Energy-efficient Resource Research Articles

An Overview of Digital Transformation and Environmental Sustainability: Threats, Opportunities, and Solutions

Digital transformation, powered by technologies like AI, IoT, and big data, is reshaping industries and societies at an unprecedented pace. While these innovations promise smarter energy management, precision agriculture, and efficient resource utilization, they also introduce serious environmental challenges. This paper examines the dual impact of digital technologies, highlighting key threats such as rising energy consumption, growing e-waste, and the increased extraction of raw materials. By synthesizing the existing literature, this study highlights mitigation strategies that include adopting energy-efficient practices, integrating renewable energy, and implementing circular economy principles. It emphasizes the need for a balanced approach—making the most of technological advances while protecting the environment. By identifying gaps in the current research, this paper also suggests future areas to explore to ensure that digital progress does not come at the expense of our planet. This review advocates for an integrated strategy to achieve sustainable digital growth aligned with global climate goals.

Dynamic energy efficient resource allocation in multi-user multi-IRS mmWave 6G networks

This study introduces a novel approach for energy-efficient resource allocation in millimeter-wave networks, assisted by multiple intelligent reflecting surfaces (IRS). The proposed framework optimizes the dynamic ON/OFF control and phase shifts of IRS elements, along with beamforming (BF) at access points (AP), under practical constraints. Unlike existing methods, our model enhances energy efficiency (EE) by optimizing a fixed number of ON IRS elements. We present innovative algorithms, including a modified nested fractional programming (NFP) for BF and a simulated annealing (SA)-type algorithm for phase shift and element selection. Our results demonstrate a 6.5-fold improvement in EE under a realistic scenario compared to benchmark, highlighting the effectiveness of our approach as a crucial strategy for future 6G networks.

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.

Effect of exogenous ketone supplementation on cardiac energetics, function, and perfusion in type 2 diabetes, heart failure, and healthy participants- A single center, open labelled clinical study

Abstract Background Type 2 diabetes (T2D) confers loss of cardiac metabolic flexibility in fuel selection and impaired mitochondrial function. Enhanced ketone metabolism observed in T2D may represent a compensatory adaptive mechanism as ketones are a more energy efficient resource than carbohydrates or fatty acids. Favorable energetic properties of ketones may potentially mitigate the energy starved state in T2D and heart failure (HF). Purpose To test whether exogenous oral ketone supplementation would reverse cardiac energetic deficit, as well as improve cardiac function and perfusion in patients with T2D, with HF with reduced ejection fraction (HFrEF), and healthy volunteers (HV). Methods Three groups of patients were recruited: patients with T2D (n=33); patients with HFrEF (n=29, 14 with T2D); and HVs(n=25). Participants underwent cardiovascular magnetic resonance and spectroscopy to assess left ventricular (LV) function, rest and dobutamine stress perfusion and energetics (phosphocreatine/adenosine triphosphate ratio[PCr/ATP]). After the initial visit, participants had an identical second visit following 2 weeks of oral ketone supplementation (30g daily). Results Participants were matched in age and sex distribution. There were no significant differences in LV ejection fraction (LVEF) between T2D and HV groups with significantly lower LVEF in HFrEF group. Both the T2D group and the HFrEF group showed significant reductions in PCr/ATP ratio and the rest and stress GLS compared to the HV. The resting and dobutamine-stress myocardial blood flow (MBF) were only significantly reduced in patients with HFrEF compared to T2D and HV groups. Subgroup analyses of HFrEF group with and without T2D showed no significant differences in energetics, perfusion, or functional assessments. Significant increase in PCr/ATP ratio was seen only in T2D group from baseline after 2 weeks of ketone supplementation (Visit-1:1.6[1.5,1.8] vs Visit-2:1.9[1.7,2.1]; P=0.01). This significant increment was not observed in HFrEF or HV groups. Two weeks of ketone supplementation was not associated with any significant changes in LVEF, GLS, rest or dobutamine-stress myocardial blood flow, or in myocardial lipid content (Table-1). Conclusions Short-term ketone supplementation is associated with isolated significant improvements in resting cardiac energetics in patients with T2D and preserved LVEF, without changes cardiac function or perfusion. In contrast to patients with T2D and preserved LVEF, short-term ketone supplementation does not lead to any significant improvements in energetics in patients with HFrEF suggesting loss of mitochondrial plasticity with transition to HFrEF. Moreover, healthy volunteers experience no further increments of myocardial energetics from the normal range values at baseline with ketone supplementation.

