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Advances in Photovoltaic Technologies

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 10906

Special Issue Editors


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Guest Editor
Department of Chemical and Materials Engineering, Chang Gung University, Taoyuan City 33302, Taiwan
Interests: perovskite solar cells; dye-sensitized solar cells and modules; electrochemistry; photodetector; X-ray image sensor
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Guest Editor
Center for Green Technology, Chang Gung University, Taoyuan 33302, Taiwan
Interests: perovskite solar cells; dye-sensitized solar cells; Si-based solar cells; solar module technology

Special Issue Information

Dear Colleagues,

Solar energy is one of the viable pathways to build a global energy consumption with net-zero emissions by 2050. The report of BloombergNEF (BNEF) states that at least 455GW of new solar PV capacity should be installed each year by 2030 and 20TW of solar systems should be installed by 2050. Solar cells and modules with high efficiency, low cost, better reliability and wide applications should be invented to reach the target of net-zero emissions. For this Special Issue on “Advances in Photovoltaic Technologies”, we invite authors to submit articles on the following topics:

  • Novel methods to improve the efficiency or enhance the light harvesting of solar cells and modules.
  • New materials for photovoltaics to achieve a higher power output or better reliability with solar cells and modules.
  • Emerging solar technologies such as heterojunctions (HIT), tunnel oxide passivated carrier-selective contacts (TOPCon) for crystalline Si solar technology, thin film technology, perovskite cells, dye-sensitized solar cells (DSSC) and organic solar cells (OPV) for organic–inorganic solar technology, quantum dot solar cell technology and 2T or 4T tandem solar cell technology.
  • New PV applications for lightweight, flexible, Internet of Things (IoT), semi-transparent, outer space or hydrogen production.

Prof. Dr. Kun-Mu Lee
Dr. Wei-Hao Chiu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

