Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073533 - 29 January 2026

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Jun Guo^1,*, Maoyuan Chen¹, Yuyang Li¹, Sibo Feng^2,3, Guangyu Fu³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.068894 - 27 January 2026

Abstract The author proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network. The model plans the configuration of photovoltaic (3.8 MW), wind power (2.5 MW), energy storage (2.2 MWh), and SVC (1.2 Mvar) through interaction between upper and lower layers, and modifies lines 2–3, 8–9, etc. to improve transmission capacity and voltage stability. The author uses normal distribution and Monte Carlo method to model load uncertainty, and combines Weibull distribution to describe wind speed characteristics. Compared to the traditional… More >

Sk. A. Shezan^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073418 - 27 December 2025

Abstract Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization. However, the high penetration of intermittent renewable sources often causes frequency deviations, voltage fluctuations, and poor reactive power coordination, posing serious challenges to grid stability. Conventional Interconnection Flow Controllers (IFCs) primarily regulate active power flow and fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks. To overcome these limitations, this study proposes an enhanced Interconnection Flow Controller (e-IFC) that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller (IRFC) within a unified adaptive… More >

Supriya Wadekar^1,*, Shailendra Mittal¹, Ganesh Wakte², Rajshree Shinde²

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.071761 - 27 December 2025

Abstract Rapid evolutions of the Internet of Electric Vehicles (IoEVs) are reshaping and modernizing transport systems, yet challenges remain in energy efficiency, better battery aging, and grid stability. Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand, thus increasing energy costs and battery aging. This study proposes a smart charging station with an AI-powered Battery Management System (BMS), developed and simulated in MATLAB/Simulink, to increase optimality in energy flow, battery health, and impractical scheduling within the IoEV environment. The system operates through… More >

Zujun Ding, Qi Xiang, Chengyi Li, Mengyu Ma, Chutong Zhang, Xinfa Gu, Jiaming Shi, Hui Huang, Aoyun Xia, Wenjie Wang, Wan Chen, Ziluo Yu, Jie Ji^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070442 - 27 December 2025

Abstract In order to address environmental pollution and resource depletion caused by traditional power generation, this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer (IDBO) with Variational Mode Decomposition (VMD). The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations. This study innovatively improves the traditional variational mode decomposition (VMD) algorithm, and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO self-optimization of key parameters K and a. On this basis, Fourier transform technology… More >

Xia Yan¹, Wei Wang¹, Kai Shen^2,*, Yanqing Feng¹, Junyi Sun¹, Xiaogang Li¹, Wentao Zhu¹, Binbin Shi¹, Guanglong Sheng^2,3

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070360 - 27 December 2025

Abstract In the development of coalbed methane (CBM) reservoirs using multistage fractured horizontal wells, there often exist areas that are either repeatedly stimulated or completely unstimulated between fracturing stages, leading to suboptimal reservoir performance. Currently, there is no well-established method for accurately evaluating the effectiveness of such stimulation. This study introduces, for the first time, the concept of the Fracture Network Bridging Coefficient (FNBC) as a novel metric to assess stimulation performance. By quantitatively coupling the proportions of unstimulated and overstimulated volumes, the FNBC effectively characterizes the connectivity and efficiency of the fracture network. A background… More >

Tiankui Sun^*, Shuyi Zhuang, Yongling Lu, Wenqiang Xie, Ning Guo, Sudi Xu

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070213 - 27 December 2025

Abstract To ensure an uninterrupted power supply, mobile power sources (MPS) are widely deployed in power grids during emergencies. Comprising mobile emergency generators (MEGs) and mobile energy storage systems (MESS), MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies, offering advantages such as flexibility and high resilience through electricity delivery via transportation networks. This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator (DWIG) intended for MEG applications, employing an improved particle swarm optimization (PSO) algorithm based on a back-propagation neural network (BPNN). A… More >

Xue Zhang¹, Jie Chen^2,*, Zhihui Zhang³, Dewei Zhang³, Yuejiao Ming³, Xinde Zhang³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069487 - 27 December 2025

Abstract The integration of wind power and natural gas for hydrogen production forms a Green and Blue Hydrogen Integrated Energy System (GBH-IES), which is a promising cogeneration approach characterized by multi-energy complementarity, flexible dispatch, and efficient utilization. This system can meet the demands for electricity, heat, and hydrogen while demonstrating significant performance in energy supply, energy conversion, economy, and environment (4E). To evaluate the GBH-IES system effectively, a comprehensive performance evaluation index system was constructed from the 4E dimensions. The fuzzy DEMATEL method was used to quantify the causal relationships between indicators, establishing a scientific input-output… More > Graphic Abstract

Zhuang Liu^#, Mingwei Ren, Kai Shi^*, Peifeng Xu

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069480 - 27 December 2025

Abstract Grid-Forming (GFM) converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags. To address this, this paper develops a multi-loop coordinated fault ride-through (FRT) control strategy based on a power outer loop and voltage-current inner loops, aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions. During voltage sags, the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support, thereby effectively suppressing the steady-state component of the fault current. To address the active power imbalance induced… More >

Jun Xiong^*, Peng Yang, Bohan Chen, Zeming Chen

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069438 - 27 December 2025

Abstract The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system. However, various defects could be produced in the secondary equipment during long-term operation. The complex relationship between the defect phenomenon and multi-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods, which limits the real-time and accuracy of defect identification. Therefore, a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed. The defect data of secondary equipment is… More >