Assessing the Damage to Environmental Pollution: Discerning the Impact of Environmental Technology, Energy Efficiency, Green Energy and Natural Resources

The existing literature covers the topic of environmental pollution, but there is a scarcity of research that specifically examines the factors contributing to financial losses caused by carbon emissions. In this perspective, this ongoing analysis provides an understanding of the impact of environmental technology, energy efficiency, renewable energy consumption, natural resources, and economic growth on carbon dioxide damage in Organization for Economic Cooperation and Development (OECD) countries from 2000 to 2021 using the “Method of Moments Quantile Regression (MMQR)”, and “Dumitrescu–Hurlin (D-H)” causality test. The findings from the MMQR revealed that environmental control technology, renewable energy consumption, and energy efficiency contribute to reducing carbon dioxide damage at different quantiles. It was also found that economic growth and natural resources contribute to the increase in carbon dioxide damage in various quantities. Additionally, a one-way causality result was obtained from environmental technology, energy efficiency, renewable energy consumption, natural resources, and economic growth towards carbon dioxide damage. These results indicate that policymakers in OECD nations should provide suggestions on the efficient utilization of renewable energy sources and environmentally friendly technologies to minimize carbon dioxide damage.

Energy efficient resource management in data centers using imitation-based optimization

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers’ infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document} Savings of 460.92 lbs CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document}/month.

Unlocking the impact of international financial support to infrastructure, energy efficiency, and ICT on CO2 emissions in India

Financial resource constraints prevent developing nations from investing in the infrastructure needed for the green transition. For this reason, it is well known that numerous financial aid programs are offered globally, mainly to wealthy developing nations whose economies are predicated on high energy consumption. It hasn't been looked into if these resources are allocated to worthwhile projects. This study examines how foreign financial assistance in this area affects India's infrastructure emissions. The effects of natural resources, economic growth, information and communication technology, energy efficiency (oil and gas), and natural resources on the environment are being investigated as well. The novel multivariate quantile on-quantile regression technique is used in this context to examine a time-series data set spanning 2000–2021. According to the findings, there is a correlation between reduced carbon emissions and increased foreign financial aid. Likewise, advancements in energy efficiency and information and communication technologies also benefit the ecosystem. However, India's emissions are rising due to its abundant natural resources and economic expansion. Policy recommendations were made regarding the need for India to use international financial aid primarily to strengthen environmentally friendly infrastructure rather than solving problems such as current account deficit or budget deficit.

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.

Energy efficient resource allocation based on virtual network embedding for IoT data generation

Energy efficient resource allocation based on virtual network embedding for IoT data generation

Energy efficiency, economic growth, and natural resource rent: A trilemma analysis of environmental sustainability in Africa

Climate change, exacerbated by a global economic model reliant on carbon-intensive industrialization and unchecked growth, disproportionately affects vulnerable regions such as Africa. Therefore, transitioning to an ecologically sustainable development paradigm is imperative for developing countries facing climate disruptions. The current study analyzes the trilemma effect of energy efficiency, economic growth, and total natural resource rent on environmental quality in Africa from 2010 to 2018 using SMB-DEA and PMG ARDL methods. The results are as follows: first, SMB-DEA analysis found traditional and renewable energy efficiency at 89 % and 58 %, respectively, indicating infrastructure and policy deficiencies in optimizing renewables. Second, economic expansion poses significant environmental costs; energy efficiency has an insignificant ameliorating effect on efficiency; and counterintuitively, natural resource rents become less environmentally harmful over time, though remain detrimental in the short term. Third, country-specific analyses presented heterogeneous outcomes, reflecting the complexity of global climate solutions. Fourth, transforming inefficient energy-growth-emission systems onto sustainable trajectories requires 262 % CO2 emission cuts and 46.7 % increased renewables capacity to reach efficiency frontiers. Fifth, Bidirectional causal relationships exist between income, energy efficiency, emissions, and resource rents, mutually influencing one another. These findings support suggestions for upscaling green energy capacity and improving green energy efficiency.

Energy Efficient Task Offloading and Resource Allocation in Air-Ground Integrated MEC Systems: A Distributed Online Approach

Energy Efficient Task Offloading and Resource Allocation in Air-Ground Integrated MEC Systems: A Distributed Online Approach

Energy Efficient Resource Allocation Framework Based on Dynamic Meta-Transfer Learning for V2X Communications

Energy Efficient Resource Allocation Framework Based on Dynamic Meta-Transfer Learning for V2X Communications

Energy Efficient Resource Allocation for Wireless Powered Short Packet Communication Networks

Energy Efficient Resource Allocation for Wireless Powered Short Packet Communication Networks

Multi-objective stochastic-robust based selection-allocation-operation cooperative optimization of rural integrated energy systems considering supply-demand multiple uncertainties

The carbon neutrality goal places greater demands on integrated energy system (IES) in rural areas. In line with this trend, a multi-objective stochastic-robust based selection-allocation-operation cooperative optimization model is established for planning of rural IES in this paper. Firstly, this paper construct three typical IESs at three levels (user, township and user-township interaction) and make economic selection. Secondly, in order to coordinate the objectives of technology-economy-environment and solve the multiple uncertainties existing in the system, a multi-objective stochastic-robust planning model is proposed. In the model, the objective function introduces indicators such as total cost, primary energy resource efficiency and renewable energy consumption rate. Then constructing stochastic hierarchical scenarios to characterize the stochastic uncertainty of parameters in the time dimension. And introducing typical scenarios into the box sets which characterize the supply-demand sides uncertainty. Finally, a sensitivity analysis is performed to discuss the influence of robustness parameters and biogas prices. The annual total cost of the user-township interaction system is 53.8 % and 8.4 % lower than that of the other two systems, respectively. And from the allocation results of selected system, primary energy resource efficiency is 0.57, total cost is 1.81 × 106 USD and renewable energy consumption rate is 99.46 %. In addition, from the operation results, it is analyzed that the output of the equipment is perfectly balanced with the user's load. Finally, it is obtained by sensitivity analysis that uncertainty budget and biogas price have an impact on the selection of discrete equipment, the capacity of continuous equipment and the total cost. And the optimal capacity of the system under different situations is given.

Comprehensive model for efficient microgrid operation: Addressing uncertainties and economic considerations

The fabrication of microgrids to harness renewable resources for local load provision has emerged as a promising concept. Efficient energy management and resource utilization within the electricity market have become crucial tasks for microgrid operation. This article presents a comprehensive model that addresses economic and technical considerations, as well as uncertainties associated with load, wind speed, and solar radiation. Specifically, the focus is on optimizing the utilization and energy consumption of controllable loads within a distributed energy system, taking into account consumer preferences and operational constraints. A two-stage planning problem is formulated to minimize microgrid operation costs and consumer payments, while considering load requirements, restrictions, and utility-imposed constraints. To solve this problem, an enhanced algorithm based on the mountain gazelle optimizer with improved local search operators is proposed, significantly enhancing its search capabilities. The retail electricity market is modeled using real-time pricing (RTP) and incentive-based regulation (IBR) tariffs to capture wholesale price fluctuations and discourage simultaneous consumption. Notably, price forecasting becomes essential in scheduling controllable loads, introducing uncertainties that are addressed by a two-point estimate method. Simulation results demonstrate the effectiveness and robustness of the proposed approach, exhibiting low standard deviation and high resilience. The results highlight the potential of the proposed method in achieving cost-effective and reliable operation of microgrids in the face of uncertainties and dynamic market conditions.