12 pages, 5595 KiB  
Article
Simulation-Based Fault Detection Remote Monitoring System for Small-Scale Photovoltaic Systems
by Hee-Won Lim, Il-Kwon Kim, Ji-Hyeon Kim and U-Cheul Shin
Energies 2022, 15(24), 9422; https://doi.org/10.3390/en15249422 - 13 Dec 2022
Cited by 1 | Viewed by 1509
Abstract
A small-scale grid-connected PV system that is easy to install and is inexpensive as a remote monitoring system may cause economic losses if its failure is not found and it is left unattended for a long time. Thus, in this study, we developed [...] Read more.
A small-scale grid-connected PV system that is easy to install and is inexpensive as a remote monitoring system may cause economic losses if its failure is not found and it is left unattended for a long time. Thus, in this study, we developed a low-cost fault detection remote monitoring system for small-scale grid-connected PV systems. This active monitoring system equipped with a simulation-based fault detection algorithm accurately predicts AC power under normal operating conditions and notifies its failure when the measured power is abnormally low. In order to lower the cost, we used a single board computer (SBC) with edge computing as a data server and designed a monitoring system using openHAB, an open-source software. Additionally, we used the Shewhart control chart as a fault detection criterion and the ratio between the measured and predicted ac power for the normal operation data as an observation. As a result of the verification test for the actual grid-connected PV system, it was confirmed that the developed remote monitoring system was able to accurately identify the system failures in real-time, such as open circuit, short circuit, partial shading, etc. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Technologies)
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15 pages, 2619 KiB  
Article
Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power Prediction
by Xiaozhi Gao, Lichi Gao, Hsiung-Cheng Lin, Yanming Huo, Yaheng Ren and Wang Guo
Energies 2022, 15(19), 7332; https://doi.org/10.3390/en15197332 - 06 Oct 2022
Viewed by 1219
Abstract
The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power [...] Read more.
The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power prediction. Here, the MVMD combined with Spearman extracts correlation features of the weather data. An equilibrium optimizer (EO) is integrated with MVMD to achieve optimal values of the long short-term memory (LSTM) parameters. Firstly, the correlation of input features is determined and selected by Spearman. The MVMD model is used to mine the high correlation features of solar radiation and conduct cross-correlation analysis to extract input feature components. Secondly, the similar weather days of the sample set are classified to ensure a good adaptability in different weather situations. Finally, the high correlation features are introduced into the photovoltaic power prediction model of EO optimized LSTM. Performance analysis using actual output power data from a PV plant shows that the proposed MVMD feature extraction method can effectively mine correlation features to achieve an optimized dataset under different seasons. Compared with the gray wolf and particle swarm optimization algorithms, the proposed model has a better optimization performance in a low discrimination of input feature decomposition components and low correlation with output power. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Technologies)
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24 pages, 4141 KiB  
Article
PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type
by Wentao Ma, Lihong Qiu, Fengyuan Sun, Sherif S. M. Ghoneim and Jiandong Duan
Energies 2022, 15(14), 5231; https://doi.org/10.3390/en15145231 - 19 Jul 2022
Cited by 6 | Viewed by 1117
Abstract
Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. [...] Read more.
Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Technologies)
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12 pages, 8403 KiB  
Article
Oxidized Nickel to Prepare an Inorganic Hole Transport Layer for High-Efficiency and Stability of CH3NH3PbI3 Perovskite Solar Cells
by Chien-Chung Hsu, Sheng-Min Yu, Kun-Mu Lee, Chuan-Jung Lin, Bo-Yi Liou and Fu-Rong Chen
Energies 2022, 15(3), 919; https://doi.org/10.3390/en15030919 - 27 Jan 2022
Cited by 5 | Viewed by 3325
Abstract
In this study, we report a perovskite solar cell (PSC) can be benefited from the high quality of inorganic nickel oxide (NiOx) as a hole transport layer (HTL) film fabricated from the physical vapor deposition (PVD) process. The power conversion efficiency [...] Read more.
In this study, we report a perovskite solar cell (PSC) can be benefited from the high quality of inorganic nickel oxide (NiOx) as a hole transport layer (HTL) film fabricated from the physical vapor deposition (PVD) process. The power conversion efficiency (PCE) of PSC is found to depend on the thickness of NiOx HTL. The NiOx thickness is optimized via quantitative investigation of the structure, optical and electrical properties. With an active area of 11.25 cm2, a PSC module (25 cm2) with a PCE of 15.1% is demonstrated, while statistically averaged PCE = 18.30% with an open voltage (Voc) 1.05 V, short-circuit current density (Jsc) 23.89 mA/cm2, and fill factor (FF) 72.87% can be achieved from 36 devices with smaller active areas of 0.16 cm2. After the stability test at 40% relative humidity (RH) and 25 °C for 1200 h, the highest performance NiOx-based PSC is shown to be about 1.2–1.8 times superior to PEDOT:PSS organic HTL based PSC at the same environment. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Technologies)
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10 pages, 1999 KiB  
Article
Efficient n-i-p Monolithic Perovskite/Silicon Tandem Solar Cells with Tin Oxide via a Chemical Bath Deposition Method
by Jiyeon Hyun, Kyung Mun Yeom, Ha Eun Lee, Donghwan Kim, Hae-Seok Lee, Jun Hong Noh and Yoonmook Kang
Energies 2021, 14(22), 7614; https://doi.org/10.3390/en14227614 - 15 Nov 2021
Cited by 7 | Viewed by 2820
Abstract
Tandem solar cells, based on perovskite and crystalline silicon absorbers, are promising candidates for commercial applications. Tin oxide (SnO2), applied via the spin-coating method, has been among the most used electron transfer layers in normal (n-i-p) perovskite/silicon tandem cells. SnO2 [...] Read more.
Tandem solar cells, based on perovskite and crystalline silicon absorbers, are promising candidates for commercial applications. Tin oxide (SnO2), applied via the spin-coating method, has been among the most used electron transfer layers in normal (n-i-p) perovskite/silicon tandem cells. SnO2 synthesized by chemical bath deposition (CBD) has not yet been applied in tandem devices. This method shows improved efficiency in perovskite single cells and allows for deposition over a larger area. Our study is the first to apply low-temperature processed SnO2 via CBD to a homojunction silicon solar cell without additional deposition of a recombination layer. By controlling the reaction time, a tandem efficiency of 16.9% was achieved. This study shows that tandem implementation is possible through the CBD method, and demonstrates the potential of this method in commercial application to textured silicon surfaces with large areas. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Technologies)
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