Chenglian Ma¹, Rui Han¹, Zhao An^2,*, Tianyu Hu², Meizhu Jin²

Energy Engineering, Vol.121, No.5, pp. 1245-1261, 2024, DOI:10.32604/ee.2024.046644

Abstract Precise forecasting of solar power is crucial for the development of sustainable energy systems. Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic (PV) power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data. To overcome these challenges, this research presents a cutting-edge, multi-stage forecasting method called D-Informer. This method skillfully merges the differential transformation algorithm with the Informer model, leveraging a detailed array of meteorological variables and historical PV power generation records. The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics,… More > Graphic Abstract

Lei Fang^*, Haiying Dong, Xiaofei Zhen, Shuaibing Li

Energy Engineering, Vol.121, No.3, pp. 661-679, 2024, DOI:10.32604/ee.2023.029823

Abstract According to the multi-time-scale characteristics of power generation and demand-side response (DR) resources, as well as the improvement of prediction accuracy along with the approaching operating point, a rolling peak shaving optimization model consisting of three different time scales has been proposed. The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination, generation power, and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response… More >

Ju Hyeon Lee¹, Jiho Shin², Jung Taek Seo^3,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 757-779, 2023, DOI:10.32604/cmc.2023.039461

Abstract As energy-related problems continue to emerge, the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration. Renewable energy is becoming increasingly important, with solar power accounting for the most significant proportion of renewables. As the scale and importance of solar energy have increased, cyber threats against solar power plants have also increased. So, we need an anomaly detection system that effectively detects cyber threats to solar power plants. However, as mentioned earlier, the existing solar power plant anomaly detection system monitors only operating information such as power generation, making it difficult to detect cyberattacks.… More >

T. E. Boukelia^1,2,*, A. Bourouis¹, M. E. Abdesselem³, M. S. Mecibah³

Energy Engineering, Vol.120, No.11, pp. 2449-2467, 2023, DOI:10.32604/ee.2023.030183

Abstract Concentrating Solar Power (CSP) plants offer a promising way to generate low-emission energy. However, these plants face challenges such as reduced sunlight during winter and cloudy days, despite being located in high solar radiation areas. Furthermore, their dispatch capacities and yields can be affected by high electricity consumption, particularly at night. The present work aims to develop an off-design model that evaluates the hourly and annual performances of a parabolic trough power plant (PTPP) equipped with a waste heat recovery system. The study aims to compare the performances of this new layout with those of the conventional Andasol 1 plant,… More >

Marwa M. Ibrahim^a,*, Mohamed Mahran Kasem^b,c

Frontiers in Heat and Mass Transfer, Vol.17, pp. 1-11, 2021, DOI:10.5098/hmt.17.20

Abstract Currently electricity generation technologies by thermal energy conversions become strong demand. The objective of this paper is to present a novel thermal study of absorber/receiver circular pipe of parabolic trough solar collector system for laminar and turbulent (k-ɛ model) fluids flow as well as two-dimensional numerical simulation is performed using CFD ANSYS FLUENT software. Significant improvements in heat transfer and velocity were discovered; the pattern of temperature distribution over the pipe absorber was displayed, and velocity vectors, pressure contours, and temperature contours were studied. The impact of increasing the heat flux towards the pipe wall is discussed. Heat transfer coefficient… More >

Yuxing Yang, Peng Zhang^*, Meng Lv

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1397-1409, 2023, DOI:10.32604/fdmp.2023.024916

Abstract The performances of turbine blades have a significant impact on the energy conversion efficiency of vertical solar power plants. In the present study, such a relationship is assessed by considering two kinds of airfoil blades, designed by using the Wilson theory. In particular, numerical simulations are conducted using the SST K − ω model and assuming a wind speed of 3–6 m/s and seven or eight blades. The two airfoils are the NACA63121 (with a larger chord length) and the AMES63212; It is shown that the torsion angle of the former is smaller, and its wind drag ratio is larger; furthermore,… More > Graphic Abstract

Xing Deng^1,2, Feipeng Da^1,*, Haijian Shao², Xia Wang³

Energy Engineering, Vol.120, No.2, pp. 385-408, 2023, DOI:10.32604/ee.2023.023480

Abstract Photovoltaic power generating is one of the primary methods of utilizing solar energy resources, with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy. In order to provide reference strategies for pertinent researchers as well as potential implementation, this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches, statistical approaches and optimization techniques for solar power generation and forecasting. Deep learning-related methods, in particular, can theoretically handle arbitrary nonlinear transformations through proper model structural design, such as hidden layer topology optimization and objective function analysis to save information that… More > Graphic Abstract

K. Madumathi^1,*, S. Chandrasekar²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1821-1836, 2023, DOI:10.32604/csse.2023.029561

Abstract Artificial intelligence, machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global Maximum Power Point (GMPP) under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions. The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions (PSC). Still the selection of an efficient learning based MPPT is complex because each model has its advantages and drawbacks. Recently,… More >

Yang Yang, Jun Hu, Lin Zhu^*, Mengchen Pei

Journal of Renewable Materials, Vol.10, No.11, pp. 2835-2849, 2022, DOI:10.32604/jrm.2022.021840

Abstract Space solar power station is a novel renewable energy equipment in space to provide the earth with abundant and continuous power. The Orb-shaped Membrane Energy Gathering Array, one of the alternative construction schemes in China, is promising for collecting space sunlight with a large-scale spherical concentrator. Both the structural and optical performances such as root mean square deformation, natural frequency, system mass, and sunlight blocking rate have significant influences on the system property of the concentrator. Considering the comprehensive performance of structure and optic, this paper proposes a novel mesh grid based on normal polyhedron projection and spherical arc bisection… More > Graphic Abstract

Zhongyao Du^1,*, Xiaoying Chen¹, Hao Wang², Xuheng Wang¹, Yu Deng¹, Liying Sun¹

Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283

Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss coefficients of PV modules. Clear-Sky… More >