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Renewable Energy Forecasting : From Models to Applications.

By: Material type: TextTextSeries: Woodhead Publishing Series in Energy SeriesPublisher: Cambridge : Elsevier Science & Technology, 2017Copyright date: ©2017Edition: 1st edDescription: 1 online resource (388 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780081005057
Subject(s): Genre/Form: Additional physical formats: Print version:: Renewable Energy ForecastingDDC classification:
  • 333.7940113
LOC classification:
  • TJ808.R464 2017
Online resources:
Contents:
Front Cover -- Renewable Energy Forecasting -- Related titles -- Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis? -- Copyright -- Contents -- List of contributors -- One - Introduction to meteorology and measurement technologies -- 1 - Principles of meteorology and numerical weather prediction -- 1.1 Introduction to meteorology for renewable energy forecasting -- 1.1.1 Atmospheric motion -- 1.1.2 Prediction across scales -- 1.1.3 Atmospheric chaos -- 1.2 Observational data and assimilation into numerical weather prediction models -- 1.2.1 Observational data -- 1.2.2 Data assimilation -- 1.2.2.1 Nudging -- 1.2.2.2 Variational assimilation -- 1.2.2.3 Ensemble Kalman filters -- 1.2.2.4 Hybrid approaches -- 1.2.3 Coupled models -- 1.3 Configuring numerical weather prediction to the needs of the problem -- 1.3.1 Fundamentals of numerical weather prediction -- 1.3.1.1 Dynamic solver -- 1.3.1.2 Parameterizations -- 1.3.2 Standard physics available in numerical weather prediction models -- 1.3.3 Configuration of numerical weather prediction models for specific applications -- 1.3.4 Model development: the WRF-Solar model -- 1.4 Postprocessing -- 1.5 Probabilistic forecasting -- 1.6 Planning for validation -- 1.7 Weather forecasting as a Big Data problem -- Acknowledgments -- References -- Further reading -- 2 - Measurement methodologies for wind energy based on ground-level remote sensing -- 2.1 Introduction -- 2.1.1 Historical background -- 2.1.2 Measuring principles for a heterodyne wind lidar -- 2.1.3 Wind lidar calibration -- 2.1.4 Climatological use of Doppler wind lidar measurements -- 2.1.5 Turbulence estimated from wind lidar measurements -- 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence.
2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements -- 2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar -- 2.1.5.4 Wind gusts from a lidar -- 2.1.6 Boundary layer depth detection from lidars -- 2.1.7 Long-range and short-range WindScanner systems -- 2.1.7.1 The long-range WindScanner system -- 2.1.7.2 The short-range WindScanner system -- References -- Two - Methods for renewable energy forecasting -- 3 - Wind power forecasting-a review of the state of the art -- 3.1 Introduction -- 3.1.1 Forecast timescales -- 3.1.2 The typical model chain -- 3.2 Time series models -- 3.2.1 Time series models for very-short-term forecasting -- 3.2.2 An explanation of the time series model improvements -- 3.3 Meteorological modeling for wind power predictions -- 3.3.1 Improvements in NWP and mesoscale modeling -- 3.3.2 Ensemble Kalman filtering -- 3.4 Short-term prediction models with NWPs -- 3.4.1 Modeling wind speed versus wind power -- 3.5 Upscaling models -- 3.6 Spatio-temporal forecasting -- 3.7 Ramp forecasting -- 3.8 Variability forecasting -- 3.9 Uncertainty of wind power predictions -- 3.9.1 Statistical approaches -- 3.9.2 Ensemble forecasts, risk indices, and scenarios -- 3.10 The ANEMOS projects and other major R&amp -- D activities -- 3.11 Conclusions -- Glossary -- Acknowledgments -- References -- 4 - Mathematical methods for optimized solar forecasting -- 4.1 Introduction -- 4.2 Regression methods -- 4.2.1 Linear stationary models -- 4.2.1.1 Autoregressive models -- 4.2.1.2 Moving average models -- 4.2.1.3 Mixed autoregressive moving average models -- 4.2.1.4 Mixed autoregressive moving average models with exogenous variables -- 4.2.2 Linear nonstationary models -- 4.2.2.1 Autoregressive integrated moving average models.
