Home - Australian Renewable Energy Agency - Date … · Web viewA key outcome of this project has been the development of uncertainty measures for solar radiation data accuracy with

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Integrated Solar Radiation Data Sources over Australia

Final report: project results and lessons learnt

Lead organisation: Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Project commencement date: 1st September 2012 Completion date: 27th August 2015

Date published:

Contact name: Alberto Troccoli

Title: Dr

Email: al b ert o .tr o cc o li @ csir o .au Phone: 02 6281 8251

Website: h tt p :// we r u .csir o .au

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Table of ContentsTable of Contents..........................................................................................................................................2

Executive Summary.......................................................................................................................................3

Project Overview...........................................................................................................................................6

Project summary....................................................................................................................................6

Project scope.........................................................................................................................................8

Outcomes ............................................................................................................................................12

Transferability .....................................................................................................................................32

Conclusion and next steps .................................................................................................................32

References ..........................................................................................................................................34

Lessons Learnt.............................................................................................................................................35

Lessons Learnt Report: Availability of quality solar data .................................................................35

Lessons Learnt Report: Analysis of direct beam data ......................................................................36

Lessons Learnt Report: Delays in signing the agreement between CSIRO and NREL .....................37

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Executive SummarySolar generating capacity in Australia has been growing to an estimated installed capacity exceeding 4,000 MW, particularly with the proliferation of grid-‐connected roof-‐top PV, as well as the more recent large scale solar installation at Nyngam, Broken Hill and Royalla (with other MW-‐scale plants due to become operational in the near term). An accurate and reliable solar resource assessment is therefore essential to assist with planning and development of new solar generation. Indeed, solar power developers and financiers regard uncertainty in the volatility (or inter-‐annual variability) of solar irradiance as a crucial element in the estimation of the power output of solar farms, and ultimately their financing.

The aim of the 36- m‐ onth, 1.4 million, project Integrated Solar Radiation Data Sources over Australia (ISRDSA) was to provide solar power developers and installers with an improved solar data resource and an enhanced understanding of its uncertainty by exploiting three sources of solar radiation data: ground based, satellite-‐ derived and atmospheric model output (Figure 1). The project, co-‐funded by the Australian Renewable Energy Agency (ARENA), was coordinated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and was executed in partnership with the Bureau of Meteorology (BoM) and the US Renewable Energy Laboratory (NREL).

Conventionally the main widely available sources of resource (or historical) solar radiation data for Australia have been ground station observations and satellite-‐derived products. The former represent the best quality data, since it provides what is actually seen at ground level, where potential solar plants are planned and/or installed. However, high-‐quality ground stations are expensive to set up and maintain. This is why the current network is sparse and often not sufficient for industry needs. Satellite-‐derived radiation data, on the other hand, offer the advantage of a much wider coverage at the expense of accuracy (it is a derived quantity) and temporal resolution (currently only hourly instantaneous data are available).

With this “Integrated Solar Radiation Data Sources over Australia” project two main features have been included so as to provide an important complement to the ground station and satellite-‐derived radiation data:

• The output of a high-‐resolution global atmospheric model developed by CSIRO, called CCAM. The Bureau of Meteorology’s model used for operational weather predictions, called ACCESS, has also been used as a reference

• Solar radiation observations from as many ground stations as practical have been collected and assembled into a self-‐consistent database.

By combining these three main sources of solar data, as depicted in Figure 1, and evaluating their respective accuracy, this project has produced a new and improved solar resource data for Australia at the regional scale to assist prospective solar power developers for resource estimation. In particular, the addition of atmospheric models output for solar resource assessment has allowed us to better address questions such as “What is the best location for a new solar monitoring station?”, “How large is the uncertainty of solar radiation at any given location?” and “How can we compute solar power yields at a given site?”.

Integrated Solar Radiation Data Sources over Australia | Page 4

Specifically, this project had three main objectives, all of which were achieved to a high standard, as discussed in the following.

1. Assess requirements for an optimal solar observations network layout

This assessment has allowed the quantification of the degree of improvement in solar radiation uncertainty (or accuracy) as a function of increased quantity, spatial distribution and quality of surface observations. The optimal network layout is described in Davy and Troccoli (2014).

This quantification provides guidance to the design of a solar radiation observation network across Australia for an improved solar resource mapping estimation for solar farm developments.

2. Development of an integrated solar radiation data set

This integrated solar radiation data set has been generated through the combination of in situ observations, satellite-‐derived data and high-‐spatial resolution model data, along with uncertainty estimates. A key element of this data set is the 34-‐year (1979-‐2012) atmospheric model run using the CCAM model, which has produced time series for the whole of Australia and at 30 minute time resolution and 10 km spatial resolution. This dataset has been used for the solar resource mapping of a proposed large-‐scale solar farm. A manuscript is being finalised for publication in an international journal (Davy et al. 2015).

3. Development of high temporal resolution solar radiation time series

High temporal resolution solar radiation time series (1 min) have been produced using lower resolution (1 hour) solar data from the integrated solar radiation data set. A statistical approach has been developed to produce solar time series for generic sites across Australia (and elsewhere).

These higher frequency time series provide suitable benchmarking for forecasting tools to be developed to help energy market operators plan and schedule large-‐scale solar power generation; they also assist with a finer assessment of solar resource by allowing to better quantify effects such as ramp events. A manuscript is being drafted for publication in an international journal.

In addition to these tasks, a major complementary task has been identified as providing a critical contribution towards a more effective project implementation and delivery. This is the development of a Solar Radiation Database, including quality control flags. With this solar radiation database we have made marked advances towards gathering solar radiation data coming from different ground station sources collected by research institutes, government organisation and commercial companies, whether for solar power, agriculture or other purposes. This database has been designed to also include solar radiation from numerical weather models and those derived from satellite at the locations for which ground stations are available. The ultimate aim is to create a repository, together with a web interface, capable of dealing with all these heterogeneous ground station observations and managing the problem of different format, quality, spatial and temporal resolutions from each of the data sources. Building a database like this is a complex technical endeavour. Thus, with this project only some of the solar radiation database building blocks have been realised.

A key outcome of this project has been the development of uncertainty measures for solar radiation data accuracy with important implications for project financing and for reducing the cost of incorporating solar energy into the grid. This project will benefit considerably from the experience of NREL experts who are working on analogous problems for the USA.


All the developments in this project have followed internationally consistent standards for deployment of solar radiation monitoring equipment, data acquisition schedules, statistical analyses and communication protocols. The project has also greatly benefitted from our active participation in the following two world-‐leading activities: the International Energy Agency (IEA) Task 46 ‘Solar Resource Assessment and Forecasting’ and/or the European COST Action ‘Weather Intelligence for Renewable Energies’ (WIRE).

Integral part of the project have been outreaching activities such as two stakeholder workshops and the convening with the ASEFS project of a Solar Resource Assessment & Forecasting Science Day in Sydney in February 2014 to discuss progress is solar resource assessment and forecasting both from an academic and industry perspectives. The event was very well received by the over fifty attendees.

The three main benefits of this project have been:

• To have markedly advanced the science of solar radiation, including its monitoring, modelling, prediction, and application to solar energy devices to assist Australia to establish itself as a worldwide leading player in this field;

• To have provided a significant contribution to bridging the gap between the meteorology community and the Australian solar community, by providing radiation data (observations, simulations and forecasts) that is critical for modelling solar power stations and predicting their annual output.

• To have prepared the ground for potentially successful commercial opportunities in the linkage between the meteorology and solar energy communities.

The key barrier to continue to accrue these benefits is in the delivery and dissemination of the data and associated information. Discussions have started with the Australian Renewable Energy Mapping Infrastructure (AREMI) project to make a version of the solar radiation data developed with this project to be available to the public through the AREMI’s web-‐interface. At present the AREMI’s portal appears to be the best vehicle for the sustainability of the provision to the public of the solar radiation data developed with this project.

