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Authors:Jan Kleissl Abstract: Journal of Renewable and Sustainable Energy, Volume 14, Issue 3, May 2022. Countries with some of the best solar resources suffer disproportionately from soiling and abrasion, which reduces system conversion efficiencies and decreases equipment lifetime. This Special Collection covers climatological analyses, soiling metrology, best installation practices to reduce soiling and abrasion, and improvements to equipment and materials to mitigate soiling and abrasion. Citation: Journal of Renewable and Sustainable Energy PubDate: 2022-05-20T04:50:43Z DOI: 10.1063/5.0097947
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Authors:Guiting Song, Robert Huva, Yangyang Zhao Abstract: Journal of Renewable and Sustainable Energy, Volume 14, Issue 3, May 2022. Numerical Weather Prediction (NWP) models over limited areas enable the simulation of local atmospheric processes in more detail and with a higher degree of accuracy when compared to global models. Limited-area NWP models can outperform their global counterparts due to higher resolution (ability to explicitly simulate processes) and tailored physics (global models, unless run as a physics ensemble, have one set of parameterization schemes for the whole globe). However, increased accuracy from an NWP model is not guaranteed and can vary based on the location and variable of interest. In this paper, we present a method for combining the output of a limited-area NWP model, the Weather Research and Forecasting model (WRF) and its global model—the European Center for Medium Range Weather Forecasting (ECMWF) deterministic model. We simulate day-ahead global horizontal irradiance for a location in Qinghai, China. WRF model configurations optimized by the type of day (cloud amount) are then implemented based on the ECMWF model forecast of cloud amount. A regression model to combined ECMWF and WRF model forecasts is then trained. The optimized coefficients (weights) of ECMWF and WRF show increasing WRF importance with higher cloud amounts and the combination out-performs the ECMWF input by 5.2% and the best WRF configuration by 7.2% on a 2.5-month testing set. The performance of the combined model increased with observed cloud amount where the combined model out-performed the ECMWF model by 12.6% for cloudy days indicating the relative importance of physical downscaling for the simulation of clouds. Citation: Journal of Renewable and Sustainable Energy PubDate: 2022-05-18T01:59:38Z DOI: 10.1063/5.0079115
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Authors:Hwayoung Jeon, Brad Hartman, Harvey Cutler, Rebecca Hill, Yuchen Hu, Tao Lu, Martin Shields, David D. Turner Abstract: Journal of Renewable and Sustainable Energy, Volume 14, Issue 3, May 2022. Each year the U.S. government makes significant investments in improving weather forecast models. In this paper, we use a multidisciplinary approach to examine how utilities can benefit from improved wind-speed forecasts to more efficiently use wind-generated electricity and subsequently increase economic activity. Specifically, we examine how improvements to the National Oceanic and Atmospheric Administration's high-resolution rapid refresh model (HRRR) wind forecasts can provide (1) cost savings for utilities and (2) increase in real household income. To do so, we compare 12-h-ahead wind forecasts with real-time observations for two HRRR model transitions (i.e., when one model is operational, the other is being tested). We compare estimates of actual and predicted wind power under the publicly available and developmental models, with reduced forecast errors allowing for better utility decision-making and lower production costs. We then translate potential cost savings into electricity price changes, which are entered as exogenous shocks to eight regional computable general equilibrium models constructed for the U.S. Overall, we find that households would have seen a potential $60 million increase in real income for our sample (13% of all contiguous U.S. land-based turbine capacity), which had the updated HRRR models been in place during the two transition periods; applying our estimated savings for the sample of turbines to the entire array of turbines shows a potential real household income increase in approximately $384 million during these time frames. Citation: Journal of Renewable and Sustainable Energy PubDate: 2022-05-17T02:45:19Z DOI: 10.1063/5.0081905
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Authors:Marc Alexandre Allard, Marius Paraschivoiu Abstract: Journal of Renewable and Sustainable Energy, Volume 14, Issue 3, May 2022. In this paper, the concept of positioning micro-scale wind turbines on the roof of buildings is being studied for a Darrieus wind turbine, located above the roof of a cubic building at two different positions and operating under different wind flow conditions. The turbine has a height of 2 m and is positioned at 1 or 2 m from the top of the roof of a 30.5 m cubic building. The simulation methodology based on 3D unsteady computational fluid dynamics is first presented, including mesh details and experimental validation of the unperturbed flow baseline configuration. The simulation of different configurations shows that the turbine's Coefficient of Power can reach 0.55 by positioning it above the side corners of the building when the wind reaches the building at 45°. This position indicates that the synergy between the building and the turbine is quite strong such that the turbine should be placed not on top of the frontal corner but on top of one of the side corners. The power produced by this turbine at this location is 464 W. This placement leads to a significant increase in comparison with the maximum coefficient of power (Cp) of 0.32 (equivalent to a power of 378 W) when the turbine does not interact with a building. This increase in performance is very impressive. An increase of 25% in the power extracted can lead to better integration of wind turbines on roofs. Citation: Journal of Renewable and Sustainable Energy PubDate: 2022-05-12T10:14:06Z DOI: 10.1063/5.0079971
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Authors:Zhonghua Huang, Yawei Li, Yifan Zhu, Hao Zhou Abstract: Journal of Renewable and Sustainable Energy, Volume 14, Issue 3, May 2022. The non-uniformity of temperature distribution and thermal stress distribution in the on-site high-temperature molten salt receiver is frequently reflected in the receiver's unstable operating circumstances. As a result, the focus of this research is on the transient thermal performance of the receiver in non-steady-state situations. An in-house software was used to estimate the transient temperature distribution of a lab-scale receiver using a three-dimensional transient model built for the receiver's thermal performance calculation. The transient thermal performance of a lab-scale receiver was studied under some variable operating conditions, such as the startup process, varying mass flow rate, varying radiation flux, and varying ambient wind speed, using a combination of numerical prediction and variable-condition experiment on a lab-scale receiver. When the working environment changed, the temperature distribution of the receiver took around 12 s to return to a stable condition. In addition, when the working environment changed, the transient temperature fluctuations of the receiver were given and evaluated in depth in this study. Citation: Journal of Renewable and Sustainable Energy PubDate: 2022-05-09T11:51:54Z DOI: 10.1063/5.0085499
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Authors:Hao Lu, Yunpeng Zhang, Peng Hao, Jiao Ma, Hui Zhong, Tingkun Gu, Ming Yang, Li Zhang Abstract: Journal of Renewable and Sustainable Energy, Volume 14, Issue 3, May 2022. The current–voltage (I–V) equation in the equivalent circuit model of the photovoltaic (PV) module is implicit, and the dependence of model parameters on environmental conditions is uncertain, which causes inconvenience in output performance prediction. In this paper, a novel method based on the power-law model (PLM) is proposed to predict the I–V characteristics and output power of PV modules under varying operating conditions. The relationship between parameters in the PLM and manufacturer datasheet information is established. The irradiance and temperature dependences of shape parameters in PLM are obtained and investigated thoroughly. Due to inherent simplicity and explicit expression of PLM, the proposed method predicts the I–V characteristics and output power without using any iterative process, which reduces the computational complexity. The proposed method is validated by different types PV modules and under a wide range of environmental conditions. Comparing with traditional methods based on a single-diode model, the proposed method has better agreements with experimental results in all irradiance and temperature intervals. The accuracy and effectiveness are verified both in short-term and long-term output power prediction. The proposed method is simple and suitable to predict the actual output properties of PV modules under varying operating conditions. Citation: Journal of Renewable and Sustainable Energy PubDate: 2022-05-06T10:15:29Z DOI: 10.1063/5.0088190