Search Results (22,965)

This study assesses the technical feasibility of utilizing aggregated domestic freezers in Turkey as a distributed resource for frequency regulation. A dynamic thermal–electrical model was developed to simulate freezer responses under frequency deviation scenarios representative of real-world grid conditions. The modeled sample of 100,000 deep freezers (80 W each) can deliver approximately 3.2 MW of instantaneous down-regulation under a 40% initial duty cycle. Extrapolating to the estimated 4.7 million eligible freezers nationwide yields a total potential headroom of roughly 150–225 MW, depending on duty-cycle assumptions. The compressor duty cycle and allowable temperature range were identified as key factors influencing both regulation capacity and endurance. Although linear reference temperature control enabled effective participation in FCR-N within the simulated timeframes, it also led to cycle synchronization and peak loads following disturbances. Implementing strategies such as randomized reconnection delays could mitigate these effects. The wide availability of domestic freezers, minimal consumer impact, and broad geographic distribution suggest that this resource represents a promising complement to existing frequency regulation assets, particularly in enhancing grid stability amid increasing renewable energy penetration. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

This research addresses the urgent need to reduce carbon emissions in the footwear manufacturing industry by utilizing digital twin technology with project management frameworks. It focuses on identifying critical emission sources across the entire life cycle of shoe production from (i) material sourcing, (ii) manufacturing, and (iii) transportation, to (iv) end-of-life disposal. By data collection, infusing project management, and integrating digital twin approaches, the study offers a dynamic, data-driven method to simulate, monitor, and optimize carbon reduction strategies in real time. An extensive literature review and industry data analysis informs the assessment of carbon emissions and energy consumption patterns. Based on these insights, a tailored project management approach is followed to analyze the feasibility of the footwear sector to adopt sustainable practices such as renewable energy adoption, eco-friendly material sourcing, and closed-loop production systems. Validation was conducted using plant simulation software to model emissions scenarios and evaluate the effectiveness of proposed interventions. Case studies from leading brands, including Nike, Adidas, and Puma, were examined for Scope 1, 2 and 3, to extract the best practices and strategic insights. The research underscores the importance of combining digital tools with sustainability goals to create an environmentally conscious manufacturing ecosystem, highlights the role of policymakers in incentivizing green practices, and emphasizes collaborative industry efforts to accelerate change. The paper concludes by highlighting that digital twin systems provide effective, scalable solutions for reducing carbon emissions in footwear manufacturing. Full article

With the increasing penetration of renewable energy in power systems, the effective utilization of pumped storage power plant (PSP) clusters for peak shaving has become an important issue in system operation. In this study, an intraday scheduling model for PSP clusters is formulated to minimize the variance of the system net load, while accounting for operational constraints, including power balance, unit operation, and reservoir energy evolution. The resulting model is a mixed-integer nonlinear programming (MINLP) problem, which is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Case studies are conducted on an improved IEEE 39-bus system under both conventional scenarios and extreme renewable energy conditions. The results show that, under a unified peak-shaving objective, PSP clusters exhibit a stable structure of role differentiation even in conventional operating conditions. As the system peak-shaving pressure increases, this differentiation is progressively reinforced along existing functional roles, shifting from renewable energy absorption to peak-period generation support. It tends to converge under high operational stress due to the coupling between load and renewable variability. Further analysis indicates that when capacity differences among PSPs are eliminated, the differentiation structure is significantly weakened, suggesting that physical capability differences constitute an important foundation for the formation of role differentiation. Full article

