AustralianSolarEnergy ForecastingSystem* …...3! ExecutiveSummary*...

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Australian Solar Energy Forecasting System

Final report: project results and lessons learnt

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

Project commencement date: 7th January 2013 Completion date: 30th May 2016

Date published:

Contact name: John Ward

Title: Dr

Email: [email protected] Phone: +61 2 4960 6072

Website: http://arena.gov.au/project/australian-solar-energy-forecasting-system-asefs-phase-1/

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

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

Project Overview ................................................................................................................................... 5

Project summary ............................................................................................................................ 5

Project scope ................................................................................................................................. 9

Outcomes .................................................................................................................................... 13

Transferability .............................................................................................................................. 39

Conclusion and next steps ........................................................................................................... 39

References ................................................................................................................................... 41

Lessons Learnt ..................................................................................................................................... 42

Lessons Learnt Report: Delays with Solar Flagship program ....................................................... 42

Lessons Learnt Report: Unexpected rapid increase in rooftop solar installations ...................... 43

Lessons Learnt Report: Lack of solar forecast data thorough the Researcher Access ................. 44

Lessons Learnt Report: Delays in signing the agreement between CSIRO and NREL .................. 45

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Executive Summary The 30-‐month, 7.6 million, project Australian Solar Energy Forecasting System (ASEFS) addressed the issue of solar power integration into the grid by means of a two-‐pronged approach:

1. The development of an operational infrastructure component, also referred to as ASEFS, and to be installed at, and operated by, the Australian Energy Market Operator (AEMO)

2. The development of an advanced forecasting research program, via the production of world leading solar forecasting techniques and tools aimed at improving the forecasts produced by the operational system and at creating national capability in the area of solar irradiance and power forecasting

Solar generating capacity in the National Energy Market (NEM) 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). Solar forecasting is therefore essential to assist with the provision of accurate supply and demand forecast models necessary to increase commercial viability and ensure stability of the electricity grid.

The project, co-‐funded by the Australian Renewable Energy Agency (ARENA), was coordinated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). The development of the operational infrastructure component was undertaken by Overspeed GmbH, a company involved in development of Australian Wind Energy Forecasting System (AWEFS), and AEMO’s Information Management and Technology (IMT) department. The development of an advanced solar forecasting research program was contributed by the Bureau of Meteorology (BoM), the University of New South Wales (UNSW), the University of South Australia (UniSA), the US Renewable Energy Laboratory (NREL) and CSIRO in close consultation with AEMO.

The state of solar energy forecasting development is such that only basic techniques, mostly developed overseas, were ready for implementation in an operational ASEFS. Developed around such basic techniques, the ASEFS project successfully installed a solar forecasting system, also called ASEFS, at AEMO, manager of the NEM. This is enabling the enhanced integration of solar energy generation at all time scales, from 5 mins to 2 years, into the national grid and is allowing operators of larger systems to participate in the NEM. This system has been configured as an extension to AWEFS, which has been successfully operating within AEMO market systems since 2008. Without such forecasting systems wind and solar renewable energy generation would be subject to increasing levels of curtailment, undermining both their viability and their significant contribution to greenhouse gas reduction.

Up to a few months before the end of ASEFS, June 2015, none of the large-‐scale solar farms (larger than 30 MW) were actually commissioned, meaning that they were not reporting their SCADA data and as a consequence no solar forecasting was available for such planned farms. In the absence of registered large-‐scale solar generators in ASEFS, the solution was to run the solar forecasts in a non-‐production environment using two small-‐scale test solar farms to exercise the forecasting models. The Black Mountain (Canberra) and the Norwest (Sydney) test solar farms replicated (scaled) fixed,

Australian Solar Energy Forecasting System|

non-‐tracking solar generators with scaled energy conversion models, providing scaled “MW” output and onsite weather data to ASEFS. The normalised mean accuracy error for the different time horizons were tested against the required system specifications and the results were within the ASEFS agreed accuracy targets.

One of the key outcomes of the ASEFS project is that it has allowed to advance, and in some cases to create, a solid knowledge in solar forecasting for Australian Institutions as well as NREL. Strengthening of expertise in the very active area of solar forecasting requires further long-‐term investments without which Australia will not be competitive in supporting the solar industry. In a way, this has happened already with the reliance of AEMO on the services of Overspeed. However, there are many other commercial applications and opportunities which the Australian research community could tap into (e.g. interactions between battery storage and PV panels) and for which the acquired expertise could be gainfully applied. At the same time, large gaps in funding opportunities could lead to a migration of expertise into other areas of research/industry, something that has already happened.

Conversations have already started around extending the R&D work developed under ASEFS by combining the various techniques which have thus far been developed in isolation. For instance, tracking of clouds from sky cameras and satellite could be merged to provide a more comprehensive picture of cloud evolution. Work on a proposal to provide advanced solar forecasting solutions to the solar and battery storage industries is underway.

