Energy Resources Research Articles

Mega-scale solar-wind complementarity assessment for large-scale hydrogen production and storage (H2PS) in Algeria: A techno-economic analysis

Green hydrogen (GH2) is produced using renewable energy resources (RERs) such as solar photovoltaic (PV) and wind energy. However, relying solely on a single source, H2 production systems may encounter challenges due to the intermittent nature, time-of-day variability, and seasonal changes associated with these energies. This paper addresses the assessment of mega-scale solar-wind complementarity and the economic viability of large-scale H2 production and storage in Algeria, considering various climatic conditions. A techno-economic feasibility analysis is conducted by examining various factors, including resource complementarity, electricity and hydrogen productivity, solar and wind contributions, capacity factors (CFs), total net present cost (TNPC), the levelized cost of hydrogen (LCOH) production, and storage (LCOS). The findings reveal that among the studied locations (Tamanrasset, M'sila, Hassi R'mel, and Adrar), Tindouf ranked first in H2 production, with the highest amount at 11,267 kg and the lowest LCOH at 2.999 $/kg. Wind energy accounted for 69% (3,846,922 kWh) of the total (5,572,571 kWh), while solar energy contributed 31% (1,738,171 kWh). Economically, the LCOH-TNPC for the various locations ranged from 2.99 $/kg-77,042,696 $ to 3.70 $/kg-99,000,488 $. The total electricity-hydrogen generated is estimated at 25,840.251 MWh-529,395 kg. Wind power makes the major contribution, accounting for 63%–70%, compared to the PV system's share, ranging from 31% to 37%. Compared to relying on a single source, leveraging solar-wind complementarity presents opportunities for more consistent energy utilization and reliable hydrogen production. The obtained results are essential for decision-makers and policymakers in Algeria, indicating that the country can successfully achieve energy diversification and transition for future sustainability, and become a major player in the international hydrogen market.

METHODOLOGICAL APPROACH TO ASSESSING THE IMPACT OF ENERGY EFFICIENCY ON THE FINANCIAL PERFORMANCE OF A GAS COMPANY

Currently, the use of energy-saving technologies that reduce costs and improve the production process is the most important factor in the development of the gas industry. The relevance of the research topic is due to the fact that in order to resolve issues related to reducing energy costs in the gas sector of the Russian economy, optimal use of non-renewable energy resources, and reducing technological losses, an integrated approach to assessing energy efficiency is required, as well as a methodology for assessing the impact of energy efficiency indicators on the financial performance of a company. The purpose of this article is to develop a methodological approach to assessing the impact of the level of energy efficiency on the financial performance of a gas company. The research methodology is based on correlation and regression analysis tools. As part of the study, a system of energy efficiency indicators for a gas company's activities has been formed and, using correlation and regression analysis tools, a methodology for assessing the impact of the level of energy efficiency on the financial performance of a gas company is proposed. An algorithm for correlation and regression analysis is presented, which makes it possible to formalize a step-by-step assessment of the impact of the level of energy efficiency on the financial performance of a gas company. The proposed methodological approach allows us to identify correlation-regression dependencies between the company’s key performance indicators (sales revenue and net profit) and the proposed energy efficiency indicators, determine the most significant indicators that affect the company’s performance indicators and determine the equation for the relationship between the company’s performance and energy efficiency indicators. Testing of the proposed approach using the example of the gas company PJSC Gazprom made it possible to quantify how changes in the energy intensity of gas production and the energy intensity of gas transportation will affect changes in sales revenue and net profit of the company. It has been established that the energy efficiency indicator such as the specific energy intensity of gas transportation has the greatest impact on the financial performance of the company. Regression equations have been established to describe the nature of the relationship between energy efficiency indicators and sales revenue and net profit of a gas company.

