Sustainable Computing-Informatics & Systems最新文献

{"title":"QTE-IoT: Q-learning-based task scheduling scheme to enhance energy consumption and QoS in IoT environments","authors":"Ali Ghaffari ,&nbsp;Vesal Firoozi ,&nbsp;Ali Maleki ,&nbsp;Mohammad Sadegh Sirjani ,&nbsp;Maedeh Abedini Bagha","doi":"10.1016/j.suscom.2025.101247","DOIUrl":"10.1016/j.suscom.2025.101247","url":null,"abstract":"<div><div>As the proliferation of Internet of Things (IoT) devices continues unabated, the demand for efficient task scheduling mechanisms becomes increasingly critical. Task scheduling in the IoT is pivotal for optimizing resource utilization, minimizing latency, and enhancing the overall system’s performance. This research proposes a novel method called QTE-IoT, standing for a Q-learning-based task scheduling scheme to enhance energy consumption and QoS in IoT environments. QTE-IoT commences by categorizing tasks into three classes: time-sensitive tasks, security tasks, and normal tasks. This classification is achieved using a multi-layer perceptron artificial neural network. Subsequently, time-sensitive tasks are offloaded to the fog layer and scheduled using the proposed African Vulture Algorithm combined with Q-learning, which we designate as QAVA. Security tasks are offloaded to the private cloud, while normal tasks are offloaded to the public cloud. For task scheduling in private and public cloud environments, QTE-IoT employs a proposed enhanced version of Artificial Rabbits Optimization integrated with the Q-learning algorithm, known as QARO. Additionally, the QTE-IoT method incorporates a monitoring agent to oversee resource workload, thereby preventing congestion and delays. Simulation results on instances of the HCSP benchmark dataset demonstrate that QTE-IoT outperforms other state-of-the-art methods in various performance metrics. QTE-IoT achieves significant improvements compared to other methods and algorithms, including a 6–12 % reduction in energy consumption. Furthermore, QTE-IoT exhibits substantial improvements in load imbalance (42–79 %), response time (25–40 %), and deadline satisfaction (6–39 %) compared to existing approaches.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101247"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continual learning-based regression testing for scalable VLSI verification across hierarchical design layers","authors":"Sindhu Nalla ,&nbsp;G. Nagarajan","doi":"10.1016/j.suscom.2025.101259","DOIUrl":"10.1016/j.suscom.2025.101259","url":null,"abstract":"<div><div>The high complexity and rapid evolution of Very Large-Scale Integration (VLSI) designs are pressing the limits of traditional regression testing especially in maintaining test relevance across design iterations. This paper introduces a Continual Learning-Based Regression Testing (CLRT) framework specifically designed for scalable VLSI verification across hierarchical abstraction levels such as logic, design, and chip. The framework overcomes the drawbacks of static test models through the stationary learning techniques, which are ingrained in the framework, and the ability to continuously learn and adjust the test strategy against every new design change and test results. To enforce the above property, our approach is based on a two-layer learning mechanism: the first layer is a supervised learning model with historical test outcomes for detecting regression-sensitive regions in the design space through the second one (an online continuous learning module) that can sequentially adapt to new data without catastrophic forgetting. This allows the system to remember learned test behavior and simultaneously adapt to changing design configurations. A hybrid feature selection mechanism is utilized for the extraction of the effective parameters, which should be extracted from design-level netlist, logic-level signals traces and fault logs at the chip bubbled status for a thorough cross-layer coverage. Experimental verification was performed on ITC-99 and Open Cores VLSI benchmark designs. The proposed CLRT framework achieved a remarkable reduction of 28.6 % in test redundancies and improvements of 35.2 % in fault detection accuracy when comparing to the traditional regression suites. Moreover, the system maintained a stable performance across variations of design, and this made it robust in dynamic testing conditions. The findings validate<!--> <!-->that CL models, if effectively integrated into rebase lining regression testing flows, can drastically augment the efficiency, flexibility, and scalability of the VLSI verification. Not only does this work offer a connecting point between machine learning and hierarchical VLSI testing, but it also paves the way for future self-improving test infrastructures in semiconductor design automation.