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The Kingdom of Saudi Arabia is experiencing a surge in electricity demand, with power generation increasing 4 times in 25 years from 1990 to 2014. Despite the abundant primary renewable energy sources, the country has overlooked them in the past in national energy policies. However, in recent years, renewable energy has become a part of the Kingdom of Saudi Arabia’s energy conservation policy due to climate changes, technological progress, economies of scale, and increased competitiveness in supply chains. The Saudi government has created the King Abdullah City for Atomic and Renewable Energy (KACARE) to develop national strategies for effectively utilizing renewable and nuclear energy. This paper reviews the current state of the art of the renewable energy technologies available on the market and evaluates the installation of renewable energy plants near Saudi Arabia’s East Coast for a new town, focusing on technical rather than economic aspects. The paper provides a wide review of the possible technical solutions to exploit the producibility of different renewable sources, considering the challenging climate conditions typical of desert areas. The analysis of a real case study shows a high availability of wind and solar irradiance that allow a net energy production of 354 and 129 GWh, respectively. In addition, the comparison between a typical ground-mounted photovoltaic (PV) system and an emerging floating PV reveals that for the same installed power, occupied area, and environmental conditions, the latter has a 4% greater performance ratio due to the cooling effect of water.

Surface wave technology for high-speed communications is a current research topic aimed to respond to increasing data rate demands on existing copper infrastructures. Also, the topic of surface waves has recently gained importance in the modeling of transmission line towers hit by lightning strikes with spectral content in the megahertz band. The single-wire transmission line structure (return conductor absent) cannot support TEM waves; it supports a TM Sommerfeld wave fully described by two propagation constants and two characteristic impedances. Nonetheless, the literature on single-wire transmission-line structures has been employing quasi-static per unit length constitutive parameters, inductance and capacitance, borrowed from ordinary two-conductor transmission line TEM analysis. This work develops, discusses, and compares various possible definitions of these constitutive parameters using different physical approaches: TEM-approach, circuit-approach, and energy-approach. Numerical results for nonmagnetic and magnetic wires, copper and steel wires, respectively, are obtained in the range 1 Hz to 1 GHz. Our analysis shows that in some circumstances the TEM and circuit approaches may lead to nonphysical results, but, remarkably, all the approaches seem to converge to a common dominant term either in the per unit length capacitance or in the per unit length inductance, whose product is frequency-invariant. Considering the different approaches under discussion, the differences among the observed results for the per unit length constitutive parameters are negligibly small, of second-order importance.

Abstract In this study, the implementation of different corona discharge models in a transient program for overhead multiconductor lines based on the implicit Crank‐Nicolson Finite Difference Time Domain method is presented. Among different corona models available in the literature, two empirical models (Gary's and Suliciu's models) and a physics‐based model (Malik's model) are discussed, which are broadly used in EMT programs, and their predictions under fast‐front lightning surges and slow‐front switching impulses are assessed. The authors critically review the dynamic capacitance approach in terms of numerical accuracy, when dealing with slowly varying voltages, and applicability, regarding dispersive models assuming a non‐instantaneous charge‐voltage relation. The alternative voltage‐controlled generator approach is proposed, which is successfully included in the implicit Crank‐Nicolson scheme. One of the objectives of this study is to show how different corona models may predict overvoltages with huge discrepancies, depending on the transient waveform and the propagation distance, despite the similarity of the q‐v hysteresis curves. More evident discrepancies between models under fast‐front voltage waves are reported, rather than for switching impulses. The consistency of these results in terms of the models' features and q‐v curves is analysed, highlighting the necessity for new engineering tools for corona simulation of general predictive capability.

Abstract The transmission of an electromagnetic field produced by a current loop of finite radius through a coaxial circular aperture in a perfectly conducting plate is evaluated through a rapidly convergent formulation in an exact form. By applying the equivalence principle, the problem is first formulated in the Hankel transform domain, obtaining a set of dual integral equations in which the equivalent surface magnetic current density defined on the aperture is not known. The set of dual integral equations is regularised in a second‐kind Fredholm integral equation by applying the Abel integral‐transform technique. The solution is achieved by expanding the unknown in a set of orthogonal basis functions that correctly reproduce the behaviour of the equivalent magnetic current at the edge of the aperture. Finally, under particular assumptions, a low‐frequency solution is extracted in a closed form. Numerical results are reported to validate the accuracy and efficiency of the proposed formulations.

The electromagnetic interaction between a circular disk with finite conductivity and finite thickness and a coaxial circular loop of constant current is addressed here. The finite conductivity and thickness of the material disk lead to the adoption of suitable generalized boundary conditions, and the problem is thereby reduced to the solution of two sets of dual integral equations in the Hankel transform domain. Such equations are then solved by expanding the spectral unknowns in Neumann series of Bessel functions. An alternative formulation that is valid for purely conductive screens with no magnetic properties, which is computationally much faster, is proposed as well. The magnetic shielding effectiveness of the structure is studied in detail, pointing out its dependencies and possible critical situations.

