• Energy Research
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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.

Gas-based power plants have attracted more attention in providing electrical energy worldwide because of their lower costs and air pollution. In addition, the use of multi-carrier energy systems has several advantages, such as sustainability benefits and improving performance in supplying the energy demand. This study aims to optimize the total operation cost of multi-carrier energy systems considering the uncertain parameters. The storage technology and consumption side assist the operator in achieving lower costs based on conceptions of demand response programs. Therefore, this study presents a comprehensive mathematical model for the coordinated operation of integrated multi-carrier energy systems while the operational constraints of both gas and power networks are considered. Furthermore, this paper utilizes a new uncertainty modeling method based on Hong's two-point estimate method for addressing the uncertainties of load consumption and wind generation. The proposed model is applied to a gas and power multi-carrier energy system through four case studies. The results affirm the high performance of the presented method and investigate the influence of demand response programs in both sides of energy carriers.

The virtual energy hub (VEH), a combination of virtual power plant and energy hub concepts, faces many uncertainties due to its constituent distributed energy resources. This paper presents the deep learning-based scheduling of VEH for participation in electrical and thermal markets using bidirectional long short-term memory (BLSTM) network, which offers excellent accuracy in forecasting uncertain parameters by concurrent using past and future dependencies. In addition to applying learning methods, energy storage systems can also influence the optimal management of uncertainties. To provide the required electrical storage equipment, the VEH employs plug-in hybrid CNG-electric vehicles (PHGEVs) that can use both electrical energy and compressed natural gas (CNG) to fulfill their energy needs. The alternative fuel can tackle the limitations of prolonged charging of electric vehicles and excess load caused by these vehicles at peak hours. To supply the secondary fuel of PHGEVs, the modeled VEH includes a CNG station, which compresses the natural gas imported from the natural gas grid before delivering it to the vehicles. Furthermore, phase change material-based thermal energy storage (PCMTES) is considered in the VEH configuration, which unlike other common thermal energy storage systems, operates at a constant temperature during the charging and discharging period. Lastly, the simulation of the developed system illustrates that PHGEVs can reduce the imposed cost in unforeseen situations by up to 26 percent and increase the system's flexibility.

Accurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation forecasting in different regions of Iran. Generalizability, data independence, forecasting for regions where no training data is available, and preserving the security and privacy of data are prominent features of the proposed method. The federated network was designed with an architecture of 9 clients to perform the training process and extract the salient features from the data associated with each region in each client via the CNN technique. Then, the generalized global supermodel is produced based on the extracted features in each client to forecast the wind power in new and unknown regions such as Mahshahr, Bojnord, and Lootak that had no training data available and had no effect on global supermodel generation. Various scenarios were developed to test the robustness of the suggested methodology. In the first scenario, wind power forecasting is performed based on the suggested technique. In this scenario, the accuracy of the generalized supermodel to forecast wind power generation in each of the Mahshahr, Bojnord, and Lootak regions is 84%, 85%, and 74%, respectively. The second scenario models the scaling attack by changing the wind speed parameters to evaluate the performance of forecasting models against the data integrity attack. In this scenario, an evaluation of the forecast results based on various performance metrics is conducted highlighting the accuracy reduction of the forecast model, due to the damage caused by cyber-attacks on the input data. In the third scenario, the detection of cyber-attack is done based on the image processing-based technique. The presented results emphasize the accurate performance and high generalizability of the cyber-resilient global supermodel in forecasting wind power in various regions of Iran.

Abstract Objective. In the present hadrontherapy scenario, there is a growing interest in exploring the capabilities of different ion species other than protons and carbons. The possibility of using different ions paves the way for new radiotherapy approaches, such as the multi-ions treatment, where radiation could vary according to target volume, shape, depth and histologic characteristics of the tumor. For these reasons, in this paper, the study and understanding of biological-relevant quantities was extended for the case of 4He ion. Approach. Geant4 Monte Carlo based algorithms for dose- and track-averaged LET (Linear Energy Transfer) calculations, were validated for 4He ions and for the case of a mixed field characterised by the presence of secondary ions from both target and projectile fragmentation. The simulated dose and track averaged LETs were compared with the corresponding dose and frequency mean values of the lineal energy, y D ¯ and y ¯ F , derived from experimental microdosimetric spectra. Two microdosimetric experimental campaigns were carried out at the Italian eye proton therapy facility of the Laboratori Nazionali del Sud of Istituto Nazionale di Fisica Nucleare (INFN-LNS, Catania, I) using two different microdosimeters: the MicroPlus probe and the nano-TEPC (Tissue Equivalent Proportional Counter). Main results. A good agreement of L ¯ d Total and L ¯ t Total with y ¯ D and y ¯ T experimentally measured with both microdosimetric detectors MicroPlus and nano-TEPC in two configurations: full energy and modulated 4He ion beam, was found. Significance. The results of this study certify the use of a very effective tool for the precise calculation of LET, given by a Monte Carlo approach which has the advantage of allowing detailed simulation and tracking of nuclear interactions, even in complex clinical scenarios.

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed model forecasts the load with the correlation coefficients (R) of 99.78%, 99.57%, 99.33%, and 99.76% for spring, summer, autumn, and winter, respectively. The presented results show that the proposed VAEBiLSTM method with the highest R values and minimum forecasting errors compared to the LSTM and SVR methods has high effectiveness and performance.

Pd nanoparticles (NPs)/reduced graphene oxide (RGO) nanocomposite was prepared in a one-pot process by using Euphorbia stenoclada extract as antioxidant media in the absence of any surfactant, dangerous reactants or using external energy input. Catalytic potential of the fabricated Pd-RGO nanocomposite was examined for the degradation of environmental contaminants including Cr(VI), 4-nitrophenol (4-NP), Congo red (CR), methylene blue (MB) and methyl orange (MO). The Pd-RGO nanocomposite has been thoroughly characterized by employing X-ray diffraction, UV–Vis and TEM studies. Furthermore, recyclability and reusability aspects of the nanocomposite were monitored for multiple uses without much change in catalytic activity.