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101259"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid AI framework for detecting cyberattacks and predicting cascading failures in power systems","authors":"Lalit Agarwal ,&nbsp;Bhavnesh Jaint ,&nbsp;Anup K. Mandpura","doi":"10.1016/j.suscom.2025.101222","DOIUrl":"10.1016/j.suscom.2025.101222","url":null,"abstract":"<div><div>The power grid is a critical infrastructure, relies on Supervisory Control and Data Acquisition (SCADA), a computer-based system for real-time monitoring and control of the grid. However, these systems are increasingly being targeted by cyberattackers, posing significant risks to grid stability and security. Existing security solutions focus on either attack detection by verifying their signatures or predicting their cascading failure to isolate the failed component from the rest of the working components. In the current paper, our objective is to detect new or existing attacks and predict their cascading failure. This research accomplish the objective by introducing a new multi-model framework that combines three models, XGBoost, Transformer, and Graph Neural Networks (GNNs), to identify both known and unknown cyberattacks with forecast their cascading impacts on power grid systems. The XGBoost model detects the known attack patterns, which includes Data Injection, Remote Tripping Command Injection, Relay Setting Change Attacks. The Transformer model identifies the deviations from established attack patterns, which result in the discovery of new threats. Our evaluation of grid infrastructure attacks utilizes a GNN-based cascading failure prediction model that represents the power grid as a graph to forecast failure propagation through interconnected nodes. Through rigorous testing using an real world dataset, our framework shows exceptional detection performance while maintaining effective generalization to new attacks and strong cascading failure prediction capabilities. The results showcase accuracy up to 98. 6% and a score of 0.98 F1 in multisource datasets, outperforming single-model baselines.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101222"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrate multiple energy sources of the microgrid: Enhancing performance and sustainability in multi-energy systems","authors":"Xiaolin Zhang,&nbsp;Zhi Liu","doi":"10.1016/j.suscom.2025.101181","DOIUrl":"10.1016/j.suscom.2025.101181","url":null,"abstract":"<div><div>This paper introduces a novel hybrid optimization framework for Multi-Energy Systems that jointly addresses cost efficiency, uncertainty, and demand-side flexibility. The proposed model uniquely integrates electric and thermal Load Response Plans within a unified structure and incorporates a Negative Risk Limit to explicitly control downside financial exposure under volatile conditions. A key innovation lies in the combination of scenario-based stochastic modeling and robust optimization to manage uncertainties in renewable generation, market prices, and consumer demand. The Flower Pollination Algorithm, a nature-inspired metaheuristic, is employed to efficiently solve the resulting high-dimensional problem. A residential-scale case study, involving photovoltaic panels, wind turbines, combined heat and power, boilers, electric vehicles, thermal storage, and heat pumps, demonstrates the framework’s applicability. Four simulation scenarios assess the individual and combined effects of Load Response Plans and risk constraints. Results indicate that energy purchases from upstream networks are reduced with coordinated load shifting, lowering peak hour procurement by 15–30 % compared to baseline operation. Electric vehicles exhibit active charge/discharge behavior in up to 75 % of daily time slots under joint Load Response Plan and Negative Risk Limit conditions, enhancing flexibility.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101181"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient QCA‐Based Circuits for Low‐Power Medical IoT System","authors":"D. Ajitha ,&nbsp;Muhammad Zohaib ,&nbsp;Firdous Ahmad ,&nbsp;Khalid Zaman ,&nbsp;S.M. Prabin","doi":"10.1016/j.suscom.2025.101203","DOIUrl":"10.1016/j.suscom.2025.101203","url":null,"abstract":"<div><div>The Internet of Things (IoT) plays a vital role in the recent healthcare industry by providing precise diagnostic and treatment capabilities. There is a growing interest in medical IoT incorporated into healthcare systems. The processing unit of all medical IoT comprises complementary metal-oxide semiconductor (CMOS) technology. However, CMOS Medical IoT technology has become integrated into biomedical hardware systems at the nanoscale regime. Due to regulatory, ethical, and technological challenges, including slow processing speeds, high power consumption, and slow switching frequencies, particularly in the gigahertz (GHz) range. On the other hand, compared to traditional computers, quantum technology will accelerate processing by an order of magnitude and affect all artificial and medical (AI) and medical IoT processing applications. Quantum-dot cellular automata (QCA) present a promising alternative digital hardware system in medical IoT. QCA technology makes an optimal choice for establishing circuit design frameworks for AI in medical IoT applications, where low-cost, real-time, energy-efficient performance is crucial. Moreever, encryption and decryption circuits have been used in medical IoT operations to protect sensitive patient data while it is being transmitted and stored. The essential arithmetic and logic unit (ALU) is proposed in this context, which is the foundation for processing and computational units for medical IoT systems at the nanoscale devices. A systematic approach is involved in integrating adders, multiplexers, an ALU, and a logic unit to enhance processor drive and privacy via encryption and decryption in medical IoT. The experimental outcomes reveal that the recommended design overtakes the previous design by 15.48 % in terms of cells and 16.07 % in terms of area. The designs are accurately simulated using the QCADesigner-E 2.0.3 software tool.