Figure 1 – The three main sources of solar radiation: satellite-‐derived data, ground station observations and atmospheric model data

Project Overview

Project summaryThe aim of the 36- m‐ onth, 1.4 million, project Integrated Solar Radiation Data Sources over Australia (ISRDSA) was to provide solar power developers and installers with an improved solar data resource and an enhanced understanding of its uncertainty by exploiting three sources of solar radiation data: ground based, satellite-‐ derived and atmospheric model output.

The project, co-‐funded by the Australian Renewable Energy Agency (ARENA), was coordinated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and was executed in partnership with the Bureau of Meteorology (BoM) and the US Renewable Energy Laboratory (NREL).

The ISRDSA project commenced on 1st September 2012. Since then there had been some delays, particularly with the signing of the agreement between CSIRO and NREL, and to a lesser extent with the agreement between CSIRO and BoM too. The agreement with NREL was only officially signed in early 2015.

The ISRDSA project has successfully achieved the great majority of its tasks. Specifically, this project had three main objectives, all of which were achieved to a high standard, as discussed in the following.



This quantification provides guidance to the design of a solar radiation observation network across Australia for an improved solar resource mapping estimation for solar farm developments.


This integrated solar radiation data set has been generated through the combination of in situ observations, satellite-‐derived data and high-‐spatial resolution model data, along with uncertainty estimates. A key element of this data set is the 34-‐year (1979-‐2012) atmospheric model run using the CCAM model, which has produced time series for the whole of Australia and at 30 minute time resolution and 10 km spatial resolution. This dataset has been used for the solar resource mapping of a proposed large-‐scale solar farm. A manuscript is being finalised for publication in an international journal (Davy et al. 2015).



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In addition to these tasks, a major complementary task has been identified as providing a critical contribution towards a more effective project implementation and delivery. This is the development of the CSIRO Weather and Energy Research Unit (WERU) Solar Radiation Database, including: quality control flags, the development of metadata management and processes for each data source; a more flexible way to deal with time aggregation so as to speed up access to various averaging periods (5 min, 10 min, etc.); a more elaborated web interface which allows to both visualise the location and the values of the data and also to download the selected data.

The ISRDSA project has organised two well-‐attended stakeholder workshops, one at the start of the project and the other half-‐way through the project. These have been very useful towards a better understanding of solar industry concerns around solar radiation data provision and their uncertainty. Participant included experts from a variety of organisations, from government agencies, to network operators and regulators. The feedback obtained via the above mentioned stakeholder workshops as well as other direct interactions have allowed this project to pursue a research programme relevant to the solar industry, as demonstrated for instance by the service provided by CSIRO to an Australian solar power developer. In addition, in collaboration with the ASEFS project, the ISRDSA project convened a Solar Resource Assessment & Forecasting Science Day in Sydney in February 2014 to discuss progress is solar resource assessment and forecasting both from an academic and industry perspectives. The event was very well received by the over fifty attendees.

This project has been developed by following international standards for deployment of solar radiation monitoring equipment, data acquisition schedules, statistical analyses and communication protocols. Aside from keeping abreast with the international literature, this process has been greatly facilitated by our participation in international leading-‐edge activities such as the International Energy Agency (IEA) Task 46 ‘Solar Resource Assessment and Forecasting’ and the European COST Action ‘Weather Intelligence for Renewable Energies’ (WIRE). This interaction has happened via attendance to meetings 9at least once a year) as well as regular email contacts. The direct participation of NREL in the project has also been highly beneficial.

Specifically, our involvement in the IEA Task 46 and the WIRE network has allowed us to benchmark our tools against worldwide standards. This has been improving the way solar resource information is delivered to energy market operators and solar power generation developers.

Our research findings have been communicated at around 15 scientific and/or industry events and have disseminated our results through international publications: one accepted publication, two nearly ready for submission and regular input to the IEA Task 46 reporting. In addition, our solar ground station observations are made available through our web site h tt p :// we r u .csir o .au and have attracted wide interest.


Project scopeHigh quality solar radiation data from ground stations, together with their uncertainty, are critical to solar power plants planning, project finance and due diligence processes. Yet, provision of ground station data over Australia is still very patchy and often insufficient for banks and project financiers to gain confidence in prospective solar power plant generation and revenue projections. Despite current efforts to enhance the solar radiation data set several gaps are still present.

Conventionally the main widely available sources of resource (or historical) solar radiation data for Australia have been ground station observations and satellite-‐derived products. The former represent the best quality data, since it provides what is actually seen at ground level, where potential solar plants are planned and/or installed. However, high-‐quality ground stations are expensive to set up and maintain. This is why the current network is sparse and often not sufficient for industry needs. Satellite-‐derived radiation data, on the other hand, offer the advantage of a much wider coverage at the expense of accuracy (it is a derived quantity) and temporal resolution (currently only hourly instantaneous data are available).

With this “Integrated Solar Radiation Data Sources over Australia” project two main features have been included so as to provide an important complement to the ground station and satellite-‐derived radiation data:

• The output of a high-‐resolution global atmospheric model developed by CSIRO, called CCAM (Conformal Cubical Atmospheric Model). The Bureau of Meteorology’s model used for operational weather predictions, called ACCESS, has also been used as a reference

• Solar radiation observations from as many ground stations as practical will be collected and assembled into a self-‐consistent database; basic quality assurance procedures will also be provided.

By combining these three main sources of solar data, as depicted in Figure 1, and evaluating their respective accuracy, this project has produced a new and improved solar resource data for Australia at the regional scale to assist prospective solar power developers for resource estimation. In particular, the addition of atmospheric models output for solar resource assessment has allowed us to better address questions such as “What is the best location for a new solar monitoring station?”, “How large is the uncertainty of solar radiation at any given location?” and “How can we compute solar power yields at a given site?”.

In order to provide a better understanding of the temporal and spatial correlation and variance of incoming solar radiation across Australia to support the solar energy industry it is important to understand the features of each of the three sources:

• Ground station observations;• Satellite-‐derived solar irradiance, and;• Atmospheric model data.

Ground observations measure the amount of incoming solar energy that reaches the surface. The data are characterised as high temporal resolution (up to 1 second) but spatially sparse. The best set of ground observations in Australia is the Bureau of Meteorology’s network of solar radiation instruments due to the quality of data (instrument calibration and maintenance) and long time


series for many stations. CSIRO has also been monitoring the three solar irradiance components at two high-‐quality monitoring stations, one in Canberra and the other in Newcastle. Global irradiance only is measured at a number of additional sites spread across the Australian Capital Territory. Other sources of ground global irradiance observations have been acquired from a collaborative network of South Australian agriculture sensors and a New South Wales Department of Primary Industries solar monitoring network. Some work has been done to define new quality flags for the South Australian data based on time series analyses and expected values.

Satellite-‐derived solar data are characterised by continental spatial coverage and instantaneous pixel values at reasonable spatial (0.05 degrees or approximately 5 km) and temporal (hourly, instantaneous) resolution. The satellite data are from a selection of geostationary satellites, which provide the hourly temporal resolution at the cost of reduced spatial resolution (higher spatial resolution satellites are generally polar-‐orbiting). The satellite data are processed with cloud masking procedures and a one-‐dimensional radiative transfer model to estimate the global radiation at the surface. Direct solar estimates are derived from the global estimates and the solar zenith angle. A bias correction is also applied to the satellite data. This bias correction is currently computed by regressing the overall average bias of all the Bureau of Meteorology’s radiation stations (on a monthly basis), although there are plans to consider the geographical and physical

clearness index) distribution of the error (I. Grant, pers. comm.).

The atmospheric model data are characterised by their potential for more flexible spatial and temporal resolutions as well as for providing a large amount of additional meteorological data. The relative accuracy is constrained by the parameterisation of atmospheric physics in the model and the available resolution is limited by the computer capacity required to generate high spatial and temporal resolution data. Data from two models are available for this project, namely CCAM and ACCESS. However, given the limited temporal coverage of the ACCESS model, this will be mainly used to benchmark the CCAM model for the overlapping period.

