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The authors compare the energy consumption and CO2 emissions from vehicles using internal combustion engines (ICE), battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and two types of hybrid vehicles, BEV-ICE hybrid and BEV-FCEV hybrid. This paper considers several scenarios for four countries’ electricity production from primary energy sources to estimate total CO2 release. Energy consumption of the vehicle per 100 km, emissions during manufacturing, battery production, and lifecycle of the vehicle are considered in the total amount evaluation of CO2 released. The results show that with current technologies for battery manufacturing, and a significant proportion of national grid electricity delivered by fossil fuels, BEV is the best choice to reduce carbon emissions for shorter driving ranges. In the case of electricity generation mainly by low-carbon sources, FCEV and BEV-FCEV hybrid vehicles end up with lower carbon dioxide emissions. In contrast, with electricity mainly generated from fossil fuels, electric vehicles do not reduce CO2 emissions compared to combustion cars.

This paper presents a pre-normative roadmap that foresees the developments in the charging of heavy-duty electric vehicles (HD-EVs). It supports and facilitates the future standardization efforts of charging technologies by creating an overview of the popularity of charging technologies and the end users’ needs. The required input for the work was collected using a comprehensive investigation on the available charging technologies and their standardization, reviewing the existing roadmaps and research work, and conducting surveys and interviews of end users and technical stakeholders. According to the findings, a pantograph on the roof of a vehicle and plug-based charging are currently the most used charging interfaces. This trend is likely to continue in the future, since (1) pantographs on vehicle roofs, (2) pantographs on infrastructure, and (3) plugs were graded as charging interfaces with the highest potential by the participants of the technical survey. Static and conductive charging technologies show more potential than dynamic and wireless charging technologies. Nevertheless, inductive charging may be a future charging solution for HD-EVs if the current bottlenecks in the technology can be addressed. These bottlenecks include high prices, slightly lower efficiency, lack of standardization, the maximum achievable power, and safety concerns. Furthermore, interoperability was repeatedly mentioned as the main challenge for today’s charging technologies.

In this short communication, we conducted first-principles calculations to explore the stability of boron monochalcogenides (BX, X = S, Se or Te), as a new class of two-dimensional (2D) materials. We predicted BX monolayers with two different atomic stacking sequences of ABBA and ABBC, referred in this work to 2H and 1T, respectively. Analysis of phonon dispersions confirm the dynamical stability of BX nanosheets with both 2H and 1T atomic lattices. Ab initio molecular dynamics simulations reveal the outstanding thermal stability of all predicted monolayers at high temperatures over 1500 K. BX structures were found to exhibit high elastic modulus and tensile strengths. It was found that BS and BTe nanosheets can show high stretchability, comparable to that of graphene. It was found that all predicted monolayers exhibit semiconducting electronic character, in which 2H structures present lower band gaps as compared with 1T lattices. The band-gap values were found to decrease from BS to BTe. According to the HSE06 results, 1T-BS and 2H-BTe show, respectively, the maximum (4.0 eV) and minimum (2.06 eV) electronic band gaps. This investigation introduces boron monochalcogenides as a class of 2D semiconductors with remarkable thermal, dynamical, and mechanical stability.

Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems.

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain.

This paper presents a comparative review of three different widely used power inverters, namely the conventional six-switch inverter; the reduced switch count four-switch inverter; and the eight-switch inverter. The later inverter can be reconfigured as a neutral-point diode-clamped inverter at the failure of one inverter leg. The three power inverters are compared and discussed with respect to cost, complexity, losses, common mode voltage, and control techniques. The paper is intended to serve as a guide regarding selecting the appropriate inverter for each specific application. Simulation results are presented to demonstrate the performance of the three power inverters, followed by a comprehensive comparison between the three power inverters.

A recent survey of cybersecurity assessment methods proposed by the scientific community revealed that their practical adoption constitutes a great challenge. Further research that aimed at identifying the reasons for that situation demonstrated that several factors influence the applicability, including the documentation level of detail, the availability of supporting tools, and the continuity of support. This paper presents the European Energy Information Sharing and Analysis Centre (EE-ISAC)—a cybersecurity platform for the energy sector that has been adopted by multiple organisations. The platform facilitates sharing information about cybersecurity incidents, countermeasures, and assessment results. Prospectively, it is envisaged to be integrated with the threat intelligence platform that enables real-time situational awareness. By considering both fault and attack scenarios together, threat awareness can be mapped onto operational contexts to prioritise decisions and responses. This paper analyses EE-ISAC’s approach based on the conceptual applicability framework developed during the research, to improve the applicability and usefulness of this platform for energy sector participants and to identify areas that require further development.

The wave energy resources in the Indian Ocean can be considered as a potential alternative to fossil fuels. However, the wave energy resources are subject to short-term fluctuations and long-term changes due to climate change. Hence, considering sustainable development goals, it is necessary to assess both short-term (intra-annual) variation and long-term change. For this purpose, the simulated wave characteristics were utilized, and the wave power and its variation and change were analyzed in the whole domain and nearshore areas. The short-term fluctuation was investigated in terms of monthly and seasonal variations and the future change was discussed based on absolute and relative changes. Both analyses show that the Southern Indian Ocean, despite experiencing extreme events and having higher wave energy potential, is more stable in terms of both short and long-term variation and change. The assessment of the total and exploitable storages of wave energy and their future change revealed the higher potential and higher stability of the nearshores of the Southern Indian Ocean. It can be concluded that based on various factors, the south of Sri Lanka, Horn of Africa, southeast Africa, south of Madagascar and Reunion and Mauritius islands are the most suitable areas for wave energy extraction.

A Positive Energy District (PED) is a portion of urban area with defined boundaries that can produce energy in excess of its own consumption. The aim of this study is to analyse design variations among the six projects (12 case studies) of PED belonging to the European Smart Cities and Communities programme. Thus, it will be possible to identify the reasons behind the energy choices related to generation, storage and distribution that appear in the different geographical areas. To achieve this, different data were collected by consulting official documents and creating questionnaires that were communicated with the project representatives. Thus, the result of this study is a catalogue of the energy system solutions adopted in the studied PEDs with a critical analysis of the different motivations behind them in order to outline general trends in the geographical areas with similar characteristics. In conclusion, this study defined which technological choices are the most common in territories with similar profiles and how divergent those with different profiles are. Furthermore, applied to a large catalogue of PED, the methodology identified would make it possible to create different operating models for different territorial types and urban settlements.

Large databases of legacy hydrocarbon reservoir and well data provide an opportunity to use modern data mining techniques to improve our understanding of the subsurface in the presence of uncertainty and improve predictability of reservoir properties. A data mining approach provides a way to screen dependencies in reservoir and fluid data and enable subsurface specialists to estimate absent properties in partial or incomplete datasets. This allows for uncertainty to be managed and reduced. An improvement in reservoir characterisation using machine learning results from the capacity of machine learning methods to detect and model hidden dependencies in large multivariate datasets with noisy and missing data. This study presents a workflow applied to a large basin-scale reservoir characterization database. The study aims to understand the dependencies between reservoir attributes in order to allow for predictions to be made to improve the data coverage. The machine learning workflow comprises the following steps: (i) exploratory data analysis; (ii) detection of outliers and data partitioning into groups showing similar trends using clustering; (iii) identification of dependencies within reservoir data in multivariate feature space with self-organising maps; and (iv) feature selection using supervised learning to identify relevant properties to use for predictions where data are absent. This workflow provides an opportunity to reduce the cost and increase accuracy of hydrocarbon exploration and production in mature basins.