Narrow Results

Thermoelectric (TE) materials that convert heat directly into electricity are crucial for waste heat recovery and renewable energy applications. This study focuses on Mg₃X₂ ($\mathrm{\text{X = P}}$, As, and Sb) compounds, which have emerged as promising candidates for TE applications due to their abundance, non-toxicity, and potential for high efficiency across a broad temperature range. We employed density functional theory and the VASP simulation code to compute the structural, electronic, and TE properties of Mg₃X₂ compounds. Structural analysis indicates that Mg₃P₂ and Mg₃As₂ crystalize in cubic form and Mg₃Sb₂ acquire a trigonal structure. Mg₃P₂ and Mg₃As₂ have direct bandgaps of 1.60 and 1.42eV, while Mg₃Sb₂ has an indirect bandgap of 0.24eV. Mg₃P₂ and Mg₃As₂, with their lower thermal conductivities, show high figures of merit (ZT), particularly at low temperatures. Mg₃Sb₂, though exhibiting a higher thermal conductivity, demonstrates superior power factors at elevated temperatures. The mechanical stability and phonon dispersion curves analysis confirms that all compounds meet the criteria for structural stability, suggesting their suitability for practical applications. This work highlights the potential of Mg₃X₂ compounds as viable TE materials and provides insights for future experimental and theoretical studies aimed at improving their performance.

Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms.

Today, the utilization and management of renewable energy have become integral to the development of smart cities. This paper explores the application of Artificial Intelligence (AI) in analyzing energy storage and renewable energy systems within smart city contexts. We introduce a joint optimization method that combines two-stage input feature selection and parameter training in traditional prediction models. This method integrates physical, statistical, and AI modeling techniques. Additionally, we conduct comprehensive experiments on feature input selection using a Binary Genetic Algorithm (BGA) and two-stage model parameter optimization based on a Natural Evolution Strategies (NES). The experimental results provide strong support for the conclusions of this study. Finally, we simulate the full charge process of energy storage in a smart city and predict the full charge data for batteries B0005, B0006, B0007, and B0029. The results indicate that the NES-BGA-ELM approach outperforms the BGA-ELM method in terms of error reduction.

Every fifth inhabitant of our planet has no access to electric lighting. Most of them are poor people living in remote areas of developing countries. Recent progress in solid-state lighting technologies offers good opportunities to develop, commercialize and introduce off-grid lighting systems based on application of white light emitting diodes (WLEDs) in combination with photovoltaic solar panels, wind generators or tiny hydro power plants. Though strongly dependent on the mainstream progress in implementation of LEDs for general lighting, application of this technology in developing world has specific challengers, difficulties and even advantages. Lighting technology the developing world is up to leapfrog from splinters and kerosene wick lamps directly to LED lamps leaving incandescent and fluorescence lamps behind. Achievements and problems, history and future of implementation of solid-state lighting in remote villages of developing counties are discussed in this chapter.

Many countries rely on the international energy market as their main energy supplier, thus leading to issues of insecurity. Energy insecurity can potentially hinder economic growth and cause sustainability problems. This paper builds on cross-country panel data and estimates the relationship between energy insecurity and economic growth. We explore the multi-dimensional feature of energy insecurity through energy dependency, renewable energy share, and price effects. Our results show statistically significant negative impacts on growth due to energy insecurity, but the effects are mostly relevant to developing economies. Moreover, we show that the development renewable energy sector can mitigate the negative effects.

Renewable energy decreases the threat of energy insecurity. Solar energy systems are proving to be an easy and cheaper solution to energy crisis worldwide. Developing countries including Pakistan have augmented efforts to boost the current solar capacity. The identification of desired solar power point plant fabrication requires robust analysis of several factors. Adequate research has not been done on the site selection process for solar projects in Pakistan. This study identifies and proposes multiple point states having solar energy potential. Based on the multiple main and sub-criteria obtained from the literature, a mathematical formulation has been used to install the solar projects at rural areas of Pakistan. An empirical assessment of the off-grid rural solar power projects in Pakistan has also been conducted in this case study. The results reveal that mass, money supply and ratio are important, while the transmission on the matrix, the cost of land and the sun were generated. The study found the district of Barkhan (R2) of Baluchistan to be the most ideal site, followed by Jacobabad (R3) and Mastung (R3). With respect to economic viability, this study found that solar energy systems provide electricity which is much cheaper than conventional energy supplied in the country. This study could be useful reference for South Asian countries including India, Bangladesh, and Afghanistan, which are currently electrifying remote and rural areas through solar energy system.