It is also important to distinguish between the three solar radiation components because their availability and accuracy varies markedly among them. What is normally referred to as solar radiation is technically called global horizontal irradiance (GHI). This is the amount of downward incoming solar (or shortwave) radiation seen by the ground and it is the main driver of PhotoVoltaic (PV) solar technology. As shown in Figure 2, GHI is the sum of various contributions: transmitted, reflected and scattered radiation and direct normal irradiance (DNI). Practically these contributions are grouped into two components, the DNI (also known as direct beam) and the diffuse radiation (the sum of all other contributions).

It is worth noting that ground station observations measure DNI, whereas atmospheric models normally compute the DHI component. Although it is straightforward to derive one from the other via the solar zenith angle as in the relationship above, the relatively coarse temporal resolution of solar radiation time series, especially that from models (typically one hour), may introduce approximation errors. In the case of satellite-‐derived data, GHI is usually the main output, with DNI derived from GHI by means of statistical or physical relationships.

In order to take into account the recommendations of the first stakeholder workshop, two main modifications to the solar data used for this project have been introduced:

1. The longest available historical solar data from the Bureau stations have been acquired;2. A model run for the 34-‐year period (1979-‐2012) using the CCAM model has been set-‐up.


Figure 2 – The three solar radiation (or irradiance) components.

The Conformal Cubical Atmospheric Model (CCAM)

Given the central role of the CCAM model of this project some additional detail is provided here.

CCAM is a global atmospheric model principally designed for climate modelling and regional climatedownscaling. In contrast to limited-‐area models, the CCAM employs a cubic conformal grid, thereby avoiding the need for any special treatment of simulation boundaries. Through the use of the Schmidt transformation, this grid is highly stretchable such that the places of interest can be resolved at desired fine spatial resolutions while maintaining a global configuration.

CCAM is a very versatile model which has been used for various applications, from regional climate downscaling for climate change studies, to wind speed and solar time series for the power industry, to localised wind forecasts for sailing boat competitions such as the America’s cup or the London Olympics, to urban modelling. Thus, CCAM can be used for a variety of scenarios, spatial and temporal resolutions and can therefore overcome some of the limitations of ground station observations and satellite-‐derived solar data.

The CCAM output used here is part of a large-‐scale study of atmospheric processes affecting renewable energy production. The CCAM simulation covers the entire Australia centring at longitude 133°, latitude -‐27.5°. With 312 grid points across each of the six panels of a cube, the spatial resolution is approximately 0.09°.

To develop the time series relevant to the high quality solar stations we constrain the large-‐scale atmospheric circulation with the simultaneous atmospheric fields which describe the state of the atmosphere. In this case, these are taken from the ERA-‐Interim reanalysis of the European Centre for Medium-‐Range Weather Forecasts (ECMWF), which covers the period from 1979 to near present. Its resolution is 6-‐hourly in time and 1.5° × 1.5° in space. The ERA-‐Interim reanalysis product is derived from a combination of model information and observations in an optimal way and consists of the


best estimate of various atmospheric parameters (such as components of wind velocity, pressure, temperature, and relative humidity).

In order to better characterize the volatility (or inter-‐annual variability) of the solar resource, a much longer model run was set-‐up by using the atmospheric model CCAM (ACCESS does not currently have the framework to perform long historical runs). Given the complexity introduced by the combination of an extended period and the high horizontal resolution, considerable work was required to adapt the CCAM code to run over this historical period.

Time series for most meteorological variables, therefore including solar radiation data, are available for the whole of Australia over the 34-‐year period (1979-‐2012) and at 30 minute time resolution and 10 km spatial resolution.

Figure 3 – An example of a CCAM grid, similar to the one used in this work. To enhance readability, only every third grid line is plotted.


OutcomesThe main outcomes are presented according to the following structure:

• The Weather and Energy Research Unit (WERU) solar radiation database system• Assessment of solar radiation• An optimal solar ground stations layout• Integration of solar radiation sources• Interannual variability• Validating the Improvements in NREL’s National Solar Radiation Data Base• Synthetic high temporal resolution solar radiation time series

The Weather and Energy Research Unit (WERU) solar radiation database systemThe objective of the Weather and Energy Research Unit (WERU) solar radiation database is to gather solar radiation data coming from different ground station sources collected by research institutes, government organisation and commercial companies, whether for solar power, agriculture or other purposes. This database is designed to also include solar radiation from numerical weather models and those derived from satellite at the locations for which ground stations are available. The ultimate aim is to create a repository, together with a web interface, capable of dealing with all these heterogeneous ground station observations and managing the problem of different format, quality, spatial and temporal resolutions from each of the data sources. The initial purpose of the WERU solar radiation database was to provide a tool to assist the scientific research carried out with this project. However, building a database like this is a complex technical endeavour. Thus, with this project only some of the WERU solar radiation database building blocks have been realised.

Overview of Available ground stations

The ISRDSA project aims to bring together solar radiation data from multiple networks of ground stations. A key outcome of this activity is to provide consistent quality and format ground station data for comparison with model and satellite data, and so provide a spatially explicit and rigorous view of solar radiation resources across Australia.

The current highest quality and easily accessible ground station data is from the network operated by the Bureau of Meteorology. This project seeks to collect, process and analyse ground station data from a number of other known networks with varying data quality and format but, importantly with a larger spatial and perhaps temporal spread than the Bureau network alone.

The spatial distribution of ground stations from the Bureau (red), NSW Department of Primary Industries (black) and the OzFlux network (blue) are shown in Figure 4 Not shown are the ground stations associated with the South Australian network, as these are all tightly clustered around Adelaide relative to the scale of the map in Figure 4, and the CSIRO network, which will be included in the next version of the figure.

While the Bureau network of stations cover the majority of macro-‐scale areas that are serviced by the other networks, it can be seen that the other networks provide more spatially explicit coverage


than the Bureau network alone, in particular through NSW and southern Queensland. This project, which will study the viability of the non-‐Bureau ground station networks for national integrated solar resource assessment, will also provide valuable information on the locations where more (or less) solar ground stations should be deployed, which may be important as the Bureau assesses which of their stations will be maintained into the future.

Figure 4 – Overview of the location of available ground station networks.

Data system overview

The data system has three main parts:

• Data processing and storage;• Fast access database, and;• The web application service that exposes this data to the end user through an interactive

web interface.

Most of the work has focussed on the data storage (system infrastructure and database design) and processing component, which has not identified any issue with the overall design that would force an amendment. While the overall design does remain flexible, it is encouraging that the system has not required a change due to the processing of source data and the implementation of the database schema.

Due to budget constraints at CSIRO, this part of the work, in strong support but not an official component of ISRDSA, could not be completed. Most of the components needed to continue building a robust data system is however available for potential future projects, at both national and international levels.


Figure 5 – Schematic of the overall design of the Solar Radiation Database system.

Assessment of solar radiationSolar radiation data derived from satellite and from the output of the two atmospheric models, CCAM and ACCESS, are compared to ground station observations. Although ground observations are affected by errors (instrumental, calibration, and others), these errors are generally smaller than those for satellite-‐derived and atmospheric model data. Thus, for our purposes ground observations can be taken as the true value of radiation. Also, whenever the two components GHI and DNI are available the comparison is carried out for both.

The results are presented as annual or monthly means, which are computed from hourly values, except for the ACCESS output for which daily values were available. The statistics used are: mean bias as a fraction of the monthly mean value, root mean square error (RMSE) as fraction of the monthly mean, and Pearson correlation coefficient (or just correlation). For the ACCESS model, the relative mean absolute error (MAE) is used instead of the relative RMSE. These statistics are commonly used for the assessment of solar irradiance.

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Satellite vs ground measurements

The instantaneous satellite-‐derived data over Australia has been compared to 60- m‐ inute averages of the Bureau’s ground station observations, centred on the nominal satellite data time stamp. Mean statistics improve for averaging periods up to about 60 minutes under broken cloud conditions, though these statistics are not much dependent on the averaging period for clear sky or overcast conditions. Similar results are obtained for the USA data, even if in this case a 30- m‐ inute averaging period was adopted.

