Subtask A: Solar Resource Applications for High Penetration of Solar Technologies (Lead: SUNY/Albany, USA – Dr. Richard Perez)
This Subtask will develop the necessary data sets to allow system planners and utility operators to understand short-term resource variability characteristics, in particular up and down ramp rates, to better manage large penetrations of solar technologies in the grid system. Although this work is primarily focused toward PV systems, which react almost instantaneously to cloud passages over individual panels, the information is also useful for solar thermal and CSP systems which are also variable resources that can impact the ability of utilities to meet load demands.
Activity A.1: Short-Term Variability (Lead: Uni-Agder/Norway - Hans Georg Beyer)
This activity will undertake 3 sub-activities:
A1.1: Characterization and modeling of solar irradiance sets with sub-hourly time resolution with application to solar energy systems
A1.2: Space and time characteristics of solar irradiance fields with application to grid integration studies
A1.3: Analysis dedicated to `irradiance enhancements` (irradiances larger than extraterrestrial irradiance) with application to system layout issues, such as inverter-sizing for PV-systems.
In sub-Activity A.1.1 irradiance time series with time resolution down to one minute are collected and analyzed. Data sets are inspected for their statistical characteristics in terms of frequency distributions and temporal characteristics with respect to the analysis of both PV and CSP systems. This knowledge is to be exploited for the direct analysis of the fluctuating characteristics of the solar energy systems and for the setup of schemes for the generation of synthetic data sets to be used for system studies for sites without measured high resolution data sets.
Activity A.2: Integration of solar with other RE technologies (lead: CENER/Spain - Martin Gaston)
This activity is concerned with hybrid power generation involving solar and other renewable technologies (e.g., wind, biomass). Hybrid generation has pertinence to various scales from remotely operating hybrid installations, to autonomous and/or interconnected microgrids and larger scales, The focus of this activity will be placed on weather data and irradiance data requirements to address such questions and will initially focus on smaller scale issues – autonomous hybrid systems.
Activity A.3: Spatial and Temporal Balancing Studies of the Solar and Wind Energy Resource (Lead: U-Jaen/Spain - David Pozo)
This Activity is concerned with the analysis and modeling of solar and renewable resource data to address: (1) the spatial balancing of the solar resource (both GHI and DNI) across various distance scales; (2) the spatial and temporal balancing of both the solar and wind resources across various distance scales, and (3) the determination of the requirements for, and the eventual improvement of solar radiation forecasting associated with this balancing.
Subtask B: Standardization and Integration Procedures for Data Bankability (Lead: DLR/Spain: Stefan Wilbert)
Activity B.1: Measurement best practices (Lead: DLR/Spain - Stefan Wilbert)
Manuals on best practices for obtaining measured irradiance data sets that provide bankable data for financial institutions will be prepared. The standardization and characterization of commonly used instruments such as the Rotating Shadowband Irradiometers (RSIs) is directly connected to this objective.
Sub Activities include:
B.1.1 Preparation of a manual on best practices based on state of the art (main topic)
B.1.2 First steps to include further alternative irradiance sensors into future versions of the manual
B.1.3 R&D: New measurands and new data for solar resource assessment and enhanced uncertainty analysis for RSIs
Activity B.2: Gap-Filling, QC, Flagging, Data Formatting (Lead: Mines-ParisTech, France: Bella Espinar)
This activity documents best practices in filling missing data gaps, conducting data quality control, and flagging potentially erroneous data values when creating an archive of a database.
B.2.1: Defining methods for plausibility tests and limits
B.2.2: Defining standards for gap filling methods
B.2.3: Defining standards for flagging system
B.2.4: Data formats, interchange formats
Activity B.3: Merging Ground and Modeled Data Sources (Lead: CIEMAT/Spain – Jesús Polo)
This activity explores procedures of merging short-term ground measurements with long term satellite derived data for extrapolating quality ground data to longer term climatic data sets, allowing for long-term cash flow analyses of projects. Satellite-derived data are typically adapted to ground measurement data with two objectives:
Reduction of the bias (systematic deviation),
Improvement of the frequency distribution of irradiance values
This activity needs to be coordinated with D.1 since there are R&D and synergies with D.1.