4.2.2.2 Autoregressive integrated moving average models with exogenous variables -- 4.3 Artificial intelligence techniques -- 4.3.1 Artificial neural networks -- 4.3.1.1 Simple preceptron -- 4.3.1.2 Multilayer perceptron -- 4.3.2 k-nearest neighbors -- 4.4 Hybrid systems -- 4.5 State of the art in solar forecasting -- 4.5.1 Exogenous data -- 4.5.2 Data preprocessing -- 4.5.3 Probabilistic forecasts -- 4.5.4 Increasing forecasting spatial coverage -- 4.5.5 Ramp forecasts -- 4.5.6 Forecast assessment -- 4.6 Conclusions -- References -- 5 - Short-term forecasting based on all-sky cameras -- 5.1 Introduction -- 5.2 Sky camera systems -- 5.2.1 All-sky camera types -- 5.3 Image processing techniques -- 5.3.1 Cloud properties -- 5.3.1.1 Cloud coverage -- 5.3.1.2 Detection of raindrops -- 5.3.1.3 Cloud classification -- 5.3.1.4 Cloud height estimation -- 5.3.1.5 Aerosol optical properties -- 5.4 Geometrical calibration of all-sky cameras -- 5.5 Case study: solar resource and forecasting methodology in the frame of DNICast project -- 5.6 Conclusions and future trends -- References -- 6 - Short-term solar power forecasting based on satellite images -- 6.1 Introduction -- 6.2 Surface solar irradiance retrieval from meteorological geostationary satellite -- 6.2.1 Different approaches -- 6.2.2 From surface solar irradiance to solar power -- 6.2.3 Characteristics of surface solar irradiance retrieval from satellite -- 6.2.4 Local combination with in situ pyranometric measurements -- 6.3 Different approaches for satellite-based forecasting -- 6.3.1 Forecast based only on temporal information -- 6.3.2 Forecast based on spatial and temporal information -- 6.3.2.1 Forecasting approaches based on cloud motion vectors -- 6.3.2.2 The forecasting approaches based on a statistical modeling of the spatiotemporal variability.
6.3.3 Postprocessing of satellite-based forecasts with in situ measurements -- 6.4 Conclusion and perspectives -- Acronyms -- References -- 7 - Wave energy forecasting -- 7.1 Introduction -- 7.2 Characteristics of the data -- 7.3 The physics models -- 7.4 Statistical and time series models -- 7.5 Physics versus statistics -- 7.6 Wave energy converters -- 7.7 Simulating wave farms -- 7.8 Conclusions -- References -- 8 - Forecasting intrahourly variability of wind generation -- 8.1 Introduction -- 8.2 Meteorological causes of hour-scale wind variability -- 8.3 Observing hour-scale wind variability -- 8.4 Forecasting wind power variability -- 8.5 Correlation between wind fluctuations at spatially distributed sites -- 8.6 Using wind variability information to improve wind farm operations and scheduling -- 8.7 Conclusions and future developments -- Acknowledgments -- References -- 9 - Characterization of forecast errors and benchmarking of renewable energy forecasts -- 9.1 Introduction -- 9.2 ANEMOS benchmark -- 9.2.1 Setup and data description -- 9.2.2 Forecast evaluation -- 9.2.2.1 Wusterhusen test case (Fig. 9.1) -- 9.2.2.2 Alaiz test case (Fig. 9.2) -- 9.2.2.3 Sotavento test case (Fig. 9.3) -- 9.2.2.4 Klim test case (Fig. 9.4) -- 9.2.2.5 Tunø Knob (offshore) test case (Fig. 9.5) -- 9.2.2.6 Golagh test case (Fig. 9.6) -- 9.3 WIRE benchmark -- 9.3.1 Setup and data description -- 9.3.2 Modeling approaches -- 9.3.3 Forecast evaluation -- 9.3.4 Comparison with ANEMOS benchmark and high-resolution model run -- 9.4 Discussion and conclusions -- References -- Three - Applications of forecasting to power system management and markets -- 10 - Wind power in electricity markets and the value of forecasting -- 10.1 Introduction -- 10.2 Electricity market context -- 10.2.1 Overview of various markets and their timeline -- 10.2.2 Day-ahead market mechanism.