During CO₂ storage in deep saline aquifers, low-permeability lenticular shale layers alter CO₂ migration and affect dissolution trapping, but their impacts remain unclear. In this study, a two-dimensional radial numerical model coupling gas–brine two-phase flow and mass transfer is developed to simulate CO₂ plume evolution and dissolution beneath discontinuous lenticular shale layers. In the model, lenticular shale interlayers are represented as discontinuous low-permeability barriers, and their geometry is characterized by radial length and vertical thickness. The blocking effect of lenticular shale layers induces bypass flow, promotes lateral plume spreading, and prolongs contact time between CO₂ and brine, which increases dissolution during 250 to 1000 days of injection. When the permeability anisotropy ratio is 0.001, upward migration of CO₂ is suppressed and a high-concentration retention zone forms beneath the lenticular shale layer. As the radial length of the lenticular shale layers increases from 150 to 250 m, the plume expands and the bypass-flow path lengthens, which strengthens lateral CO₂ spreading and redistributes dissolved CO₂ concentration. In contrast, varying the thickness of the lenticular shale layers from 6 to 10 m has a relatively limited influence on the extent of bypass flow and the morphology of the concentration field. Full article

Upon the challenges of climate change and the demand for energy sustainability, nuclear power (NP) units not only provide clean electricity but are also equipped for cogeneration to achieve energy cascade utilization; this represents a key avenue for improving the overall efficiency and achieving the comprehensive utilization of nuclear energy. However, following the heating retrofitting stage, there exists a risk that the supply control valve of the unit may accidentally open completely during operation, which increases the risk of over-powering. Therefore, this study designs response schemes for second-generation large pressurized water reactor NP plants (NPPs) under the accidental full-open condition of the heat-supply control valve. Specifically, an integrated model encompassing the nuclear steam supply system, secondary circuit system, thermal energy supply system (TESS), and related control systems was constructed using the optimal estimation program and 3KeyMaster simulation platform. Subsequently, two response schemes were designed for the accidental full-open valve scenario under two operation modes—namely, the “Reactor Follows Turbine + TESS” and “Turbine Follows TESS” modes. Finally, on the basis of the established simulation platform, the scenario of accidental full opening of the heat-supply control valve was simulated and verified. Ultimately, the results indicate that the response scheme implemented under the “Turbine Follows TESS” mode is more effective in suppressing nuclear overpower when the heat supply control valve accidentally opens fully. Thus, overall, this study provides a feasible accident response strategy and critical technical reference for NPPs involving cogeneration and energy cascade utilization. Full article

Symmetry plays a crucial role in balancing production efficiency and energy consumption within distributed manufacturing systems. This study leverages symmetric decision-making structures in resource allocation and maintenance scheduling to achieve an equilibrium between productivity and sustainability. To address the multi-factory collaboration requirements for large-scale orders, a distributed dual-resource flexible job shop scheduling model considering multiple speeds and preventive maintenance on energy consumption is constructed. It aims to minimize the maximum completion time and total machine energy consumption. An artificial bee colony algorithm with adaptive scout bees is proposed to solve the model. An improved decoding method is designed according to the model characteristics to enhance convergence speed. Neighborhood structures based on preventive maintenance and machine speeds are designed, and a dynamic neighborhood search strategy is proposed to improve the local search capability. Three food source generation methods are defined as actions, and Q-learning is employed to dynamically select actions, ensuring population diversity while improving population quality. Extensive experiments are conducted to validate the effectiveness of the improved strategies, and the superiority of the proposed algorithm is verified through performance comparisons with state-of-the-art algorithms. Full article

Traditional cloud pricing models are allocation-centric, where users are charged based on reserved resources rather than workload energy consumption. However, modern AI workloads exhibit substantial and heterogeneous power behavior, limiting the effectiveness of such allocation-centric pricing. This paper presents a comprehensive experimental study of nine widely used workloads across 50 controlled configurations, including standalone and concurrent executions under varying resource partitions. Our results show that total system power is largely unaffected by how resources are divided among co-located workloads, except in cases of explicit resource under-provisioning or severe resource contention. Across 45 workload–core groups, 41 exhibit a coefficient of variation below 3% across different co-located workloads, demonstrating structural stability of workload-level power profiles under heterogeneous execution environments. In contrast, deployment choice (e.g., CPU versus GPU execution) can shift the same model into distinct power regimes. Based on measured power decomposition and scaling behavior, we derive an empirical categorization framework distinguishing GPU-dominant and CPU-dominant workloads, further characterized by utilization and memory dimensions. From an energy perspective, CPU utilization (for CPU-dominant workloads) and SM utilization (for GPU-dominant workloads) emerge as the primary determinants of power magnitude, while memory-related parameters contribute marginally to overall power. These findings provide empirical evidence that allocation-based pricing is a weak proxy for actual energy cost and motivate energy-aligned cloud management strategies grounded in workload power profiles. As our findings are derived from a controlled single-node experiment, evaluations under more realistic data center environments will be required for further generalization. Full article

