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  • Open Access

    ARTICLE

    Improved Multi-Fusion Black-Winged Kite Algorithm for Optimizing Stochastic Configuration Networks for Lithium Battery Remaining Life Prediction

    Yuheng Yin, Lin Wang*

    Energy Engineering, Vol.122, No.7, pp. 2845-2864, 2025, DOI:10.32604/ee.2025.065889 - 27 June 2025

    Abstract The accurate estimation of lithium battery state of health (SOH) plays an important role in the health management of battery systems. In order to improve the prediction accuracy of SOH, this paper proposes a stochastic configuration network based on a multi-converged black-winged kite search algorithm, called SBKA-CLSCN. Firstly, the indirect health index (HI) of the battery is extracted by combining it with Person correlation coefficients in the battery charging and discharging cycle point data. Secondly, to address the problem that the black-winged kite optimization algorithm (BKA) falls into the local optimum problem and improve the… More >

  • Open Access

    ARTICLE

    A Partitioned Yaw Control Algorithm for Wind Farms Using Dynamic Wake Modeling

    Yinguo Yang1, Lifu Ding2,*, Yang Liu1, Bingchen Wang2, Weihua Wang1, Ying Chen2

    Energy Engineering, Vol.122, No.7, pp. 2571-2587, 2025, DOI:10.32604/ee.2025.065716 - 27 June 2025

    Abstract This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn (FLOW Redirection and Induction Dynamics) dynamic wake model. First, the impact of wakes on turbine effective wind speed is analyzed, leading to a quantitative method for assessing wake interactions. Based on these interactions, a partitioning method divides the wind farm into smaller, computationally manageable zones. Subsequently, a heuristic control algorithm is developed for yaw optimization within each partition, reducing the overall computational burden associated with multi-turbine optimization. The algorithm’s effectiveness is evaluated through case More >

  • Open Access

    ARTICLE

    Transformer-Enhanced Intelligent Microgrid Self-Healing: Integrating Large Language Models and Adaptive Optimization for Real-Time Fault Detection and Recovery

    Qiang Gao1, Lei Shen1,*, Jiaming Shi2, Xinfa Gu2, Shanyun Gu1, Yuwei Ge1, Yang Xie1, Xiaoqiong Zhu1, Baoguo Zang1, Ming Zhang1, Muhammad Shahzad Nazir2, Jie Ji2

    Energy Engineering, Vol.122, No.7, pp. 2767-2800, 2025, DOI:10.32604/ee.2025.065600 - 27 June 2025

    Abstract The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems. Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multi-modal data fusion. This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large language models (LLMs) with adaptive optimization, achieving three key innovations: (1) A hierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction, (2) Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds (Daubechies-4 basis, 6-level decomposition), and… More >

  • Open Access

    ARTICLE

    Simulation on H2S Migration and Elutriation during Cyclic Operationof Underground Sour Gas Storage

    Siji Chen1, Gang Chen2, Wei Wang2, Han Liu1, Mukun Ouyang1, Wanhong Zhang1, Lianghua Zhang1, Wei Tang1, Shilai Hu2,*

    Energy Engineering, Vol.122, No.7, pp. 2819-2843, 2025, DOI:10.32604/ee.2025.065481 - 27 June 2025

    Abstract The construction and operation of sulfur-containing gas storage are often more difficult than a non-sulfur storage facility due to the need to prevent environmental contamination from H2S leaks, as well as the corrosive effects of H2S on production facilities. Rapid elutriation of H2S from the reservoir during the construction of the gas storage is an effective way to avoid these problems. However, the existing H2S elutriation method has low efficiency and high economic cost, which limits the development of reconstructed gas storage of sulfur-containing gas reservoirs. To improve the efficiency of H2S elutriation in sulfur-containing gas reservoirs and… More >

  • Open Access

    ARTICLE

    Low Carbon Economic Dispatch of a Multi-Principal Integrated Energy System Considering CCS and P2G

    Yunfeng Liu, Ximin Cao*, Yanchi Zhang

    Energy Engineering, Vol.122, No.7, pp. 2865-2890, 2025, DOI:10.32604/ee.2025.065419 - 27 June 2025

    Abstract Against the backdrop of China’s “dual-carbon” target, clean energy generation currently accounts for about 3.8 trillion kilowatt-hours, or 39.7 percent of total power generation, establishing a reasonable market trading mechanism while enhancing the low-carbon economic benefits of the integrated energy system (IES) and optimizing the interests of various entities within the distribution system has become a significant challenge. Consequently, this paper proposes an optimization strategy for a low-carbon economy within a multi-agent IES that considers carbon capture systems (CCS) and power-to-gas (P2G). In this framework, the integrated energy system operator (IESO) acts as the primary… More >

  • Open Access

    ARTICLE

    In-Situ Study on the Effect of Gas Stove Structure on Flame Combustion Characteristics Based on Spectral Diagnosis