Energy efficient dynamic resource allocation in C-RAN using tunicate swarm and whale optimization algorithm

Energy efficient dynamic resource allocation in C-RAN using tunicate swarm and whale optimization algorithm

Analyze the effect of energy efficiency, natural resources, and the digital economy on ecological footprint in OCED countries: The mediating role of renewable energy

Considering the urgent target of carbon neutrality and environmental sustainability, this paper analyzes the nexus of energy efficiency, natural resources, and the digital economy on ecological footprint in OECD countries from 2002 to 2020. We apply the Method of Moments Quantile Regression (MMQR) to evaluate the long-run relationship of variables and check the mediating effect of renewable energy between the digital economy and ecological footprint. Through a series of empirical analyses, the primary results are as follows. First, it has been discovered that energy efficiency and natural resources contribute to decreasing ecological footprint. Second, the digital economy exerts a detrimental impact on the lowering of the ecological footprint. Third, the mediating outcome suggests that the digital economy cannot promote an ecological footprint decrease with the development of renewable energy. Based on the findings, we also recommend some ideas for OECD countries’ economy, energy, and environmental improvements.

Hydrogen-fueled microgrid energy management: Novel EMS approach for efficiency and reliability

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability.

Enhancing Kurdistan's manufacturing companies' sustainable waste management: A norm activation approach to green accounting, CSR, and environmental auditing oversight

The significance of accurate energy production prediction cannot be overstated, especially in the context of achieving carbon neutrality and balancing traditional and clean energy sources. Unlike conventional models with simplified assumptions or limited data inputs hindering energy usage optimization, waste reduction and efficient resource allocation, we introduced a novel structural equation modelling approach to eight manufacturing industries' sustainable waste management practices (SWMPs) in Iraq. This comprehensive analysis, conducted with Smart PLS software on 375 responses aims to enhance energy production predictions’ accuracy and support sustainability goals contribute to achieving carbon neutrality goals and promote a balanced energy mix that supports sustainability and environmental stewardship. The findings reveal noteworthy insights: notably, chemical manufacturing companies exhibit a substantial advantage from green accounting practices, witnessing a 78.1 % and 45.8 % improvement in environmental auditing oversight and SWMPs, respectively, compared to other manufacturing sectors. Compared to conventional grey models, our model demonstrates that a 1-unit improvement in CSR enhances environmental auditing oversight effectiveness by 33.4 % and sustainable waste management by 56.9 % across industries. By leveraging these data-driven insights and innovative approaches, we can drive positive change towards a more sustainable and resilient energy future, collectively contributing to a more resilient, efficient, and sustainable energy ecosystem that benefits societies, economies, and the environment. The heightened accuracy of energy production prediction facilitated by our novel model empowers stakeholders at regional and global levels to make informed decisions, mitigate risks, support policy development, achieve sustainability goals, formulate effective policies and foster collaboration.

Monitoring the Use of Electrical Energy and Water in Homes Based on IoT

Increased awareness of energy efficiency and sustainable resource management has encouraged the development of intelligent electrical energy and water usage monitoring systems. In this context, the Internet of Things (IoT) has become a promising solution for connecting household devices to the internet, enabling users to monitor and control electricity and water consumption in real-time. This research aims to design and implement an IoT-based monitoring system for electrical energy and water usage in homes. This system uses a powerful Arduino ESP32 microcontroller and can connect to the internet via WiFi. This microcontroller acts as a control center that collects data from sensors installed in the house. Based on these problems, this research was carried out, with the aim of creating a prototype to record electrical power loads and display the amount of electricity consumption in rupiah levels to monitor the use of electrical power loads and convert them into rupiah. To conduct this research, the author used observational research methodology or field study and literature study. In this research, it is necessary to pay attention to understanding how to think and how to carry out the results of thinking according to scientific steps.