A comprehensive degradation assessment of silicon photovoltaic modules installed on a concrete base under hot and low-humidity environments: Building applications

Khan, Firoz; Alshahrani, Thamraa; Fareed, Imran; Kim, Jae Hyun

doi:10.1016/j.seta.2022.102314

Cited by 11 publications

(11 citation statements)

References 45 publications

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“…The heat removal from PV panels ensures higher electrical productivity, in addition to the thermal energy collected from the modules that can be utilised in adsorption cooling systems and domestic hot water production [75,76]. It is worth mentioning here that the recent progress in PV technology has resulted in decreasing the deterioration of the PV [77,78], which has gradually improved the economics of PV or PVT systems.…”

Section: Pv/t or "Photovoltaic/thermal" Systemmentioning

confidence: 99%

Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals

et al. 2022

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The use of solar thermal systems (STSs) has recently reached a significant edge. The increasing research on developing an alternative power supply for limiting fossil fuel usage and climate change are the driving forces of STSs. The current work explores the recent progress in STSs’ applications, including PV/T or “photovoltaic/thermal” systems, zero-energy buildings, greenhouse solar thermal applications, solar thermal for pumping water, solar thermal refrigerators, solar chimneys, water desalination, and solar collectors, along with the benefits and challenges of these applications. Then, the potential contribution of STSs in achieving the various SDGs or “Sustainable development goals”, including barriers and research gaps, are elaborated. In brief, STSs significantly contribute to the seventeen SDGs’ achievement directly and indirectly. Recent developments in the engineering applications of STSs are strongly based on the materials of construction, as well as their design, process optimisation, and integration with multidisciplinary sciences and technologies such as modelling, nanoscience/nanotechnology, and artificial intelligence.

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Section: Pv/t or "Photovoltaic/thermal" Systemmentioning

confidence: 99%

Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals

et al. 2022

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“…For instance, the authors of [3,4] have shown that monolithic perovskite/silicon tandem solar cells recently achieved a certified efficiency of 29.1%. Recent advancements have significantly improved PV module lifetimes and their ability to perform optimally even in harsh environmental conditions [5]. Despite the advancement in PV systems, they still present significant technological and economic challenges [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

Nematirad,

Pahwa,

Natarajan

2024

Solar

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Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

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“…The above methods provide inspiration and motivation for the proposed forecasting strategy in this paper. However, the actual photovoltaic power is greatly interfered by the external environment and has the characteristics of instability and obvious intermittent fluctuation [35,36]. In addition, there are differences in photovoltaic power forecasting models under different environments, and a single model cannot meet the actual production needs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

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A comprehensive degradation assessment of silicon photovoltaic modules installed on a concrete base under hot and low-humidity environments: Building applications

Cited by 11 publications

References 45 publications

Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals

Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

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