10.2.3 Intraday adjustment and continuous trading -- 10.2.4 Balancing market mechanism -- 10.3 From market revenue to forecast value -- 10.3.1 Assumptions -- 10.3.2 Formulation of market revenues -- 10.3.3 Linkage to forecast value -- 10.4 Formulation of offering strategies -- 10.4.1 Benchmark offering strategies -- 10.4.2 Trading strategies in a single-price imbalance system -- 10.4.3 Trading strategies in a two-price imbalance system -- 10.5 Test case exemplification -- 10.5.1 Experimental setup -- 10.5.2 Trading results and value of various forecasts -- 10.6 Overall conclusions and perspectives -- References -- 11 - Forecasting and setting power system operating reserves -- 11.1 Introduction -- 11.1.1 Overview of existing methodologies -- 11.1.2 Aims and structure of this chapter -- 11.2 Integration of uncertainty forecasts in operating reserve estimation -- 11.3 Conceptual frameworks -- 11.3.1 Modeling assumptions -- 11.3.1.1 Independency of forecast errors -- 11.3.1.2 Unplanned outages of wind turbines -- 11.3.1.3 Unplanned outages of conventional generation -- 11.3.1.4 Load uncertainty -- 11.3.2 Separated energy and ancillary services market clearing -- 11.3.2.1 Framework -- 11.3.2.2 Methodology to estimate system generation margin -- 11.3.2.3 Decision-making strategy -- 11.3.3 Joint energy and ancillary services market clearing -- 11.3.3.1 Framework -- 11.3.3.2 Methodology for developing an operating reserve demand curve -- 11.4 Illustrative results -- 11.4.1 Separated energy and ancillary services market clearing -- 11.4.1.1 Assumptions -- 11.4.1.2 Illustrative results -- 11.4.1.3 Overall risk assessment -- 11.4.2 Joint energy and ancillary services market clearing -- 11.4.2.1 Assumptions -- 11.4.2.2 Illustrative results -- 11.4.3 Qualitative analysis of the methodologies -- 11.5 A look into the future -- Acknowledgments -- References.
12 - Forecasting for storage management.
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Front Cover -- Renewable Energy Forecasting -- Related titles -- Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis? -- Copyright -- Contents -- List of contributors -- One - Introduction to meteorology and measurement technologies -- 1 - Principles of meteorology and numerical weather prediction -- 1.1 Introduction to meteorology for renewable energy forecasting -- 1.1.1 Atmospheric motion -- 1.1.2 Prediction across scales -- 1.1.3 Atmospheric chaos -- 1.2 Observational data and assimilation into numerical weather prediction models -- 1.2.1 Observational data -- 1.2.2 Data assimilation -- 1.2.2.1 Nudging -- 1.2.2.2 Variational assimilation -- 1.2.2.3 Ensemble Kalman filters -- 1.2.2.4 Hybrid approaches -- 1.2.3 Coupled models -- 1.3 Configuring numerical weather prediction to the needs of the problem -- 1.3.1 Fundamentals of numerical weather prediction -- 1.3.1.1 Dynamic solver -- 1.3.1.2 Parameterizations -- 1.3.2 Standard physics available in numerical weather prediction models -- 1.3.3 Configuration of numerical weather prediction models for specific applications -- 1.3.4 Model development: the WRF-Solar model -- 1.4 Postprocessing -- 1.5 Probabilistic forecasting -- 1.6 Planning for validation -- 1.7 Weather forecasting as a Big Data problem -- Acknowledgments -- References -- Further reading -- 2 - Measurement methodologies for wind energy based on ground-level remote sensing -- 2.1 Introduction -- 2.1.1 Historical background -- 2.1.2 Measuring principles for a heterodyne wind lidar -- 2.1.3 Wind lidar calibration -- 2.1.4 Climatological use of Doppler wind lidar measurements -- 2.1.5 Turbulence estimated from wind lidar measurements -- 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence.

