Qiang Wang¹, Hao Cheng², Wenrui Zhang^2,*, Guangxi Li³, Fan Xu², Dianhao Chen⁴, Haixiang Zang⁴

Energy Engineering, Vol.122, No.2, pp. 747-764, 2025, DOI:10.32604/ee.2025.059533 - 31 January 2025

Abstract Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources. However, the fluctuations and intermittency of photovoltaic (PV) power pose challenges for its extensive incorporation into power grids. Thus, enhancing the precision of PV power prediction is particularly important. Although existing studies have made progress in short-term prediction, issues persist, particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data. These factors hinder improvements in PV power prediction performance. To overcome these challenges, this paper… More >

Chakar Khadija^*, El Mouden Mahmoud, Hajjaji Abdelowahed

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 61-70, 2025, DOI:10.32604/fdmp.2024.053458 - 24 January 2025

Abstract The primary objective of this study is to develop an innovative theoretical model to accurately predict the thermophysical properties of hybrid nanofluids designed to enhance cooling in solar panel applications. This research lays the groundwork for our future studies, which will focus on photovoltaic thermal applications. These nanofluids consist of water and nanoparticles of alumina (Al₂O₃), titanium dioxide (TiO₂), and copper (Cu), exploring volumetric concentrations ranging from 0% to 4% for each type of nanoparticle, and up to 10% for total mixtures. The developed model accounts for complex interactions between the nanoparticles and the base fluid, More >

Chao Zhou¹, Narisu Wang¹, Fuyin Ni^1,2,*, Wenchao Zhang¹

Energy Engineering, Vol.122, No.1, pp. 265-284, 2025, DOI:10.32604/ee.2024.057380 - 27 December 2024

Abstract Uneven power distribution, transient voltage, and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes. In response to these issues, this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm. The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control. Then, it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy. Precise pre-synchronization is achieved by regulating the virtual current to zero and… More >

Yun Zhang^1,*, Zifen Han², Biao Tian¹, Ning Chen², Yi Fan³

Energy Engineering, Vol.122, No.1, pp. 167-184, 2025, DOI:10.32604/ee.2024.056190 - 27 December 2024

Abstract The new energy power generation is becoming increasingly important in the power system. Such as photovoltaic power generation has become a research hotspot, however, due to the characteristics of light radiation changes, photovoltaic power generation is unstable and random, resulting in a low utilization rate and directly affecting the stability of the power grid. To solve this problem, this paper proposes a coordinated control strategy for a new energy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit. Firstly, the variational mode decomposition algorithm is… More >

Jie Lin^*, Hongchi Shen, Tingting Pei, Yan Wu

Energy Engineering, Vol.122, No.1, pp. 331-347, 2025, DOI:10.32604/ee.2024.055611 - 27 December 2024

Abstract Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unit power generation costs. The service life of these modules directly affects these costs. Over time, the performance of PV modules gradually declines due to internal degradation and external environmental factors. This cumulative degradation impacts the overall reliability of photovoltaic power generation. This study addresses the complex degradation process of PV modules by developing a two-stage Wiener process model. This approach accounts for the distinct phases of degradation resulting from module aging and environmental influences. A power degradation model based on the More > Graphic Abstract

Rahool Rai^1,2,3,*, Fareed Hussain Mangi⁴, Kashif Ahmed², Sudhakar Kumaramsay^1,5,6,*

Energy Engineering, Vol.122, No.1, pp. 153-166, 2025, DOI:10.32604/ee.2024.051789 - 27 December 2024

Abstract The growing need for sustainable energy solutions, driven by rising energy shortages, environmental concerns, and the depletion of conventional energy sources, has led to a significant focus on renewable energy. Solar energy, among the various renewable sources, is particularly appealing due to its abundant availability. However, the efficiency of commercial solar photovoltaic (PV) modules is hindered by several factors, notably their conversion efficiency, which averages around 19%. This efficiency can further decline to 10%–16% due to temperature increases during peak sunlight hours. This study investigates the cooling of PV modules by applying water to their… More >

Daixuan Zhou¹, Yujin Liu¹, Xu Wang², Fuxing Wang¹, Yan Jia^2,*

Energy Engineering, Vol.121, No.12, pp. 3573-3616, 2024, DOI:10.32604/ee.2024.055853 - 22 November 2024

Abstract With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. More > Graphic Abstract

Aws Al-Akam^1,*, Ahmed A. Abduljabbar², Ali Jaber Abdulhamed¹

Energy Engineering, Vol.121, No.12, pp. 3761-3777, 2024, DOI:10.32604/ee.2024.055198 - 22 November 2024

Abstract Photovoltaic (PV) panels are essential to the global transition towards sustainable energy, offering a clean, renewable source that reduces reliance on fossil fuels and mitigates climate change. High temperatures can significantly affect the performance of photovoltaic (PV) panels by reducing their efficiency and power output. This paper explores the consequential effect of various rooftop coverings on the thermal performance of photovoltaic (PV) panels. It investigates the relationship between the type of rooftop covering materials and the efficiency of PV panels, considering the thermal performance and its implications for enhancing their overall performance and sustainability. The… More >

Yuxuan Zhao^1,2,*, Bo Wang¹, Shu Wang¹, Wenjun Xu², Gang Ma²

Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >