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La movilidad eléctrica parece traer un cambio de paradigma en el sector del transporte por carretera en todo el mundo. El enorme consumo de combustibles fósiles y la creciente congestión del tráfico han causado preocupaciones sobre el consumo futuro de energía, el crecimiento de la economía y las emisiones de gases de efecto invernadero en los países miembros del Consejo de Cooperación del Golfo. La introducción de vehículos eléctricos (VE) en los dos países más poblados de la región, es decir, el Reino de Arabia Saudita y los Emiratos Árabes Unidos, se considera una opción prometedora para abordar la contaminación ambiental y los futuros temores relacionados con la economía de la región. Este documento presenta los impulsores clave para que los países adopten el transporte eléctrico. Este estudio de investigación investiga el impacto de la penetración de los vehículos eléctricos en la energía, la economía y el medio ambiente de Arabia Saudita y los Emiratos Árabes Unidos a través del pronóstico de existencias de vehículos eléctricos mediante el análisis de regresión lineal. Los resultados obtenidos sugieren que el crecimiento esperado en el sector eléctrico de Arabia Saudita y los Emiratos Árabes Unidos les permitirá mantener una penetración de vehículos eléctricos del 5% y el 30% para 2030, respectivamente. En este sentido, se propone un conjunto de políticas que permitirán a los países acelerar sus esfuerzos para alcanzar los objetivos previstos de reducción de emisiones de gases de efecto invernadero (GEI). Aunque la investigación presentada se centra en el estudio de caso de Arabia Saudita y los Emiratos Árabes Unidos, los hallazgos de la investigación son lo suficientemente generalizados como para aplicarse a todas las demás regiones de la región. El conjunto de políticas sugerido servirá como guía para las partes interesadas pertinentes sobre las medidas necesarias para la electrificación sostenible del transporte por carretera en Arabia Saudita y los Emiratos Árabes Unidos. La mobilité électrique semble apporter un changement de paradigme dans le secteur du transport routier dans le monde entier. L'énorme consommation de combustibles fossiles et la congestion croissante du trafic ont suscité des inquiétudes quant à la consommation d'énergie future, à la croissance économique et aux émissions de gaz à effet de serre dans les pays membres de la région du Conseil de coopération du Golfe. L'introduction de véhicules électriques (VE) dans les deux pays les plus peuplés de la région, à savoir le Royaume d'Arabie saoudite et les Émirats arabes unis, est considérée comme une option prometteuse pour lutter contre la pollution de l'environnement et les craintes liées à l'économie future. Ce document présente les principaux facteurs incitant les pays à adopter le transport électrique. Cette étude de recherche étudie l'impact de la pénétration des véhicules électriques sur l'énergie, l'économie et l'environnement de l'Arabie saoudite et des Émirats arabes unis grâce à la prévision des stocks de véhicules électriques à l'aide d'une analyse de régression linéaire. Les résultats obtenus suggèrent que la croissance attendue dans le secteur de l'électricité en Arabie saoudite et aux Émirats arabes unis leur permettra de maintenir une pénétration de 5 % et de 30 % des véhicules électriques d'ici 2030, respectivement. À cet égard, un ensemble de politiques est proposé, qui permettra aux pays d'accélérer leurs efforts pour atteindre les objectifs de réduction des émissions de gaz à effet de serre (GES). Bien que la recherche présentée se concentre sur l'étude de cas de l'Arabie saoudite et des Émirats arabes unis, les résultats de la recherche sont suffisamment généralisés pour être appliqués à toutes les autres régions de la région. L'ensemble de politiques suggéré servira de lignes directrices aux parties prenantes concernées sur les mesures nécessaires à l'électrification durable du transport routier en Arabie saoudite et aux Émirats arabes unis. Electric mobility seems to bring a paradigm shift in the road transport sector worldwide. Huge consumption of fossil fuels and ever-increasing traffic congestion have caused concerns over future energy consumption, economy growth, and greenhouse gas emissions in the Gulf Cooperation Council region's member countries. The introduction of electric vehicles (EVs) in the two most populous countries of the region, i.e., the Kingdom of Saudi Arabia and UAE is considered a promising option to address environmental pollution and future economy-related fears region. This paper presents key drivers for the countries to adopt electric transportation. This research study investigates the impact of EVs penetration on energy, economy, and environment of KSA and UAE through EV stockpile forecasting using linear regression analysis. The obtained results suggest that expected growth in KSA and UAE's power sector will enable them to keep up 5% and 30% EVs penetration by 2030, respectively. In this regard, a set of policies are proposed, which will enable the countries to pace up their efforts to achieve the intended greenhouse gases (GHG) emission reduction goals. Though the presented research is focused on the case study of KSA and UAE, the research findings are generalized enough to be applied to all other regions of the region. The suggested set of policies will serve as guidelines for the relevant stakeholders about the necessary measures required for sustainable road transport electrification in KSA and UAE. يبدو أن التنقل الكهربائي يجلب نقلة نوعية في قطاع النقل البري في جميع أنحاء العالم. تسبب الاستهلاك الهائل للوقود الأحفوري والازدحام المروري المتزايد باستمرار في مخاوف بشأن استهلاك الطاقة في المستقبل ونمو الاقتصاد وانبعاثات غازات الدفيئة في البلدان الأعضاء في مجلس التعاون الخليجي. يعتبر إدخال المركبات الكهربائية (EVs) في البلدين الأكثر اكتظاظًا بالسكان في المنطقة، أي المملكة العربية السعودية والإمارات العربية المتحدة، خيارًا واعدًا لمعالجة التلوث البيئي ومنطقة المخاوف المتعلقة بالاقتصاد المستقبلي. تعرض هذه الورقة الدوافع الرئيسية للبلدان لتبني النقل الكهربائي. تبحث هذه الدراسة البحثية في تأثير تغلغل المركبات الكهربائية على الطاقة والاقتصاد والبيئة في المملكة العربية السعودية والإمارات العربية المتحدة من خلال التنبؤ بمخزون المركبات الكهربائية باستخدام تحليل الانحدار الخطي. تشير النتائج التي تم الحصول عليها إلى أن النمو المتوقع في قطاع الطاقة في المملكة العربية السعودية والإمارات العربية المتحدة سيمكنهما من الحفاظ على انتشار السيارات الكهربائية بنسبة 5 ٪ و 30 ٪ بحلول عام 2030، على التوالي. وفي هذا الصدد، تُقترح مجموعة من السياسات التي ستمكن البلدان من تسريع جهودها لتحقيق الأهداف المنشودة لخفض انبعاثات غازات الدفيئة. على الرغم من أن البحث المقدم يركز على دراسة حالة المملكة العربية السعودية والإمارات العربية المتحدة، إلا أن نتائج البحث معممة بما يكفي لتطبيقها على جميع المناطق الأخرى في المنطقة. ستكون مجموعة السياسات المقترحة بمثابة إرشادات لأصحاب المصلحة المعنيين حول التدابير اللازمة لكهربة النقل البري المستدام في المملكة العربية السعودية والإمارات العربية المتحدة.