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101203"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning based robust control for DFIG based wind energy conversion systems","authors":"S. Kavitha ,&nbsp;B. Chinthamani ,&nbsp;John De Britto C ,&nbsp;B. Suresh Chander Kapali","doi":"10.1016/j.suscom.2025.101236","DOIUrl":"10.1016/j.suscom.2025.101236","url":null,"abstract":"<div><div>The pressing challenge of environmental change and the global goal of attaining carbon neutrality are driving a significant and widespread shift towards Renewable Energy (RE) sources.Among various RE sources, wind power stands out owing to its minimum cost, cleanliness, reliability and ecological merits. Thereby, this work focuses on a Doubly-Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS) fed to grid. The DFIG-based WECS is regulated by a Chaotic Flamingo Optimization (CFO) algorithm optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) controller. This advanced controller is employed to manage the operation of a PWM rectifier connected to the DFIG, ensuring optimal energy extraction from the wind. Moreover, an excess storage system stabilizes grid power by storing excess wind energy during high wind periods and releasing it during low wind periods.The efficacy of developed system is thoroughly assessed based on several critical metrics, including tracking efficiency (99.3 %), steady-state error and the mitigation of THD in grid system (in both simulation (1.11 %) and hardware (3.59 %)). The outcomes highlight the efficiency of CFO-ANFIS in curtailing harmonic distortion and improving grid power quality. This contributes significantly to the advancement of sustainable energy systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101236"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and performance assessment of a green hydrogen and renewable integrated hybrid industrial microgrid with advanced control strategies considering uncertainties of renewable energy","authors":"Javed Khan Bhutto ,&nbsp;Arvind Kumar ,&nbsp;Sarfaraz Kamangar ,&nbsp;Amir Ibrahim Ali Arabi ,&nbsp;Hadi Hakami","doi":"10.1016/j.suscom.2025.101267","DOIUrl":"10.1016/j.suscom.2025.101267","url":null,"abstract":"<div><div>Hybrid industrial microgrids (HIMG) are emerging as a key enabler for decarbonizing energy-intensive sectors through the integration of renewable energy and green hydrogen technologies. This paper introduces the design, control, and performance assessment of a hybrid hydrogen integrated industrial microgrid comprising 1-MWp solar photovoltaic (PV) and 1.6-MW wind generator, a 650-kW proton exchange membrane fuel cell (PEMFC), a 3-MW battery energy storage system (BESS), and a 5-MW diesel generator supplying an electrolyzer and diverse industrial loads. The PV array operates at maximum power point tracking in grid-following mode, while the BESS and wind generator operate in grid-forming mode using droop control. To guarantee the steady operation of the HIMG, control methodologies for distributed generation and system-level control techniques for bidirectional interlinking converters (BIC) are developed. Resynchronization and planned islanding strategies are proposed to ensure seamless transitions between grid-connected and islanded operation. The system’s resynchronization performance is further evaluated by introducing intentional time delays into phase-locked loop measurements, demonstrating increasing oscillatory behavior and slower dynamic response at higher delays, while low-latency conditions enable fast and well-damped frequency recovery. The performance of the proposed controller is validated through detailed MATLAB/Simulink simulations under diverse operating scenarios, including islanding, grid reconnection, load disturbances, and severe three-phase fault conditions. Comprehensive simulation scenarios, including renewable uncertainties and load fluctuations, are evaluated against international performance standards. Frequency response analysis confirms the stability and robustness of the grid-forming control under dynamic conditions. Results demonstrate improved voltage and frequency regulation, reduced total harmonic distortion in voltage and current, and significant diesel usage reduction, confirming the proposed HIMG’s technical viability and sustainability benefits for industrial applications.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101267"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEDALS: A sustainable AI framework for energy-efficient routing in 5G vehicular networks","authors":"G. Balram ,&nbsp;KDV Prasad ,&nbsp;Kamalakar Ramineni ,&nbsp;Rahul Divgan ,&nbsp;K. Ashok ,&nbsp;N.V. Phani Sai Kumar","doi":"10.1016/j.suscom.2025.101210","DOIUrl":"10.1016/j.suscom.2025.101210","url":null,"abstract":"<div><div>Intelligent transportation systems require routing protocols that optimize both performance and environmental impact simultaneously in 5G-enabled Vehicular Ad Hoc Networks (VANETs). Existing solutions often treat sustainability as a secondary constraint, which limits their effectiveness in addressing climate change goals. This study presents MEDALS (Metaheuristic-Enhanced Deep Adaptive Learning System), a hybrid framework that integrates deep reinforcement learning with metaheuristic optimization to achieve both superior performance and environmental sustainability. The system introduces the Green Performance Index (GPI), the first comprehensive metric combining energy efficiency, carbon footprint, latency, and reliability. Through extensive evaluation using industry-standard simulators, MEDALS demonstrates statistically significant improvements: MEDALS achieves 96.8 % energy efficiency (+11.6 %), 0.73 ms latency (-91.6 %), 99.7 % reliability, and 42.3 % carbon reduction while scaling to 1000 + vehicles with linear computational complexity. This will allow its practical implementation in smart cities and towards fulfillment of the sustainable development goals. This complexity augmentation of 3.3x times in the network size handling is attributed to the hybrid intelligence architecture of the framework, the adaptive deep reinforcement learning with the dual metaheuristic optimisation in intelligent fusion mechanism, and the empirically quantified O(N log N) complexity.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101210"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization of regional energy systems with exergy efficiency and user satisfaction dynamics","authors":"Xuecheng Wu,&nbsp;Qiongbing Xiong,&nbsp;Cizhen Yu","doi":"10.1016/j.suscom.2025.101213","DOIUrl":"10.1016/j.suscom.2025.101213","url":null,"abstract":"<div><div>The evolving energy landscape is increasingly integrating diverse energy sources, electricity, gas, heat, and cooling, reflecting a strategic shift driven by smart technologies and rising renewable adoption. However, the variability of renewable supply requires enhanced flexibility in demand-side management. This study presents a novel approach to optimizing regional integrated energy systems through a two-layer closed-loop model that incorporates exergy efficiency and user satisfaction dynamics. The model addresses the limitations of traditional energy systems, which often operate within the constraints of singular energy resources and fail to fully integrate renewable energies. The proposed model optimizes energy production, conversion, transmission, and consumption by using a multi-objective framework that includes economic, environmental, and exergy efficiency considerations. The proposed optimization approach significantly improves the performance of integrated energy systems. The energy efficiency is enhanced by 8.36 %, while exergy efficiency shows a notable increase of 1.61 %. Emissions are reduced by approximately 16.3 %, demonstrating the environmental benefits of the model. Though operational costs rise slightly, the trade-off favors sustainability with substantial gains in energy and environmental outcomes. The modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm outperforms traditional methods like NSGA-II and Standard PSO, achieving a higher Hypervolume value, indicating better convergence and solution diversity. This makes MOPSO a robust tool for solving multi-objective optimization problems in energy management.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101213"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable transient frequency management in eco-industrial park microgrids considering e-shared mobility storage using efficient fractional-order computing","authors":"SeyedJalal SeyedShenava,&nbsp;Peyman Zare,&nbsp;Amir Mohammadian","doi":"10.1016/j.suscom.2025.101197","DOIUrl":"10.1016/j.suscom.2025.101197","url":null,"abstract":"<div><div>The evolving architecture of rich-renewable Eco-Industrial Park Microgrids (EIP-MGs) introduces significant frequency stability challenges due to the intermittent nature and low inertia of integrated renewable energy sources. To address these limitations, advanced energy storage systems, comprising fixed and mobile electric energy storage systems, have been adopted. Among them, mobile EV energy storage, particularly in the context of e-shared mobility, offers a flexible and scalable solution for load frequency control in modern EIP-MGs. This study presents a novel framework for sustainable transient frequency management using a fractional-order computing-based hybrid cascade controller, TFOID–3DOF–TID (Tilted Fractional-Order Integral and Derivative with Three Degrees of Freedom), optimized via the Crested Porcupine Optimizer (CPO). The proposed control scheme is validated through six case studies under three industrial load disturbance scenarios, with emphasis on transient stability and real-world uncertainties. The evaluations are structured around frequency-domain design criteria based on integral error metrics, including squared and absolute formulationsaimed at analyzing efficiency, sensitivity, adaptability, robustness, stability, and computational burden. The proposed control scheme, featuring the TFOID and 3DOF-TID controllers, is evaluated in comparison with validated metaheuristic-based algorithms. Simulation results demonstrate that the CPO-based TFOID–3DOF–TID controller consistently outperforms other schemes, with improvements including a 22 %–48 % reduction in settling time, a 25 %–55 % decrease in undershoot, and a 30 %–60 % reduction in overshoot across varying scenarios. Additionally, Bode plot evaluations confirm superior phase margins and damping characteristics, while robustness margins improve by up to 60 %, affirming the controller’s resilience under non-ideal operational conditions. These findings provide practical insights for policymakers and engineers aiming to enhance the resilience and sustainability of future-ready industrial microgrids.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101197"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}