The activity focuses on reviews of existing methods for integration of data sources containing the topics B.3.1 to B.3.4
B.3.1: Acquiring ground-measured data and satellite data covering parallel period of records
B.3.2: Review of existing methods for integration of data sources
B.3.3: Definition of statistical parameters for use in characterizing the combination of ground-based and model-derived data.
B.3.4: Developing/defining procedures for data fusion of ground and satellite data.
B.3.5: Validation of the procedure applied by comparison and further improvement
Modeled solar radiation data such as satellite-derived irradiance values usually show significant differences to values measured at the ground. For solar resource assessments or TMY-creation it is essential to know the typical uncertainty of various data sets.
Uncertainty of such data sets strongly depends on the time resolution of the compared data. For example, an hourly averaged measurements when compared with concurrent satellite-derived 60 min data usually shows lots of random deviations, which are characterized by high RMSD or Standard Deviation of the Differences (SDD) values. Averaging over 3 hours or full days removes much of this ‘noise’. For many applications in solar energy monthly or even annual averages are sufficient. Averaging over a full year or more usually further reduces the random deviations, which can be characterized by the SDD of the two time-series. For such long-term averages mainly a bias remains.
However also the geographical context, like latitude, elevation, and satellite view angles play a role for the typical uncertainty of model-derived data sets. For example, satellite products tend to have better quality near the nadir position at the equator than at high latitudes, where pixel sizes are increasing, and also where the parallax error increases due to mismatch of observed and actual cloud position.
The objective of this activity is to create a series of benchmarking events. The actual benchmarking should be executed by independent experts, which are not directly involved or related to providers of solar radiation data products. The benchmarking events shall be open for all potential data providers – commercial vendors as well as free data sets. Established data products in various versions may be considered equally to prototype products, which will benefit from comparison to other products to find directions how to better improve towards the final product.
Once processes are established such benchmarking events depending on available funding and interest from data providers to deliver new products can be repeated, e.g. as annual or bi-annual events. Activity B.4 is dealing with the benchmarking of historical data sets only; it does not cover forecasted solar radiation, which is the topic of Subtask C.
Sub Activities include:
B.4.1: Proposing methodologies for benchmarking of model-derived solar radiation sets
B.4.2: Uncertainty of model-derived time-series in various time-scales
B.4.3: Geographical variability of uncertainty in model-derived data sets
Activity B.5: Evaluation of meteorological products with focus on Typical Meteorological Year and Time Series (Lead: GeoModel Solar/Slovakia - Marcel Suri)
Multiyear time series of solar radiation are often transformed to various reduced data representations with the aim of volume reduction and speeding-up of energy system simulations. Typical Meteorological Year (TMY) data represents long-term measured time series condensed into one year of hourly or sub-hourly data. In this activity the historical use of TMY data will be evaluated in the context of current best practices for simulating solar system design and output. Evaluation of alternative approaches to TMY data will be made, given that TMY data sets do not allow for evaluation of extreme high- and low-resource events. Also shorter time resolutions than one hour should be considered.
B.5.1: Reviewing the use of TMY in the solar energy industry (engineering, financing, etc.)
B.5.2: Review of methods to create TMYs and improvement of the same
B.5.3. Multiple year data sets for risk analysis of solar power plant yields
B.5.4. Standard format definition for time-series data products for solar energy
Subtask C: Solar Irradiance Forecasting (Lead: University of Oldenburg/ Germany: Elke Lorenz)
Solar irradiance forecasting provides the basis for energy management and operational strategies for many solar energy applications. Depending on the application and its corresponding time scales different forecasting approaches are appropriate. In this subtask forecasting methods covering timescales from several minutes up to seven days ahead will be developed, tested and compared in benchmarking studies. The use of solar irradiance forecasting approaches in different fields will be investigated, including PV and CSP power forecasting for plant operators and utility companies as well as irradiance forecasting for heating and cooling of buildings or districts.