This paper is aimed at modelling of a distinct smart charging station for electric vehicles (EVs) that is suitable for DC quick EV charging while ensuring minimum stress on the power grid. Operation of the charging station is managed in such a way that it is either supplied by photovoltaic (PV) power or the power grid, and the vehicle-to-grid (V2G) is also implemented for improving the stability of the grid during peak load hours. The PV interfaced DC/DC converter and grid interfaced DC/AC bidirectional converter share a DC bus. A smooth transition of one operating mode to another demonstrates the effectiveness of the employed control strategy. Modelling and control of the different components are explained and are implemented in Simulink. Simulations illustrate the feasible behaviour of the charging station under all operating modes in terms of the four-way interaction among PV, EVs and the grid along with V2G operation. Additionally, a business model is discussed with comprehensive analysis of cost estimation for the deployment of charging facilities in a residential area. It has been recognized that EVs bring new opportunities in terms of providing regulation services and consumption flexibility by varying the recharging power at a certain time instant. The paper also discusses the potential financial incentives required to inspire EV owners for active participation in the demand response mechanism.

Coral bleaching continues to be one of the most devastating and immediate impacts of climate change on coral reef ecosystems worldwide. In 2015, a major bleaching event was declared as the "3rd global coral bleaching event" by the United States National Oceanic and Atmospheric Administration, impacting a large number of reefs in every major ocean. The Red Sea was no exception, and we present herein in situ observations of the status of coral reefs in the central Saudi Arabian Red Sea from September 2015, following extended periods of high temperatures reaching upwards of 32.5°C in our study area. We examined eleven reefs using line-intercept transects at three different depths, including all reefs that were surveyed during a previous bleaching event in 2010. Bleaching was most prevalent on inshore reefs (55.6% ± 14.6% of live coral cover exhibited bleaching) and on shallower transects (41% ± 10.2% of live corals surveyed at 5m depth) within reefs. Similar taxonomic groups (e.g., Agariciidae) were affected in 2015 and in 2010. Most interestingly, Acropora and Porites had similar bleaching rates (~30% each) and similar relative coral cover (~7% each) across all reefs in 2015. Coral genera with the highest levels of bleaching (>60%) were also among the rarest (<1% of coral cover) in 2015. While this bodes well for the relative retention of coral cover, it may ultimately lead to decreased species richness, often considered an important component of a healthy coral reef. The resultant long-term changes in these coral reef communities remain to be seen.