Assessment of grid electricity systems using the life-cycle carbon-emission model

The previous study developed a life-cycle carbon emissions (LCCE) algorithm in MS Excel. Despite improvements, a comprehensive approach is needed to conduct LCCE inventory analysis using current methods. This study diverges from existing research by assessing life-cycle carbon emissions inventory data of power generation and transmission systems on studied grids, considering component lifespans, recycling pollutants, and retirement rates. The life-cycle carbon emissions inventory analysis results improve understanding of power system environmental performance, aligning with stakeholders' objectives. This study aims to enhance the environmental performance of electric power systems in Kenya, Rwanda, and Tanzania by evaluating the life-cycle carbon emissions of power generation and transmission within their national grids. The selected grids are the right participants for the study, of non-renewable and renewable grid electricity generation mixes, due to their different environmental features, potential power trade, upcoming grid interconnection, and power transmission practises at various scales. The study applied a life cycle assessment method and simulated the learning patterns using RStudio. The data (emission factors and activity) has been collected from the reports (scientific and technical) and national utility actors. The presented results show that only Kenyan generation and transmission systems have the lifetime decarbonization performance relationship between renewable energy sources dominated power systems and non-renewable energy sources dominated power systems. A major challenge of this study has been the scarcity of primary data, leading to reliance on some secondary and external sources. Therefore, future research should consider the use of more internal and primary data sources, and the use of the most current data, including new technologies adopted from cradle-to-grave of the systems. This study's findings inform better system designs, policies, and plans for improved environmental performance in electrical power systems.

Energy management of the residential smart microgrid with optimal planning of the energy resources and demand side

The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using two-layer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in first-layer optimization. Then, minimizing the consumption costs and consumers' discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lp-metric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the two-layer optimization is confirmed using testing proposed case studies in the numerical simulation.

Sustainable hybrid energy system’s reliability optimization by solving RRAP-CM with integration of metaheuristic approaches

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

DEVELOPMENT OF SCIENTIFIC FOUNDATIONS AND METHOD OF AUTONOMOUS POWER AND GAS SUPPLY OF REMOTE CONSUMERS FROM GAS DISTRIBUTION NETWORKS

The development and implementation of independent power supply systems with a high degree of autonomy is becoming an urgent task for the development of remote and inaccessible regions of the country, both due to the lack of energy capacities and accessible networks in newly developed areas, and the high cost of allocating separate power lines for remote and compact settlements. The increase in the scale of central networks is associated with high capital investments and long project implementation time, which creates inconvenience for consumers and the development of small and medium-sized enterprises. At the same time, unlike power supply, gasification issues, which are actively being resolved in all regions of the country, make it possible to connect new consumers of natural gas several times faster and cheaper, which is due to the high manufacturability and speed of construction of polyethylene gas pipelines of distribution networks, provided that gas pipelines pass in the region. In addition to lower capital and operating costs, the process of transporting energy resources through pipelines in the form of a primary source — gas — is characterized by significantly lower losses compared to water and electricity. At the same time, network gas in combination with trigeneration technologies and cryogenic hydrocarbon storage can be used not only as boiler fuel, but also as motor fuel, a source for generating electricity, heat and even cold with a replenished autonomous reserve. Thus, as an alternative solution to traditional power systems, it is proposed to use hybrid power systems — systems based on trigeneration plants with gas piston installations, turbine and screw machines, as well as cryogenic gas storage facilities that can provide the population with electricity, heat, cold and motor fuel. The article analyzed the state and development of the fuel and energy complex in Russia, conducted a study of the needs for heat, gas and electricity for an average compact settlement with small and medium-sized enterprises. Analysis and selection of equipment parameters for uninterrupted autonomous power supply of the economy was carried out.

