Ramos-Salas, J., & Valladares, J. A. (2014, June), University of Texas Pan American Solar Radiation Clearness and Variability Indexes  Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--23230

\bibitem{asee_peer_23230}
  Ramos-Salas, J., \& Valladares, J. A. (2014, June), \emph{University of Texas Pan American Solar Radiation Clearness and Variability Indexes}
  Paper presented at 2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana.
  10.18260/1-2--23230

Jaime Ramos-Salas P.E. and Jesus Alejandro Valladares.  "University of Texas Pan American Solar Radiation Clearness and Variability Indexes".  2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana, 2014, June.  ASEE Conferences, 2014.  https://strategy.asee.org/23230 Internet.  28 May, 2024

\bibitem{asee_peer_23230}
  Jaime Ramos-Salas P.E. and Jesus Alejandro Valladares.
  "University of Texas Pan American Solar Radiation Clearness and Variability Indexes".
  \emph{2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana, 2014, June}.
  ASEE Conferences, 2014.
  https://strategy.asee.org/23230 Internet.  28 May, 2024

@INPROCEEDINGS{asee_peer_23230
author = "Jaime Ramos-Salas P.E. and Jesus Alejandro Valladares"
title = "University of Texas Pan American Solar Radiation Clearness and Variability Indexes"
booktitle = "2014 ASEE Annual Conference \& Exposition"
year = "2014"
month = "June"
address = "Indianapolis, Indiana"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/23230}
number = {10.18260/1-2--23230}
}

TY  - CPAPER
AB  -            University of XXX-Solar Radiation Clearness and variability Index        Large solar farms, intended for electric power generation, require better tools to predictand classify the amount of energy and power they produce, on a day to day basis [1]. Thisanalytical tool is constructed on global horizontal irradiance (GHI) measurements on siteperformed by pyranometers. The UXXX Solar Radiation Lab (SRL) is equipped with such asensor on the rooftop of the ENGR building, and its data is available in the internet        We are working on the evaluation of the local solar resource. The Daily Clearness Index(CI) and the Variability Index (VI), used identify differences during the period when the SolarResource is available for PV Plant Generation from day-to-day [2,3]. The Clearness Index ismerely a representation of how much of the Solar Irradiance hits a horizontal surface withrespect to the Clear Sky Model Implemented[4] for the site where the study is being made. Butthe CI by itself is not enough to determine how much this resource varies during a given day.The Variability Index introduced by SANDIA Research Laboratories, as it name implies, tellsabout how the solar resource varies during a day. This variability is directly related to the cloudsblocking and of focusing the sunlight over a determined area. Although this effect is easilyappreciated when measured data is plotted along with the modeled Irradiance, these indices areessential to study the frequency with which high variability index days occur and how reliablethe Solar Resource is at a given site during high demand periods and Power Plant down time.        With the data obtained from the onsite solar resource measurements & a clear sky modelfor solar insolation and the clearness and variability indices calculated along with the averageelectric power that that can be collected, we can now estimate the size of a photovoltaicgenerating plant in the area with certain degree of expectancy for power output during thedifferent seasons and months of the year. Thus making a feasibility study more reliable, and tonot to concentrate on the power output ratings for a photovoltaic plant design.        These topics can be integrated into curriculum development, on a course such asRenewable Energy for undergraduate engineering . To assess the benefit on the student learning,questionnaires would be applied in a Likert type of format. Their responses would be scaled tofind out if there is change in the level of the participants’ knowledge, in their interest inengineering, and to assess if the activity has been planned and conducted with a high standard ofquality       References       [1]- C. Trueblood, S. C., T. Key, L. Rogers, A. Ellis, C. Hansen, E. Philpot (2013). PVMeasures Up for Fleet Duty. IEEE Power & Energy Magazine. 11: 33-44.        [2]- J. S. Stein, C. W. H., M. J. Reno (2012). The Variability Index: A new and novelmetric for quantifying Irradiance and PV output variability. World Renewable Energy Forum.Denver, CO: 208.      [3]- M. J. Reno, C. W. H., J. S. Stein (2012). Global Horizontal Irradiance Clear SkyModels: Implementation and Analysis. Albuquerque, NM, Sandia National Laboratory.       [4]- Gilbert M. Masters (2013). Renewable and Efficient Electric Power Systems. SecondEdition. Hoboken, NJ: John Wiley & Sons.
AU  - Jaime Ramos-Salas P.E.
AU  - Jesus Alejandro Valladares
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - University of Texas Pan American Solar Radiation Clearness and Variability Indexes
UR  - https://strategy.asee.org/23230
DO  - 10.18260/1-2--23230
ER  -