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Weekley, A.

Publications and source records attributed to Weekley, A..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Physics-Based GOES Satellite Product for Use in NREL's National Solar Radiation Database: Preprint

The National Renewable Energy Laboratory (NREL), University of Wisconsin, and National Oceanic Atmospheric Administration are collaborating to investigate the integration of the Satellite Algorithm for Shortwave Radiation Budget (SASRAB) products into future versions of NREL's 4-km by 4-km gridded National Solar Radiation Database (NSRDB). This paper describes a method to select an improved clear-sky model that could replace the current SASRAB global horizontal irradiance and direct normal irradiances reported during clear-sky conditions.

14 SOLAR ENERGY↗
DOE OSTI2014

Variability of Photovoltaic Power in the State of Gujarat Using High Resolution Solar Data

India has ambitious goals for high utilization of variable renewable power from wind and solar, and deployment has been proceeding at a rapid pace. The western state of Gujarat currently has the largest amount of solar generation of any Indian state, with over 855 Megawatts direct current (MWDC). Combined with over 3,240 MW of wind, variable generation renewables comprise nearly 18% of the electric-generating capacity in the state. A new historic 10-kilometer (km) gridded solar radiation data set capturing hourly insolation values for 2002-2011 is available for India. We apply an established method for downscaling hourly irradiance data to one-minute irradiance data at potential PV power production locations for one year, 2006. The objective of this report is to characterize the intra-hour variability of existing and planned photovoltaic solar power generation in the state of Gujarat (a total of 1.9 gigawatts direct current (GWDC)), and of five possible expansion scenarios of solar generation that reflect a range of geographic diversity (each scenario totals 500-1,000 MW of additional solar capacity). The report statistically analyzes one year's worth of power variability data, applied to both the baseline and expansion scenarios, to evaluate diurnal and seasonal power fluctuations, different timescales of variability (e.g., from one to 15 minutes), the magnitude of variability (both total megawatts and relative to installed solar capacity), and the extent to which the variability can be anticipated in advance. The paper also examines how Gujarat Energy Transmission Corporation (GETCO) and the Gujarat State Load Dispatch Centre (SLDC) could make use of the solar variability profiles in grid operations and planning.

14 SOLAR ENERGY↗
DOE OSTI2013

Downscaling Solar Power Output to 4-Seconds for Use in Integration Studies (Presentation)

High penetration renewable integration studies require solar power data with high spatial and temporal accuracy to quantify the impact of high frequency solar power ramps on the operation of the system. Our previous work concentrated on downscaling solar power from one hour to one minute by simulation. This method used clearness classifications to categorize temporal and spatial variability, and iterative methods to simulate intra-hour clearness variability. We determined that solar power ramp correlations between sites decrease with distance and the duration of the ramp, starting at around 0.6 for 30-minute ramps between sites that are less than 20 km apart. The sub-hour irradiance algorithm we developed has a noise floor that causes the correlations to approach ~0.005. Below one minute, the majority of the correlations of solar power ramps between sites less than 20 km apart are zero, and thus a new method to simulate intra-minute variability is needed. These intra-minute solar power ramps can be simulated using several methods, three of which we evaluate: a cubic spline fit to the one-minute solar power data; projection of the power spectral density toward the higher frequency domain; and average high frequency power spectral density from measured data. Each of these methods either under- or over-estimates the variability of intra-minute solar power ramps. We show that an optimized weighted linear sum of methods, dependent on the classification of temporal variability of the segment of one-minute solar power data, yields time series and ramp distributions similar to measured high-resolution solar irradiance data.

14 SOLAR ENERGY↗
DOE OSTI2013

Downscaling Solar Power Output to 4-Seconds for Use in Integration Studies: Preprint

High penetration renewable integration studies require solar power data with high spatial and temporal accuracy to quantify the impact of high frequency solar power ramps on the operation of the system. Our previous work concentrated on downscaling solar power from one hour to one minute by simulation. This method used clearness classifications to categorize temporal and spatial variability, and iterative methods to simulate intra-hour clearness variability. We determined that solar power ramp correlations between sites decrease with distance and the duration of the ramp, starting at around 0.6 for 30-minute ramps between sites that are less than 20 km apart. The sub-hour irradiance algorithm we developed has a noise floor that causes the correlations to approach ~0.005. Below one minute, the majority of the correlations of solar power ramps between sites less than 20 km apart are zero, and thus a new method to simulate intra-minute variability is needed. These intra-minute solar power ramps can be simulated using several methods, three of which we evaluate: a cubic spline fit to the one-minute solar power data; projection of the power spectral density toward the higher frequency domain; and average high frequency power spectral density from measured data. Each of these methods either under- or over-estimates the variability of intra-minute solar power ramps. We show that an optimized weighted linear sum of methods, dependent on the classification of temporal variability of the segment of one-minute solar power data, yields time series and ramp distributions similar to measured high-resolution solar irradiance data.

14 SOLAR ENERGY↗