Solar Energy Forecasting and Resource Assessment

Preface

Biography

Chapter 1. Terms and Definitions

1.1 Introduction

1.2 Overview of Solar-Power Conversion Technologies

1.3 Solar Power Versus Solar Irradiance

1.4 Direct, Diffuse, and Global Solar Radiation and Instrumentation

1.5 Atmospheric Properties Affecting Solar Irradiance

References

Chapter 2. Semi-Empirical Satellite Models

2.1 Satellites and Spectral Bands

2.2 Basic Principles

2.3 Clear-Sky Background

2.4 Cloud Attenuation: Cloud Index

2.5 Computing Global Irradiance

2.6 Computing Direct Normal Irradiance

2.7 Downscaling Solar Irradiance with High-Resolution Terrain Information

2.8 Sources of Uncertainty

2.9 Validation and Accuracy

2.10 Calibrating Satellite Bias using Ground Measurements

2.11 Future Advancements

References

Chapter 3. Physically Based Satellite Methods

3.1 Introduction

3.2 Satellite Observing Systems

3.3 Cloud and Aerosol Detection and Property Characterization

3.4 Relating Properties to Surface-Irradiance Parameters

3.5 Example Processing and Datasets

3.6 Future Satellite Capabilities

3.7 Critical Needs for Research

3.8 Conclusions

References

Chapter 4. Evaluation of Resource Risk in Solar-Project Financing

4.1 Introduction

4.2 Perspectives on Resource Risk in Project Financing

4.3 Data Sources, Quality, and Uncertainty

4.4 Commercial Implications of Resource Variability

4.5 Techniques for Quantifying and Managing Resource Risk

4.6 Conclusions

References

Chapter 5. Bankable Solar-Radiation Datasets

5.1 Introduction

5.2 Solar-Radiation Datasets: Characteristics, Strengths, and Weaknesses

5.3 Typical Meteorological Year (TMY) Data Files

5.4 Satellite-Derived Solar-Radiation Values

5.5 Irradiance Measurements and Uncertainties

5.6 Building a Bankable Dataset

5.7 Statistical Analysis of a Solar-Radiation Dataset for P50, P90, and P99 Evaluations

5.8 Status and Future

References

Chapter 6. Solar Resource Variability

6.1 Introduction

6.2 Quantifying Solar-Resource Variability

6.3 The Dispersion-Smoothing Effect

6.4 The General Case of an Arbitrarily Dispersed Fleet of Solar Generators

6.5 Variability Impact on the Distribution and Transmission System

6.6 A Final Note on the Smoothing Effect

References

Chapter 7. Quantifying and Simulating Solar-Plant Variability Using Irradiance Data

7.1 Causes and Impacts of PV Variability

7.2 Variability Metrics

7.3 Wavelet Variability Model

7.4 WVM Validation and Application in Puerto Rico

7.5 Conclusions

References

Chapter 8. Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation

8.1 Classification of Solar-Forecasting Methods

8.2 Deterministic and Stochastic Forecasting Approaches

8.3 Metrics for Evaluation of Solar-Forecasting Models

8.4 Applying the THI Metric to Evaluate Persistence, and Nonlinear Autoregressive Forecast Models

8.5 Conclusions

References

Chapter 9. Sky-Imaging Systems for Short-Term Forecasting

9.1 Challenges in Short-Term Solar Forecasting

9.2 Applications

9.3 Sky-Imaging Hardware

9.4 Sky-Imagery Analysis Techniques

9.5 Case Study: Copper Mountain

9.6 Future Applications

References

Chapter 10. SolarAnywhere Forecasting

10.1 The SolarAnywhere Solar Resource and Forecast Data Service

10.2 Solaranywhere Forecast Models

10.3 Model Evaluation: Standard Resolution

10.4 Performance Evaluation: 1 km, 1 min Forecasts

Concluding Remarks

References

Chapter 11. Satellite-Based Irradiance and Power Forecasting for the German Energy Market

11.1 Solar Energy Penetration in Germany

11.2 Overview of the Satellite Forecast Process

11.3 Irradiance from Satellite Data

11.4 Cloud-Motion Vectors

11.5 Evaluation

11.6 Evaluation of CMV Forecasts

11.7 PV-Power Forecasting

11.8 Summary and Outlook

References

Chapter 12. Forecasting Solar Irradiance with Numerical Weather Prediction Models

12.1 Introduction

12.2 Steps Required to Produce a NWP Forecast and Grid Resolution

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

12.4 Possible Sources of Error in Forecasted Irradiance

12.5 Present-Day Accuracy of Solar-Irradiance Forecasts

12.6 Conclusions

References

Chapter 13. Data Assimilation in Numerical Weather Prediction and Sample Applications

13.1 Introduction

13.2 DA Methods and Their Use

13.3 How does DA Work?

13.4 Solar-Energy DA Challenges

13.5 Future Trends

13.6 Conclusions

References

Chapter 14. Case Studies of Solar Forecasting with the Weather Research and Forecasting Model at GL-Garrad Hassan

14.1 Motivation: Forecasts of Irradiance, Variability, and Uncertainty

14.2 Solar Forecasting Using NWP at GL-Garrad Hassan

14.3 Case Studies on Meeting Stakeholder Needs

14.4 Summary and Conclusions

Acronyms, Symbols, and Variables

References

Chapter 15. Stochastic-Learning Methods

15.1 Introduction

15.2 Baseline Methods for Comparison

15.3 Genetic Algorithms

15.4 Qualitative Performance Assessment

15.5 Performance of Stochastic-Learning Methods with No Exogenous Variables

15.6 Sky-Imaging Data as Exogenous Variables for Solar Forecasts

15.7 Stochastic-Learning Using Exogenous Variables: The National Digital Forecasting Database

15.8 Conclusions

References

Color Plates

Index