High penetration of renewable energy sources (RESs) introduces significant power fluctuations, threatening voltage and frequency stability in modern power systems. This paper presents an integrated framework for static voltage stability assessment and stability-constrained optimization of under-frequency load shedding (UFLS) in renewable-dominated grids. A low-conservativeness analytical criterion is first derived for static voltage stability margin assessment. Then, a hybrid Deep Belief Network–Long Short-Term Memory (DBN–LSTM) model is developed for accurate renewable power forecasting, capturing temporal variability and uncertainty. Finally, UFLS-based stability-constrained dispatch is formulated to prevent voltage collapse, enhance the system stability, and minimize RES curtailment. Simulations on a modified IEEE benchmark system demonstrate that the proposed approach improves voltage and frequency stability while maintaining high renewable energy utilization. Full article

The transition from 5G to 6G wireless systems marks a paradigm shift from “connected things” to “connected intelligence,” driven by the necessity to manage hyper-heterogeneous networks and overcome the Shannon capacity limit. This Systematic Literature Review (SLR) analyzes 118 primary studies to evaluate the transformative impact of Generative AI (GenAI) and Large Language Models (LLMs) on 6G architecture. We categorize the integration of GenAI into five semantic clusters: Architecture, Management, Security, Semantics, and Edge AI. The synthesis reveals that 6G is evolving toward an “AI-Native” ecosystem where LLMs show strong promise for augmenting network orchestration through Intent-Based Networking (IBN) and generative models demonstrate significant potential to augment or transcend traditional physical layer algorithms. Furthermore, the review identifies a fundamental transition from bit-oriented to semantic-oriented communication, utilizing GenAI to reconstruct meaning from minimal data. However, critical challenges remain, particularly the “energy–intelligence paradox” and the risks of model hallucinations in critical infrastructure. We conclude that while GenAI provides the necessary cognitive flexibility for 6G, its successful deployment depends on solving the “inference gap” through split learning and extreme model quantization at the edge. Full article

The adoption of circular economy (CE) practices and artificial intelligence (AI) in the supply chain (SC) has become extremely significant in manufacturing organizations. The CE seeks to facilitate sustainable growth by managing the flow of materials and energy within closed-loop systems. The CE has resulted in the development of sustainable business models. AI capabilities transform work activities, data flows, and organizational processes. Therefore, the present study aims to develop a framework to improve circular supply chain (CSC) adoption in the automobile manufacturing sector by identifying and analyzing CE practices and AI-enabled digital strategies. The proposed framework was analyzed by employing a hybrid approach of Prioritized Weighted Average–Criteria Importance Through Intercriteria Correlation–Preference Ranking Organization Method for Enrichment Evaluations-II (PWA-CRITIC-PROMETHEE-II) under an Interval-Valued Fermatean Fuzzy (IVFF) environment. IVFF-CRITIC was employed to determine the CE practices’ weights, while IVFF-PROMETHEE-II was utilized to establish the relative index of AI-enabled digital strategies to enhance the CSC adoption. The key findings of the current study indicate that “AI-enabled infrastructure configuration for circular economy adoption in the supply chain”, “AI-integrated equipment to facilitate adaptability and mass personalization”, and “Robotics and AI-driven manufacturing and material reclamation” are the most significant AI-based digital strategies that support CE practices to enhance the adoption of a CSC and encourage case example manufacturing organizations to align their operations with AI and CE. Moreover, the outcomes of the study will deliver a comprehensive evaluation of CE practices and AI-enabled digital strategies for SC managers, based on the relative indexing obtained through the implementation of the hybrid approach. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