Global horizontal irradiance over Australia

Figure 6 (left hand side) shows that the annual mean correlation for satellite-‐derived GHI is very high (larger than 0.9) for all Bureau’s sites, indicating a remarkable performance in terms of this key statistic. It should be noted, however, that it is likely that a large fraction of this correlation is due to the diurnal cycle, which would be closely reproduced by the algorithm used to derive solar radiation from satellite data. The relative RMSE is also comparatively small, with Learmonth, Kalgoorlie-‐ Boulder and Alice Springs displaying the smallest errors (Figure 6 right hand side). With relative errors between 0.5 and 0.6 (50-‐60%), Cairns and Mount Gambier are the worst performers.

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Figure 6 – Comparison of satellite vs ground measurements for GHI: annual means of Pearson correlation (left) and relative RMSE (right) from hourly values.

Direct Normal Irradiance over Australia

The performance of the DNI in terms of mean annual correlation and relative RMSE is shown in Figure 7. The correlation is still high but smaller than for the GHI. For most stations correlation ranges from 0.8 to 0.9, but it is a little less for Darwin and Cairns (between 0.7 and 0.8, Figure 7 left hand side). The fact that correlation for DNI is worse than for GHI is expected since DNI is derived from GHI via a statistical relationship. A reduction in performance is more noticeable in the relative RMSE (Figure 7 right hand side). While error pattern broadly reflect that for GHI (Figure 6 right hand side), with the highest errors for the stations of Cairns and Mount Gambier, the relative errors are at least 0.3 (30%) larger. Even the best performing stations, e.g. Learmonth, have an error of between1.4 and 0.5. Cairns and Mount Gambier have an error larger than 100%.


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Figure 7 – Comparison of satellite vs ground measurements for DNI: annual means of Pearson correlation (left) and relative RMSE (right).

CCAM vs ground measurements

Global horizontal irradiance

The mean annual correlation for the GHI produced by CCAM is shown in Figure 8. Comparing Figure 8 with its satellite equivalent (Figure 6), it can be seen that there is deterioration in performance of CCAM relative to the satellite-‐based model. This is reflected in the results for the annual correlation (which are lower than for the satellite model) and relative RMSE (higher).

Direct normal irradiance

The mean annual correlation for the DNI produced by CCAM is shown in Figure 9. Comparing Figure 9 with its satellite equivalent (Figure 7), it can be seen again that CCAM is generally worse than the satellite-‐based model. This is reflected in the results for the annual correlation (which are lower than for the satellite model) and relative RMSE (higher). The Cairns location is the one anomaly, where the relative RMSE is lower in CCAM, possibly due to a lower bias.

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Figure 8 – Comparison of CCAM vs ground measurements for GHI: annual means of Pearson correlation (left) and relative RMSE (right).


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Figure 9 – Comparison of CCAM vs ground measurements for DNI: annual means of Pearson correlation (left) and relative RMSE (right).

An optimal solar ground stations layoutLarge solar projects cannot obtain finance without having on site measurements. These measurements are used both to quantify the short-‐term variability and the inter-‐annual variability. In the case of the latter, it is only feasible to measure for a few years at most. Therefore, it is necessary to infer the past solar irradiance by correlation with a long-‐term time series.

Long-‐term solar time series may be derived from three sources: ground measurements, satellite model and weather model. The coverage of the ground station network is quite sparse and there are many candidate locations for solar projects that would not have a ground station nearby. In such cases, satellite-‐based time series may serve as the long-‐term reference.

An important role for ground station measurements is in performing bias correction for the satellite model. Satellite model bias can vary with cloud cover, zenith angle and latitude. A more complete ground station network can help improve the satellite record and hence reduce the uncertainty in the long-‐term resource.

If ground stations are used as the long-‐term reference, it is necessary to monitor for 10 years or more – a very time-‐consuming and expensive task. Even so, having a ground station reference of a few years’ length can reduce the uncertainty in the resource at a nearby location, with the satellite information making up the remainder.

The work in this section is aimed at designing a solar monitoring network which would be optimal in some respects – especially in its coverage of areas that are close to transmission infrastructure.

Variability

A key factor in resource estimation and measurement is the variability over time. Areas where the resource is too variable may not be suited for some kinds of generation. Also, measurements are more spatially representative when the temporal variability is lower.

As a preliminary step, a measure of the resource variability was calculated. Firstly, the seasonal cycle was subtracted at each location by fitting periodic sinusoidal functions to the time series. This gives the fluctuations left over after accounting for predictable seasonal variations. Because these


fluctuations tend to vary in magnitude with the seasons and with location, the time series was then normalised by the fitted seasonal cycle. The result is a time series representing the daily fluctuation as a fraction of the daily climatological mean. For each grid point, the standard deviation of this quantity was calculated. This was performed for both global horizontal and direct normal irradiance. Generally, the fractional standard deviation is lower for GHI than for DNI, and the spatial gradients are lower also. The highest variability occurs in the east and south east of the continent. There are also areas of high DNI variability in far north Queensland and in the south west of Western Australia.

Optimising a station layout based on spatial prediction

The problem of designing an optimum spatial measurement network is one of continual interest in the fields of geostatistics and environmental monitoring. It has been applied to areas as diverse as air pollution, radioactivity and river sediment. The problem of optimal solar monitoring has also been studied but work thus far has made use of a clustering algorithm to classify different regions of Greece into areas that are similar in terms of solar irradiance variations.

In this work, we design network around techniques that are frequently used in industry to estimate the long-‐term renewable energy resource. In the wind industry this is sometimes referred to as “measure-‐correlate-‐predict”. If short-‐term measurements are available along with a long-‐term reference time series, it is possible to use linear regression to predict the “missing” measurements from the past. Since we are looking for candidate locations for monitoring, and the available measurements are so sparse, we can use the satellite irradiance as a model for the spatial correlation of the solar irradiance. This also enables testing the accuracy of a linear regression model.

Here we take the mainland of Australia and assume the placement of 16 monitoring “stations” (time series derived from satellite). Then, a multiple linear regression model is formed for every location across the mainland, with the 16 “stations” serving as predictors. The regression models are formed over the time period 2007-‐2009 and their performance is quantified using the years 2010-‐2012. Relative mean absolute error is used as the performance metric.

Results for global horizontal irradiance

In the results that follow, stations are not always placed in the areas of highest resource. This algorithm is mostly aimed at reducing the overall resource uncertainty, rather than specifically targeting areas of highest resource. However, some of the chosen locations are in areas with excellent solar resource.

Under the equal weight scenario, the 16 stations are placed fairly uniformly across the mainland. The overall regression error is largest in the coastal regions, except for the north west where it is relatively small. This largely reflects the in daily variability in the solar resource. Comparing the left and right hand plots of Figure 10, it can be seen that stations that are placed in areas of lower variability appear to have a larger spatial influence. In the left hand plot, the dark red patches around the station locations represent areas where the mean relative prediction error from the regression is quite low – less than 0.05. These dark red patches are largest in the centre of the mainland and in the north west where the variability of the global horizontal irradiance is lowest.

Results for direct normal irradiance

Many of the same observations apply for the case of direct normal irradiance. However, it can be seen that the predictability is markedly worse than for global horizontal irradiance. This is a


reflection that the spatial variability and daily variability is higher. Much of the transmission network is in areas along the east coast where the DNI resource is somewhat low. Overall, it can be seen that the placement of monitoring stations represents a compromise between at least three different goals:

1. Monitoring in areas of excellent resource in order to achieve highest energy yield.

2. The need to quantify the resource in areas with high variability.

3. The need to accommodate the existing transmission infrastructure.

The left hand plot of Figure 11 indicates that stations in areas of lower DNI variability have a larger spatial influence in terms of reducing the prediction error. This is indicated by the regions of darker red surrounding those locations.

Figure 10 – Optimal monitoring locations with equal spatial weighting. Left: plotted against contours of the relative mean absolute error. Right: plotted against the solar irradiance variability.

Figure 11 – Optimal monitoring locations with equal spatial weighting. Left: plotted against contours of the relative mean absolute error. Right: plotted against the solar irradiance variability.

