In order to create a sustainable future for the urban environment in s=Smart cities, it is necessary to develop a concept of urban transport, partially reduce the use of traditional transport, primarily cars, as well as the environmental pressure on society, which is essential to move to a sustainable urban future. In the latest discussions on the future of the urban transport system, the quality of the environment, and the possibility of its improvement are discussed, this issue became especially relevant with the onset of the pandemic, when the lockdowns were introduced. The problem of sustainable transport in urban areas has been recognized in academic studies, searching for appropriate models and solutions. The article presents the latest literature review and illustrates the newest trends with several examples. VOS Viewer software has been used to classify the different keywords, according to their co-citation, following clustering techniques. By analyzing the research conducted b... [more]

Petroleum has become a strategic resource to the national economy, and forecasting its demand is a critical step to supporting energy planning and policy-making for carbon reduction. We first conducted a characteristic analysis of end consumption for petroleum products, and the key affecting factors are identified through an extended logarithmic mean Divisia index (LMDI) method. Afterwards, the long-range energy alternatives planning system (LEAP) was adopted to predict the petroleum products demand by considering the potential impacts of different policies on the identified key factors. Through comparative analysis of three scenarios including five sub-scenarios, the findings show that the dual carbon constraints are crucial to petroleum demand control. Under the enforcement of existing carbon peaking policies, the petroleum products demand will peak around 2043 at 731.5 million tons, and the impact of energy intensity-related policies is more significant than that of activity level.... [more]

This paper proposes an improved common mode (CM) electromagnetic interference (EMI) suppression method in switching power supplies. The lossless snubber circuit can reduce du/dt and EMI in the high-frequency band. Nevertheless, it has a weak EMI suppression effect on the low-frequency band. A method combining the chaotic spread spectrum and the lossless snubber (CSS−LS) is proposed to improve the EMI suppression effect of the lossless snubber. It is an effective means to suppress CM EMI further. The paper used a Boost PFC converter as the object of analysis to study the CM EMI suppression effect of CSS−LS. Firstly, a CM EMI-equivalent model of the lossless snubber PFC converter was established. Then, the power spectral density function under chaotic spread-spectrum modulation was derived. The simulation analysis was performed. Finally, an experimental prototype was built, and relevant EMI tests were carried out. The experimental results show that CSS−LS can reduce CM EMI by 4~20 dBµV w... [more]

Generator fault diagnosis has a great impact on power networks. With the coupling effects, some uncertain factors, and all the complexities of generator design, fault diagnosis is difficult using any theoretical analysis or mathematical model. This paper proposes a bit-coding support vector regression (BSVR) algorithm for turbine generator fault diagnosis (GFD) based on a support vector machine (SVM) capable of processing multiple classification problems of fault diagnosis. The BSVR can simplify the design architecture and reduce the processing time for detection, where m classifier is needed for m class problems compared to the [m(m − 1)]/2 size of the original multi-class SVM. Compared with conventional methods, numerical test results showed a high accuracy, good robustness, and a faster processing performance.

Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning a... [more]

Electric vehicles (EVs) have the advantage of being resilient to natural disasters. However, users hesitate to donate electricity when they lose the chance to recharge at the utility. Solar electric vehicles (SEVs) save energy through vehicle-integrated photovoltaics (VIPV) and make it possible to voluntarily donate excess energy, thus maintaining facility resilience. Given that the supply of solar energy to VIPV systems is not continuous and is difficult to forecast, the contribution of VIPV to the resilience of the larger energy system has been called into question. This is the first study in which the potential of VIPV to maintain utility resilience is investigated in the context of physical factors, such as irradiance, and social factors. The actual energy yield of a VIPV car was determined using an advanced 3D solar irradiation model under a nonuniform shading distribution, with validation from actual measures of solar irradiance on five orthogonal sides of the car body. The Monte... [more]