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks.

Anion exchange membranes (AEM) have gained attention recently as a promising candidate for low-cost water electrolysis systems to produce hydrogen, linked with renewable energy resources as a sustainable alternative to fossil fuels. The development of potential materials for producing and analyzing AEM is an imperative step towards commercialization and plays a competitive role in the hydrogen production industry. In this article, we developed a composite anion exchange membrane prepared by activating a commercial support structure (Celgard® 3401) with a commercially available functional group (Fumion® FAA-3) through a phase-inversion process. Fourier-transform infrared spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) analysis demonstrated the phase-inversion procedure as an effective methodology. Furthermore, the cell performance test result (with Celgard/Fumion) was very promising and even better in comparison with a commercial membrane commonly applied in alkaline electrolysis (Fumasep). We also developed a testing procedure for membrane performance evaluation during electrolysis which is very critical considering the effect of CO2 absorption on membrane conductivity.

Islands are facing significant challenges in meeting their energy needs in a sustainable, affordable, and reliable way. Traditionally, the primary source of electricity on the islands has been imported diesel fuel, with high financial costs for most utilities. In this context, even replacing part of the traditional production with renewable energy source can reduce costs and improve the quality of life of islanders. However, integrating large amounts of renewable energy production into existing grids introduces many concerns regarding feasibility, economic analysis, and technical implementation. From this point of view, machine learning and deep learning techniques are efficient tools to mitigate these problems. Their potential results are beneficial considering isolated grids of small islands which are not connected to the national grid. In this paper, a study of the Italian island of Ponza is carried out. The isolation leads to several challenges, such as the high cost related to the transport, installation, and maintenance of renewable energy sources in a small area with several constraints and their intermittent power production, which requires the use of storage systems for dispatching purposes. The proposed study aims to identify future developments of the electricity grid by considering the deployment of both renewable energy sources and energy storage systems. Furthermore, future scenarios are depicted through the use of autoregressive and deep learning techniques to give an idea about the economic costs of both energy demand and supply.

In recent years, the explosion of renewable energy sources, the increase in the demand for electrical energy, and several improvements in related technologies have fostered research in many relevant areas of interest [...]

Within the policy framework of the energy union strategy started in 2015, the European Union (EU) finalized the “Clean Energy for all Europeans Package” (CEP) in 2019 [...]

Over the past decade, decarbonization and environmental issues have taken a key role in worldwide politics [...]

The ongoing energy crisis and the rising prices of fossil fuels have accelerated the need for a renewable energy transition [...]

Fast-front transients play an important role in the insulation design of any power system. When a stroke hits the shield wire or the tower of high-voltage overhead power lines, flashover may occur either along the span or across tower insulators, depending on the relevant voltages and insulation strength. As a result, backflashover may take place from the tower structure to the phase conductor whenever a huge impulse current flows along the tower towards considerably high footing impedances. For these reasons, tower modeling for transients studies is an important step in the insulation design, and also for lower voltage applications, where indirect lightning effects may play a predominant role. However, after decades of research on tower modeling, starting from the 1930s with the first model proposed by Jordan, no consensus has been reached neither on a widely accepted tower model nor on the quantitative effect of the tower models on insulation design. Moreover, the fundamental mechanisms at the base of the transient response of towers and the definition of some fundamental parameters have not been totally clarified yet. The aim of this review is to present the available tower models developed through the years in the power community, focussing mainly on lumped/distributed circuit models, and to help the reader to obtain a deeper understanding of them.

Research on corona discharge, shared by physics, chemistry and electrical engineering, has not arrested yet. As a dissipative process, the development of corona increases the resistive losses of transmission lines and enhances the line capacitance locally. Introducing additional losses and propagation delay, along the line, non-linearity and non-uniformity of the line parameters; therefore, corona should not be neglected. The present work is meant to provide the reader with comprehensive information on the corona macroscopic phenomenology and development, referring to the most relevant contributions in the literature on this subject. The models proposed in the literature for the simulation of the corona development are reviewed in detail, and sensitivity curves are provided to highlight their dependence on the input parameters.

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

In this work, we propose the synthesis of ZnO nanostructures through the thermal oxidation of ball-milled powders with the introduction of Mg and Sn doping species at the preliminary step of milling. We investigate the advantages and challenges of this two steps process for the production and fabrication of highly crystalline ZnO nanowires. This simple method allows us to fabricate ZnO nanowires with a higher quality core crystal at a much lower temperature and for a shorter processing time than the state-of-the-art, and decorated with by ZnO2 nanoparticles as determined via TEM analysis. The main findings will show that the crystalline core of the nanowires is of hexagonal ZnO while the nanoparticles on the surface are ZnO2 cubic type. Generally, the method proves to be suitable for applications that require a high surface-to-volume ratio, for example, catalysis phenomena, in which the presence of zinc oxides species can play an important role.

