Constructing probabilistic scenarios for wide-area solar power generation. (15th January 2018)

• Record Type: Journal Article
• Authors: Woodruff, David L.; Deride, Julio; Staid, Andrea; Watson, Jean-Paul; Slevogt, Gerrit; Silva-Monroy, César
• Is Part Of: Solar energy. Volume 160(2018)
• Page Start: 153
• Page End: 167
• Publication Date: 2018-01-15
• Subjects: Solar power forecasting -- Probabilistic scenario creation -- Unit commitment and economic dispatch -- Stochastic optimization; 00-01 -- 99-00; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2017.11.067
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: A method designed to create day-ahead, wide-area probabilistic solar power scenarios with control over emphasis of the tails. Provides a method for estimation of wide-area, grid connected solar capacity. Computational experiments are detailed, including comparisons with a method based on quantile regression. Abstract: Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this paper, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions. Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. We compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.