Jack Kiing Teck Wei¹, Mohanad Taher Mohamed Sayed Roshdy¹, Bryan Ho Liang Hui¹, Jalal Tavalaei¹, Hadi Nabipour Afrouzi^2,*

Energy Engineering, Vol.122, No.11, pp. 4755-4775, 2025, DOI:10.32604/ee.2025.069980 - 27 October 2025

Abstract In this article, a hybrid energy storage system powered by renewable energy sources is suggested, which is connected to a grid-tied electric vehicle charging bay (EVCB) in Sarawak and is examined for its techno-economic effects. With a focus on three renewable energy sources, namely hydrokinetic power, solar power, and hydrogen fuel cells, the study seeks to minimize reliance on the electrical grid while meeting the growing demand from the growing electric vehicle (EV) infrastructure. A hybrid renewable energy storage system that combines solar power, hydrogen fuel cells, hydrokinetic power, and the grid was simulated and… More >

Shekaina Justin^1,*, Wafaa Saleh², Hind Mohammed Albalawi³, J. Shermina⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5469-5487, 2025, DOI:10.32604/cmc.2025.066888 - 23 October 2025

Abstract Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles, surface radiation balance, weather and climate changes, and vegetation photosynthesis. Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry. This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period. Solar energy is the cheapest form of clean energy, and due to the intermittent nature of the energy, accurate forecasting across multiple timeframes is necessary for… More >

Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025

Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

Xu Chen^1,*, Shuai Wang¹, Kaixun He²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1779-1806, 2025, DOI:10.32604/cmc.2025.066543 - 29 August 2025

Abstract Accurate and reliable photovoltaic (PV) modeling is crucial for the performance evaluation, control, and optimization of PV systems. However, existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency. To address these challenges, we propose an adaptive multi-learning cooperation search algorithm (AMLCSA) for efficient identification of unknown parameters in PV models. AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises. It enhances the original cooperation search algorithm in two key aspects: (i) an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights, allowing better individuals to More >

Khadija Ibaararen, Mhammed Zaimi, Khadija El Ainaoui, El Mahdi Assaid^*

Energy Engineering, Vol.122, No.9, pp. 3487-3505, 2025, DOI:10.32604/ee.2025.067422 - 26 August 2025

Abstract This study investigates the complex heat transfer dynamics in multilayer bifacial photovoltaic (bPV) solar modules under spectrally resolved solar irradiation. A novel numerical model is developed to incorporate internal heat generation resulting from optical absorption, grounded in the physical equations governing light-matter interactions within the module’s multilayer structure. The model accounts for reflection and transmission at each interface between adjacent layers, as well as absorption within individual layers, using the wavelength-dependent dielectric properties of constituent materials. These properties are used to calculate the spectral reflectance, transmittance, and absorption coefficients, enabling precise quantification of internal heat… More >

Jingxian Ni, Chaomeng Wang, Shiqi Sun, Yuxuan Sun, Gang Ma^*

Energy Engineering, Vol.122, No.9, pp. 3719-3736, 2025, DOI:10.32604/ee.2025.066971 - 26 August 2025

Abstract To improve the fault diagnosis accuracy of a PV grid-connected inverter, a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed. Firstly, unlike the traditional VMD algorithm which relies on manual experience to set parameters (e.g., noise tolerance, penalty parameter, number of decompositions), this paper achieves adaptive optimization of parameters through MPA algorithm to avoid the problem of feature information loss caused by manual parameter tuning, and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters; and then, adopts the PSO algorithm for the Then, the… More >

Dayan Sun¹, Xiao Cao^2,*, Zhifeng Liang¹, Junrong Xia², Yuqi Wang³

Energy Engineering, Vol.122, No.8, pp. 3329-3346, 2025, DOI:10.32604/ee.2025.066341 - 24 July 2025

Abstract Photovoltaic (PV) power generation is undergoing significant growth and serves as a key driver of the global energy transition. However, its intermittent nature, which fluctuates with weather conditions, has raised concerns about grid stability. Accurate PV power prediction has been demonstrated as crucial for power system operation and scheduling, enabling power slope control, fluctuation mitigation, grid stability enhancement, and reliable data support for secure grid operation. However, existing prediction models primarily target centralized PV plants, largely neglecting the spatiotemporal coupling dynamics and output uncertainties inherent to distributed PV systems. This study proposes a novel Spatio-Temporal… More >

Luiz Felipe Souza Fonseca¹, Heitor do Nascimento Andrade¹, João Marcelo Fernandes Gualberto de Galiza², Raphael Abrahão¹, Hamid Boleydei³, Silvia Guillén-Lambea⁴, Monica Carvalho^1,*

Energy Engineering, Vol.122, No.8, pp. 3265-3283, 2025, DOI:10.32604/ee.2025.066218 - 24 July 2025

Abstract The application of different coatings on solar photovoltaic (PV) panels can be an efficient solution to increase performance and further mitigate the emission of greenhouse gases. This study uses the Life Cycle Assessment (LCA) methodology and the environmental payback concept to analyze the effects of the application of a nano-silica coating on a solar PV system installed in the Brazilian Northeast. Firstly, an uncoated reference 16.4 MW PV system is designed, and the detailed inventory is presented (PV panels, supporting structure, inverters, junction boxes, cables, transportation, maintenance and operation—including the replacement of equipment). The results… More > Graphic Abstract

Yunfei Xu¹, Jianfeng Liu^1,*, Tianxing Sun¹, Heran Kang¹, Xiaoqing Hao²

Energy Engineering, Vol.122, No.8, pp. 3155-3176, 2025, DOI:10.32604/ee.2025.065753 - 24 July 2025

Abstract In order to promote the utilization level of new energy resources for local and efficient consumption, this paper introduces the biogas (BG) fermentation technology into the integrated energy system (IES). This initiative is to study the collaborative and optimal scheduling of IES with wind power (WP), photovoltaic (PV), and BG, while integrating carbon capture system (CCS) and power-to-gas (P2G) system. Firstly, the framework of collaborative operation of IES for BG-CCS-P2G is constructed. Secondly, the flexible scheduling resources of the source and load sides are fully exploited, and the collaborative operation mode of CCS-P2G is proposed… More >

Ruiheng Pan^*, Shuyan Wang, Yihan Huang, Gang Ma

Energy Engineering, Vol.122, No.8, pp. 3079-3098, 2025, DOI:10.32604/ee.2025.065124 - 24 July 2025

Abstract Contemporary power network planning faces critical challenges from intensifying climate variability, including greenhouse effect amplification, extreme precipitation anomalies, and persistent thermal extremes. These meteorological disruptions compromise the reliability of renewable energy generation forecasts, particularly in photovoltaic (PV) systems. However, current predictive methodologies exhibit notable deficiencies in extreme weather monitoring, systematic transient phenomena analysis, and preemptive operational strategies, especially for cold-wave weather. In order to address these limitations, we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks (TimeGAN) for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data… More >