The Great East Japan Earthquake (GEJE) and Fukushima nuclear disaster that occurred in 2011 gave a sharp reminder to Japan’s energy security to reconsider the reduction of nuclear power dependence with a better energy mix. We use a recursive CGE model based on Japan’s renewable energy input–output model to analyze the energy composite of power generation and consumption to investigate cost-effective policy incentives to achieve an optimal energy mix to reduce the nuclear power dependence to less than 5% within 10 years. Moreover, we create scenarios of: (1) nuclear power decommission, (2) renewable energy promotion and (3) virtual power plant (VPP) implementation with public R&D expenditure and power infrastructure investment. The simulation results show that renewable energy could gradually replace nuclear power with capital-use subsidies. Under the direction of nuclear power decommission, the VPP installation could reduce the fiscal cost of wind power by 13%, solar energy by 8% and the social cost by 36%. We provide empirical evidence that the implementation of VPP should be promoted in addition to the renewable policy promotion policy to facilitate power allocation and overcapacity problems better.

Energy security, a multidimensional concept that encompasses the notion of resource availability, accessibility, environmental acceptability and cost affordability, has been widely discussed. However, the same cannot be said about energy insecurity, a concept that may not necessarily mirror energy security, but also comprises the various consequences of energy unavailability. Energy insecurity mostly affects the poorest and can lead to deepened inequalities and poor health at the household level. One solution for tackling energy insecurity could be adoption of renewable energy. Power generated from renewable sources could help in mitigating energy price fluctuations and reduce health issues, as well as encourage stable economic growth. This special issue discusses the interaction between the three concepts through careful case studies and panel analysis and proposes various policy implications for energy policymakers. This introductory paper introduces the articles selected for this special issue.

The purpose of this study is to analyze the effect of financial development (FD) on the share of renewable energy (RE) usage in Japan. The existing theoretical literature and empirical analyses covering different country cases reveal that FD might have positive, negative, or insignificant effect on RE use. Since there is no empirical investigation of the issue for Japan, the study aims to contribute to the literature. To that end, we used several time-series techniques to detect the association between RE usage and FD in Japan over the 1970–2020 period. The results obtained from the ARDL, Hatemi-J, Maki, Tsong et al. and NARDL cointegration tests showed that there is a significant cointegrated relationship between the share of RE use, FD, GDP per capita and trade openness. As for the long-run coefficients obtained from the ARDL, FMOLS, CCR and NARDL estimators revealed that increases in FD and trade openness raise the share of RE usage while increases in GDP per capita reduce it. Briefly, for the Japan case, we may suggest that improving the financial market structure of the country will bear fruit in terms of the share of cleaner and sustainable energy usage.

Using first-principles calculations, we studied the electronic, structural and thermoelectric properties of two-dimensional (2D) MXenes $M_{n+1}{\mathrm{\text{C}}}_n{\mathrm{\text{O}}}_2$ ($M$ = Ti, Zr and Hf, $n=1$, 2 and 3). The calculations are carried out within the generalized gradient approximation (GGA). We have calculated the Boltzmann transport equation for finding the thermoelectric properties such as power factor, Seebeck coefficient and electrical conductivity. For $n=1$, these materials behave as semiconductors having an indirect bandgap nature. In contrast, for $n>1$ these materials show metallic behavior. Out of these MXenes, we found that Ti₂CO₂ has a high Seebeck coefficient value, whereas the electrical conductivity of Ti₄C₃O₂ is exceptionally high. While among all these compounds, Ti₂CO₂ and Hf₄C₃O₂ have a high power factor in the 300–1200-K temperature range.