A cost-‐benefit analysis for the implementation of new forecasting improvements in AEMO operational system could not be carried out to lack of solar forecasting data produced by AEMO’s ASEFS. Despite several iterations with the technical people involved in the access to the forecasting data, these were still unavailable at the time of completion of the project. To the best of our knowledge, this difficulty arose from the fact that until very recently no solar power plant larger than 30 MW was operating. And although ASEFS has been implemented, the fact that it has been tested only on the two small test solar farms has meant that ASEFS could not run on the AEMO’s operational machines. Since the researchers access is part of AEMO’s ASEFS, our understanding is that the issue of making solar forecasting data available will continue to be pursued until resolved.

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Project Overview

Project summary

The 30-‐month, 7.6 million, project Australian Solar Energy Forecasting System (ASEFS) addressed the issue of solar power integration into the grid by means of a two-‐pronged approach:

• The development of an operational infrastructure component, also referred to as ASEFS, and to be installed at, and operated by, the Australian Energy Market Operator (AEMO)

• The development of an advanced forecasting research program, aimed at improving the forecasts produced by the operational system and at creating national capability in the area of solar irradiance and power forecasting

The project, co-‐funded by the Australian Renewable Energy Agency (ARENA), was coordinated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). The development of the operational infrastructure component was undertaken by Overspeed GmbH, a company involved in development of Australian Wind Energy Forecasting System (AWEFS), and AEMO’s Information Management and Technology (IMT) department. The development of an advanced solar forecasting research program was contributed by the Bureau of Meteorology (BoM), the University of New South Wales (UNSW), the University of South Australia (UniSA), the US Renewable Energy Laboratory (NREL) and CSIRO in close consultation with AEMO. The project structure along with the partners’ specific tasks are illustrated in Figure 1.

Figure 1 – ASEFS project structure. NWP stands for Numerical Weather Prediction, PV for PhotoVoltaic, ECM for energy conversion model, and CSP for Concentrating Solar Power.


The ASEFS project commenced on 7th January 2013. Since then there had been some delays, particularly with the signing of the agreement between CSIRO and NREL. As of June 2014, however, NREL consistently contributed to ASEFS, as have all other partners. Due to these delays, the project finished in June 2015, hence six months later than originally planned.

ASEFS successfully installed an operational system to predict solar power at the AEMO. ASEFS is enabling the enhanced integration of solar energy generation at all scales into the national grid and allows operators of larger systems to participate in the National Energy Market (NEM). This system has been configured as an extension to the Australian Wind Energy Forecasting System (AWEFS), which has been successfully operating within AEMO market systems since 2008. Without such forecasting systems wind and solar renewable energy generation will be subject to increasing levels of curtailment, undermining both their viability and their significant contribution to greenhouse gas reduction.

This ASEFS operational system provides an operational system that uses basic forecasting techniques to cover all the AEMO-‐required forecasting timeframes, which range from five minutes to two years. Also, the system was intended to cater for large-‐scale photovoltaic and solar-‐thermal plants as well as distributed small-‐scale photovoltaic systems. In the lead-‐up to the AEMO ASEFS go-‐live in May 2014, AEMO continuously monitored all intending large-‐scale solar generators. There were a number of intending solar generators that were due to be commissioned around June 2014 (hence the planned May 2014 go-‐live), but the change in policies around renewables resulted in a number of intending solar generators to be delayed, and some withdrawn. Even up to a few months before the end of ASEFS in June 2015, none of the large-‐scale solar farms (larger than 30 MW) were actually commissioned, meaning that they were not reporting their SCADA data and as a consequence no solar forecasting was available for such planned farms.

In the absence of registered large-‐scale solar generators in ASEFS, the solution was to run the solar forecasts in a non-‐production environment using two small-‐scale test solar farms to exercise the forecasting models. The Black Mountain (Canberra) and the Norwest (Sydney) test solar farms replicated (scaled) fixed, non-‐tracking solar generators with scaled energy conversion models, providing scaled “MW” output and onsite weather data to ASEFS. The normalised mean accuracy error for the different time horizons were tested against the required system specifications and the results were within the ASEFS agreed accuracy targets.

However from an operational point of view, without any registered semi-‐scheduled generators in the ASEFS, the system is restricted in the following:

• Ability to monitor the live forecasting performance of ASEFS against accuracy targets

• Availability of live, large scale solar generators data for researcher access

AEMO have also been working with intending solar generators to see if there is interest to register as a non-‐scheduled solar generator (i.e. less than 30MW rating), for proof of concept and readiness purposes.

The state of solar energy forecasting development is such that only basic techniques, mostly developed overseas, are ready for implementation in an operational ASEFS. This is why R&D is required on a range of forecast approaches necessary to improve on these basic techniques and


satisfy AEMO’s (as well as other users) full requirements for such a system in the longer term. Thus, through the advanced forecasting research program, ASEFS has been instrumental in advancing the development of leading-‐edge forecasting technologies. Such technologies range from:

• Improved radiative-‐transfer modelling for NWP – including cloud schemes and aerosols

• Short term satellite-‐based schemes using locally available real-‐time data, combined with NWP

• Short-‐term schemes based on on-‐site and peripheral met data and sky camera imaging

• Improved Concentrated Solar Thermal (CST) power conversion models

• Basic forecasting schemes based on more complex NWP fields (cloud character, synoptic class)

• Development of basic intermittency prediction schemes at all time scales

• Investigation and testing of distributed PV generation data sets, upscaling-‐schemes for distributed PV, testing and further development of distributed PV power prediction techniques

A number of research institutions – BoM, UNSW, UniSA, NREL and CSIRO, have provided technical input and undertaken research and development on enhancements to the system. Specifically, the involvement of NREL has helped strengthened collaboration between the world’s leading Australian and US researchers in the solar forecasting area.