2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements -- 2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar -- 2.1.5.4 Wind gusts from a lidar -- 2.1.6 Boundary layer depth detection from lidars -- 2.1.7 Long-range and short-range WindScanner systems -- 2.1.7.1 The long-range WindScanner system -- 2.1.7.2 The short-range WindScanner system -- References -- Two - Methods for renewable energy forecasting -- 3 - Wind power forecasting-a review of the state of the art -- 3.1 Introduction -- 3.1.1 Forecast timescales -- 3.1.2 The typical model chain -- 3.2 Time series models -- 3.2.1 Time series models for very-short-term forecasting -- 3.2.2 An explanation of the time series model improvements -- 3.3 Meteorological modeling for wind power predictions -- 3.3.1 Improvements in NWP and mesoscale modeling -- 3.3.2 Ensemble Kalman filtering -- 3.4 Short-term prediction models with NWPs -- 3.4.1 Modeling wind speed versus wind power -- 3.5 Upscaling models -- 3.6 Spatio-temporal forecasting -- 3.7 Ramp forecasting -- 3.8 Variability forecasting -- 3.9 Uncertainty of wind power predictions -- 3.9.1 Statistical approaches -- 3.9.2 Ensemble forecasts, risk indices, and scenarios -- 3.10 The ANEMOS projects and other major R&amp -- D activities -- 3.11 Conclusions -- Glossary -- Acknowledgments -- References -- 4 - Mathematical methods for optimized solar forecasting -- 4.1 Introduction -- 4.2 Regression methods -- 4.2.1 Linear stationary models -- 4.2.1.1 Autoregressive models -- 4.2.1.2 Moving average models -- 4.2.1.3 Mixed autoregressive moving average models -- 4.2.1.4 Mixed autoregressive moving average models with exogenous variables -- 4.2.2 Linear nonstationary models -- 4.2.2.1 Autoregressive integrated moving average models.

4.2.2.2 Autoregressive integrated moving average models with exogenous variables -- 4.3 Artificial intelligence techniques -- 4.3.1 Artificial neural networks -- 4.3.1.1 Simple preceptron -- 4.3.1.2 Multilayer perceptron -- 4.3.2 k-nearest neighbors -- 4.4 Hybrid systems -- 4.5 State of the art in solar forecasting -- 4.5.1 Exogenous data -- 4.5.2 Data preprocessing -- 4.5.3 Probabilistic forecasts -- 4.5.4 Increasing forecasting spatial coverage -- 4.5.5 Ramp forecasts -- 4.5.6 Forecast assessment -- 4.6 Conclusions -- References -- 5 - Short-term forecasting based on all-sky cameras -- 5.1 Introduction -- 5.2 Sky camera systems -- 5.2.1 All-sky camera types -- 5.3 Image processing techniques -- 5.3.1 Cloud properties -- 5.3.1.1 Cloud coverage -- 5.3.1.2 Detection of raindrops -- 5.3.1.3 Cloud classification -- 5.3.1.4 Cloud height estimation -- 5.3.1.5 Aerosol optical properties -- 5.4 Geometrical calibration of all-sky cameras -- 5.5 Case study: solar resource and forecasting methodology in the frame of DNICast project -- 5.6 Conclusions and future trends -- References -- 6 - Short-term solar power forecasting based on satellite images -- 6.1 Introduction -- 6.2 Surface solar irradiance retrieval from meteorological geostationary satellite -- 6.2.1 Different approaches -- 6.2.2 From surface solar irradiance to solar power -- 6.2.3 Characteristics of surface solar irradiance retrieval from satellite -- 6.2.4 Local combination with in situ pyranometric measurements -- 6.3 Different approaches for satellite-based forecasting -- 6.3.1 Forecast based only on temporal information -- 6.3.2 Forecast based on spatial and temporal information -- 6.3.2.1 Forecasting approaches based on cloud motion vectors -- 6.3.2.2 The forecasting approaches based on a statistical modeling of the spatiotemporal variability.