Activity C.1: Short-term forecasting (up to 7 days ahead) (co-leads: U of Oldenburg/Germany: Elke Lorenz and SUNY/Albany/USA: Richard Perez )
The development and improvement of methods to forecast GHI and DNI is a major subject of activity C1. Different forecast horizons, ranging from minutes up to several days ahead are addressed using specific methodology and data. Activities C1.1 to C1.5 cover different forecasting approaches, characterized by the used data sources, corresponding methods and time scales. A second focus of this activity, addressed in Subactivity C1.6, is the comparison of these approaches in benchmarking studies focusing on different models, time scales or forecast parameters.
C1.1. Time series models based on ground measured irradiance data
C1.2. Total sky imagers
C1.3. Motion vectors from satellite data
C1.4 NWP forecasts models
C1.5. Statistical models integrating different data sources
C1.6. Benchmarking studies
Activity C.2: Integration of solar forecasts into operations (Lead: IRSolav/Spain: Louis Martin)
This activity examines the important issue of how solar forecasts are used for different applications, including utility operations, management of PV or CSP power plants, and thermal management of buildings. A critical aspect of this task is to seek input from users, e.g. utility operators on the specific types of irradiance or power output forecasts they need in order to improve system operations and reduce the overall cost of energy and maximize the use of renewable energy within the system.
C.2.1: Link with industry
C.2.2: Applications of solar forecasting
Subtask D: Advanced Resource Modeling (Lead: Mines ParisTech/France: Philippe Blanc)
Although most of the work in Task 36 involved the testing and evaluation of existing solar resource methodologies, some specific new methodologies have been identified that could be developed within a new task. These methodologies are driven by specific information requests from energy developers and planners. They can include new data sets required for the control and heating and cooling in buildings, solar resource forecasting for CSP plant operations, and the impact of climate change on solar resources, both from an historical perspective as well as estimates of future impacts.
Activity D.1: Improvements to existing solar radiation retrieval methods (Lead: Uni-Jaén, José A. Ruiz Arias).
The objective of this activity is to consider state-of-the-art and new solar radiation modeling approaches or other sources for input parameters to improve the accuracy and/or to increase the spatial, spectral and angular resolutions of solar resource data sets derived from satellite.
An overview of the different advanced available satellite-derived solar radiation methodologies will be given as well as the corresponding requirements of their input parameters. This activity will also evaluate the latest products coming out of the U.S. National Oceanic and Atmospheric Administration, such as the GOES Surface Irradiance Product (GSIP), which offers a promising solution for providing near real-time irradiance values throughout the western hemisphere at 4-km resolution.
Sub Activities include:
D.1.1 Direct/diffuse transposition model, radiative transfer code for direct/diffuse and angular distribution of irradiance, circumsolar (sunshape) analysis
D.1.2 Spectrally resolved irradiance
D.1.3 Enhanced atmospheric parameters for radiative transfer code based modeling: aerosol optical depth, enhanced cloud parameters, including 3-D cloud characterization.
D.1.4 Other model upgrades: pixel desegregation (down-scaling solar irradiation data) for high spatial resolution solar maps
Activity D.2: Long term analysis and forecasting of solar resource trends and variability (co-Leads: NASA-LaRC/USA: Paul Stackhouse, and Meteotest/Switzerland: Jan Remund)
In this activity, studies of long-term solar data sets, both observed as well as satellite derived, will continue to asses episodes of “global dimming” and “global brightening”, important for evaluating potential long-term cash flow implications from solar systems. The uncertainties of the variability are characterized from large continental to regional scales. Efforts will be undertaken to link the results of IPCC climate change scenarios to predictions of future solar resource variations.
Sub Activities include:
D.2.1: Influence of leading atmospheric patterns (as NAO and ENSO) on the interannual variability of GHI and DNI.