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Hoff T.E.,Clean Power Research | Perez R.,University at Albany | Kleissl J.,University of California at San Diego | Renne D.,National Renewable Energy Laboratory | Stein J.,Sandia National Laboratories
Progress in Photovoltaics: Research and Applications | Year: 2013

Metrics used in assessing irradiance model accuracy, such as root mean square error and mean absolute error, are precisely defined. Their relative (%) counterpart, however, can be subject to interpretation and may cover a wide range of values for a given set of data depending on reporting practice. This note evaluates different approaches for the reporting of relative metrics quantifying the dispersion accuracy of a model and formulates recommendations for the most appropriate approach. Copyright © 2012 John Wiley & Sons, Ltd. Source


Perez R.,University at Albany | Zweibel K.,George Washington University | Hoff T.E.,Clean Power Research
Energy Policy | Year: 2011

This article identifies the combined value that solar electric power plants deliver to utilities' rate payers and society's tax payers. Benefits that are relevant to utilities and their rate payers include traditional, measures of energy and capacity. Benefits that are tangible to tax payers include environmental, fuel price mitigation, outage risk protection, and long-term economic growth components. Results for the state of New York suggest that solar electric installations deliver between 15 and 40 ¢/kWh to ratepayers and tax payers. These results provide economic justification for the existence of payment structures (often referred to as incentives) that transfer value from those who benefit from solar electric generation to those who invest in solar electric generation. © 2011 Elsevier Ltd. Source


Perez R.,University at Albany | Kivalov S.,University at Albany | Schlemmer J.,University at Albany | Hemker K.,University at Albany | And 2 more authors.
Solar Energy | Year: 2010

This paper presents a validation of the short and medium term global irradiance forecasts that are produced as part of the US SolarAnywhere (2010) data set. The short term forecasts that extend up to 6-h ahead are based upon cloud motion derived from consecutive geostationary satellite images. The medium term forecasts extend up to 6-days-ahead and are modeled from gridded cloud cover forecasts from the US National Digital Forecast Database.The forecast algorithms are validated against ground measurements for seven climatically distinct locations in the United States for 1. year. An initial analysis of regional performance using satellite-derived irradiances as a benchmark reference is also presented. © 2010 Elsevier Ltd. Source


Perez R.,University at Albany | Kivalov S.,University at Albany | Schlemmer J.,University at Albany | Hemker Jr. K.,University at Albany | Hoff T.E.,Clean Power Research
Solar Energy | Year: 2012

In this article, we report on the correlation between the irradiance variability observed at two neighboring sites as a function of their distance, and of the considered variability time scale. Correlation is the factor that determines whether the combined relative fluctuations of two solar systems add up when correlation is high, or attenuate when correlation is low.Using one-dimensional virtual networks in 24 US locations and cloud motion derived from satellites as experimental evidence, we observe station pair correlations for distances ranging from 100. m to 100. km and from variability time scales ranging from 20. s to 15. min.Within the limits of the assumptions from one-dimensional virtual networks, results show that the relationship between correlation, distance and time scale is predictable and largely independent of location and prevailing insolation conditions. Further, results indicate that the distance at which station pairs become uncorrelated is a quasi linear function of the considered time scale. © 2012 Elsevier Ltd. Source


Hoff T.E.,Clean Power Research | Perez R.,University at Albany
Solar Energy | Year: 2012

This paper introduces a novel approach to estimate the maximum short-term output variability that an arbitrary fleet of PV systems places on any considered power grid. The paper begins with a model that demonstrates that the maximum possible variability for N identical, uncorrelated PV systems equals the total installed capacity divided by 2N. The paper then describes a general methodology that is applicable to arbitrary PV fleets. A key input to this generalized approach is the correlation, or absence thereof, existing between individual installations in the fleet at the considered variability time scale. In this respect, the article includes a presentation of new experimental evidence from hourly satellite-derived irradiances relating distance and fluctuation time scales in three geographic regions in the United States (Southwest, Southern Great Plains, and Hawaii) and from recent high density network measurements that both confirm and extend conclusions from previous studies, namely: (1) correlation coefficients decrease predictably with increasing distance, (2) correlation coefficients decrease at a similar rate when evaluated versus distance divided by the considered variability time scale, and (3) the accuracy of results is improved by including an implied cloud speed term. © 2011 Elsevier Ltd. Source

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