Cloud base height from sky imager and cloud speed sensor

Wang, Guang; Kurtz, Ben; Kleissl, Jan

doi:10.1016/j.solener.2016.02.027

Cited by 39 publications

(34 citation statements)

References 17 publications

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“…Moreover, the weather above southern Spain with multiple cloud layers being frequently present might be more complex than the weather elsewhere. For instance, on the pacific coast of the United States, cumulus clouds at low altitudes seem to be predominantly present …”

Section: Validation Of Nowcasted Irradiance Mapsmentioning

confidence: 99%

Validation of an all‐sky imager–based nowcasting system for industrial PV plants

Kuhn¹,

Nouri²,

Wilbert³

et al. 2017

Progress in Photovoltaics

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International audienceBecause of the cloud-induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward-facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial-sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all-sky cameras (“WobaS-4cam”). The WobaS-4cam system is operational at 2 solar energy research centers and at a commercial 50-MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS-4cam system is considered to be at least state of the art

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Section: Validation Of Nowcasted Irradiance Mapsmentioning

confidence: 99%

Validation of an all‐sky imager–based nowcasting system for industrial PV plants

Kuhn¹,

Nouri²,

Wilbert³

et al. 2017

Progress in Photovoltaics

View full text Add to dashboard Cite

show abstract

“…Recently, ramp rate regulations (Lave et al (2013), Marcos et al (2014), Chen et al (2017)) in several countries with high solar grid penetrations have further stressed the need of cloud speed measurements. The Cloud Shadow Speed Sensor (CSS) can be used to derive such cloud motion vectors and can be a part of a camera-based solar nowcasting system (Wang et al, 2016). A singular allsky imager can measure angular speeds of clouds, but cannot provide absolute speeds in [m/s].…”

Section: Introductionmentioning

confidence: 99%

Field validation and benchmarking of a cloud shadow speed sensor

Kuhn¹,

Wirtz²,

Wilbert³

et al. 2018

Solar Energy

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With ramp rate regulations for photovoltaic plants being discussed in many countries, the speed of clouds has gained signicant importance lately. Besides, measuring cloud velocities and directions is of interest for validations of numerical weather predictions and solar nowcasting systems. Recently, the Cloud Shadow Speed Sensor (CSS) was developed and validated in San Diego for low cumulus clouds. In this publication, the CSS is studied under dierent weather and cloud conditions in the desert of Tabernas in southern Spain. Furthermore, a novel shadow camera based low-cost, low-maintenance approach to determine cloud shadow motion vectors is presented and used as a reference to benchmark the CSS. In comparison, the absolute velocities derived from the CSS and the shadow camera on 59 days for ±5 min temporal medians show deviations of

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“…Also, the combination of one sky imager's CMV in pixels per second with another device's absolute CMV in meters per second device can be used to determine the cloud height. Examples of such systems are described in Wang, Kurtz, and Kleissl (2016) and Kuhn et al (2017a). Cloud shadows maps at the surface (see Figure 7-2, bottom) are produced by projecting the forecasted cloud scenes with their assigned height using information about the position of the sun.…”

Section: -6mentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

2017

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Designing, financing, and operating successful solar heating, concentrating solar power, and photovoltaic systems requires reliable information about the solar resource available and its variability over time. In the past, seasonal and daily variability has been studied and understood; however, with new solar technologies becoming more important in energy supply grids, small time-scale effects are critical to successful deployment of these important low carbon technologies. A vital part of the bankability of solar projects is to understand the variability of the solar resource so that supply and storage technologies can be optimized.This handbook is the result of 10 years of international collaboration carried out by experts from the International Energy Agency's (IEA's) Solar Heating and Cooling (SHC), Solar PACES, and Photovoltaic Power Systems Technology Collaboration Programmes. Under IEA SHC Task 46: Solar Resource Assessment and Forecasting, experts from 11 countries produced information products and best practices on solar energy resources that will greatly benefit project developers and system operators as well as assist policymakers in advancing renewable energy programs worldwide.Meteorologists, mathematicians, solar technology specialists, and other key solar resource experts from around the world joined forces to further our understanding of the sun's temporal and spatial variability through benchmarking satellite-derived solar resource data and solar forecasts, developing best practices for measuring the solar resource, and conducting research to improve satellite-based algorithms. The results of IEA SHC Task 46 are useful to a wide range of users of solar heating and cooling, photovoltaics, and concentrating solar power systems and of building developers and owners as well as anyone else who needs to understand and predict sunlight for agricultural or other purposes.The earlier edition of the handbook, which was published in 2015, is used worldwide as a reference for each stage of a solar energy project. Since that time, there has been substantial growth in the interest in high-quality "bankable" solar resource data. This revision adds significant new methods so it will be even more useful.This publication is a summary that details the fundamentals of solar resources as well as captures the state of the art. For those wanting more depth, it also provides the references where more detailed information can be found.I would like to acknowledge the leadership of the National Renewable Energy Laboratory and express appreciation to the U.S. Department of Energy for producing the handbook and incorporating results from IEA SHC Task 46. Ken Guthrie Chair, IEA Solar Heating and Cooling Technology Collaboration Programme June 2017This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. Preface Dave Renné Dave Renné Renewables, LLCAs the world looks for carbon-free sources to meet final energy demand associated with heat, electrical power, and transport, ...

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Cloud base height from sky imager and cloud speed sensor

Cited by 39 publications

References 17 publications

Validation of an all‐sky imager–based nowcasting system for industrial PV plants

Validation of an all‐sky imager–based nowcasting system for industrial PV plants

Field validation and benchmarking of a cloud shadow speed sensor

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

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