It should be noted that forecasting solar irradiance and solar power is a relatively recent research area and one which is receiving a lot of attention internationally. Furthermore, solar forecasting is a very challenging area of research and application. Specifically the representation and the forecasting of cloud movements and aerosols concentrations, which are key to the proper estimation of solar irradiance on the ground, are amongst the most difficult scientific aspects of meteorology. Nonetheless, with ASEFS it has been demonstrated that the project partnership has produced very promising advances in this area of science, while also targeting industry requirements and applications. Specific findings and advances are documented in the Outcomes section.

Those techniques developed under ASEFS which will prove to provide better forecasts than the current basic techniques in the operational ASEFS system could be incorporated into the operational system. ASEFS should have also provided researcher access to allow for the benchmarking, by Australian institutions, of such advanced solar forecasting techniques against the current ASEFS system. However, the lack of operational ASEFS data – in turn due to the lack of large-‐scale solar generators – implied that researchers could not access the ASEFS system directly (as done with AWEFS). To alleviate the lack of direct connectivity, AEMO attempted to extract solar forecasting data from the test system so that ASEFS partners could assess their developments against these forecast data. This task however proved more difficult than planned and ASEFS data had not be released at the time of completion of this project. Lack of ASEFS data also implied that a proper cost-‐benefit analysis for the implementation of new forecasting improvements in the ASEFS system could not be carried out.


An important component of ASEFS has also been that of stakeholder engagement as a way to ensure relevance and quality of project outputs. One such mechanism has been the establishment of an Industry Advisory Committee whose role was to:

• Advise on requirements and issues for forecasting of solar output for large scale solar systems for both short (5 minutes ahead) and long term (2 years ahead) time scales

• Establish technical standards relating to solar farms • Agree on a governance for release of data to research organisations • Discuss the progress and testing of ASEFS, in particular the testing and tuning of the energy

conversion model for accuracy.

The committee, chaired by AEMO, met on two occasions and was participated by Clean Energy Council, Sunpower Corporation, Energy Network Association, Grid Australia, AEMO, ARENA and CSIRO.

In addition, in collaboration with the ARENA co-‐funded project Integrated Solar Radiation Data Sources over Australia, ASEFS organised 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.

Last but not least, ASEFS partners have produced more than 10 scientific publications for peer-‐reviewed journals and gave over 50 presentations at various public events, from conferences to industry meetings, to summer schools, to ARENA staff meetings. Details of publications and select presentations are available through the technical milestone reports.


Project scope

Electricity supply systems attempt to balance supply and demand requirements at time scales from seconds to years. Scheduling of generation assets is made against forecast demand. Increasing levels of non-‐forecast variable renewable generation increases the uncertainty in supply forecasts leading to inefficient generator scheduling and potentially resulting in system contingency services failing to cope.

Recent developments in solar power generation technology and costs, renewable energy targets, carbon pricing and government incentives have made utility-‐scale solar power generation a credible alternative to thermal and wind generation currently deployed in the NEM. Subsidies associated with programs such as the Solar Flagships are expected to drive investment in large-‐scale solar generation in the near term. Indeed, the main driver for the Australian Solar Energy Forecasting System (ASEFS) project was the need to have a forecasting system in place in time for the commissioning of large-‐scale solar farms. At the time of planning ASEFS two large-‐scale solar plants, supported by the federal Solar Flagship program, were due to be commissioned within the timeframe of development of ASFES. Subsequently, solar generating capacity in the National Energy Market (NEM) has been largely-‐unexpectedly growing to an estimated installed capacity exceeding 4,000 MW, particularly with the proliferation of grid-‐connected roof-‐top PV (see Figure 2). Solar forecasting is therefore essential to assist with the balancing of supply and demand.

Australia has a system where the market system is coupled to the physical network operation at the 5 minute level. The Solar Energy Forecasting Extension to AEMO Australian Wind Energy Forecasting System (AWEFS) was needed for the same reason the original wind forecasting system was introduced, namely because power plants larger than 30 MW are required to participate in the NEM. Increasing amounts of variable renewable energy eventually requires unsustainable amounts of expensive spinning reserve and frequency control services as well as threatening system security. Accurate forecasting can minimise these costs and maximise the amount of renewable energy which can be hosted in the electricity system. The importance of this issue was recognised by the incorporation of a forecasting requirement into the rules for the connection of intermittent renewable generators >30MW nameplate capacity in the NEM.