An analysis was performed to estimate the radius around a station within which it could be considered effective at modelling the surrounding irradiance. The overall area with prediction error less than some fixed amount was calculated. Taking the square root of this quantity gives something


akin to a radius (distance). For a given error level in the lower range 0.05—0.09, on average the radius of effectiveness was about 7—8 times higher for GHI than for DNI. As the allowable error amount is allowed to increase, the difference between GHI and DNI becomes smaller. Naturally, these results would vary from location to location. Also, the satellite model does not capture the full variability of actual ground measurements, as was shown in the previous report.

Additional results can be found in Davy and Troccoli (2014).

Integration of solar radiation sourcesFor solar resource assessment, two main sources of data have traditionally been used: ground measurements and satellite-‐derived modelling. More recently, it has been found that reanalysis-‐ based irradiance estimates can be useful when the satellite irradiance is not available. This suggests that there is opportunity to combine all three data products in a way that is better than either the satellite or reanalysis on its own. This is particularly important for Australia, where there is a heavy reliance on the satellite irradiance to provide estimates over vast amounts of terrain where there are no nearby ground stations.

The satellite-‐derived irradiance has errors that depend on clear sky index and solar zenith angle, as well as other variables such as aerosols, cloud properties and satellite viewing angle. Corrections can be performed if ground measurements are available, assuming ground stations represent the truth. Ground stations are the most reliable source of solar irradiance measurements, but these are sparsely located. Successful attempts have been made at fusion of model and measurements, but this has generally used daily mean irradiance and a relatively dense ground station network. Recently, an optimal interpolation approach for combining numerical weather model estimates of irradiance with ground measurements for monthly-‐averaged data has also been published. These methods rely on knowledge of the spatial co-‐ variance of the errors in the data. In Australia, the sparse monitoring network makes estimating the spatial covariance highly problematic. This suggests that a more empirical approach may be more appropriate.

Some random error in gridded satellite irradiance is inevitable due to its spatially averaged nature, in contrast with ground stations. Regarding systematic errors, polynomial regression models have been used for correcting the bias of the satellite irradiance as a function of satellite clear sky index (the ratio of the irradiance to the clear sky value) and cosine of the solar zenith angle. Fourth-‐order models have been reported in the literature in a fore- casti‐ ng context. Third-‐order polynomials have also been used for hourly data. Here, we adopt nonparametric generalised additive models (GAM) using cubic smoothing splines, and demonstrate a useful performance gain. Expanding upon this regression model, we investigate the value of including irradiance derived from a reanalysis weather model (expressed as a clear sky index) as an additional predictor for the hourly measured solar irradiance. As part of this analysis, an estimate for the error variance as a function of the two irradiance sources and the zenith angle is developed. We investigate the extent to which the weather model irradiance contributes knowledge regarding the uncertainty in the combined irradiance estimate. From here we explore spatial interpolation of the regression functions. Generally speaking this process results in an improvement in RMSE compared with the raw satellite irradiance, but there is a risk of increased absolute bias when the distance from a ground station is very large. The implications of this for resource estimation are discussed.


The main contributions of this work can be summarised into three main areas. Firstly, in bias-‐ correcting satellite irradiance against ground measurements, there is a performance gain in using GAM regression with smoothing splines, together with interactions between zenith angle and clear sky index, compared with fourth-‐order polynomial models (Figure 12 and Figure 13 for GHI; analogous figures for DNI can be found in Davy et al. 2015). The trade-‐off is an increase in complexity – the lack of simple set of coefficients which can be tabulated. Given the importance of accurate solar radiation assessments, this is likely to be a worthwhile trade-‐off in most applications. Whether this performance gain has practical significance when combined with weather model forecast error is something that needs to be further assessed.

Secondly, including solar irradiance derived from the CCAM weather model as an additional predictor provides a further increase in RMSE performance. The reasons for this may be due to the instantaneous nature of the satellite irradiance which, when integrated spatially over a grid square, has inherent random deviation when compared with the nearest ground station. The analysis of conditional variance from the regression models suggests that satellite irradiance has highest variance for large zenith angles, and that the addition of CCAM irradiance as a predictor can reduce variance under some conditions and when it is in broad agreement with the satellite.

Thirdly, we explore the potential for spatial interpolation of these regression models and find that there are improvements in RMSE compared with the un-‐ corrected satellite irradiance. However, there is some uncertainty in the bias performance depending on the distance to the nearest ground station. For GHI, there was a reduction in RMSE at most sites, but an increase in absolute bias at one-‐third of locations, as a result of the spatial interpolation of the regression functions over long distances. The interpolation distances involved in this exercise were very large. The closest pair of ground stations is about 300 km apart. Three ground stations are more than 700 km remote from the nearest other station. This exercise therefore provides no information on extrapolating the regression coefficients in a small region surrounding the ground station, and the decay in performance with distance. It is therefore not possible to specify the optimal distance from ground station over which this technique could be applied. The possibility of an increased bias with respect to the satellite-‐ derived irradiance would be problematic for solar resource estimation because the long term mean is an important measure in quantifying the resource. It would therefore be unwise to employ this method operationally in regions that are a long way from a measurement station. However, the method should work quite well within some region around the measurement stations, where the regression functions remain valid and the atmospheric turbidity properties are similar.

Of the stations where bias increases, it is difficult to isolate the reasons why this has occurred. Interpolating across different climate zones is clearly problematic. In this work the use of a constant turbidity value (ignoring daily and seasonal fluctuations) is a source of error when interpolating between climatic types. For instance, Wagga Wagga is quite different climatologically to its two closest neighbours, Mildura and Cobar. The short measurement record of some stations (Woomera, Townsville, Longreach and Cobar) is also a possible factor in model error, since in this work the regression models developed from their data are applied across the years prior to the stations being operational.

Overall, the work here shows that there are opportunities for substantial performance gains through bias correction of the satellite irradiance via additional ground stations. These additional ground stations could be operated over short-‐term campaigns. The work in Davy and Troccoli (2014)


provides an objective framework for establishing new ground stations in addition to the ones recently operated by the BoM. Combining the satellite-‐derived irradiance with weather model irradiance, using appropriate regression models calibrated to the ground stations, can provide additional accuracy.

Figure 12 – Change in mean cross- validated‐ RMSE when CCAM is included as predictor for GHI.

Figure 13 – Leave- one-‐ out‐ cross validation of spatial interpolation. RMSE change, GAM vs raw satellite irradiance for GHI.

Interannual variabilityFinancing for solar projects is based on estimates of the annual production and its uncertainty. Generally speaking, a project will need conduct on-‐site monitoring for a year or so, and then infer the past solar irradiance by correlation with another data source, such as satellite or weather model.


Usually a figure is produced known as P90, which is the level of production that we are 90 per cent confident of exceeding in any given year.

The length and the quality of the reference data both impact on the P90 calculations. As the length of the reference dataset increases, there is greater information about the long term interannual variability in solar production.

Crucially, the reference dataset needs to be consistent over time. Systematic changes or drifts in the reference data due to calibration or instrumentation can introduce large unspecified errors in the P90 estimates. As shown in the previous section, satellite-‐derived data is more subject to changes than CCAM is. This is due to the fact that while solar radiation derived from satellite relies on a limited stream of (satellite) observations (essentially cloud images), CCAM is constrained by a much more abundant set of (satellite and ground) data, encompassing a wide range of meteorological variables.

In this part of the report we investigate the use of CCAM as a long term reference for solar P90 calculations.

Estimating measurements using reference data

We will assume that ground measurements have been taken spanning one calendar year. We then try to model these measurements using either CCAM or satellite irradiance as predictors. In fact we use the very same models used to estimate the bias as a function of clearness index and cosine of SZA, i.e. generalised additive models based on hourly observations. These are capable of fitting smooth nonlinear functions to the data. We then apply this model to the remaining calendar years and validate it using the ground station data.

These calculations were performed for all measurement sites on the mainland, and all available calendar years. The example results are presented here for GHI.