Geopolymer concrete is preferred over OPC due to its use of energy waste such as fly ash, making it more sustainable and energy-efficient. However, limited research has been done on its seismic characterization in confined masonry, highlighting a gap in sustainable earthquake-resistant structures. Our study compares the performance of alkali-activated fly-ash-based geopolymer concrete bare frame and confined masonry wall panels with conventional concrete. Experimental results showed that geopolymer concrete bare frame has 3.5% higher initial stiffness and 1.0% higher lateral load-bearing capacity compared to conventional concrete. Geopolymer concrete confined masonry exhibited 45.2% higher initial stiffness and 4.1% higher ultimate seismic capacity than traditional concrete. The experimental results were verified using a numerical simulation technique with ANSYS-APDL, showing good correlation. Comparison with previously tested masonry walls revealed that GPC confined masonry has simila... [more]

Auxiliary diesel heaters are commonly used in all types of vehicles in cold climates and conditions around the world. Electric buses used in public transport utilise diesel-burning auxiliary heaters to provide thermal comfort for passengers under cold weather conditions while maintaining the operational range otherwise reduced by electric heating. However, the downside of utilising diesel burners is that they cause similar exhaust pollutants to conventional diesel vehicles. Because the emission control for auxiliary heaters is lax, the diesel burners typically lack any exhaust aftertreatment (EAT), resulting in potentially high local emissions. As the public transport sectors around the world seem to transit from traditional internal combustion engine-vehicles to battery electric applications, the significance of the emissions caused by diesel auxiliary heaters is continuously increasing. EVs are generally considered zero-emission vehicles but the implementation of diesel burners is ev... [more]

Solar photovoltaic self-consumption is an attractive approach to increase autarky and reduce emissions in the building sector. However, a successful deployment in urban rooftops requires both accurate and low-computational-cost methods to estimate the self-consumption potential and economic feasibility, which is especially scarce in the literature on net billing schemes. In the first part of this study, a bottom-up GIS-based techno-economic model has helped compare the self-consumption potential with net metering and net billing in a Mediterranean municipality of Spain, with 3734 buildings in total. The capacity was optimized according to load profiles obtained from aggregated real measurements. Multiple load profile scenarios were assessed, revealing that the potential self-sufficiency of the municipality ranges between 21.9% and 42.5%. In the second part of the study, simplified regression-based models were developed to estimate the self-sufficiency, self-consumption, economic paybac... [more]

To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling... [more]

This review article aims to describe the main applications of gas hydrates in industrial processes and the related advantages and limitations. In particular, gas storage, energy storage, gas transportation, final disposal of greenhouse gases, desalination, wastewater treatments, food concentration, and other technologies are described in detail. Similarly, the benefits and disadvantages of the solutions, currently adopted to improve the process efficiency, are discussed in the text. A particular focus on the use of additives and their capability to intervene during the formation of hydrates and on the replacement process is provided. The second part of the article deals with the use of small-chain hydrocarbons as aid gases during formation, to improve the efficiency and the competitivity of hydrate-based processes. First, the thermodynamic properties of hydrates, containing only these compounds, are described. Then, based on a collection of experimental data available elsewhere in the... [more]

The Crofer 22 APU alloy is a frequently used metallic material to manufacture interconnects in solid oxide fuel cells. However, the formation and evaporation of Cr2O3 not only increases the electrical resistance but also leads to the Cr-related degradation over the service time. In order to investigate the growth kinetics of Cr-oxide, i.e., Cr2O3, the multi-phase field model coupled with reliable CALPHAD databases is employed. The phase field simulation results are benchmarked with the predictions of Wagner’s theory. Moreover, we evidence the influence of the temperature and Cr concentration on the ferritic matrix phase and the oxygen concentration at the Cr2O3/gas interface on the growth kinetics of Cr-oxide, paving the way for further investigations of Cr-related solid oxide fuel cell degradation processes.

Efforts to control climate change with the aim of achieving carbon neutrality by 2050 have had the most significant impact on businesses operating in the energy sector, which produce large amounts of greenhouse gas (GHG) emissions. In light of such policies, oil and gas companies have set goals aimed at reducing GHG emissions and achieving carbon neutrality, but the issue remains open as to how such activities and progress towards these goals can be evaluated. This study attempts to assess the activities and progress of oil and gas companies towards carbon neutrality, with a focus on quantitative evaluation of goal achievement. First, an algorithm was developed for selecting global oil and gas companies for the analysis that reported their activities in 2022. Using this algorithm, a list of companies was compiled and their goals with regard to carbon neutrality were analyzed. Second, an assessment of how information is presented in corporate reports and which activities aimed at achiev... [more]

Electric motors are one of the largest consumers of electricity and are responsible for 40−45% of the world’s energy consumption [...]