The Development of Metal-Free Porous Organic Polymers for Sustainable Carbon Dioxide Photoreduction

A viable tactic to effectively address the climate crisis is the production of renewable fuels via photocatalytic reactions using solar energy and available resources like carbon dioxide (CO2) and water. Organic polymer material-based photocatalytic materials are thought to be one way to convert solar energy into valuable chemicals and other solar fuels. The use of porous organic polymers (POPs) for CO2 fixation and capture and sequestration to produce beneficial compounds to reduce global warming is still receiving a lot of interest. Visible light-responsive organic photopolymers that are functionally designed and include a large number of heteroatoms and an extended π-conjugation allow for the generation of photogenerated charge carriers, improved absorption of visible light, increased charge separation, and decreased charge recombination during photocatalysis. Due to their rigid structure, high surface area, flexible pore size, permanent porosity, and adaptability of the backbone for the intended purpose, POPs have drawn more and more attention. These qualities have been shown to be highly advantageous for numerous sustainable applications. POPs may be broadly categorized as crystalline or amorphous according to how much long-range order they possess. In terms of performance, conducting POPs outperform inorganic semiconductors and typical organic dyes. They are light-harvesting materials with remarkable optical characteristics, photostability, cheap cost, and low cytotoxicity. Through cocatalyst loading and morphological tweaking, this review presents optimization options for POPs preparation techniques. We provide an analysis of the ways in which the preparative techniques will affect the materials’ physicochemical characteristics and, consequently, their catalytic activity. An inventory of experimental methods is provided for characterizing POPs’ optical, morphological, electrochemical, and catalytic characteristics. The focus of this review is to thoroughly investigate the photochemistry of these polymeric organic photocatalysts with an emphasis on understanding the processes of internal charge generation and transport within POPs. The review covers several types of amorphous POP materials, including those based on conjugated microporous polymers (CMPs), inherent microporosity polymers, hyper-crosslinked polymers, and porous aromatic frameworks. Additionally, common synthetic approaches for these materials are briefly discussed.

The role of environmental tax in guiding global climate policies to mitigate climate changes in European region

AbstractThe purpose of this study is to perform a practical inquiry into the influence of environmental tax laws on the execution of climate‐related financial policies. Our research will assess the effectiveness of environmental tax measures in 23 European countries from 2011 to 2020. The panel‐corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model are used in the empirical examination of the link between environmental tax laws and the implementation of climate‐related financial measures. This study is based on panel data with cross‐sectional dependence. The results of our estimation highlight the need to improve policy effectiveness by using all four ecological tax indicators. These include total environmental tax revenue, energy tax revenue, pollution and resource tax revenue, and transportation tax revenue. Furthermore, we provide actual evidence clarifying the process by which the implementation of environmental tax policies improves the efficacy of climate‐related financial policies in the short term as well as the long term. According to the findings, a third of environmental tax policy indicators have a long‐term effect on the implementation of climate‐related financial measures, with no short‐term effects seen. Our findings are critical for economists and policy‐makers who support the environmental tax as an effective tool to promote a country's climate policy implementations.

The status of artisanal coal mining in Afghanistan: Using remote sensing to assess remaining artisanally mineable Jurassic coal resources

Coal mining is critical to the functioning of the current Afghan economy providing jobs, tax revenue, and foreign exchange. Taxes on coal may constitute the Taliban Government’s single largest source of operating income. Prior to the start of mining, Afghanistan had between 215 M and 430 M tonnes of hypothetical category Jurassic coal resources at depths accessible to artisanal miners. Since 1940, between 51 M and 89 M tonnes of coal have been extracted. Technologically primitive mining practices result in suboptimal coal extraction from disorganized room and pillar mines leaving significant amounts of coal unmineable. Frequent tunnel collapse, gas and coal dust explosions, and uncontrolled widespread mine fires have destroyed or made inaccessible significant additional volumes of remaining artisanally mineable resources. The haphazard unmapped development of the existing Jurassic coal makes it impossible to redevelop these fields using more efficient mining technologies. If new mines are to be developed, they will have to tap subsurface coals not accessible to artisanal miners. 66 % of the area mapped as Lower to Middle Jurassic rock does not host outcropping coal beds. This area, as well as some of the areas beneath thin veneers of lowest Cretaceous sediment are potential exploration targets. Jurassic coals are very gassy. Developing coalbed methane prospects is a more effective and less environmentally destructive means to access the energy contained in the Jurassic coals.

Are social justice and sustainability interdependent? If so, how and under what conditions?