Figure 2 – Australian PV Institute (APVI) Solar Map (http://pv-‐map.apvi.org.au accessed 18 Jul 2015)


In recognition of the potential growth of the solar generation industry the Department of Resources, Energy and Tourism funded CSIRO in 2011 to undertake a feasibility study to investigate the extension of the AWEFS system to solar power generation. This study concluded that it was feasible in principle to extend the AWEFS system to solar but that there needed to be significant development of some key components (see Figure 3).

The current AWEFS system uses two weather forecast feeds from Numerical Weather Prediction (NWP) model output – one from the USA and the other from Europe – to drive the shorter-‐term forecasts. It employs up to 6 different wind power forecasting techniques in a modular arrangement with a decision engine to determine the most suitable combination for current conditions based on historical performance. These modules have well-‐established performance capabilities and most importantly, defined uncertainties. The ASEFS has adopted adopt an analogous approach.

Figure 3 – Proposed solar forecasting system in addition, and in parallel, to AWEFS

While a key objective of the project was the development of an operational solar forecasting system, it is was also recognized that emphasis should be placed on the development of improved forecasting techniques, therefore requiring extensive research work which would also lead to new skills and possible important innovations by the Australian research community. Given the requirements of systems such as AEMO’s to be able to produce forecasts at 5-‐minute intervals, and up to 2-‐year horizons the need for specialized forecasting tools is of central concern. Considering also the infancy of solar forecasting research, ASEFS provided a great opportunity for the Australian research community to acquire new skills and at the same time produce some great innovations with strong potential for commercialization into solar industry and energy markets more generally.

To exemplify the richness and complexity of approaches adopted to predict solar irradiance and power, Figure 4 shows the most common basic elements. All given modeling steps may involve physical or statistical models or a combination of both.

Forecasting surface solar irradiance is the first and most essential step in most PV power prediction systems. Depending on the application and the corresponding requirements with respect to forecast horizon and temporal and spatial resolution, different models and data sources are used (see Figure 5). NWP models are applied to derive forecasts of several days ahead. For very short-‐term horizons, irradiance forecasts may be obtained by detection and extrapolation of cloud motion, based on satellite images for forecasts of several hours ahead and on ground-‐based sky imagers for sub-‐hourly forecasts with a very high spatial and temporal resolution. Measured irradiance data, forming the


basic input to time series models, are another valuable data source for very short-‐term forecasting in the range of minutes to hours. Furthermore, measured data are required for any statistical post-‐processing procedure, applied to optimize forecasts derived with a physical model for a given location (Lorenz et al. 2015).

To derive PV power forecasts from the predicted global horizontal irradiance different approaches may be applied. Explicit physical modeling involves con-‐ version of the irradiance from the horizontal to the angle of tilt of the module plane, followed by the application of a PV simulation model. Here, characteristics of the PV system configuration are required in addition to the meteorological input data, implying information on nominal power, tilt and orientation of a PV system as well as a characterization of the module efficiency in dependence of irradiance and temperature. Alternatively, the relation between PV power output and irradiance forecasts and other input variables may be established on the basis of historical datasets of measured PV power with statistical or learning approaches. In practice, often both approaches are combined and statistical post-‐processing using measured PV power data is applied to improve predictions with a physical model (Lorenz et al. 2015).

Although the conversion from solar irradiance into solar power for PV systems is relatively straightforward, there are some technical aspects, which require close attention. In fact, normally measurements and predictions of solar irradiance are given on a plane parallel to the ground – the global horizontal irradiance (GHI) – in practice PV systems are on planes other than the horizontal one. So unless the global irradiance on the PV planed is directly measured, a rotation of the irradiance signal is normally required: this is a non-‐trivial transformation. Moreover, given the dependency of PV panels on other physical variables, particularly temperature but also dust, these quantities need to be measured and appropriately modeled in the solar forecast system.

While the major focus of the ASEFS project is on providing power forecasts for PV systems, the prediction of the direct beam (or direct normal irradiance, DNI) which is critical for Concentrating Solar Power (CSP) – also referred to as Concentrated Solar Thermal (CST) – systems will also be assessed. In fact, DNI is also an essential element in deriving the global irradiance component on PV planes when only GHI is available. The power conversion from irradiance (specifically DNI) to electricity in the case of CSP is much more complex than that for PV, due to intermediate conversion steps from radiation to thermal energy to electricity, and to storage mechanisms. In addition, the solar receiver can be highly non-‐linear, and this relation is also dependent on the type of CSP technology. It is apparent therefore that a considerable amount of research needs to be devoted to the understanding of the CSP, and to a lesser extent the PV, conversion process so as to produce the most accurate solar power prediction possible.


