Shuxin Tan¹, Wei Yan², Lei Zhao¹, Xianglin Zhang^3,*, Ziqiang Man², Yu Lu², Teng Liu², Gaoyue Zhong², Weiqun Liu², Linjun Shi³

Energy Engineering, Vol.122, No.12, pp. 5175-5197, 2025, DOI:10.32604/ee.2025.068692 - 27 November 2025

Abstract The integration of large-scale renewable energy introduces frequency instability challenges due to inherent intermittency. While doubly-fed pumped storage units (DFPSUs) offer frequency regulation potential in pumping mode, conventional strategies fail to address hydraulic-mechanical coupling dynamics and operational constraints, limiting their effectiveness. This paper presents an innovative primary frequency control strategy for double-fed pumped storage units (DFPSUs) operating in pumping mode, integrating an adaptive parameter calculation method. This method is constrained by operational speed and power limits, addressing key performance factors. A dynamic model that incorporates the reversible pump-turbine characteristics is developed to translate frequency deviations… More >

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >

Kunjabihari Swain¹, Murthy Cherukuri^1,*, Indu Sekhar Samanta², Bhargav Appasani^3,*, Nicu Bizon^4,5, Mihai Oproescu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1993-2015, 2025, DOI:10.32604/cmes.2025.067121 - 26 November 2025

Abstract Transmission line faults pose a significant threat to power system resilience, underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring, economic loss prevention, and blackout avoidance. Extreme learning machine (ELM) offers a compelling solution for rapid classification, achieving network training in a single epoch. Leveraging the Internet of Things (IoT) and the virtual instrumentation capabilities of LabVIEW, ELM can enable the swift and precise identification of transmission line faults. This paper presents a regularized radial basis function (RBF) ELM-based fault detection and classification system for transmission lines, utilizing a LabVIEW More >

Alex Segura^*, Joaquim Meléndez

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 649-672, 2025, DOI:10.32604/cmes.2025.070954 - 30 October 2025

Abstract The increasing use of renewable energy sources, combined with the increase in electricity demand, has highlighted the importance of energy flexibility management in electrical grids. Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation, ensuring the stability and efficiency of the grid. Thus, Local Flexibility Markets (LFMs) are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level, where the Distribution System Operators (DSOs) are the flexibility-demanding actors, and prosumers are the flexibility providers. This paper investigates the… More >

Ihab Taleb, Guillaume Guerard^*, Frédéric Fauberteau, Nga Nguyen, Pascal Clain

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 597-619, 2025, DOI:10.32604/cmes.2025.070517 - 30 October 2025

Abstract Isolated power systems, such as those on islands, face acute challenges in balancing energy demand with limited generation resources, making them particularly vulnerable to disruptions. This paper addresses these challenges by proposing a novel control and simulation framework based on a holonic multi-agent architecture, specifically developed as a digital twin for the Mayotte island grid. The primary contribution is a multi-objective optimization model, driven by a genetic algorithm, designed to enhance grid resilience through intelligent, decentralized decision-making. The efficacy of this architecture is validated through three distinct simulation scenarios: (1) a baseline scenario establishing nominal… More >

Zengyao Tian^1,2, Li Lv^1,*, Wenchen Deng¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5359-5376, 2025, DOI:10.32604/cmc.2025.069427 - 23 October 2025

Abstract Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems. While graph neural networks show promise for this task, existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions. To address these dual challenges, we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning. Its core innovations are two synergistic modules: (1) The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations. It employs an information-driven optimization loss within a… More >

Remah Younisse^1,#, Mouhammd AlKasassbeh^1,#, Amjad Aldweesh^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4921-4941, 2025, DOI:10.32604/cmc.2025.067818 - 23 October 2025

Abstract Smart grid systems are advancing electrical services, making them more compatible with Internet of Things (IoT) technologies. The deployment of smart grids is facing many difficulties, requiring immediate solutions to enhance their practicality. Data privacy and security are widely discussed, and many solutions are proposed in this area. Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks. The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems, which is commonly solved via fog computing. This… More >

Yuqian Qi^*, Yanbo Che, Liangliang Liu, Jiayu Ni, Shangyuan Zhang

Energy Engineering, Vol.122, No.10, pp. 3999-4017, 2025, DOI:10.32604/ee.2025.068054 - 30 September 2025

Abstract The process of including renewable energy sources in power networks is moving quickly, so the need for innovative configuration solutions for grid-side ESS has grown. Among the new methods presented in this paper is GA-OCESE, which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks. This is one of the methods suggested in this study, which aims to enhance the sizing, positioning, and operational characteristics of structured ESS under dynamic grid conditions. Particularly, the aim is to maximize efficiency. A multiobjective genetic algorithm, the GA-OCESE framework, considers all these factors simultaneously. Besides… More >

Sheng Bi^1,2,*, Jiayan Wang¹, Dong Su¹, Hui Lu¹, Yu Zhang¹

Energy Engineering, Vol.122, No.10, pp. 4135-4151, 2025, DOI:10.32604/ee.2025.066735 - 30 September 2025

Abstract By comparing price plans offered by several retail energy firms, end users with smart meters and controllers may optimize their energy use cost portfolios, due to the growth of deregulated retail power markets. To help smart grid end-users decrease power payment and usage unhappiness, this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection. An enhanced state-based Markov decision process (MDP) without transition probabilities simulates the decision issue. A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue. Several adjustments to the sampling and data… More >