Sustainable Computing-Informatics & Systems最新文献_第4页

QTE-IoT: Q-learning-based task scheduling scheme to enhance energy consumption and QoS in IoT environments QTE-IoT：基于q学习的任务调度方案，提高物联网环境下的能耗和QoS

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-11-07 DOI: 10.1016/j.suscom.2025.101247

Ali Ghaffari , Vesal Firoozi , Ali Maleki , Mohammad Sadegh Sirjani , Maedeh Abedini Bagha

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.

随着物联网（IoT）设备的激增，对高效任务调度机制的需求变得越来越重要。物联网中的任务调度对于优化资源利用率、最小化延迟和提高整体系统性能至关重要。本研究提出了一种名为QTE-IoT的新方法，即基于q学习的任务调度方案，以提高物联网环境中的能耗和QoS。QTE-IoT将任务分为三类：时间敏感任务、安全任务和正常任务。这种分类是使用多层感知器人工神经网络实现的。随后，将时间敏感任务卸载到雾层，并使用提出的结合q -学习的非洲秃鹫算法进行调度，我们将其称为QAVA。安全任务卸载到私有云，正常任务卸载到公有云。对于私有云和公共云环境中的任务调度，QTE-IoT采用了人工兔子优化的拟议增强版本，集成了q -学习算法，称为QARO。此外，QTE-IoT方法包含一个监视代理来监督资源工作负载，从而防止拥塞和延迟。HCSP基准数据集实例的仿真结果表明，QTE-IoT在各种性能指标上优于其他最先进的方法。与其他方法和算法相比，QTE-IoT实现了显着改进，包括能耗降低6 - 12% %。此外，与现有方法相比，QTE-IoT在负载不平衡（42-79 %）、响应时间（25-40 %）和截止日期满意度（6-39 %）方面表现出实质性的改善。

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引用次数: 0

Continual learning-based regression testing for scalable VLSI verification across hierarchical design layers 跨分层设计层的可扩展VLSI验证的持续基于学习的回归测试

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-11-24 DOI: 10.1016/j.suscom.2025.101259

Sindhu Nalla , G. Nagarajan

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.

超大规模集成电路（VLSI）设计的高复杂性和快速发展正在挑战传统回归测试的极限，特别是在保持跨设计迭代的测试相关性方面。本文介绍了一个基于持续学习的回归测试（CLRT）框架，专为跨层次抽象级别（如逻辑、设计和芯片）的可扩展VLSI验证而设计。该框架通过在框架中根深蒂固的静态学习技术，以及根据每一个新的设计变化和测试结果不断学习和调整测试策略的能力，克服了静态测试模型的缺点。为了强化上述特性，我们的方法基于两层学习机制：第一层是具有历史测试结果的监督学习模型，用于检测设计空间中的回归敏感区域，第二层（在线连续学习模块）可以顺序适应新数据而不会发生灾难性遗忘。这允许系统记住学习的测试行为，同时适应不断变化的设计配置。利用混合特征选择机制提取有效参数，从芯片起泡状态下的设计级网表、逻辑级信号迹线和故障日志中提取有效参数，实现全面的跨层覆盖。在tc -99和Open Cores VLSI基准设计上进行了实验验证。与传统回归套件相比，所提出的CLRT框架的测试冗余度显著降低28.6 %，故障检测准确率显著提高35.2% %。此外，该系统在各种设计中都保持稳定的性能，这使其在动态测试条件下具有鲁棒性。研究结果验证了CL模型，如果有效地集成到rebase lining回归测试流程中，可以极大地提高VLSI验证的效率、灵活性和可扩展性。这项工作不仅提供了机器学习和分层VLSI测试之间的连接点，而且还为半导体设计自动化中未来自我改进的测试基础设施铺平了道路。

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引用次数: 0

Hybrid AI framework for detecting cyberattacks and predicting cascading failures in power systems 用于检测网络攻击和预测电力系统级联故障的混合人工智能框架

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.suscom.2025.101222

Lalit Agarwal , Bhavnesh Jaint , Anup K. Mandpura

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.

电网是关键的基础设施，依靠基于计算机的监控和数据采集（SCADA）系统对电网进行实时监测和控制。然而，这些系统越来越多地成为网络攻击者的目标，对电网的稳定和安全构成重大风险。现有的安全解决方案要么侧重于通过验证其签名来检测攻击，要么侧重于预测其级联故障，从而将失败的组件与其他工作组件隔离开来。在本文中，我们的目标是检测新的或现有的攻击并预测它们的级联失败。本研究通过引入一种新的多模型框架来实现这一目标，该框架结合了三个模型，XGBoost、Transformer和图神经网络（gnn），以识别已知和未知的网络攻击，并预测其对电网系统的级联影响。XGBoost模型可以检测已知的攻击模式，包括数据注入、远程脱扣命令注入、中继设置更改攻击。Transformer模型识别与已建立的攻击模式的偏差，这会导致发现新的威胁。我们对电网基础设施攻击的评估利用了基于gnn的级联故障预测模型，该模型将电网表示为一个图，以预测通过互联节点的故障传播。通过使用真实世界数据集的严格测试，我们的框架显示出卓越的检测性能，同时保持对新攻击的有效泛化和强大的级联故障预测能力。结果显示准确率高达98。6%，在多源数据集中得分为0.98 F1，优于单模型基线。

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引用次数: 0

Integrate multiple energy sources of the microgrid: Enhancing performance and sustainability in multi-energy systems 整合微电网的多种能源：提高多能源系统的性能和可持续性

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-08-14 DOI: 10.1016/j.suscom.2025.101181

Xiaolin Zhang, Zhi Liu

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.

本文介绍了一种新型的多能源系统混合优化框架，该框架共同解决了成本效率、不确定性和需求侧灵活性问题。所提出的模型独特地将电力和热负荷响应计划集成在统一的结构中，并包含负风险限制，以明确控制不稳定条件下的下行财务风险。一个关键的创新在于将基于场景的随机建模和鲁棒优化相结合，以管理可再生能源发电、市场价格和消费者需求的不确定性。采用自然启发的元启发式算法——花授粉算法，有效地解决了由此产生的高维问题。住宅规模的案例研究，包括光伏板、风力涡轮机、热电联产、锅炉、电动汽车、储热和热泵，展示了该框架的适用性。四个模拟场景评估负载响应计划和风险约束的单独和综合影响。结果表明，与基线运行相比，通过协调负荷转移减少了上游网络的能源采购，高峰时间采购减少了15 - 30% %。在联合负荷响应计划和负风险限制条件下，电动汽车在高达75% %的每日时隙中表现出主动充放电行为，增强了灵活性。

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引用次数: 0

Efficient QCA‐Based Circuits for Low‐Power Medical IoT System 用于低功耗医疗物联网系统的高效QCA电路

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-04 DOI: 10.1016/j.suscom.2025.101203

D. Ajitha , Muhammad Zohaib , Firdous Ahmad , Khalid Zaman , S.M. Prabin

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.

物联网（IoT）通过提供精确的诊断和治疗功能，在最近的医疗保健行业中发挥着至关重要的作用。人们对将医疗物联网纳入医疗保健系统的兴趣越来越大。所有医疗物联网的处理单元均采用互补金属氧化物半导体（CMOS）技术。然而，CMOS医疗物联网技术已经集成到纳米级生物医学硬件系统中。由于监管、道德和技术方面的挑战，包括处理速度慢、功耗高、开关频率慢，特别是在千兆赫（GHz）范围内。另一方面，与传统计算机相比，量子技术将使处理速度加快一个数量级，并影响所有人工和医疗（AI）以及医疗物联网处理应用。量子点元胞自动机（QCA）在医疗物联网中是一种很有前途的数字硬件系统。QCA技术为在医疗物联网应用中建立人工智能电路设计框架提供了最佳选择，在医疗物联网应用中，低成本、实时、节能性能至关重要。此外，加密和解密电路已用于医疗物联网操作，以保护正在传输和存储的敏感患者数据。在此背景下提出了基本算术和逻辑单元（ALU），它是纳米级设备医疗物联网系统处理和计算单元的基础。系统的方法涉及集成加法器、多路复用器、ALU和逻辑单元，以通过医疗物联网中的加密和解密增强处理器驱动和隐私。实验结果表明，推荐的设计在单元数上超过了15.48 %，在面积上超过了16.07 %。利用qcaddesigner - e2.0.3软件工具对设计进行了精确仿真。

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引用次数: 0

Machine learning based robust control for DFIG based wind energy conversion systems 基于机器学习的DFIG风能转换系统鲁棒控制

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-10-27 DOI: 10.1016/j.suscom.2025.101236

S. Kavitha , B. Chinthamani , John De Britto C , B. Suresh Chander Kapali

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.

环境变化的紧迫挑战和实现碳中和的全球目标正在推动向可再生能源（RE）的重大和广泛转变。在各种资源中，风能因其成本最低、清洁、可靠和生态优点而脱颖而出。因此，本文研究了一种基于双馈感应发电机（DFIG）的并网风能转换系统（WECS）。基于dfig的wcs由混沌火烈鸟优化算法优化的自适应神经模糊推理系统（ANFIS）最大功率点跟踪（MPPT）控制器控制。这种先进的控制器用于管理连接到DFIG的PWM整流器的操作，确保从风中获得最佳的能量。此外，多余的存储系统通过在大风期储存多余的风能并在低风期释放风能来稳定电网电力。基于几个关键指标，包括跟踪效率（99.3 %）、稳态误差和网格系统中THD的缓解（仿真（1.11 %）和硬件（3.59 %）），对所开发系统的有效性进行了全面评估。结果表明了CFO-ANFIS在抑制谐波失真和改善电网质量方面的有效性。这对可持续能源系统的发展有重大贡献。

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引用次数: 0

Design and performance assessment of a green hydrogen and renewable integrated hybrid industrial microgrid with advanced control strategies considering uncertainties of renewable energy 考虑可再生能源不确定性的绿色氢能与可再生能源集成混合工业微电网设计与性能评价

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-11-29 DOI: 10.1016/j.suscom.2025.101267

Javed Khan Bhutto , Arvind Kumar , Sarfaraz Kamangar , Amir Ibrahim Ali Arabi , Hadi Hakami

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.

混合工业微电网（HIMG）正在成为通过整合可再生能源和绿色氢技术使能源密集型行业脱碳的关键推动者。本文介绍了由1 mwp太阳能光伏（PV）和1.6 mw风力发电机、650 kw质子交换膜燃料电池（PEMFC）、3 mw电池储能系统（BESS）和5 mw柴油发电机组成的混合氢集成工业微电网的设计、控制和性能评估。光伏阵列以最大功率点跟踪方式运行，采用电网跟随模式，而BESS和风力发电机采用下垂控制方式运行，采用电网形成模式。为了保证HIMG的稳定运行，研究了分布式发电的控制方法和双向互连变流器的系统级控制技术。提出了再同步和计划孤岛策略，以确保并网和孤岛运行之间的无缝过渡。通过在锁相环测量中引入有意的时间延迟，进一步评估了系统的再同步性能，显示出在高延迟下振荡行为增加，动态响应变慢，而低延迟条件下可以实现快速且阻尼良好的频率恢复。通过详细的MATLAB/Simulink仿真，验证了所提控制器在各种运行场景下的性能，包括孤岛、电网重连、负载扰动和严重三相故障条件。综合模拟情景，包括可再生能源的不确定性和负荷波动，根据国际性能标准进行评估。频率响应分析证实了动态条件下网格成形控制的稳定性和鲁棒性。结果表明，改进的电压和频率调节，降低了电压和电流的总谐波失真，并显著减少了柴油的使用，证实了所提出的HIMG在工业应用中的技术可行性和可持续性效益。

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引用次数: 0

MEDALS: A sustainable AI framework for energy-efficient routing in 5G vehicular networks 奖牌：5G车辆网络中节能路由的可持续人工智能框架

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.suscom.2025.101210

G. Balram , KDV Prasad , Kamalakar Ramineni , Rahul Divgan , K. Ashok , N.V. Phani Sai Kumar

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.

智能交通系统需要在支持5g的车辆自组织网络（VANETs）中同时优化性能和环境影响的路由协议。现有的解决方案往往将可持续性视为次要制约因素，这限制了它们在解决气候变化目标方面的有效性。本研究提出了奖牌（元启发式增强型深度适应学习系统），这是一个混合框架，将深度强化学习与元启发式优化相结合，以实现卓越的性能和环境可持续性。该系统引入了绿色绩效指数（GPI），这是首个综合能源效率、碳足迹、延迟和可靠性的指标。通过使用行业标准模拟器进行广泛评估，奖牌显示了统计上显着的改进：奖牌实现了96.8% %的能源效率（+11.6 %），0.73 毫秒的延迟（-91.6 %），99.7 %的可靠性，以及42.3 %的碳减排，同时扩展到1000辆 + 车辆，具有线性计算复杂性。这将使其能够在智慧城市中实际实施，并实现可持续发展目标。在网络规模处理方面，这种3.3倍的复杂度提升归功于框架的混合智能架构、智能融合机制中具有双元启发式优化的自适应深度强化学习以及经验量化的O（N log N）复杂度。

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引用次数: 0

Multi-objective optimization of regional energy systems with exergy efficiency and user satisfaction dynamics 基于能效和用户满意度动态的区域能源系统多目标优化

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-23 DOI: 10.1016/j.suscom.2025.101213

Xuecheng Wu, Qiongbing Xiong, Cizhen Yu

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.

不断发展的能源格局正日益整合各种能源，包括电力、天然气、热能和制冷，这反映了智能技术和不断增长的可再生能源采用推动的战略转变。然而，可再生能源供应的可变性要求提高需求侧管理的灵活性。本研究提出了一种通过两层闭环模型优化区域综合能源系统的新方法，该模型结合了能源效率和用户满意度动态。该模型解决了传统能源系统的局限性，传统能源系统通常在单一能源的约束下运行，无法完全整合可再生能源。提出的模型通过使用包括经济、环境和能源效率考虑在内的多目标框架来优化能源生产、转换、传输和消费。所提出的优化方法显著提高了综合能源系统的性能。能源效率提高8.36 %，火用效率显著提高1.61 %。排放量减少了约16.3% %，证明了该模型的环境效益。尽管运营成本略有上升，但这种权衡有利于可持续性，并在能源和环境方面取得实质性成果。改进的多目标粒子群优化（Multi-Objective Particle Swarm Optimization， MOPSO）算法优于NSGA-II和Standard PSO等传统方法，实现了更高的Hypervolume值，具有更好的收敛性和解的多样性。这使得MOPSO成为解决能源管理中多目标优化问题的有力工具。

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引用次数: 0

Sustainable transient frequency management in eco-industrial park microgrids considering e-shared mobility storage using efficient fractional-order computing 基于高效分数阶计算的生态工业园区微电网暂态频率管理

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.suscom.2025.101197

SeyedJalal SeyedShenava, Peyman Zare, Amir Mohammadian

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.

由于集成可再生能源的间歇性和低惯性，丰富可再生生态工业园区微电网（eip - mg）架构的不断发展带来了显著的频率稳定性挑战。为了解决这些限制，采用了先进的储能系统，包括固定和移动电力储能系统。其中，移动电动汽车储能，特别是在电动共享出行的背景下，为现代eip - mg的负载频率控制提供了灵活和可扩展的解决方案。本研究提出了一种新的可持续瞬态频率管理框架，该框架使用基于分数阶计算的混合级联控制器TFOID-3DOF-TID（倾斜分数阶积分和三自由度导数），通过冠状Porcupine Optimizer （CPO）进行优化。通过三种工业负荷扰动情景下的六个案例研究验证了所提出的控制方案，重点研究了暂态稳定性和现实世界的不确定性。评估是围绕基于积分误差度量的频域设计标准构建的，包括平方和绝对公式，旨在分析效率、灵敏度、适应性、鲁棒性、稳定性和计算负担。采用TFOID和3DOF-TID控制器的控制方案与经过验证的元启发式算法进行了比较。仿真结果表明，基于cpo的TFOID-3DOF-TID控制器始终优于其他方案，在不同场景下，其改进包括沉降时间减少22 % -48 %，过冲减少25 % -55 %，超调减少30 % -60 %。此外，波德图评估确认了优越的相位裕度和阻尼特性，而鲁棒性裕度提高了60% %，确认了控制器在非理想操作条件下的弹性。这些发现为旨在增强面向未来的工业微电网的弹性和可持续性的政策制定者和工程师提供了实用的见解。

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引用次数: 0