The increasing importance of sustainability in the manufacturing industry necessitates that, alongside economic efficiency, resource and energy consumption must also be taken into account when planning manufacturing processes. In particular, the mechanical processing of components on machine tools offers significant potential to reduce energy consumption during machining. This article presents a method for planning energy-efficient machining processes, illustrated through the example of turning. The proposed planning methodology is based on mathematical optimization and determines economically optimal cutting parameters while simultaneously ensuring efficient energy utilization. The effectiveness of the planning approach is subsequently validated through a case study involving a sample-turned component. Full article

Extremely Large-Scale Multiple-Input Multiple-Output (XL-MIMO) is positioned as a transformative technology for sixth-generation (6G) networks, effectively turning base stations into high-resolution sensing and communication hubs. However, the practical deployment of XL-MIMO is hindered by the “curse of dimensionality,” specifically the prohibitive overhead associated with Channel State Information (CSI) sensing and feedback, alongside the computational latency of massive antenna arrays. To resolve the conflict between high-resolution sensing requirements and limited bandwidth resources, this paper proposes a novel two-stage beamforming architecture that synergizes physics-aware dimensionality reduction with deep learning. First, by exploiting the inherent sparsity of XL-MIMO channels in the angle-delay domain, we design a Spatial–Frequency Concentration Block (SFCB). This module functions as a hard-attention sensing mechanism, performing efficient source-end dimensionality reduction on raw CSI at the User Equipment (UE) via precise feature extraction and adaptive energy truncation. Second, we develop a highly adaptable Direct Integrated Precoding Network (DIP-I). Departing from the conventional “sense-reconstruct-then-precode” paradigm, DIP-I learns end-to-end mapping to directly regress the optimal precoding matrix at the Base Station (BS). Comprehensive simulations utilizing the COST 2100 and QuaDRiGa hybrid channel models demonstrate that, under a massive 512-antenna configuration, the proposed framework achieves exceptional beamforming gain. Furthermore, it significantly reduces sensing data overhead and inference latency, offering a superior trade-off between spectral efficiency and hardware resource consumption for future 6G sensing-communication integrated systems. Full article

The prolonged low temperature in cold regions significantly inhibits the initiation of straw composting and lignocellulose degradation, thereby restricting straw resource utilization. In this study, 24 cellulose-degrading strains capable of stable growth under low-temperature conditions were screened. Based on multiple indicators, including carboxymethyl cellulase (CMCase) activity, strain LDT1 was identified as the best-performing isolate under low-temperature conditions and as Paenarthrobacter nitroguajacolicus. Subsequently, an efficient mutant strain, LDT1-8, was obtained through atmospheric and room-temperature plasma mutagenesis. The CMCase activity of LDT1-8 at 10 °C increased to 74.25 U/mL, representing a 21.72% increase compared to the wild-type strain. In a straw degradation system at 10 °C, LDT1-8 significantly accelerated early-stage degradation kinetics, with straw degradation rates at 3 and 6 d being 72.72% and 38.15% higher than those of the wild-type strain, respectively. Multi-enzyme profiling further indicated enhanced activities of multiple lignocellulose-degrading enzymes at low temperatures, accompanied by a partial shift in the optimal temperature of some enzymes (e.g., laccase) toward lower temperatures. Whole-genome sequencing revealed increased gene numbers related to energy, amino acid, and lipid metabolism in LDT1-8. Comparative genomic analysis suggested that mutations were mainly enriched in regulatory regions, accompanied by local structural variations. Transcriptional analyses further verified the coordinated upregulation of genes involved in cellulose and hemicellulose degradation, cold adaptation, and transcriptional and protein homeostasis processes in LDT1-8. Overall, this study provides an efficient microbial resource and a mechanistic basis for straw bioconversion in cold regions. Full article