scholarly journals Dependence of PV Module Temperature on Incident Time-Dependent Solar Spectrum

2020 ◽  
Vol 10 (3) ◽  
pp. 914 ◽  
Author(s):  
Joseph Appelbaum ◽  
Tamir Maor

The operating temperature of photovoltaic (PV) modules affects the photovoltaic conversion process. The operating temperature depends on various environmental conditions and on material-dependent properties of the PV modules. Many expressions for the operating temperature have been proposed in the literatures, some are simplified working Equation as NOCT (Nominal Operating Cell Temperature), and others are more complex, being based on a combination of the energy balance Equation and NOCT. The present study offers a new approach (model) for determining the PV module temperature based on the energy balance Equation and on the solar spectrum irradiance. While using the new model, the operating temperature has been determined for four module technologies: c-Si, a-Si/ μ c-Si, CdTe, and CIGS and it shows that the operating temperatures for the different cell types are close to the manufacturers’ NOCT data-sheet temperatures. For c-Si technology, for example, the simulation resulted in 43.2° and 46° for the spectrum and NOCT models, respectively. The proposed new model offers a new approach for determining the operating temperature of PV modules.

2013 ◽  
Vol 284-287 ◽  
pp. 1163-1167 ◽  
Author(s):  
Huan Liang Tsai ◽  
Chao Jia Yang

This paper presents a novel photovoltaic (PV) model for a commercial PV module, which is augmented with an energy balance equation to simultaneously describe cell temperature and PV electricity output characteristics. Having the thermal and electrical characteristics of commercial PV module available from the manufacturer datasheet, the proposed PV model is implemented on the Simulink environment and verified under the standard test condition (STC) and nominal operating cell temperature (NOCT) condition. The NOCT verification with a commercial PV module datasheet is first addressed. Through experimental measurement of a commercial PV module in real operation from June 1 to August 31, 2011, the proposed model demonstrates the good estimation performance of both cell temperatures and output electricity characteristics. Comparing with ones of the other methods, the predicted output characteristics of the proposed model have a better agreement with the measured ones of an operating PV module.


Author(s):  
Shaun Lovejoy ◽  
Roman Procyk ◽  
Raphael Hébert ◽  
Lenin Del Rio Amador

2021 ◽  
Author(s):  
Roman Procyk ◽  
Shaun Lovejoy ◽  
Raphaël Hébert ◽  
Lenin Del Rio Amador

<p>We present the Fractional Energy Balance Equation (FEBE): a generalization of the standard EBE.  The key FEBE novelty is the assumption of a hierarchy of energy storage mechanisms: scaling energy storage.  Mathematically the storage term is of fractional rather than integer order.  The special half-order case (HEBE) can be classically derived from the continuum mechanics heat equation used by Budyko and Sellers simply by introducing a vertical coordinate and using the correct conductive-radiative surface boundary conditions (the FEBE is a mild extension).</p><div> <p>We use the FEBE to determine the temperature response to both historical forcings and to future scenarios.  Using historical data, we estimate the 2 FEBE parameters: its scaling exponent (<em>H</em> = 0.38±0.05; <em>H</em> = 1 is the standard EBE) and relaxation time (4.7±2.3 years, comparable to box model relaxation times). We also introduce two forcing parameters: an aerosol re-calibration factor, to account for their large uncertainty, and a volcanic intermittency exponent so that the intermittency volcanic signal can be linearly related to the temperature. The high frequency FEBE regime not only allows for modelling responses to volcanic forcings but also the response to internal white noise forcings: a theoretically motivated error model (approximated by a fractional Gaussian noise). The low frequency part uses historical data and long memory for climate projections, constraining both equilibrium climate sensitivity and historical aerosol forcings. <span>Parameters are estimated in a Bayesian framework using 5 global observational temperature series, and an error model which is a theoretical consequence of the FEBE forced by a Gaussian white noise.</span></p> <p>Using the CMIP5 Representative Concentration Pathways (RCPs) and CMIP6 Shared Socioeconomic Pathways (SSPs) scenario, the FEBE projections to 2100 are shown alongside the CMIP5 MME. The Equilibrium Climate Sensitivity = 2.0±0.4 <sup>o</sup>C/CO<sub>2</sub> doubling implies slightly lower temperature increases.   However, the FEBE’s 90% confidence intervals are about half the CMIP5 size so that the new projections lie within the corresponding CMIP5 MME uncertainties so that both approaches fully agree.   The mutually agreement of qualitatively different approaches, gives strong support to both.  We also compare both generations of General Circulation Models (GCMs) outputs from CMIP5/6 alongside with the projections produced by the FEBE model which are entirely independent from GCMs, contributing to our understanding of forced climate variability in the past, present and future.</p> <p>Following the same methodology, we apply the FEBE to regional scales: estimating model and forcing parameters to produce climate projections at 2.5<sup>o</sup>x2.5<sup>o</sup> resolutions. We compare the spatial patterns of climate sensitivity and projected warming between the FEBE and CMIP5/6 GCMs. </p> </div>


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