AbstractSustainability often entails dealing with issues of justice in the distribution of goods and bads, such as resources and restrictions. Applications of justice to concrete situations, including what is deserved and entitled, call for specifications of several issues, like the meaning of “deservingness” and “entitlement” and how justice should be accomplished. This is a key aspect for sustainability, as social arrangements that are not experienced as fair may not be long‐lived. Conversely, justice may be an empty notion unless it is sustainable. With this in mind, we provide an inventory of factors to analyze the relationship between (un)sustainability and social (in)justice from a social psychological perspective. We (a) clarify the gist of the two subjective notions, (b) discuss their interdependences, (c) provide a list of factors for analyses, and (d) illustrate how they may be applied to two cases, one at a global level (food accessibility) and the other at a local level (distribution of renewable energy resources in communities). The proposed framework forms the basis of empirical analyses of the social dimension of sustainability.

The Impacts on Regional Development and “Resource Curse” by Energy Substitution Policy: Verification from China

With the increasing “resource curse” phenomena relating to energy resources in China, research concerning energy substitution policies has become more meaningful. In this paper, a dynamic CGE model was built to evaluate the impacts of energy substitution policies on regional growth and the “resource curse”. The results show the following: (1) Compared to other regions, an energy substitution policy exerts a more considerable influence on economic growth in regions with high “resource curse”. (2) The changes in carbon emissions in regions with no or low “resource curse” are modest. The primary factor contributing to the rapid decline might be energy structure adjustments. As the intensity of substitution varies, the regional disparities in marginal emission reduction costs are expected to increase. (3) Energy substitution policies reduce the severity of high “resource curse” regions. As the intensity of energy substitution increases, the extent of “resource curse” regions decreases accordingly. Some suggestions are given as follows: (1) Further promote energy substitution and speed up the transformation of energy production and consumption modes in China. (2) Accelerate the energy transformation in “resource curse” regions to promote regional sustainable development. (3) Improve energy substitution policies in transportation, industry and other fields.

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Real-time energy management simulation for enhanced integration of renewable energy resources in DC microgrids

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management.

An Empirical Study on Lightweight CNN Models for Efficient Classification of Used Electronic Parts

The problem of electronic waste (e-waste) presents a significant challenge in our society as outdated electronic devices are frequently discarded rather than recycled. To tackle this issue, it is important to embrace circular economy principles. One effective approach is to desolder and reuse electronic components, thereby reducing waste buildup. Automated vision-based techniques, often utilizing deep learning models, are commonly employed to identify and locate objects in sorting applications. Artificial intelligence (AI) and deep learning processes often require significant computational resources to perform automated tasks. These computational resources consume energy from the grid. Consequently, a rise in the use of AI can lead to higher demand for energy resources. This research empirically develops a lightweight convolutional neural network (CNN) model by exploring models utilising various grayscale image resolutions and comparing their performance with pre-trained RGB image classifier models. The study evaluates the lightweight CNN classifier’s ability to achieve an accuracy comparable to pre-trained red–green–blue (RGB) image classifiers. Experiments demonstrate that lightweight CNN models using 100 × 100 pixels and 224 × 224 pixels grayscale images can achieve accuracies on par with more complex pre-trained RGB classifiers. This permits the use of reduced computational resources for environmental sustainability.

Mesocorticolimbic circuit mechanisms of social dominance behavior.

Social animals, including rodents, primates, and humans, partake in competition for finite resources, thereby establishing social hierarchies wherein an individual's social standing influences diverse behaviors. Understanding the neurobiological underpinnings of social dominance is imperative, given its ramifications for health, survival, and reproduction. Social dominance behavior comprises several facets, including social recognition, social decision-making, and actions, indicating the concerted involvement of multiple brain regions in orchestrating this behavior. While extensive research has been dedicated to elucidating the neurobiology of social interaction, recent studies have increasingly delved into adverse social behaviors such as social competition and hierarchy. This review focuses on the latest advancements in comprehending the mechanisms of the mesocorticolimbic circuit governing social dominance, with a specific focus on rodent studies, elucidating the intricate dynamics of social hierarchies and their implications for individual well-being and adaptation.