We have undertaken an ab initio investigation of emerging metal lead-free halide double perovskite materials for renewable energy applications using the WIEN2k simulation code. These materials have garnered significant attention from the research community due to their potential utility in electronic devices. Through an analysis of their electronic structure, we have ascertained that these materials exhibit characteristics of direct band gap semiconductors, falling within the energy range spanning 0.755 to 1.825eV. Furthermore, to check their suitability for use in photovoltaic devices, optical properties have been investigated. The thermoelectric potential of these materials has been explored using the BoltzTraP simulation code. The study of thermoelectric parameters indicates that the studied materials are effective thermoelectric materials with a strong potential for n-type doping. Additionally, thermodynamic parameters have been investigated to check their thermal stability, required to make them promising candidates for a wide range of renewable energy applications.

Growing concerns on the energy crisis impose great challenges in development and deployment of the smart grid technologies into the existing electrical power system. A key enabling technology in smart grid is distributed generation, which refers to the technology that power generating sources are located in a highly distributed fashion and each customer is both a consumer and a producer for energy. An important optimization problem in distributed generation design is the insertion of distributed generators (DGs), which are often renewable resources exploiting e.g., photovoltaic, hydro, wind, ocean energy.

In this paper, a new power loss filtering based sensitivity guided cross entropy (CE) algorithm is proposed for the distributed generator insertion problem. This algorithm is based on the advanced CE optimization technique which exploits the idea of importance sampling in performing optimization. Our experimental results demonstrate that on large distribution networks, our algorithm can largely reduce (up to 179.3%) power loss comparing to a state-of-the-art sensitivity guided greedy algorithm with small runtime overhead. In addition, our algorithm runs about 5× faster than the classical CE algorithm due to the integration of power loss filtering and sensitivity optimization. Moreover, all existing techniques only test on very small distribution systems (usually with < 50 nodes) while our experiments are performed on the distribution networks with up to 5000 nodes, which matches the realistic setup. These demonstrate the practicality of the proposed algorithm.

A novel Four Switch Infinite Level Inverter (FSILI) is proposed in this paper. In conventional multilevel inverters, as the number of levels increases the output voltage becomes more sinusoidal. Unlike conventional multilevel topologies, the output voltage level in the proposed topology depends upon the switching frequency. Since the switching frequency is very high, the output voltage level approaches infinity, thus the name Infinite Level Inverter. Proposed topology requires only one inductor and capacitor reducing the size, weight and thus cost of the overall system. Inherent buck operation is happening in the proposed topology with a sine varying duty ratio PWM control. Steady-state analysis and design of the inverter are carried out. The proposed topology is simulated using Matlab/Simulink to evaluate the theoretical analysis and operation. A hardware prototype is also developed to validate the operation of proposed FSILI.

A high voltage gain DC–DC converter with a Y-source network is proposed in this paper for applications such as fuel cells and PV systems. The proposed converter topology is a combination of a Y-source module with a switched capacitor boost module. It is a single switch topology to produce high voltage gain at the load. The superiority of the proposed topology is the high voltage transfer ratio and continuous input current. The voltage gain of the converter can be achieved up to twenty times the input voltage at a duty cycle of 25% itself. The steady state analysis of the converter is carried out using mathematical equations and the working of the converter is explained. The design and analysis of the passive components in the circuit are explained. To illustrate the highlights of the converter, the proposed topology is compared with similar converters in the literature. The simulation of the converter is carried out using MATLAB/SIMULINK. The circuit is verified in the laboratory using a 100W prototype.

Recent years have witnessed a growing trend towards the development and deployment of distributed generation (DG). It is shown that electric distribution networks with DGs can encounter two types of local bifurcations: saddle-node bifurcation and structure-induced bifurcation. The structure-induced bifurcation occurs when a transition between two structures of the distribution network takes place due to limited amount of reactive power supports from renewable energies. The saddle-node bifurcation occurs when the underlying distribution network reaches the limit of its delivery capability. The consequence of structure-induced bifurcation is an immediate instability induced by reactive power limits of renewable energy. It is numerically shown that both types of local bifurcations can occur at both small distribution networks and large-scale distribution networks with DGs. Physical explanations of these two local bifurcations are provided. Studies of local bifurcations in distribution networks provide insights regarding how to design controls to enhance distribution networks with DGs.