Figure 14 shows calculations for Wagga Wagga. The statistical models are calibrated in 2008 (left plot) and 2010 (right plot). In both cases, a statistical model based on CCAM seems to be fairly capable of capturing the interannual fluctuations at Wagga Wagga. At least, the fluctuations are not dissimilar to those produced using the satellite data.

In general, CCAM seemed to perform quite well at the inland measurement sites. At some coastal sites and some of the subtropical/tropical sites, CCAM–based estimates seemed to depart from the satellite. Figure 15 shows some results for Rockhampton, where 2004 and 2010 were used for calibration in the left and right plots respectively. It can be seen that 2010 was a low year. If 2010 is used for calibration, the CCAM-‐based estimates don’t capture the interannual fluctuations as well as the satellite estimates. Similarly, when 2004 is used for calibration, the low of 2010 is not well predicted by CCAM.

From this exercise it can be concluded that CCAM may be a useful tool for the calculation of P90 solar production at inland sites. The main advantage of CCAM is in the additional years (1979—1989) prior to the beginning of the satellite product in 1990. These extra years provide valuable information about interannual fluctuations in solar energy production. Further calculations are required to quantify the likely reduction in uncertainty, and therefore the potential increases in P90 values, as a result of including these extra years.


Figure 14 – Using CCAM (red) and Satellite (green) to infer interannual variablility in mean GHI at Wagga Wagga. Left: 2008 used for calibration (as indicated by the plus sign). Right: 2010 used for calibration.

Figure 15 – Using CCAM (red) and Satellite (green) to infer interannual variablility in mean GHI at Rockhampton. Left: 2004 used for calibration. Right: 2010 used for calibration.


Validating the Improvements in NREL’s National Solar Radiation Data BaseThis section presents the validation results of the current NREL National Solar Radiation Database (NSRDB) dataset compared to ground measurements. The NSRDB has become the industry-‐standard for establishing long-‐term solar resources. Data products such as a Typical Meteorological Year (TMY), Typical Direct Year (TDY), and Typical Global Year (TGY) have been produced from the NSRDB dataset. NSRDB and TMY variants are the basis for system performance and economic models. The recent NSRDB update, which is used in this validation, was developed by NREL in collaboration with the University of Wisconsin and NOAA to produce a physics-‐based satellite-‐derived solar radiation data. The satellite-‐based data are available every 30 minutes for 4-‐km-‐resolution pixels. The available data fields include solar radiation and meteorological information that can be used in economic and production models such as the System Advisor Model (SAM). The current release of the data set encompasses the years from 2005 to 2012. The model uses a two-‐stage scheme that retrieves cloud properties and uses those properties in a radiative transfer model to calculate surface radiation. The cloud properties are generated using the AVHRR Pathfinder Atmospheres-‐ Extended (PATMOSx) algorithms. Our original use of the SASRAB model for solar resource assessment resulted in an underestimate of DNI and GHI during clear-‐sky conditions and especially during when the solar zenith angle was low. This was because the SASRAB algorithm uses a background reflectance field to calculate solar insolation for the current image, which is generated by recording the second-‐darkest value for each image pixel from the previous 28 days. This is a visible channel measurement that is adversely affected by the Earth’s surface reflectivity. Thus, desert environments, snow, or any high-‐albedo conditions that existed at the time of the background calibration forces the model to assume it was actually caused by high atmospheric aerosols. This results in the SASRAB clear-‐sky shortwave radiation results being lower than the actual surface radiation. At the time of this algorithm’s development, in the early 1980s, aerosol content over land via satellite imagery did not exist; however, with satellite aerosol data now available, NREL used this opportunity to replace the SASRAB model with newer clear sky radiative transfer models. These models use high resolution Aerosol Optical Depth (AOD) data derived from MODIS/MISR and Aeronet network ground stations. The time-‐series irradiance data for each pixel is quality-‐checked to ensure that they are within acceptable physical limits and gaps were filled. The GOES-‐East satellite measurements are shifted by 15 minutes in time from the GOES- W‐ est satellite. To provide the data on a uniform temporal map the GOES-‐East data had to be shifted in time by 15 minutes. Finally, the GOES East and West data sets were blended to create a contiguous national dataset of irradiance data for the period from 2005 to 2012.

The purpose of this study is to investigate the performance of the NSRDB satellite derived data compared to ground observations. The comparison also includes scenarios of different sky conditions where all times are clear and cloudy sky conditions. The comparison was conducted for the period covering 2005-‐2012 for 8 ground stations.

The half-‐hourly averaged comparison results (Table 1) show that NSRDB satellite derived data have a systematic (bias) difference under clear sky condition ranging from 17 to 37 W/m2 for GHI and -‐1 to 34 W/m2 for DNI. However, under cloudy sky condition the bias ranges from -‐ 29 to 11 W/m2 for GHI and - 6‐ 8 to 30 W/m2 for DNI. The random errors (RMSE) under clear sky condition ranging from 36 to


68 W/m2 for GHI and 78 to 169 W/m2 for DNI, and under cloudy conditions the ranges are 98 to 135 W/m2 for GHI and 178 to 275 W/m2 for DNI.

Overall, the results of the comparison demonstrate good agreement between the current NSRDB datasets and surface measured solar radiation. The biases and random differences are significantly improved when compared to the previous version of the NSRDB. These improvements are attributed to improvement in both the models and inputs to those models. This research and the results are especially relevant to ARENA for three reasons:

a) The Australian BOM is adopting the same framework from producing satellite-‐based solar radiation datasets as NREL. Future collaborative research under ARENA between BOM, CSIRO and NREL is expected to significantly improve the capability to develop solar projects with lower production uncertainty in Australia.

b) NREL is working on developing example aerosol datasets for Australia as part of the project and this dataset will provide a useful input for BOM.

c) NREL’s development and use of newer radiative transfer models will be especially useful for ARENA research and development as these models can be used directly by Australian organizations such as CSIRO, BOM and UNSW.

Table 1 – GHI and DNI Half-‐hourly Statistics results (MBE, MAE, RMSE and R2) for the 7 SURFRAD locations in W/m2

Sky condition

Site Code

TBL DRA GCM PSU BON FPK SXF

GHI DNI GHI DNI GHI DNI GHI DNI GHI DNI GHI DNI GHI DNI

clear MBE 17 2 20 14 37 34 30 29 22 - 1‐ 26 27 22 13

MAE 22 56 23 42 41 103 37 119 30 109 29 72 28 89

RMSE 39 98 36 78 68 153 64 169 50 148 44 117 45 132

R2 0.98 0.43 0.99 0.62 0.95 0.46 0.95 0.37 0.97 0.40 0.98 0.45 0.97 0.40

cloudy MBE - 29‐ - 68‐ - 16‐ - 41‐ 11 24 4 30 1 13 - 15‐ - 14‐ -‐6 10

MAE 87 194 67 175 69 113 76 125 66 114 64 145 63 119

RMSE 135 275 107 249 108 178 117 201 101 182 100 215 98 187

R2 0.77 0.40 0.85 0.39 0.83 0.56 0.77 0.43 0.83 0.53 0.81 0.44 0.82 0.55

All MBE - 19‐ - 52‐ 3 - 11‐ 22 28 11 30 8 9 -‐4 - 2‐ 3 11

MAE 73 163 43 103 58 109 66 124 54 112 54 125 52 109

RMSE 121 247 77 178 94 169 106 193 89 172 88 193 85 171

R2 0.84 0.61 0.94 0.74 0.91 0.77 0.85 0.68 0.90 0.75 0.89 0.70 0.91 0.77


Synthetic high temporal resolution solar radiation time seriesHigh temporal resolution datasets of solar irradiance are often required for an appropriate assessment of the expected energy to be produced by a solar power plant, which is critical for their planning, project finance and due diligence process. This is because rapid changes in solar power output due to intermittent clouds are a common occurrence, and it is important to understand these rapid changes in order to effectively integrate a solar power plant into the electricity grid.

