Building a bankable solar radiation dataset

Vignola, Frank; Grover, Cathy A.; Lemon, Nick; McMahan, Andrew C.

doi:10.1016/j.solener.2012.05.013

Cited by 84 publications

(32 citation statements)

References 4 publications

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“…Estimates of long‐term changes in system performance are usually based on an assumption of expected degradation rates for the PV modules. On the other hand, changes in the solar resource are often neglected or considered to be negligible . Recent publications show, however, that this factor does need to be considered .…”

Section: Introductionmentioning

confidence: 99%

Yield predictions for photovoltaic power plants: empirical validation, recent advances and remaining uncertainties

Müller

Hardt

Armbruster

et al. 2015

Progress in Photovoltaics

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Yield predictions are performed to predict the solar resource, the performance and the energy production over the expected lifetime of a photovoltaic (PV) system. In this study, we compare yield predictions and monitored data for 26 PV power plants located in southern Germany and Spain. The monitoring data include in‐plane irradiance for comparison with the estimated solar resource and energy yield for comparison with predicted performance. The results show that because of increased irradiance in recent years (‘global brightening’) the yield predictions systematically underestimate the energy yield of PV systems by about 5%. Because common irradiance databases and averaging times were used for the yield predictions analysed in this paper, it is concluded that yield predictions for areas where the global brightening effect occurred in general underestimated the energy yield by the same magnitude. Using recent satellite‐derived irradiance time series avoids this underestimation. The observed performance ratio of the analysed systems decreases by 0.5%/year in average with a relatively high spread between individual systems. This decrease is a main factor for the combined uncertainty of yield predictions. It is attributed to non‐reversible degradation of PV cells or modules and to reversible effects, like soiling. Based on the results of the validation, the combined uncertainty of state of the art yield predictions using recent solar irradiance data is estimated to about 8%. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Yield predictions for photovoltaic power plants: empirical validation, recent advances and remaining uncertainties

Müller

Hardt

Armbruster

et al. 2015

Progress in Photovoltaics

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“…Vignola et al. compared meteorological data from various sources and concluded that long‐term data like 22 years NASA SSE are better for systems with long life‐time (the life time of this hybrid system is 25 years). The meteorological data are collected from NASA due to availability of long‐term and free of charge data.…”

Section: Methodsmentioning

confidence: 99%

Optimization and comparison analysis for application of PV panels in three villages

Fadaeenejad

Radzi

Fadaeenejad

et al. 2014

Energy Science & Engineering

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In recent years, research about the application and optimization of PV panels has grown rapidly. Especially for stand-alone solar systems there have been some problems related to first stages of design and application. Finding the optimum slope of PV panels in order to maximize the absorbed solar radiation is considered as one of the major obstacles. Although in Malaysia there have been some research works about finding of optimal tilt angle, however, this research work aims to start investigation about slope of PV panels for three villages without electricity and prior research. The Liu and Jordan model is applied to find the monthly optimal tilt angle and fixed tilt angle for the application of solar energy in these rural locations. The results demonstrate that the optimal tilt angle in these locations is less than 5°.

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“…The beam transposition factor is calculated straightforward from simple geometric considerations: cos^ cos 0 7 (4)…”

Section: Transposition Modelsmentioning

confidence: 99%

“…Long-term energy estimations are a key step to determine the feasibility of a photovoltaic (PV) project [1][2][3]. They depend to a large extent on the calculation of the operating conditions, mainly on the effective in-plane irradiation [4][5][6][7]. When lacking on-site measurements a common practice is to turn to available solar radiation databases.…”

Section: Introductionmentioning

confidence: 99%

From broadband horizontal to effective in-plane irradiation: A review of modelling and derived uncertainty for PV yield prediction

Moretón

Lorenzo

Pinto

et al. 2017

Renewable and Sustainable Energy Reviews

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This article presents an analysis on irradiance modelling and its impact on energy estimation uncertainty. In particular, the calculation of the effective in-plane irradiance from broadband horizontal irradiance is assessed. Thirty-one models for irradiance distribution, decomposition, transposition and spectral correction have been considered. The simulation results have been compared with experimental records obtained during up to three years in 19 locations of the Mediterranean belt with static and tracking systems. Models performance has been evaluated in terms of hourly to yearly weighted errors and frequency distribution similarity. Finally, some bestperforming model combinations have been selected. Table 1 Irradiation nomenclature. Symbol Irradiation concept G(0) Hourly broadband horizontal Gd(0) Daily broadband horizontal Gdm(0) Monthly mean of daily broadband horizontal

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Building a bankable solar radiation dataset

Cited by 84 publications

References 4 publications

Yield predictions for photovoltaic power plants: empirical validation, recent advances and remaining uncertainties

Yield predictions for photovoltaic power plants: empirical validation, recent advances and remaining uncertainties

Optimization and comparison analysis for application of PV panels in three villages

From broadband horizontal to effective in-plane irradiation: A review of modelling and derived uncertainty for PV yield prediction

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