Forecasting solar radiation for a given region is an emerging field of study. It will help to identify the places for installing large-scale photovoltaic-systems, designing energy-efficient buildings and energy estimation. The different machine learning kernel-based approaches for prediction problems uses either a local or global kernel. These models can provide either strong training capability or good generalization performance. In this paper, a new hybrid kernel is proposed using the combination of a local and global kernel. A novel algorithm Hybrid Kernel-based Extreme Learning Machine is proposed for predictive modelling of Incident Solar Radiation (ISR) time series using new hybrid kernel. The proposed algorithm uses the surface, atmospheric, cloud properties obtained from the MODIS instrument and observed ISR at time t – 1 to predict ISR at time t. This study is conducted for 41 diverse sites of Australia for the period of 2012–2015. Further, the proposed model is experimented with other time series datasets to prove its efficacy. It is shown that the proposed methodology outperforms five other benchmarking methods in terms of MAE and Willmott’s Index (WI). Therefore, the suggested approach can be used for modelling solar energy at a national scale using remotely-sensed satellite footprints.

An ocean turbine generator will be subject to variations in water current velocity at one end and resistive load at the other. Measurements acquired during bench testing of the turbine's drivetrain can be used to predict the effect of these variables on the turbine during deployment. This paper outlines an ongoing series of tests involving vibration data captured at various velocities and loads. This data is analyzed to detect the machine's state using two approaches. In the first approach, machine learning techniques are used to discern between states given adjacent values for velocity or load. The purpose of that approach is to assess a learner's ability to draw the fine distinctions needed for subsequent fault identification. The second approach computes a power spectrum for each velocity and load combination over enough trials as to construct an operating envelope. The purpose of that approach is to identify as abnormal, incoming data having parts of its spectrum that fall outside that envelope. A case study applies each approach to baseline data and data for a deterioration scenario. The rationale behind each approach, their assumptions, and their limitations are also discussed.

A hybrid energy system integrates renewable energy sources like wind, solar, micro-hydro and biomass, fossil fuel power generators such as diesel generators and energy storage. Hybrid energy system is an excellent option for providing electricity for remote and rural locations where access to grid is not feasible or economical. Reliability and cost-effectiveness are the two most important objectives when designing a hybrid energy system. One challenge is that the existing methods do not consider the time-varying characteristics of the renewable sources and the energy demand over a year, while the distributions of a power source or demand are different over the period, and multiple power sources can often times complement one another. In this paper, a reliability analysis method is developed to address this challenge, where wind and solar are the two renewable energy sources that are considered. The cost evaluation of hybrid energy systems is presented. A numerical example is used to demonstrate the proposed method.

China seeks to develop biofuels industry despite production difficulties.

Shengquan and Novozymes team up to commercialize cellulosic ethanol.

China, Denmark establish first national renewable energy think tank.

Bayer HealthCare, Tsinghua University extend innovative drug discovery partnership.

CUHK launches non-invasive prenatal test for Down Syndrome.

World's 1st handmade cloned transgenic sheep born in China.

American genetics helps meet Chinese demands for more protein.

China's colorectal cancer drug market expected to grow to more than $400 million in 2016.

Medistem appoints director of Chinese operations.

AUSTRALIA — Childhood CT scans slightly raise cancer risk.

AUSTRALIA — There's a very simple solution to your lack of vitamin D.

INDIA — India develops cheap rotavirus vaccine.

JAPAN — 'Tug of war' method to measure the copy number limits of all genes in budding yeast.

SINGAPORE — SG Austria co-edits just released book on living cell bioencapsulation.

SINGAPORE — Nano Today's 2013 impact factor increases from 15.355 to 17.689.

SINGAPORE — Cholesterol beats coronaviruses, Avian flu and Swine flu.

THE PHILIPPINES — Philippines maps out plan to switch to 100% renewables in 10 years.

EUROPE — Roche launches first sugar-transferase for new glyco-engineering portfolio.

EUROPE — Older liver cancer patients respond to radioembolization equally as well as younger patients.

NORTH AMERICA — Protein helps colon cancer move and invade.

NORTH AMERICA — FDA approval of VIBATIV(R) (telavancin) for the treatment of bacterial pneumonia.

NORTH AMERICA — “On Demand Medical Research” is up and running.

UNITED KINGDOM — Diabetes rises sharply among UK's young adults.

UNITED KINGDOM — 'Mental illness' isn't all about brain chemistry: it's about life.

UNITED KINGDOM — Public to see impact of medical research funding.