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Solar Energy Forecasting and Resource Assessment

Book Description

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.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
  6. Biography
  7. Chapter 1. Terms and Definitions
    1. 1.1 Introduction
    2. 1.2 Overview of Solar-Power Conversion Technologies
    3. 1.3 Solar Power Versus Solar Irradiance
    4. 1.4 Direct, Diffuse, and Global Solar Radiation and Instrumentation
    5. 1.5 Atmospheric Properties Affecting Solar Irradiance
    6. References
  8. Chapter 2. Semi-Empirical Satellite Models
    1. 2.1 Satellites and Spectral Bands
    2. 2.2 Basic Principles
    3. 2.3 Clear-Sky Background
    4. 2.4 Cloud Attenuation: Cloud Index
    5. 2.5 Computing Global Irradiance
    6. 2.6 Computing Direct Normal Irradiance
    7. 2.7 Downscaling Solar Irradiance with High-Resolution Terrain Information
    8. 2.8 Sources of Uncertainty
    9. 2.9 Validation and Accuracy
    10. 2.10 Calibrating Satellite Bias using Ground Measurements
    11. 2.11 Future Advancements
    12. References
  9. Chapter 3. Physically Based Satellite Methods
    1. 3.1 Introduction
    2. 3.2 Satellite Observing Systems
    3. 3.3 Cloud and Aerosol Detection and Property Characterization
    4. 3.4 Relating Properties to Surface-Irradiance Parameters
    5. 3.5 Example Processing and Datasets
    6. 3.6 Future Satellite Capabilities
    7. 3.7 Critical Needs for Research
    8. 3.8 Conclusions
    9. References
  10. Chapter 4. Evaluation of Resource Risk in Solar-Project Financing
    1. 4.1 Introduction
    2. 4.2 Perspectives on Resource Risk in Project Financing
    3. 4.3 Data Sources, Quality, and Uncertainty
    4. 4.4 Commercial Implications of Resource Variability
    5. 4.5 Techniques for Quantifying and Managing Resource Risk
    6. 4.6 Conclusions
    7. References
  11. Chapter 5. Bankable Solar-Radiation Datasets
    1. 5.1 Introduction
    2. 5.2 Solar-Radiation Datasets: Characteristics, Strengths, and Weaknesses
    3. 5.3 Typical Meteorological Year (TMY) Data Files
    4. 5.4 Satellite-Derived Solar-Radiation Values
    5. 5.5 Irradiance Measurements and Uncertainties
    6. 5.6 Building a Bankable Dataset
    7. 5.7 Statistical Analysis of a Solar-Radiation Dataset for P50, P90, and P99 Evaluations
    8. 5.8 Status and Future
    9. References
  12. Chapter 6. Solar Resource Variability
    1. 6.1 Introduction
    2. 6.2 Quantifying Solar-Resource Variability
    3. 6.3 The Dispersion-Smoothing Effect
    4. 6.4 The General Case of an Arbitrarily Dispersed Fleet of Solar Generators
    5. 6.5 Variability Impact on the Distribution and Transmission System
    6. 6.6 A Final Note on the Smoothing Effect
    7. References
  13. Chapter 7. Quantifying and Simulating Solar-Plant Variability Using Irradiance Data
    1. 7.1 Causes and Impacts of PV Variability
    2. 7.2 Variability Metrics
    3. 7.3 Wavelet Variability Model
    4. 7.4 WVM Validation and Application in Puerto Rico
    5. 7.5 Conclusions
    6. References
  14. Chapter 8. Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation
    1. 8.1 Classification of Solar-Forecasting Methods
    2. 8.2 Deterministic and Stochastic Forecasting Approaches
    3. 8.3 Metrics for Evaluation of Solar-Forecasting Models
    4. 8.4 Applying the THI Metric to Evaluate Persistence, and Nonlinear Autoregressive Forecast Models
    5. 8.5 Conclusions
    6. References
  15. Chapter 9. Sky-Imaging Systems for Short-Term Forecasting
    1. 9.1 Challenges in Short-Term Solar Forecasting
    2. 9.2 Applications
    3. 9.3 Sky-Imaging Hardware
    4. 9.4 Sky-Imagery Analysis Techniques
    5. 9.5 Case Study: Copper Mountain
    6. 9.6 Future Applications
    7. References
  16. Chapter 10. SolarAnywhere Forecasting
    1. 10.1 The SolarAnywhere Solar Resource and Forecast Data Service
    2. 10.2 Solaranywhere Forecast Models
    3. 10.3 Model Evaluation: Standard Resolution
    4. 10.4 Performance Evaluation: 1 km, 1 min Forecasts
    5. Concluding Remarks
    6. References
  17. Chapter 11. Satellite-Based Irradiance and Power Forecasting for the German Energy Market
    1. 11.1 Solar Energy Penetration in Germany
    2. 11.2 Overview of the Satellite Forecast Process
    3. 11.3 Irradiance from Satellite Data
    4. 11.4 Cloud-Motion Vectors
    5. 11.5 Evaluation
    6. 11.6 Evaluation of CMV Forecasts
    7. 11.7 PV-Power Forecasting
    8. 11.8 Summary and Outlook
    9. References
  18. Chapter 12. Forecasting Solar Irradiance with Numerical Weather Prediction Models
    1. 12.1 Introduction
    2. 12.2 Steps Required to Produce a NWP Forecast and Grid Resolution
    3. 12.3 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 Transfer
    4. 12.4 Possible Sources of Error in Forecasted Irradiance
    5. 12.5 Present-Day Accuracy of Solar-Irradiance Forecasts
    6. 12.6 Conclusions
    7. References
  19. Chapter 13. Data Assimilation in Numerical Weather Prediction and Sample Applications
    1. 13.1 Introduction
    2. 13.2 DA Methods and Their Use
    3. 13.3 How does DA Work?
    4. 13.4 Solar-Energy DA Challenges
    5. 13.5 Future Trends
    6. 13.6 Conclusions
    7. References
  20. Chapter 14. Case Studies of Solar Forecasting with the Weather Research and Forecasting Model at GL-Garrad Hassan
    1. 14.1 Motivation: Forecasts of Irradiance, Variability, and Uncertainty
    2. 14.2 Solar Forecasting Using NWP at GL-Garrad Hassan
    3. 14.3 Case Studies on Meeting Stakeholder Needs
    4. 14.4 Summary and Conclusions
    5. Acronyms, Symbols, and Variables
    6. References
  21. Chapter 15. Stochastic-Learning Methods
    1. 15.1 Introduction
    2. 15.2 Baseline Methods for Comparison
    3. 15.3 Genetic Algorithms
    4. 15.4 Qualitative Performance Assessment
    5. 15.5 Performance of Stochastic-Learning Methods with No Exogenous Variables
    6. 15.6 Sky-Imaging Data as Exogenous Variables for Solar Forecasts
    7. 15.7 Stochastic-Learning Using Exogenous Variables: The National Digital Forecasting Database
    8. 15.8 Conclusions
    9. References
  22. Color Plates
  23. Index