Ground station observations are one potential source of high temporal resolution data, but their networks are usually sparsely and irregularly distributed such that, for any given location of interest, it is unlikely that a suitable nearby site would be available. Another potential data source are gridded datasets derived from satellites or numerical weather prediction (NWP) models. These datasets benefit from a continuous spatial coverage, and are often available for a long time period. However, the temporal resolution is usually hourly at best. For example, in Australia an hourly satellite-‐derived solar radiation dataset produced by the Bureau of Meteorology is available with a spatial resolutionof 0.05°x0.05°, or about 5x5 km2, while one- m‐ inute resolution ground station data series are oftenseparated by hundreds of kilometres.

The aim of this stage of the project is therefore to provide a methodology and a tool for generating high temporal resolution solar radiation time series for any location in Australia.

The integrated dataset developed in the previous stage of this project is used as the base data for the high temporal resolution data. This dataset combines satellite-‐derived data with numerical model and ground station data in order to generate a more accurate and complete hourly gridded

dataset. The integrated dataset has a spatial resolution of 0.1°x0.1° (ca 10x10 km2).

In order to develop methods for generating high temporal resolution time series, data from ground measurement stations have been used together with statistical modelling techniques which combine the high temporal resolution station data with the high spatial resolution integrated dataset. The approach used has been developed from that used by NREL who used the variability in the spatial and temporal datasets, and the probabilistic relationship between the two, to develop algorithms to model sub-‐hourly irradiance data in western United States.

Thus, a tool is developed that provides realistic solar radiation time series at a generic location in Australia with the necessary time resolution for an accurate assessment of the energy production by a solar power plant.

Model development

The approach used involves making inferences about the temporal variability of solar radiation within each hour from the spatial variability of the mini-‐grid of values from the gridded dataset for that hour. The justification for this approach is that a higher spatial variability of cloudiness indicates that intermittent cloud conditions are likely to be prevalent, which would lead to higher temporal variability at the point of interest. There is a probability that clouds within 40 km may pass across the sun within the following hour.

In order to make comparisons between the spatial and temporal mean and variability, the distance-‐ weighted mean and standard deviation were calculated for each hour of the mini-‐grid time series.


The hourly mean and standard deviation of the 1- m‐ inute observed CI data were also calculated. The comparison was restricted to hours with solar zenith angle (SZA) < 80° and less than 10% missing observed data. There is a strong positive relationship between the spatial and temporal means, and a moderate positive relationship for the variability. These relationships give added confidence to the approach taken. Alice Springs is the sunniest location, which also leads to it having the lowest variability and the highest correlations.

Assessment of Results

The results were verified using a number of different metrics, as well as by visual inspection of plots comparing observed with modelled time series. The metrics were designed to assess how successful the methods are in meeting the aims of replicating the statistical properties of observed high temporal resolution solar data, including autocorrelation and the probability distribution, and in maintaining an accurate overall solar resource estimate, including the seasonal cycle and spatial differentiation of the resource. Verification results were used in the development of methods to compare alternative approaches. Results are shown here in order to provide an assessment of the quality of the final methods used.

Figure 16 shows time series of the synthetic solar radiation compared with the observed data for example hours at Wagga Wagga. Plots are representative examples where the observed class was the same as the modelled class. It can be seen that the modelled series are generally similar in character, level and variability to the observed series. Greater differences often occur when the modelled class is different to the observed class, although good results can still be obtained for similar classes.

Subsequent results were converted back from CI to GHI, as this is the quantity of interest. Figure 17 shows that the total annual GHI values from the synthetic series match well to observed values, with very similar interannual variability and correlation coefficients ranging from 0.79 to 0.97. Monthly totals, normalised by the monthly mean for the calendar month, also match very well with correlation in excess of 0.85 for all sites. These relationships are due to the methods which link the synthetic series to the observed gridded data each hour. There is, however, a slight bias in the overall solar resource in the synthetic series compared to the observed, particularly at Adelaide where the modelled total is 3.5% lower than that observed. Part of this bias is probably caused by a bias in the satellite-‐derived gridded data, which for Adelaide is 1.4 % lower than the ground station data.

Discussion

A methodology has been described which has been shown to generate realistic high temporal resolution time series of solar radiation for any location in Australia. The methods have the advantage that they do not require the availability of any local ground station data, and only rely on the availability of gridded datasets for which operational products derived from satellite images exist covering Australia, as well as other areas including Europe and North America.

The methods model the unique characteristics of solar radiation by classifying each hour into a typical weather situation. The classification makes use of the spatial variability of the gridded input dataset, and is calibrated to observed one- m‐ inute datasets from four ground stations. The resulting time series are semi-‐synthetic, in that they are based on the real cloud information incorporated into the gridded datasets, and tied to the solar radiation value for the nearest grid point on each


hour. This leads to high temporal resolution time series which have been shown to match closely the observed monthly and interannual variability. The synthetic part of the high resolution time series is the minute-‐to- m‐ inute variability within each hour. The low variability situations are modelled using an autoregressive process which is applied to ramps of clear-‐sky index. The autoregressive model and its innovations are based on empirical analyses of autocorrelation and the probability distribution of ramps, grouped by class, to ensure that the time series produced are realistic. High variability situations are modelled as a transition between states of clear-‐sky conditions and different levels of cloud opacity. Again, the methods are calibrated to ground station observations through an empirical analysis of state durations and probabilities.

There is limited availability of observed high temporal resolution, high quality, long period solar radiation data across Australia. This means that it is difficult to carry out a spatial calibration of the methods due to the large distances between available ground stations. The approach taken was to combine together data from four representative stations in order to tailor the methods to Australian conditions, for characteristics such as ramp distribution and class probabilities by spatial segment. However, there are climatological differences between the stations which lead to some difference in the characteristics being modelled. A potential improvement to the methods described would be to make a full assessment of the spatial variability of these characteristics using all available data, for example grouped by climate zone or using spatial interpolation techniques.

Other alternative methods for generating simulated time series could also be explored further, for example using a Markov process to model the dependence between adjacent observations or the Iterative Amplitude Adjusted Fourier Transform (IAAFT). The Markov process has been shown to produce good results, but requires a long series of training data for robust results. The IAAFT method is able to make surrogate data which has the same autocorrelations and probability distribution as the data. However it is uncertain whether it could replicate the particular characteristics of solar radiation data, which the current methods have done by careful consideration of modelling the different conditions which affect solar radiation and its short-‐term changes (ramps).


Figure 16 – Examples comparing observed and modelled time series of GHI CI for Wagga Wagga; times are in AEST


Figure 17 – Observed and modelled annual total global horizontal solar radiation (kWh) for four stations, 2003/4 to 2012/13 (years start from September and the end year is shown)

Meetings and Stakeholder Engagement

Regular project meetings as well as stakeholder workshops were integral to the execution and success of the project. Details of the stakeholder workshops can be found in the technical final project report. A Solar Forecasting & Storage Stakeholder Workshop, where solar radiation and other meteorological data relevant for renewables were also central to the discussion, was also recently held (10 August 2015).


TransferabilityThe three main benefits of this project have been:

• To have markedly advanced the science of solar radiation, including its monitoring, modelling, prediction, and application to solar energy devices to assist Australia to establish itself as a worldwide leading player in this field;

• To have provided a significant contribution to bridging the gap between the meteorology community and the Australian solar community, by providing radiation data (observations, simulations and forecasts) that is critical for modelling solar power stations and predicting their annual output.

• To have prepared the ground for potentially successful commercial opportunities in the linkage between the meteorology and solar energy communities.

This project has been supporting the anticipated rapid growth of the Australian solar industry through access to more reliable solar radiation data. Access to high quality radiation data enables financiers and government approvers to proceed more easily and rapidly with new developments. For Government bodies, the availability of the solar resource data and output prediction tools, provides a technical basis for developing policies that foster solar technology deployment. Developing solar power stations is assumed to be part of Australia’s energy mix in reducing greenhouse gas emissions.

