat gain through outer layer could improve with increaseof fold depth and reach at least 33% higher than the Base Case, meanwhile, the total electricity generationpotential of folded cases could be up to 169% higher than the Base Case; thus, the study proved that if thearchitectural design is of interest, it is highly recommended to consider folded DSFs as a design option.

Power-to-methane (PtM) is a prospective solution to the mismatching between the supply and consumption of renewable energy resources (RES) by converting renewable power into methane. However, the continuous fluctuation of RES causes the PtM system to deviate from the design condition in the vast majority of cases, and thus it is significantly vital to study the operating characteristics of the PtM system under off-design conditions. This paper proposes a comprehensive investigation framework from design to off-design steps for the performance improvement of a PtM system combining solid oxide electrolysis cell with methanation reactor, and solar energy is selected as renewable energy input. Firstly, the system with the total exergy efficiency (ηEX,tot) of 11.83% and levelized cost of exergy (LCOE) of 150.76 $/MWh is selected as the optimal design condition based on the homogeneous assessment from both thermodynamic and economic aspects, by means of Non-dominated sorting genetic algorithm-II. Then, based on the optimal design point, the off-design performances are quantitatively investigated under varying solar radiation and key operating parameters, in terms of synthetic natural gas (SNG) yield and ηEX,tot. The results indicate that with the increment in solar radiation, the SNG yield rises, while the ηEX,tot increases first and then decreases. Finally, the multi-objective optimization based on the Artificial Neural Network models is implemented for the system under off-design conditions to acquire the best trade-off between hourly SNG yield and ηEX,tot. The off-design optimization solutions reveal that the hourly optimal SNG yield is located in the range of 275.06–946.53 kW, achieving a total annual SNG yield of 1697 MWh/y, and the hourly optimal ηEX,tot mainly varies in the range of 10.40–11.40%.

This perspective covers the use of molybdenum disulfide and related compounds, generally termed MoSx, as electro- or photoelectrocatalysts for the hydrogen evolution reaction (HER). State of the art solutions as well as the most illustrative results from the extensive electro- and photoelectrocatalytic literature are given. The research strategies currently employed in the field are outlined and future challenges pointed out. We suggest that the key to optimising the HER activity of MoS2 is divided into (1) increasing the catalytic activity of the active site, (2) increasing the number of active sites of the catalyst, and (3) improving the electrical contact to these sites. These postulations are substantiated by examples from the existing literature and some new results. To demonstrate the electrocatalytic properties of a highly conductive MoS2 hybrid material, we present the HER activity data for multi-wall MoS2 nanotubes on multi-wall carbon nanotubes (MWMoS2@MWCNTs). This exemplifies the typical data collected for the electrochemical HER. In addition, it demonstrates that the origin of the activity is closely related to the amount of edges in the layered MoS2. The photoelectrocatalytic HER is also discussed, based on examples from literature, with an emphasis on the use of MoSx as either (1) the co-catalyst providing the HER activity for a semiconductor, e.g. Mo3S+4on Si or (2) MoS2 as the semiconductor with an intrinsic HER activity. Finally, suggestions for future catalyst designs are given.

AbstractThe wood pellet market is booming in Europe. The EU 2020 policy targets for renewable energy sources and greenhouse gas (GHG) emissions reduction are among the main drivers. The aim of this analysis is to map current European national wood pellet demand and supplies, to provide a comprehensive overview of major market types and prices, and to discuss the future outlook in light of raw material supply. Approximately 650 pellet plants produced more than 10 million tonnes of pellets in 2009 in Europe. Total European consumption was about 9.8 million tonnes, of which some 9.2 million tonnes is within the EU‐27, representing a modest 0.2% of Gross Energy Consumption (75 EJ level in 2008). The prices of most pellet types are increasing. While most markets of non‐industrial pellets are largely self‐sufficient, industrial pellet markets depend on the import of wood pellets from outside the EU‐27. Industrial pellet markets are relatively mature, compared to non‐industrial ones, because of their advanced storage facilities and long‐term price‐setting. However, industrial pellet markets are unstable, depending mainly on the establishment or the abolishment of public support schemes.Following our scenarios, additional 2020 demand for woody biomass varies from 105 million tonnes, based on market forecasts for pellets in the energy sector and a reference growth of the forest sector, to 305 million tonnes, based on maximum demand in energy and transport sectors and a rapid growth of the forest sector. Additional supply of woody biomass may vary from 45 million tonnes from increased harvest levels to 400 million tonnes after the recovery of slash via altered forest management, the recovery of waste wood via recycling, and the establishment of woody energy plantations in the future. Any short‐term shortages within the EU‐27 may be bridged via imports from nearby regions such as north west Russia or overseas. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd

Self-organizing nanopatterns can enable economically competitive, industrially applicable light-harvesting platforms for thin-film solar cells. In this work, we present transparent solar cell substrates having quasiperiodic uniaxial nanowrinkle patterns with high optical haze values. The self-organized nanowrinkle template is created by controlled heat-shrinking of metal-deposited pre-stretched polystyrene sheets. A scalable UV nanoimprinting method is used to transfer the nanopatterns to glass substrates on which single-junction hydrogenated amorphous silicon p-i-n solar cells are subsequently fabricated. The structural and optical analyses of the solar cell show that the nanowrinkle pattern is replicated throughout the solar cell structure leading to enhanced absorption of light. The efficient broadband light-trapping in the nanowrinkle solar cells results in very high 18.2 mA/cm2 short-circuit current density and 9.5% energy-conversion efficiency, which respectively are 35.8% and 39.7% higher than the values obtained in flat-substrate solar cells. The cost- and time-efficient technique introduces a promising new approach to customizable light-management strategies in thin-film solar cells.

Reliability and cost-effectiveness in the operation of the multiple microgrid (MMG) system depend on the skillful management of its energy resources. Traditional energy management approaches are physics-model-based, which rely on the precise system parameters (e.g., line parameters) of the electricity and heat network. It is difficult to establish the precise system parameters in practice because they depend on a variety of factors. In this context, this paper proposes a physics-model-free control framework for the energy management of MMGs with heat-electricity energy, consisting of proposed surrogate model and multi-agent deep reinforcement learning (MADRL) approach. An important step is to use historical data to train a surrogate model in supervised manner that can imitate the realistic power and thermal flow calculations. Meanwhile, the energy management problem is reformulated as a Markov game. It is solved by the proposed MADRL-based approach by modeling each MG controller as an agent with a specific objective. The historical trajectories representation, parameter space technology, and deep dense architecture in reinforcement learning are introduced in MADRL to overcome the negative impact brought by the time series data from the input state on the decision-making process and construct an efficient exploration mechanism to overcome inefficient optimization of the MMG system in a multi-agent setting. During training period of MADRL, the trained surrogate models integrate into the environment of the MADRL, which can develop optimal energy management strategy based on the continuous interaction with the surrogate models. The proposed surrogate model enabled MADRL approach can reduce the reliance on precise physical systems and prevent having an impact on the real system while being trained involving trial and error process. Simulation results demonstrate the effectiveness of the proposed control framework.

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified.