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  • Open Access

    ARTICLE

    Recent Advancements in the Optimization Capacity Configuration and Coordination Operation Strategy of Wind-Solar Hybrid Storage System

    Hongliang Hao1, Caifeng Wen2,3, Feifei Xue2,*, Hao Qiu1, Ning Yang2, Yuwen Zhang1, Chaoyu Wang1, Edwin E. Nyakilla1

    Energy Engineering, Vol.122, No.1, pp. 285-306, 2025, DOI:10.32604/ee.2024.057720 - 27 December 2024

    Abstract Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources. This paper proposes a wind-solar hybrid energy storage system (HESS) to ensure a stable supply grid for a longer period. A multi-objective genetic algorithm (MOGA) and state of charge (SOC) region division for the batteries are introduced to solve the objective function and configuration of the system capacity, respectively. MATLAB/Simulink was used for simulation test. The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system, with a combination of a 300 More >

  • Open Access

    ARTICLE

    A Multi-Objective Genetic Algorithm Based Load Balancing Strategy for Health Monitoring Systems in Fog-Cloud

    Hayder Makki Shakir, Jaber Karimpour*, Jafar Razmara

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545 - 26 January 2024

    Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource… More >

  • Open Access

    ARTICLE

    Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

    Xiaokan Wang*, Qiong Wang

    Journal on Internet of Things, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jiot.2021.010228 - 16 March 2021

    Abstract A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve. In the train control system, the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme, and the initial population can be formed by the way. The fitness function can be designed by the design requirements of the train control stop error, time error and energy consumption. the effectiveness of new individual can be ensured by checking the validity More >

  • Open Access

    ARTICLE

    Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation

    Juan Du1,*, Peng Dong2, Vijayan Sugumaran3

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 715-723, 2020, DOI:10.32604/iasc.2020.010105

    Abstract A dynamic optimized production scheduling which takes into account demand fluctuation and uncertainty is very critical for the efficient performance of Prefabricated Component Supply Chain. Previous studies consider only the conditions in the production factory and develop corresponding models, ignoring the dynamic demand fluctuation that often occurs at the construction site and its impact on the entire lifecycle of prefabricated construction project. This paper proposes a dynamic flow shop scheduling model for prefabricated components production, which incorporates demand fluctuation such as the advance of due date, insertion of urgent component and order cancellation. An actual More >

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