Conclusion and next stepsThe aim of the 36- m‐ onth, 1.4 million, project Integrated Solar Radiation Data Sources over Australia (ISRDSA) was to provide solar power developers and installers with an improved solar data resource and an enhanced understanding of its uncertainty by exploiting three sources of solar radiation data: ground based, satellite- ‐ derived and atmospheric model output. The project, co-‐funded by ARENA, was coordinated by CSIRO and was executed in partnership with the BoM and the NREL.

This project had three main objectives, all of which were achieved to a high standard:




This integrated solar radiation data set has been generated through the combination of in situ observations, satellite-‐derived data and high-‐spatial resolution model data, along with uncertainty estimates. This dataset has been used for the solar resource mapping of a proposed large-‐scale solar farm. A manuscript is being finalised for publication in an international journal (Davy et al. 2015).





In addition to these tasks, a major complementary task has been identified as providing a critical contribution towards a more effective project implementation and delivery. This is the development of a Solar Radiation Database, including quality control flags. With this solar radiation database we have made marked advances towards gathering solar radiation data coming from different ground station sources collected by research institutes, government organisation and commercial companies, whether for solar power, agriculture or other purposes. This database has been designed to also include solar radiation from numerical weather models and those derived from satellite at the locations for which ground stations are available. The ultimate aim is to create a repository, together with a web interface, capable of dealing with all these heterogeneous ground station observations and managing the problem of different format, quality, spatial and temporal resolutions from each of the data sources. Building a database like this is a complex technical endeavour. Thus, with this project only some of the solar radiation database building blocks have been realised.

A key outcome of this project has been the development of uncertainty measures for solar radiation data accuracy with important implications for project financing and for reducing the cost of incorporating solar energy into the grid. This project will benefit considerably from the experience of NREL experts who are working on analogous problems for the USA.

The two stakeholder workshops carried out with this project, as well as the many interactions with industry experts at other events, have indicated a strong need for more accurate solar resource assessment data. A tangible demonstration of this need is service work provided by CSIRO to a solar power developer for the resource assessment at an Australian site using the data developed with this project. In addition to our interactions with Australian-‐based industry experts, our involvement in international leading-‐edge activities such as the International Energy Agency (IEA) Task 46 ‘Solar Resource Assessment and Forecasting’ and the European COST Action ‘Weather Intelligence for Renewable Energies’ (WIRE) have allowed our research to be showcased at international forums and benchmarked against analogous work produced by other world experts in this field. This process has allowed us to further consolidate our understanding of the potential for the commercialization of these data.

The benefits of this project relate to the availability of new uncertainty information for solar radiation data. Such uncertainty estimation naturally yields to more reliable data. This has been achieved by integrating numerical weather predictions and satellite data and through the in-‐depth analysis of several sources of radiation data. Given the new technology and terminology introduced with this project, the accrual of such benefits is going to be part of an ongoing process.

The key barrier to continue to accrue these benefits is in the delivery and dissemination of the data and associated information. Discussions have started with the Australian Renewable Energy Mapping Infrastructure (AREMI) project to make a version of the solar radiation data developed with this project to be available to the public through the AREMI’s web-‐interface. At present the AREMI’s portal appears to be the best vehicle for the sustainability of the provision to the public of the solar radiation data developed with this project even though a web portal specifically for solar radiation


and other meteorological data, to complement the current Solar PV mapping portal, may be also developed.

While a version of the integrated solar radiation data set is going to be made publicly available, higher resolution data, including solar energy yields for specific devices and locations, is available through a commercial service. In order to ensure that the tools developed with this project are directly relevant to the solar power industry, CSIRO will continue to meet and discuss with industrial counterparts. CSIRO might also engage with an industrial advisor to assist in the planning and set up of a commercial entity to provide services related to the output of this project. A commercialisation plan for the ISRDSA project can be found in the technical final project report.

ReferencesDavy, R. J. and Troccoli, A., 2014. Continental- scale spatial optimisation of a solar irradiance ‐monitoring network. Solar Energy 109, 36–44.Davy, R. J., Huang J. and Troccoli, A., 2015. Integration of hourly solar radiation sources: ground station, satellite and numerical weather prediction model. Manuscript under revision.

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Lessons Learnt

Lessons Learnt Report: Availability of quality solar dataProject Name: Integrated Solar Radiation Data Sources over Australia

Knowledge Category: TechnicalKnowledge Type: TechnologyTechnology Type: Solar PVState/Territory: National

Key learningHigh-‐standard solar radiation-‐based tools for the solar industry require quality observations. In principle a number of solar radiation data sources are available – e.g. solar radiation collected for agriculture purposes. In practice, however, their quality is disparate. So, although these data can be used, the effort required to quality control and assure them should not be underestimated.

Implications for future projectsGiven the scarcity of solar radiation monitoring sites, it would be very beneficial for the solar industry to be able to collect solar radiation observations from as many ground stations as practical and to assemble them into a self-‐consistent database so as to improve solar resource assessments. Therefore, it needs to be appreciated that while not naturally lending itself to innovation, work devoted to data quality control and assurance, as well as to properly catalogue the data, is key.

Knowledge gapNone

Background

Objectives or project requirements

The plan was to collect solar radiation observations from as many ground stations as practical in order to have a stronger base than just the BoM’s observations for the development of solar radiation tools in this project.

Process undertaken

As documented in the report, we have gathered solar radiation data from different ground station sources collected by research institutes, government organisation and commercial companies, whether for solar power, agriculture or other purposes.

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Lessons Learnt Report: Analysis of direct beam dataProject Name: Integrated Solar Radiation Data Sources over Australia

Knowledge Category: TechnicalKnowledge Type: TechnologyTechnology Type: Solar PVState/Territory: National

Key learningThe dearth of quality direct beam (the direct normal irradiance, DNI) observations together with the higher complexity of these data compared to the more standard global horizontal irradiance (GHI) have meant that more time had to be spent in cleaning and interpreting these direct beam data.

Implications for future projectsFocussing only on direct beam rather than including also GHI may be a way to plan future projects. However, since most of the lessons learned with GHI can be transferred to direct beam, allowing for more project resources would be a better way to ensure that more robust direct beam analyses and products are achieved.

Knowledge gapA fuller analysis of direct beam, particularly in the context of high resolution (1 min) time series, could not be carried out in a satisfactory way.

Background


The project plan was to develop analogous tools for both global solar radiation and direct beam. Given the complexities of handling direct beam data, but also due to the predominance of PV – for which global radiation is sufficient – compared to concentrating solar power plants – for which direct beam is critical – a slight priority was given to tools for global radiation data. However, approaches developed for global radiation are normally transferrable to direct beam.

Process undertaken

Whenever possible analysis and developments for both global solar radiation and direct beam were carried out in parallel.

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Lessons Learnt Report: Delays in signing the agreement between CSIRO and NRELProject Name: Integrated Solar Radiation Data Sources over Australia

Knowledge Category: TechnicalKnowledge Type: Human ResourcesTechnology Type: Solar PVState/Territory: Non-‐state specific

Key learningThe signing of the agreement between CSIRO and NREL, a subcontractor to CSIRO in the project, took much longer than anticipated. Such unexpected delay, due to the complexity of the two organisations involved, led to both lengthy negotiations and delays in the execution of the project.

Implications for future projectsIt is difficult to anticipate legal obstacles in specific project agreements but circulation of terms and conditions ahead of the planned exchange of contracts could help iron out potential legal issues in time for the execution of the project.

Knowledge gapNone

Background


The agreement between CSIRO and NREL, a subcontractor to CSIRO in the project, should have been signed at the start of the project. The ISRDSA project commenced in September 2012 but the agreement with NREL was only officially signed in early 2015.

Process undertaken

Many email and phone communications, including lengthy negotiations had been necessary in order to reach an agreement between CSIRO and NREL. However, despite the agreement being finally signed in early 2015, NREL had managed to contribute to ISRDSA ahead of that date, when it was apparent substance issues had been resolved.

Documents

Home - Australian Renewable Energy Agency - Date … · Web viewA key outcome of this project has been the development of uncertainty measures for solar radiation data accuracy with