    Jin Feng1, Juntao Wei2,3,*, Yuanyuan Jing1, Xudong Song1,*, Zhengdong Gu3, Yonghui Bai1, Manoj Kumar Jena4,5, Weiguang Su1, Guangsuo Yu1,6

    Energy Engineering, Vol.122, No.7, pp. 2637-2652, 2025, DOI:10.32604/ee.2025.065407 - 27 June 2025

    Abstract This study systematically investigated the effects of different gas stove structures on flame combustion characteristics using spectral diagnostic techniques, aiming to provide optimized design guidelines for clean energy applications. To explore the combustion behaviors of various gas stove structures, UV cameras, high-speed cameras, and K-type thermocouples were employed to measure parameters such as flame OH radicals (OH*), flame morphology, pulsation frequency, flame temperature, and heat flux. The results demonstrate that flame stability was achieved at an inner/outer cover flow rate ratio of 0.5/4.0 L/min, beyond which further flow rate increases led to reduced combustion efficiency.… More >

  • Open Access

    ARTICLE

    Optimization of Fracture Propagation in Coal Seams Using Discrete Lattice Method: Case Study of the L Block, China

    Xuesong Xing1, Li Wang1, Guangai Wu1, Chengyong Peng1,2,3, Yanan Hou1, Jingyu Zi1, Biao Yin2,3,*

    Energy Engineering, Vol.122, No.7, pp. 2911-2930, 2025, DOI:10.32604/ee.2025.065384 - 27 June 2025

    Abstract Hydraulic fracturing, an effective method for enhancing coal seam productivity, largely determines coalbed methane (CBM) production, which is significantly influenced by geological and engineering factors. This study focuses on the L block to investigate the mechanisms influencing efficient fracture propagation and enhanced stimulated reservoir volume (SRV) in fracturing. To explore the mechanisms influencing effective fracture propagation and enhanced SRV, the L block was selected as the research object, with a comprehensive consideration of geological background, reservoir properties, and dynamic production data. By combining the discrete lattice method with numerical analysis and true triaxial experimental simulation,… More >

  • Open Access

    ARTICLE

    Energy Recycling System for Harnessing Industrial Rotational Kinetic Energy

    Md Tanjil Sarker1,*, See Wei Jing1, Gobbi Ramasamy1,*, Siva Priya Thiagarajah1, Md. Golam Sadeque2

    Energy Engineering, Vol.122, No.7, pp. 2891-2909, 2025, DOI:10.32604/ee.2025.065331 - 27 June 2025

    Abstract Industrial processes often involve rotating machinery that generates substantial kinetic energy, much of which remains untapped. Harvesting rotational kinetic energy offers a promising solution to reduce energy waste and improve energy efficiency in industrial applications. This research investigates the potential of electromagnetic induction for harvesting rotational kinetic energy from industrial machinery. A comparative study was conducted between disk and cylinder-shaped rotational bodies to evaluate their energy efficiency under various load conditions. Experimental results demonstrated that the disk body exhibited higher energy efficiency, primarily due to lower mechanical losses compared to the cylinder body. A power… More >

  • Open Access

    ARTICLE

    Occupancy Based Building Energy Analysis Using Discrete Event Simulation

    Rupa Das1, Roseline Mostafa2, Bhaskaran Gopalakrishnan2,*

    Energy Engineering, Vol.122, No.7, pp. 2931-2956, 2025, DOI:10.32604/ee.2025.064887 - 27 June 2025

    Abstract Highly energy-efficient buildings have generated remarkable interest over the last few years. There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devices, as they contribute to energy-efficient buildings and assist in gaining flexibility for the human occupancy-based energy loads. In this context, the integrated energy profile of a building can be ascertained by effective research approaches, as this knowledge would be beneficial to understand the demographics with respect to human occupancy and activities, as well as estimate varying energy consumption over time. Utility data from… More > Graphic Abstract

    Occupancy Based Building Energy Analysis Using Discrete Event Simulation

  • Open Access

    ARTICLE

    Forecasting Solar Energy Production across Multiple Sites Using Deep Learning

    Samira Marhraoui1,2,*, Basma Saad3, Hassan Silkan1, Said Laasri2, Asmaa El Hannani3

    Energy Engineering, Vol.122, No.7, pp. 2653-2672, 2025, DOI:10.32604/ee.2025.064498 - 27 June 2025

    Abstract Photovoltaic (PV) power forecasting is essential for balancing energy supply and demand in renewable energy systems. However, the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation. This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies: Hybrid-Si, Mono-Si, and Poly-Si, across three forecasting horizons: 1-step, 12-step, and 24-step. Among the tested models, the Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture exhibited superior performance, particularly for the 24-step horizon, achieving R2 = 0.9793 and MAE = 0.0162 for More >

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