The high penetration of renewable energy resources’ (RESs) and electric vehicles’ (EVs) demands to power systems can stress the network reliability due to their stochastic natures. This can reduce the power quality in addition to increasing the network power losses and voltage deviations. This problem can be solved by allocating RESs and EV fast charging stations (FCSs) in suitable locations on the grid. So, this paper proposes a new approach using the red kite optimization algorithm (ROA) for integrating RESs and FCSs to the distribution network through identifying their best sizes and locations. The fitness functions considered in this work are: reducing the network loss and minimizing the voltage violation for 24 h. Moreover, a new version of the multi-objective red kite optimization algorithm (MOROA) is proposed to achieve both considered fitness functions. The study is performed on two standard distribution networks of IEEE-33 bus and IEEE-69 bus. The proposed ROA is compared to dung beetle optimizer (DBO), African vultures optimization algorithm (AVOA), bald eagle search (BES) algorithm, bonobo optimizer (BO), grey wolf optimizer (GWO), multi-objective multi-verse optimizer (MOMVO), multi-objective grey wolf optimizer (MOGWO), and multi-objective artificial hummingbird algorithm (MOAHA). For the IEEE-33 bus network, the proposed ROA succeeded in reducing the power loss and voltage deviation by 58.24% and 90.47%, respectively, while in the IEEE-69 bus it minimized the power loss and voltage deviation by 68.39% and 93.22%, respectively. The fetched results proved the competence and robustness of the proposed ROA in solving the problem of integrating RESs and FCSs to the electrical networks.

A higher penetration of EVs may pose several challenges to the power systems, including reliability issues. To analyze the impact of EVs on the reliability of power systems, a detailed EV charging infrastructure is considered in this study. All possible charging locations (home, workplace, public locations, and commercial fast chargers) and different charging levels (level 1, level 2, and DC fast charging) are considered, and seven charging infrastructures are determined first. Then, the reliability impact of each charging infrastructure is determined using the two widely used reliability indices, i.e., the loss of load expectation (LOLE) and the loss of energy expectation (LOEE). The impact of mixed charging infrastructure portfolios is also analyzed by considering two different cases, which included the equal share of all charging infrastructure and charging infrastructure share based on consumer preferences. The performance is analyzed on a well-known reliability test system (Roy Billinton Test System) and different penetration levels of EVs are considered in each case. Test results have shown that fast-charging stations have the worst reliability impact. In addition, it was also observed that mixed charging portfolios have lower reliability impacts despite having a fair share of fast-charging stations.

Biomass is a renewable energy source because it is contained in organic material such as plants. This paper introduces a modified hunger games search for solving global optimization and biomass distributed generator problems. The hunger search algorithm is a very recent optimization algorithm. Despite its merits, it still needs some modifications. The proposed approach includes a new binary τ-based crossover strategy with satisfaction fulfillment step mechanisms. This new algorithm is designed to improve the original hunger games search algorithm by addressing some of its shortcomings, specifically, in solving problems related to global optimization such as finding the best possible solutions for biomass distributed generators. To assess the power of the new approach, its performance was evaluated on the IEEE CEC’2020 test suite against five recent and competitive algorithms. This comparison process included applying the Wilcoxon sign rank and Friedman statistical tests. Reducing the system losses and enhancing the network’s voltage profile are two main issues in the stability of radial distribution networks. Optimal allocation of biomass distributed generators in radial distribution networks can not only improve their stability but also guarantee good service to the customers. Consequently, this research work suggests an effective strategy based on the proposed approach to produce the optimal positions, sizes, and power factors of the biomass distributed generators in the network. Accordingly, the target is to mitigate the network’s active power loss such that the power flow and the bus voltage have to be maintained at their standard limits. Three distribution networks were considered for validating the superiority of the new proposed algorithm. These networks are the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. The obtained results were compared with the gravitational search algorithm, whale optimization algorithm, grey wolf optimizer, Runge Kutta method, and the original hunger search algorithm. The new approach outperformed the other considered approaches in obtaining the optimal parameters, which mitigated the power loss to 11.6300, 5.2291, and 145.489 kW, with loss reduction of 94.49%, 97.68%, and 88.79% for the three networks, respectively.

Since the rules and regulations strongly emphasize environmental preservation and greenhouse gas GHG reduction, researchers have progressively noticed a shift in the transportation means toward electromobility. Several challenges must be resolved to deploy EVs, beginning with improving network accessibility and bidirectional interoperability, reducing the uncertainty related to the availability of suitable charging stations on the trip path and reducing the total service time. Therefore, suggesting DQN supported by AIoT to pair EVs’ requests and station invitations to reduce idle queueing time is crucial for long travel distances. The author has written a proposed methodology in MATLAB to address significant parameters such as the battery charge level, trip distance, nearby charging stations, and average service time. The effectiveness of the proposed methodology is derived from hybridizing the meta-heuristic techniques in searching DQN learning steps to obtain a solution quickly and improve the servicing time by 34%, after solving various EV charging scheduling difficulties and congestion control and enabling EV drivers to policy extended trips. The work results obtained from more than 2145 training hypothetical examples for EVs’ requests were compared with the Bayesian Normalized Neural Network (BASNNC) algorithm, which hybridize the Beetle Antennae Search and Neural Network Classifier, and with other methods such as Grey Wolf Optimization (GWO) and Sine-cosine and Whale optimization, revealing that the mean overall comparison efficiencies in error reduction were 72.75%, 58.7%, and 18.2% respectively.

Abstract Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.