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances.

The development of smart and intelligent regenerative biomaterials for skeletal muscle tissue engineering is an ongoing challenge, owing to the requirement of achieving biomimetic systems able to communicate biological signals and thus promote optimal tissue regeneration. Electrospinning is a well-known technique to produce fibers that mimic the three dimensional microstructural arrangements, down to nanoscale and the properties of the extracellular matrix fibers. Natural and synthetic polymers are used in the electrospinning process; moreover, a blend of them provides composite materials that have demonstrated the potential advantage of supporting cell function and adhesion. Recently, the decellularized extracellular matrix (dECM), which is the noncellular component of tissue that retains relevant biological cues for cells, has been evaluated as a starting biomaterial to realize composite electrospun constructs. The properties of the electrospun systems can be further improved with innovative procedures of functionalization with biomolecules. Among the various approaches, great attention is devoted to the “click” concept in constructing a bioactive system, due to the modularity, orthogonality, and simplicity features of the “click” reactions. In this paper, we first provide an overview of current approaches that can be used to obtain biofunctional composite electrospun biomaterials. Finally, we propose a design of composite electrospun biomaterials suitable for skeletal muscle tissue regeneration.

Surge arresters may represent an efficient choice for limiting lightning surge effects, significantly reducing the outage rate of power lines. The present work firstly presents an efficient numerical approach suitable for insulation coordination studies based on an implicit Crank–Nicolson finite difference time domain method; then, the IEEE recommended surge arrester model is reviewed and implemented by means of a local implicit scheme, based on a set of non-linear equations, that are recast in a suitable form for efficient solution. The model is proven to ensure robustness and second-order accuracy. The implementation of the arrester model in the implicit Crank–Nicolson scheme represents the added value brought by the present study. Indeed, its preserved stability for larger time steps allows reducing running time by more than 60 % compared to the well-known finite difference time domain method based on the explicit leap-frog scheme. The reduced computation time allows faster repeated solutions, which need to be looked for on assessing the lightning performance (randomly changing, parameters such as peak current, rise time, tail time, location of the vertical leader channel, phase conductor voltages, footing resistance, insulator strength, etc. would need to be changed thousands of times).

Micro- and nano-scale materials and systems based on zinc oxide are expected to explode in their applications in the electronics and photonics, including nano-arrays of addressable optoelectronic devices and sensors, due to their outstanding properties, including semiconductivity and the presence of a direct bandgap, piezoelectricity, pyroelectricity and biocompatibility. Most applications are based on the cooperative and average response of a large number of ZnO micro/nanostructures. However, in order to assess the quality of the materials and their performance, it is fundamental to characterize and then accurately model the specific electrical and piezoelectric properties of single ZnO structures. In this paper, we report on focused ion beam machined high aspect ratio nanowires and their mechanical and electrical (by means of conductive atomic force microscopy) characterization. Then, we investigate the suitability of new power-law design concepts to accurately model the relevant electrical and mechanical size-effects, whose existence has been emphasized in recent reviews.

ZnO-based nanomaterials are a subject of increasing interest within current research, because of their multifunctional properties, such as piezoelectricity, semi-conductivity, ultraviolet absorption, optical transparency, and photoluminescence, as well as their low toxicity, biodegradability, low cost, and versatility in achieving diverse shapes. Among the numerous fields of application, the use of nanostructured ZnO is increasingly widespread also in the biomedical and healthcare sectors, thanks to its antiseptic and antibacterial properties, role as a promoter in tissue regeneration, selectivity for specific cell lines, and drug delivery function, as well as its electrochemical and optical properties, which make it a good candidate for biomedical applications. Because of its growing use, understanding the toxicity of ZnO nanomaterials and their interaction with biological systems is crucial for manufacturing relevant engineering materials. In the last few years, ZnO nanostructures were also used to functionalize polymer matrices to produce hybrid composite materials with new properties. Among the numerous manufacturing methods, electrospinning is becoming a mainstream technique for the production of scaffolds and mats made of polymeric and metal-oxide nanofibers. In this review, we focus on toxicological aspects and recent developments in the use of ZnO-based nanomaterials for biomedical, healthcare, and sustainability applications, either alone or loaded inside polymeric matrices to make electrospun composite nanomaterials. Bibliographic data were compared and analyzed with the aim of giving homogeneity to the results and highlighting reference trends useful for obtaining a fresh perspective about the toxicity of ZnO nanostructures and their underlying mechanisms for the materials and engineering community.

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.