Figure 4 – Overview of key modelling steps in PV power prediction (from Lorenz et al. 2015)

Figure 5 – Solar forecasting techniques for different timescales. NWP stands for Numerical Weather

Predictions; SCADA stands for supervisory control and data acquisition


Outcomes The two main outcomes of ASEFS are:

1. The development of an operational system, with architectural extension to AWEFS system, installed at AEMO and able to provide power forecasting capability for solar power plants

2. A range of R&D activities with the main aim to improve upon the basic ASEFS operational system. Such R&D activities include:

a. Improved radiative-‐transfer modelling for NWP – including cloud schemes and aerosols

b. Short term satellite-‐based schemes using locally available real-‐time data, combined with NWP

c. Short-‐term schemes based on on-‐site and peripheral met data and sky camera imaging

d. Improved CST power conversion models e. Basic forecasting schemes based on more complex NWP fields (cloud character,

synoptic class) f. Development of basic intermittency prediction schemes at all time scales g. Investigation and testing of distributed PV generation data sets, upscaling-‐schemes

for distributed PV, testing and further development of distributed PV power prediction techniques

The development of the operational ASEFS In terms of operational forecasting system, AEMO requires solar energy forecasting with corresponding uncertainties at three timescales to match their scheduling requirements:

1. Short time frame -‐ 5 minute interval, 2 hour horizon, updated every 5 minutes (50% probability of exceedance required)

2. Medium time frame -‐ 30 minute interval, 8 day horizon, updated every 30 minutes (10%, 50%, 90% probability of exceedance)

3. Long time frame -‐ 30 minute interval, 2 year horizon, updated every day (10%, 50%, 90% probability of exceedance)

The ASEFS baseline system has been successfully delivered, installed and commissioned into the live market system at AEMO. The ASEFS has been developed by a sub-‐contractor, the German company Overspeed, namely the company which developed and installed AWEFS at AEMO. The ASEFS was live in the AEMO system by the target date of May 2014. Performance assessments of ASEFS have been provided at the 6–month and 11–month mark of the system operation. The initial performance of the system in the user–acceptance testing has exceeded the requirements outlined in the system specifications (see Table 1).

In the absence of any large power plants connected to the NEM, the ASEFS system has been developed and tested using a series of smaller PV plants, which also have quality meteorological data available – three from the Canberra CSIRO network and one installed at the AEMO operations centre in North–western (Norwest) Sydney. These provided 10–second data feeds to Overspeed in Germany. Two NWP model feeds, one from the US and the other from Europe (as with AWEFS), were used as main weather predictors to the ASEFS. After commissioning the live ASEFS at AEMO,


this has been operating with the two SCADA feeds – one from AEMO and one from the main CSIRO solar research facility at Black Mountain in Canberra.

At a first step of the evaluation process the performance of solar generation forecasts have been evaluated against the Solar Generation Accuracy Targets presented in Table 1. This Table includes targets for different horizons varying from 5 min ahead to 6 days ahead. The targets refer to individual solar farms. The performance of the forecasts is measured by the Normalised Mean Absolute Error (NMAE) measure. If the performance of the system satisfies all the targets corresponding to a specific milestone then the evaluation process is completed.

The ASEFS solution has been operating online since the industry go-‐live on the 30 May 2014. AEMO has been continuously monitoring all intending large scale solar generators from the May 2014 go-‐live period to date. There were a number of intending solar generators that were due to be commissioned around June 2014 (hence the planned May 2014 go-‐live), but the change in policies around renewables resulted in a number of intending solar generators being delayed, and some withdrawn.

As such, ASEFS is currently operating in a non-‐production environment using two small scale test solar farms to exercise its forecasting models:

• CSIRO – Black Mountain (Canberra) Solar Facility (1.5kW) • AEMO – Norwest Solar Facility (Sydney) (1.5kW)

Both solar facilities meet the requirements of the energy conversion model (ECM) and relay real-‐time output and weather data to ASEFS for forecasting. The Norwest and Black Mountain test solar farms replicate (scaled) fixed, non-‐tracking solar generators with scaled energy conversion models, providing scaled “MW” output and onsite weather data to ASEFS. The normalised mean absolute error for the different time horizons can be found in Table 2 and Figure 6 for Black Mountain found in Table 3 and Figure 7 for Norwest. The results are within the ASEFS agreed accuracy targets, also indicated as dotted lines in the two Figures.

Table 1 – ASEFS target specifications in terms of Normalised Mean Absolute Error (NMAE)

Timeframe GoLive+6months GoLive+11m

5 minutes ahead 18.5% 17.6%

1 hour ahead 19.3% 18.3%

4 hours ahead 20.7% 19.7%

12 hours ahead 22.4% 21.3%

24 hours ahead 23.5% 22.3%

40 hours ahead 24.4% 23.2%

6 days ahead 27.2% 25.7%


Table 2 – Accuracy in terms of NMAE for the Black Mountain Test Systems

5 minutes ahead

1 hour ahead (60 min)

4 hours ahead

(240 min)

12 hours ahead

(720 min)

24 hours ahead (1440 min)


6 days ahead (8640 min)