The timely discovery of low-frequency oscillations in power systems and accurate identification of their modal parameters is critical in numerous applications. Therefore, we investigated the feasibility of using multi-channel signals and established a relative theory. An algorithm based on the underdetermined blind source separation (UBSS) algorithm was proposed using this theory. First, the energy ratio function was used to determine the fault occurrence time. Then, the Bayesian information criterion was used to estimate the number of fault sources, and the boundary conditions were set to determine the number of fault sources. Next, the UBSS algorithm was used to analyze raw data, extract individual components that characterize faults, and subsequently measure low-frequency oscillation modal parameters through the Hilbert transform. Finally, the fast independent component analysis (FastICA) algorithm was used to separate noise signal from raw data. This separation considerably reduced... [more]

Greenhouse gas (GHG) emissions from the transport sector and their effect on air quality are now a major concern, and the electrification of road freight transport is seen as one potential solution. However, this presents a challenge with the increased electricity demand on a depot’s grid connection, and increased costs if this has to be upgraded. This study seeks to evaluate the impact on costs of introducing solar (PV) panels and a Battery Energy Storage System (BESS) when a company electrifies its fleet, based on two different priorities. On one hand, avoiding the use of the grid at peak price periods at the expense of upgrading the power connection. On the other hand, avoiding any power connection upgrade at the expense of incurring excess capacity charges. These two priorities aim to represent real-life challenges that logistics and commercial companies are facing when it comes to fleet electrification. The choice of prioritising one over the other may be driven by operational req... [more]

Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational efforts with high-speed computing hardware to deal with model complexity and commitments to obtain efficient, reliable, and resilient solutions. Quantum computing (QC) is presented as a strong candidate to help MLs reach their best performance especially for cybersecurity issues and digital defense. This paper presents quantum support vector machine (QSVM) model to detect distributed denial of service (DDoS) attacks on smart micro-grid (SMG). An evaluation of our approach against a real dataset of DDoS attack instances shows the effectiveness of our proposed model. Finally, conclusions and some open issues and challenges of the fitting of ML with QC are presented.

Because of the rising demand for CO2 emission limits and the high cost of fuel, the electrification of heavy-duty vehicles has become a hot topic. Manufacturers have tried a variety of designs to entice customers, but the outcomes vary depending on the application and availability of recharging. Without affecting vehicle range, plug-in hybrids provide a potential for the automobile industry to reach its CO2 reduction objectives. However, the actual CO2 emission reductions will largely rely on the energy source, user behavior, and vehicle design. This research compares a series plug-in hybrid medium-duty truck against two baselines: nonhybrid and pure electric commercial trucks. As well as evaluating and contrasting the different tools to quantify CO2 emissions, this manuscript offers fresh information on how to simulate various powertrain components used in electrified vehicles. According to the findings, plug-in hybrids with batteries larger than 50 kWh can reduce emissions by 30%, wh... [more]

While hydro turbines generate over 40% of the world’s total renewable energy, these traditional turbines present a great environmental concern due to the potential of their sharp blades to damage aquatic lives and ecology, along with their harmful noise and vibration during operation. One effective solution to these environmental issues is to substantially increase the skew of these blades, which would result in a much safer blade operation for aquatic animals (such as fish, etc.) and a substantial reduction in noise and vibration. Adding skew to turbine rotors is known to reduce cavitation and noise, and hence, to mitigate the environmental impact on underwater fauna and flora. However, adding skew will compromise the power performance of the turbines. This study aimed to identify the effect of rotor skew on the hydrodynamic power performance of a series of horizontal-axis turbine rotors that were manufactured using 3D printing technology and tested in a towing tank. The diameter of t... [more]

