book

Solar Energy Forecasting and Resource Assessment

by Jan Kleissl

June 2013

Intermediate to advanced

462 pages

14h 15m

English

Academic Press

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1.1 Introduction1.2 Overview of Solar-Power Conversion Technologies1.3 Solar Power Versus Solar Irradiance1.4 Direct, Diffuse, and Global Solar Radiation and Instrumentation1.5 Atmospheric Properties Affecting Solar IrradianceReferences
2.1 Satellites and Spectral Bands2.2 Basic Principles2.3 Clear-Sky Background2.4 Cloud Attenuation: Cloud Index2.5 Computing Global Irradiance2.6 Computing Direct Normal Irradiance2.7 Downscaling Solar Irradiance with High-Resolution Terrain Information2.8 Sources of Uncertainty2.9 Validation and Accuracy2.10 Calibrating Satellite Bias using Ground Measurements2.11 Future AdvancementsReferences
3.1 Introduction3.2 Satellite Observing Systems3.3 Cloud and Aerosol Detection and Property Characterization3.4 Relating Properties to Surface-Irradiance Parameters3.5 Example Processing and Datasets3.6 Future Satellite Capabilities3.7 Critical Needs for Research3.8 ConclusionsReferences
4.1 Introduction4.2 Perspectives on Resource Risk in Project Financing4.3 Data Sources, Quality, and Uncertainty4.4 Commercial Implications of Resource Variability4.5 Techniques for Quantifying and Managing Resource Risk4.6 ConclusionsReferences
5.1 Introduction5.2 Solar-Radiation Datasets: Characteristics, Strengths, and Weaknesses5.3 Typical Meteorological Year (TMY) Data Files5.4 Satellite-Derived Solar-Radiation Values5.5 Irradiance Measurements and Uncertainties5.6 Building a Bankable Dataset5.7 Statistical Analysis of a Solar-Radiation Dataset for P50, P90, and P99 Evaluations5.8 Status and FutureReferences
6.1 Introduction6.2 Quantifying Solar-Resource Variability6.3 The Dispersion-Smoothing Effect6.4 The General Case of an Arbitrarily Dispersed Fleet of Solar Generators6.5 Variability Impact on the Distribution and Transmission System6.6 A Final Note on the Smoothing EffectReferences
7.1 Causes and Impacts of PV Variability7.2 Variability Metrics7.3 Wavelet Variability Model7.4 WVM Validation and Application in Puerto Rico7.5 ConclusionsReferences
8.1 Classification of Solar-Forecasting Methods8.2 Deterministic and Stochastic Forecasting Approaches8.3 Metrics for Evaluation of Solar-Forecasting Models8.4 Applying the THI Metric to Evaluate Persistence, and Nonlinear Autoregressive Forecast Models8.5 ConclusionsReferences
9.1 Challenges in Short-Term Solar Forecasting9.2 Applications9.3 Sky-Imaging Hardware9.4 Sky-Imagery Analysis Techniques9.5 Case Study: Copper Mountain9.6 Future ApplicationsReferences
10.1 The SolarAnywhere Solar Resource and Forecast Data Service10.2 Solaranywhere Forecast Models10.3 Model Evaluation: Standard Resolution10.4 Performance Evaluation: 1 km, 1 min ForecastsConcluding RemarksReferences
11.1 Solar Energy Penetration in Germany11.2 Overview of the Satellite Forecast Process11.3 Irradiance from Satellite Data11.4 Cloud-Motion Vectors11.5 Evaluation11.6 Evaluation of CMV Forecasts11.7 PV-Power Forecasting11.8 Summary and OutlookReferences
12.1 Introduction12.2 Steps Required to Produce a NWP Forecast and Grid Resolution12.3 Comparison of Model Configurations for Four Operational Models (ECMWF, NAM, GFS, RAP): Spatial and Temporal Coverage, Deep and Shallow Cumulus, Turbulent Transport, Cloud Fraction, Cloud Overlap, Stratiform Microphysics, Aerosol, Shortwave Radiative Transfer12.4 Possible Sources of Error in Forecasted Irradiance12.5 Present-Day Accuracy of Solar-Irradiance Forecasts12.6 ConclusionsReferences
13.1 Introduction13.2 DA Methods and Their Use13.3 How does DA Work?13.4 Solar-Energy DA Challenges13.5 Future Trends13.6 ConclusionsReferences
14.1 Motivation: Forecasts of Irradiance, Variability, and Uncertainty14.2 Solar Forecasting Using NWP at GL-Garrad Hassan14.3 Case Studies on Meeting Stakeholder Needs14.4 Summary and ConclusionsAcronyms, Symbols, and VariablesReferences
15.1 Introduction15.2 Baseline Methods for Comparison15.3 Genetic Algorithms15.4 Qualitative Performance Assessment15.5 Performance of Stochastic-Learning Methods with No Exogenous Variables15.6 Sky-Imaging Data as Exogenous Variables for Solar Forecasts15.7 Stochastic-Learning Using Exogenous Variables: The National Digital Forecasting Database15.8 ConclusionsReferences

Overview

Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators’ concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, 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 only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world’s most renowned experts.
Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis.
Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.