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Title: Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.


Abstract not provided.

; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the UVIG held September 27-29, 2016 in Denver, CO.
Country of Publication:
United States

Citation Formats

Silva-Monroy, Cesar Augusto, Watson, Jean-Paul, Staid, Andrea, and Woodruff, David L. Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.. United States: N. p., 2016. Web.
Silva-Monroy, Cesar Augusto, Watson, Jean-Paul, Staid, Andrea, & Woodruff, David L. Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.. United States.
Silva-Monroy, Cesar Augusto, Watson, Jean-Paul, Staid, Andrea, and Woodruff, David L. 2016. "Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.". United States. doi:.
title = {Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.},
author = {Silva-Monroy, Cesar Augusto and Watson, Jean-Paul and Staid, Andrea and Woodruff, David L.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 9

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  • Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less
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  • From nuclear utilities planning methods symposium; Chattanooga, Tennessee (16 Jan 1974). In collection of papers presented at the nuclear utilities planning methods symposium. The concept of using a probabilistic simulation for estimatmg the operating cost of generating plants within an electrical utility was first introduced in 1967, and has recently been gaining acceptance in the United Stetes. Basically, the probabilistic simulation technique develops the cost of operating a uttlity system over an extended period of time by forecastmg the power to be generated by each plant. The basic model for this technique requires the following information: system load duration curve,more » loading order of units, generating unit characteristics, and energy supplied by energy-limited units such as hydroelectric units. The major advantage of the technique is its capability for simulating the effects of random events such as unit forced-outages. The basic probabilistic simulation model and modifications that have been made to represent more effectively the operation of large thermal units Wring low load periods and to take into consideration the effect of hydroelectric and pumped-storage units are presented. The model is versatile and can be modified easily. Computation times have averaged between 0.001 and 0.01 sec/unit on an IBM 360/91 computer with a memory requirement of approximately 80 K bytes. The probabtlistic simulation model is used as a subroutine for estimating operating costs in conjunction with optimization techniques for studying utility planning problems. (6 references) (auth)« less
  • 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-minutemore » 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.« less