Naphtha is an important distillation product of crude oil, and is used as a raw material for first-generation products such as ethylene, propylene, gasoline, xylene (BTX), and others. However, due to the different sources of crude oil, differences in naphtha composition impact the quality of conversion processes. Parameters such as pressure, charge flow, and temperature need to be adjusted for conversion efficiency. This work aims to compare naphtha samples from different origins, through the analysis of distillation curve (ASTM D86), density (ASTM D4052), total sulfur (ASTM D4294), and n-paraffins, iso-paraffins, olefins, naphthene, and aromatics (PIONA, ASTM D5134). Among these parameters evaluated in naphtha, the ones that showed the greatest correlation with the type of oil and its origin was the amount of total sulfur, number of aromatics, and paraffins. The three imported evaluated naphtha presented values greater than 200 mg/kg of total sulfur, aromatics above 9%w, and paraffins... [more]

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the “WinJi Gearbox Fault De... [more]

Expansion energy is the main factor of coal and gas outbursts, and the gas desorption around the outburst hole is developed in variable pressure conditions. While studying the impact of moisture on gas desorption characteristics, atmospheric pressure desorption is usually used, but its characteristics under variable pressure conditions have not been thoroughly investigated. In this study, typical outburst coal samples with different water contents from the Jincheng mining area of China were selected as research objects, and the effects of water on gas displacement, desorption, desorption rate, and gas desorption index (K1) of drilling cuttings under step-by-step depressurization were analyzed by means of stepwise depressurization and atmospheric desorption experiments. The research conclusions suggest that (1) the amount of gas replacement, which augments rapidly during the inception, increases with the growth of water content under the experimental conditions, and then the rate decrea... [more]

The energy conversion of nickel-impregnated coconut shells using supercritical water has not yet been explored. The impregnation process was conducted at room temperature and a pH of 5.80 for 72 h. Characterization of the prepared sample confirmed the presence of nickel nanoparticles with an average size of 7.15 nm. The gasification of control and impregnated samples was performed at 400−500 °C, biomass loading from 20 to 30 wt% and residence time from 20 to 60 min. The response surface methodology (RSM) approach, with a Box−Behnken method, was used to design the experiments. The optimization model showed that the non-catalytic process at 500 °C, 60 min and 20 wt% of biomass loading could promote an H2 yield of 8.8 mol% and gasification efficiency of 47.6%. The gasification of nickel-impregnated coconut shells showed significantly higher gasification efficiency (58.6%) and hydrogen yield (17.2 mol%) with greater carbon and hydrogen efficiencies (109.4 and 116.9%) when compared to the n... [more]

Over the last few decades, climate change and global warming have intensified a serious threat that may deteriorate global sustainable development. The factors significantly contributing to global warming are greenhouse gases, mainly carbon dioxide emissions. Therefore, it is crucial to consider the variables affecting carbon emissions considerably. This study examines symmetric (linear) and asymmetric (non-linear) effects of green technology innovation (GTI), economic policy uncertainty (EPU) along with foreign direct investment (FDI), and economic development (GDP) on carbon emissions (CO2) by utilizing yearly time series data between 1970−2018 in Italy. We employed linear and non-linear autoregressive distributed lag (ARDL) approaches to examine short- and long-run estimates. The symmetric results show that GTI and EPU mitigate environmental degradation in the long run and intensify in the short run, whereas FDI increases environmental issues over the long and short run. Nevertheles... [more]

Electric vehicles (EVs) are becoming increasingly popular, and it is important for utilities to understand their charging characteristics to accurately estimate the demand on the electrical grid. In this work, we developed simulation models for different EV charging scenarios in the home sector. We used them to predict maximum demand based on the increasing penetration of EV consumers. We comprehensively reviewed the literature on EV charging technologies, battery capacity, charging situations, and the impact of EV loads. Our results suggest a method for visualizing the impact of EV charging loads by considering factors such as state of charge, arrival time, charging duration, rate of charge, maximum charging power, and involvement rate. This method can be used to model load profiles and determine the number of chargers needed to meet EV user demand. We also explored the use of a time-of-use (TOU) tariff as a demand response strategy, which encourages EV owners to charge their vehicles... [more]