Mar-‐14 5.62% 6.45% 7.72% 8.34% 8.69% 9.19% 13.28%

Apr-‐14 5.13% 8.13% 10.00% 10.07% 10.42% 10.70% 13.56%

May-‐14 4.13% 6.36% 7.67% 7.68% 8.14% 8.28% 11.96%

Jun-‐14 4.65% 7.95% 9.80% 9.88% 10.14% 11.37% 13.66%

Jul-‐14 4.38% 7.10% 9.37% 9.19% 9.41% 9.44% 11.46%

Aug-‐14 5.74% 14.15% 15.84% 15.95% 15.70% 15.91% 16.26%

Sep-‐14 5.43% 9.49% 11.06% 11.37% 11.97% 12.04% 14.87%

Oct-‐14 3.91% 6.06% 7.65% 7.84% 8.15% 7.89% 9.92%

Nov-‐14 4.20% 4.76% 5.27% 5.37% 5.62% 5.66% 7.60%

Dec-‐14 5.59% 5.69% 6.42% 6.63% 6.73% 7.48% 8.38%

Jan-‐15 6.02% 5.45% 6.11% 5.92% 6.01% 6.14% 8.75%

Feb-‐15 6.40% 6.86% 7.99% 8.19% 8.20% 8.70% 9.97%

Mar-‐15 4.73% 5.25% 6.52% 6.77% 6.92% 6.89% 9.05%

Table 3 – Accuracy in terms of NMAE for the Norwest Test Systems

5 minutes ahead

1 hour ahead (60 min)

4 hours ahead

(240 min)

12 hours ahead

(720 min)



6 days ahead (8640 min)

Mar-‐14 5.99% 8.17% 9.32% 9.19% 9.38% 9.89% 15.02%

Apr-‐14 6.12% 6.85% 7.70% 8.22% 8.54% 9.17% 12.30%

May-‐14 5.32% 7.76% 8.50% 8.32% 8.45% 9.91% 11.11%

Jun-‐14 4.39% 7.03% 7.75% 7.53% 7.51% 8.49% 11.17%

Jul-‐14 3.44% 5.99% 6.41% 6.79% 7.24% 7.65% 9.75%

Aug-‐14 7.01% 7.87% 8.93% 9.37% 10.46% 10.30% 12.75%

Sep-‐14 7.69% 7.96% 8.87% 9.11% 9.27% 9.34% 13.73%

Oct-‐14 5.00% 7.24% 8.40% 8.60% 8.33% 9.03% 11.48%

Nov-‐14 5.83% 6.81% 8.46% 8.42% 8.54% 8.73% 11.17%

Dec-‐14 6.83% 7.15% 9.03% 9.07% 8.80% 9.00% 11.67%

Jan-‐15 5.75% 5.75% 6.84% 6.67% 7.28% 7.76% 12.22%

Feb-‐15 8.87% 9.35% 10.73% 10.79% 10.94% 11.04% 11.96%

Mar-‐15 6.66% 7.39% 8.31% 8.58% 8.90% 9.21% 12.14%


Figure 6 – Black Mountain test farm forecast performance (in terms of Normalized Mean Absolute Error). The dotted lines are the corresponding target specifications for each horizon time.

Figure 7 – As in Figure 6 but for the Norwest test farm.


R&D activities in support to the operational ASEFS The R&D component of ASEFS has produced a varied and rich output. This output has been documented in a comprehensive way in the milestone reports. In this section select highlights are presented.

Review of solar forecasting approaches

One of the first tasks in ASEFS was to review existing solar forecasting techniques.

Forecasting methods can be broadly characterized as physical or statistical. The physical approach uses numerical weather prediction and PV models to generate solar power forecasts, whereas the statistical approach relies primarily on historical data to train models (Pelland et al., 2013). In the literature, researchers have developed a variety of methods for solar power forecasting, such as the use of NWP models (Marquez and Coimbra, 2011; Mathiesen and Kleissl, 2011; Chen et al., 2011), tracking cloud movements from satellite images (Perez et al, 2007), and tracking cloud movements from direct ground observations with sky cameras (Perez et al, 2007; Chow et al., 2011; Marquez and Coimbra, 2013a). NWP models are the most popular method for forecasting solar irradiance several hours or days in advance. Mathiesen and Kleissl (2011) analyzed the global horizontal irradiance in the continental United States forecasted by three popular NWP models: the North American Model, the Global Forecast System (GFS), and the European Centre for Medium-‐Range Weather Forecasts (ECMWF). Chen et al. (2011) developed an advanced statistical method for solar power forecasting based on artificial intelligence techniques. Crispim et al. (2008) used total sky imagers (TSI) to extract cloud features using a radial basis function neural network model for time horizons from 1 to 60 minutes. Chow et al. (2011) also used TSI to forecast short-‐term global horizontal irradiance. The results suggested that TSI was useful for forecasting time horizons up to 15 to 25 minutes-‐ahead. Marquez and Coimbra (2013a) presented a method using TSI images to forecast 1-‐minute averaged direct normal irradiance at the ground level for time horizons between 3 and 15 minutes. Loren et al. (2007) showed that cloud movement–based forecasts likely provide better results than NWP forecasts for forecast timescales of 3 to 4 hours or less. Beyond that, NWP models tend to perform better. A brief description of solar forecasting methods is summarized in Table 4.

