Solar Energy Forecasting and Resource Assessment
- 1st Edition - July 16, 2013
- Latest edition
- Author: Jan Kleissl
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 9 7 1 7 7 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 9 7 7 7 2 - 4
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 barrie… Read more
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.
Scientists and engineers working within the power utility, renewable energy industry, or other related energy fields, as well as within the areas of atmospheric science and meteorology. Solar energy professionals particularly, including research scientists, project developers, system operators, planners and engineers.
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
- Edition: 1
- Latest edition
- Published: July 16, 2013
- Language: English
JK
Jan Kleissl
Affiliations and expertise
Associate Professor, Department of Mechanical & Aerospace Engineering; Co-Director, Center for Energy Research, UC San Diego, USARead Solar Energy Forecasting and Resource Assessment on ScienceDirect