The book, Solar Energy Forecasting and Resource Assessment, provides an authoritative voice on the accuracy of long-term resource projections and addresses grid operators’ concerns about variable short-term power generation, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward.
The chapter by Matthew Lave and Joshua Stein (both in Sandia’s Photovoltaic and Distributed Systems Integration Dept.) and Jan Kleissl (with the University of California–San Diego), “Quantifying and simulating solar-plant variability using irradiance data,” presents metrics for characterizing/simulating solar-power-plant output variability.
Their wavelet variability model (WVM) simulates a solar plant’s variability using a point sensor’s irradiance measurements as input and estimating the variability reduction at each timescale. The WVM is an ideal tool to create virtual time series of power-plant output that can be used in the design phase to simulate the sizes and operation of ramp-rate mitigation tools such as solar forecasting and energy storage.