Table 4 – Solar forecasting methodologies

Methods Description / Comment Forecast

Horizons

Physical approach

NWP models NWP models are the most popular method for

forecasting solar irradiance more than 6 hours or days in advance

6 hours to days ahead

Total Sky Imagery (TSI)

Use TSI to extract cloud features or to forecast short-‐term global horizontal irradiance

0 to 2 hours ahead

Statistical approach

Statistical methods

Statistical methods were developed based on autoregressive or artificial intelligence techniques

for short-‐term forecasts

0 to 6 hours ahead

Persistence forecasts

Persistence of cloudiness performs well for very short-‐term forecasts

0 to 4 hours ahead


Forecast metrics

An assessment of various forecast metrics was also carried out. These can be broadly divided into four categories:

1. Statistical metrics for different time and geographic scales, including distributions of forecast errors, Pearson’s correlation coefficient, (normalized) root mean square error (RMSE), (normalized) fourth root mean quartic error (4RMQE), maximum absolute error (MaxAE), mean absolute error (MAE), mean absolute percentage error (MAPE), mean bias error (MBE), Kolmogorov–Smirnov test integral (KSI), OVER, skewness, and kurtosis

2. Uncertainty quantification and propagation metrics, including standard deviation and information entropy of forecast errors

3. Ramp characterization metrics, including the swinging door algorithm signal compression

4. Economic metrics, including non-‐spinning reserves service represented by 95th percentiles of forecast errors.

A brief description of each metric is summarized in Table 5. A standardized set of forecasting metrics was established based on multiple discussions with various system operators and utilities that are participating in the solar forecasting research effort at NREL. The Australian Energy Market Operator (AEMO) is procuring solar forecasts from commercial vendors. It is expected that such standardized metrics that are considered valuable to U.S. operators will also be beneficial to AEMO to evaluate the value of solar forecasting in its operations.

Assessment of GFS solar forecasts

The GFS, one of the two weather feeds for the ASEFS operational model, was developed by the National Oceanic and Atmospheric Administration (NOAA) and provides operational global weather forecasts up to 196 hours at 6 hourly intervals. The model is initialized every 6 hours, so a new set of forecasts is available four times per day: 0 UTC, 6 UTC, 12 UTC, and 18 UTC. The Global Data Assimilation System (GDAS) is used by the GFS model to place observations into a gridded model space for the purpose of initializing weather forecasts with observed data. GDAS adds the following types of observations to a gridded, 3D, model space: surface observations, balloon data, wind profiler data, aircraft reports, buoy observations, radar observations, and satellite observations. Gridded GDAS output data can be used to start the GFS model. The GDAS model output is also available four times per day and contains forecasts for 3 hours, 6 hours, and 9 hours.

As part of ASEFS, a forecast validation for solar radiation using output data from the GFS and GDAS model forecasts has been carried out. These forecasts are compared to high-‐quality solar radiation data available every minute from NOAA’s Surface Radiation Budget Network (SURFRAD) network and ground data from nine stations maintained by the Australian Bureau of Meteorology (BOM).

The verification of the forecasts was conducted using ground data from the BOM at nine sites for which 2011 data was avaialable: Adelaide, Alice Springs, Cocos Island, Darwin, Melbourne, Rockhampton, Wagga Wagga, Broome, and Cape Grim. Scatter plots of the average ground station data and the GFS forecast are shown in Figure 8 for the 24-‐hour forecasts. The GFS data is plotted on the vertical axis, and the ground station data is plotted along the horizontal axis. Notice that the data is well correlated over the time period covered. For nearly all the sites, with the possible


exception of Cocos Island, the data appears to have correlated well, also regardless of the forecast hour (i.e., 12-‐hour, 24-‐hour, or 36-‐hour forecast; only 24-‐hour forecast is shown).

Table 5 – Proposed Metrics for Solar Forecasting

Metric Description/Comment

Statistical Metrics

Distribution of forecast errors

Provides a visualization of the full range of forecast errors and variability of solar forecasts at multiple

temporal and spatial scales

Pearson’s correlation coefficient

Linear correlation between forecasted and actual solar power

RMSE and NRMSE Suitable for evaluating the overall accuracy of the forecasts while penalizing large forecast errors in a

square order

RMQE and NRMQE Suitable for evaluating the overall accuracy of the forecasts while penalizing large forecast errors in a

quartic order

MaxAE Suitable for evaluating the largest forecast error

MAE and MAPE Suitable for evaluating uniform forecast errors

MBE Suitable for assessing forecast bias

KSI or KSIPer Evaluates the statistical similarity between the

forecasted and actual solar power

OVER or OVERPer Characterizes the statistical similarity between the forecasted and actual solar power on large forecast

errors

Skewness Measures the asymmetry of the distribution of forecast errors; a positive (or negative) skewness

leads to an overforecasting (or underforecasting) tail

Excess kurtosis

Measures the magnitude of the peak of the distribution of forecast errors; a positive (or negative) kurtosis value indicates a peaked (or flat) distribution, greater or less than that of the normal distribution

Uncertainty Quantification

Metrics

Rényi entropy Quantifies the uncertainty of a forecast; it can utilize all of the information present in the forecast error

distributions

Standard deviation Quantifies the uncertainty of a forecast

Ramp Characterization

Metrics

Swinging door algorithm

Extracts ramps in solar power output by identifying the start and end points of each ramp

Economic Metrics

95th percentile of forecast errors

Represents the amount of nonspinning reserves service held to compensate for solar power forecast

errors


Figure 8 – Twenty-‐four-‐hour GFS forecast compared to station data.