6.3.3 Postprocessing of satellite-based forecasts with in situ measurements -- 6.4 Conclusion and perspectives -- Acronyms -- References -- 7 - Wave energy forecasting -- 7.1 Introduction -- 7.2 Characteristics of the data -- 7.3 The physics models -- 7.4 Statistical and time series models -- 7.5 Physics versus statistics -- 7.6 Wave energy converters -- 7.7 Simulating wave farms -- 7.8 Conclusions -- References -- 8 - Forecasting intrahourly variability of wind generation -- 8.1 Introduction -- 8.2 Meteorological causes of hour-scale wind variability -- 8.3 Observing hour-scale wind variability -- 8.4 Forecasting wind power variability -- 8.5 Correlation between wind fluctuations at spatially distributed sites -- 8.6 Using wind variability information to improve wind farm operations and scheduling -- 8.7 Conclusions and future developments -- Acknowledgments -- References -- 9 - Characterization of forecast errors and benchmarking of renewable energy forecasts -- 9.1 Introduction -- 9.2 ANEMOS benchmark -- 9.2.1 Setup and data description -- 9.2.2 Forecast evaluation -- 9.2.2.1 Wusterhusen test case (Fig. 9.1) -- 9.2.2.2 Alaiz test case (Fig. 9.2) -- 9.2.2.3 Sotavento test case (Fig. 9.3) -- 9.2.2.4 Klim test case (Fig. 9.4) -- 9.2.2.5 Tunø Knob (offshore) test case (Fig. 9.5) -- 9.2.2.6 Golagh test case (Fig. 9.6) -- 9.3 WIRE benchmark -- 9.3.1 Setup and data description -- 9.3.2 Modeling approaches -- 9.3.3 Forecast evaluation -- 9.3.4 Comparison with ANEMOS benchmark and high-resolution model run -- 9.4 Discussion and conclusions -- References -- Three - Applications of forecasting to power system management and markets -- 10 - Wind power in electricity markets and the value of forecasting -- 10.1 Introduction -- 10.2 Electricity market context -- 10.2.1 Overview of various markets and their timeline -- 10.2.2 Day-ahead market mechanism.

10.2.3 Intraday adjustment and continuous trading -- 10.2.4 Balancing market mechanism -- 10.3 From market revenue to forecast value -- 10.3.1 Assumptions -- 10.3.2 Formulation of market revenues -- 10.3.3 Linkage to forecast value -- 10.4 Formulation of offering strategies -- 10.4.1 Benchmark offering strategies -- 10.4.2 Trading strategies in a single-price imbalance system -- 10.4.3 Trading strategies in a two-price imbalance system -- 10.5 Test case exemplification -- 10.5.1 Experimental setup -- 10.5.2 Trading results and value of various forecasts -- 10.6 Overall conclusions and perspectives -- References -- 11 - Forecasting and setting power system operating reserves -- 11.1 Introduction -- 11.1.1 Overview of existing methodologies -- 11.1.2 Aims and structure of this chapter -- 11.2 Integration of uncertainty forecasts in operating reserve estimation -- 11.3 Conceptual frameworks -- 11.3.1 Modeling assumptions -- 11.3.1.1 Independency of forecast errors -- 11.3.1.2 Unplanned outages of wind turbines -- 11.3.1.3 Unplanned outages of conventional generation -- 11.3.1.4 Load uncertainty -- 11.3.2 Separated energy and ancillary services market clearing -- 11.3.2.1 Framework -- 11.3.2.2 Methodology to estimate system generation margin -- 11.3.2.3 Decision-making strategy -- 11.3.3 Joint energy and ancillary services market clearing -- 11.3.3.1 Framework -- 11.3.3.2 Methodology for developing an operating reserve demand curve -- 11.4 Illustrative results -- 11.4.1 Separated energy and ancillary services market clearing -- 11.4.1.1 Assumptions -- 11.4.1.2 Illustrative results -- 11.4.1.3 Overall risk assessment -- 11.4.2 Joint energy and ancillary services market clearing -- 11.4.2.1 Assumptions -- 11.4.2.2 Illustrative results -- 11.4.3 Qualitative analysis of the methodologies -- 11.5 A look into the future -- Acknowledgments -- References.

12 - Forecasting for storage management.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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