Green investment and its transformative impact on energy use in agriculture: A holistic approach towards carbon dioxide emissions

The extensive use of non-renewable energy sources in agriculture and associated unsustainable practices have contributed greatly to climate change issues. Africa's lack of green initiatives has exacerbated environmental pollution in the last decade. In that regard, this study investigates the transformative impact of green investment on carbon emissions in agriculture fueled by dependency on non-renewable energy use in the sector. It utilizes advanced econometric techniques, including dynamic ordinary least squares, fully modified ordinary least squares, and the causality tests to conduct a rigorous analysis. The findings reveal a concerning rise in agricultural pollution via crop and livestock production. However, green investment is found to have a positive impact on carbon dioxide emissions, hence a potent tool for significantly reducing carbon emissions. The causality analysis unveils a bidirectional causal relationship between green investment and agricultural pollution and a unidirectional causality between green investment and carbon emissions, shedding light on the complex interplay between these variables. Also, non-renewable energy is found to increase carbon emissions in the region. The outcomes of this research contribute valuable insights and lay a foundation for future research and policymakers to enhance the future of agricultural research and sustainability. It also offers a roadmap for developing effective policies that balance the immediate needs of the agricultural sector with the imperative of long-term environmental sustainability. Finally, it has a global significance on the relevance of green initiatives to boost sustainable agriculture and reduce pollution.

Enhanced uranium extraction using a nanostructured photothermal hydrogel membrane

Uranium is the basic resource in nuclear energy and a sword of two sides. The accidental release of radioactive uranium to the environment may cause serious impact on the environment and living creatures. Herein, a nanostructured hydrogel-based solar evaporator with in-situ formed nanostructures is designed for simultaneous solar evaporation and uranium extraction from polluted water. To be specific, poly(acrylic acid) and gelatin with embedded carbon black nanoparticles coated on a cotton cloth self-assemble into nanostructures via salting-out and acid treatment to reduce the light reflectivity and enhance the specific surface area for uranium adsorption. Numerical simulation confirms that the evaporation-induced flow accelerates the diffusion of uranyl ions towards the adsorbent. Under solar irradiation of 1 kW m−2 a maximum uranium adsorption capacity of 443.2 ± 18.1 mg g−1 is achieved in 20 ppm uranium-spiked water, meanwhile a high solar evaporation rate of 3.75 kg m−2 h−1 is reached, hence, the solar evaporator is ranked among the most powerful adsorbents/evaporators. This work provides a cost-effective and scalable approach for dealing with uranium-polluted water.

6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles

The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge, as well as with a centralized system.

National-level resilience: Innovative framework for Energy-Water-Food nexus evaluation

The escalating demand for energy, water, and food (EWF) resources, alongside external stressors such as climate change, is placing significant strain on national systems. Therefore, this paper introduces a novel framework for assessing the resilience of interconnected EWF systems at a national level. The framework utilizes a hybrid approach of network systems modeling and optimization models, integrating sectoral and dependency impacts to offer a comprehensive perspective on the interconnected resilience of EWF components. Applied to Qatar's EWF nexus, the framework revealed significant interdependencies, particularly between water and energy components, that led to cascading failures and challenged nexus resilience. Under single failure scenarios, unmet demands for energy and water ranged from 9 to 59 GWh and 97,000 to 359,000 m3, respectively, with resilience indices from 0.86 to 0.99. In double failure scenarios, significantly higher unmet demands caused the resilience index to drop to 0.43. Due to limited freshwater availability, the food sector is highly vulnerable to groundwater supply disruptions, resulting in an 85.6 % reduction in total crop production under extreme scenarios. Furthermore, the study highlighted resilience-enhancement strategies, such as utilizing treated wastewater for agricultural, industrial, and commercial activities and diversifying energy sources to reduce the risk of cascading failures, which increased the nexus's resilience by 17 %. The proposed framework serves as an essential tool for identifying critical components within the EWF nexus and planning strategies to enhance both operational and structural resilience.