Improved radiative-‐transfer modelling for NWP

A number of model development projects have been conducted under this task. The first is an implementation of the fast surface solar radiation scheme (SUNFLUX) into the ACCESS NWP model. The second involved testing several changes to the model physical parameterization schemes in the ACCESS NWP models to evaluate their impact on the surface solar radiation. The third consisted of some trials testing a number of different approximations of the two-‐stream radiative transport scheme at the heart of the radiation parameterization. These are essentially variants of the


approximations used to calculate the angular mean over all incident and scattering angles in each layer of the atmosphere. In the process of checking the results of the two-‐stream variants an erroneous assumption in the formulation used to derive the direct solar radiation in the parameterization scheme was uncovered which was consistent with too much direct beam radiation. A new version of the ACCESS-‐C model was developed with this assumption corrected and a number of monthly forecasts run to assess whether the surface radiation verification scores improved and to verify the standard NWP forecast elements to ensure no degradation in the results. This version has recently been applied to the global and regional models for short test periods as well but complete results are not yet available.

The verification of the ACCESS-‐R surface solar radiation suggests that the model tends to over-‐estimate the direct component and underestimate the diffuse component. One possible cause for this is the assumptions built into the radiative transport two-‐stream approximation. There are a large number of possible different two stream schemes available, differentiated by their different assumptions about the approximations for the angular integrations required for a full (and computationally expensive) radiative transfer calculation. The variant selected for the ACCESS system was chosen to give accurate global surface and top of atmosphere radiative fluxes and atmospheric heating rates. The Unified Model (UM) on which ACCESS is based has a number of two stream schemes already coded in. Figure 9 shows a comparison of the diffuse surface radiation from a number of these for clear sky and ice and water cloud cases (the standard UM choice is the one on the far right). These results show that changing the two stream approximation is not likely to increase the diffuse component substantially. However, in investigating these alternatives it was discovered that there is a fundamental problem in all the schemes as implemented in the UM radiative transfer parameterization which led directly to the experiments described below.

Figure 9 – A comparison of the diffuse surface solar radiation from the different possible two-‐stream codes implemented in the ACCESS system for a number of idealised cases for clear sky and water and ice cloud

Short term satellite-‐based schemes using locally available real-‐time data, combined with NWP

Short-‐term forecasting of GHI is carried out as a two-‐step process.

The first step uses the HELIOSAT approach to pre-‐calculate the clear sky irradiance. This depends on Linke Turbidity values used in the clear sky model. The Linke turbidity factor has no unit. It typically


ranges between 3 (clear skies) to 7 (heavily polluted skies). The Linke turbidity factor refers to the whole solar spectrum, that is, spectrally integrated attenuation, which includes presence of gaseous water vapour and aerosols.

The second step makes use of MTSAT images to calculate the ground albedo and the cloud motion vectors (CMVs). CMVs are turned into forecasts by advection of present clouds using the derived CMVs to form a future cloud image. The forecasted image is transformed into cloud index using ground albedo determined from multiple MTSAT images. The cloud transmission attenuation coefficient (k*T) is approximated from the cloud index. It is then used to scale the pre-‐calculated clear sky irradiance to produce GHI.

The derivation process of CMVs (shown in Figure 10) involves pre-‐processing 3 successive images and then tracking (maximum cross correlation) the tracers (distinct features) both forward and backward in time. A 2D field (Latitude, Longitude) of parameters including u wind, v wind and quality index is currently produced (Local CMV product). The CMV algorithm was used to derive displacement vectors using special case study data obtained from BOM at 10-‐minute intervals over Mildura and Mount Gambier. A shorter time scale reduces errors in CMV produced from changing cloud properties. The errors in observed (MISR mapped) and estimated CMVs for the two sites are shown in the Table 6.

Figure 10 – Derivation process of CMVs


Table 6 – Comparison of CMV product

RMSE (m s-‐1) MBE (m s-‐1) MAE (m s-‐1)

Mildura

u-‐component 15.8 -‐0.2 10.7

v-‐component 12.6 0.7 7.7

Speed 12.5 -‐7.3 9.2

Mount Gambier

u-‐component 14.8 1.7 10.7

v-‐component 12.6 1.3 7.7

Speed 11.3 -‐5.1 9.2

Short-‐term irradiance forecasts with WRF

The Weather Research and Forecasting (WRF) model is a mesoscale numerical weather prediction system with immense capabilities in atmospheric research and operational forecasting. The growing interests in solar irradiance forecasting for the management and operation of solar power systems requires WRF solar irradiance forecasting capabilities to be also explored in Australia. Aerosols play a major role in attenuating irradiance during clear-‐sky conditions. Most of the daily variability in DNI is associated with clouds, however regions where aerosols are significant may also account for the observed variability. Australian atmosphere is also present with a number of aerosol sources such as soil-‐dust, sea salt, biomass burning, and secondary organic aerosols and sulphates, which can affect irradiance forecasts in Australia. WRF was used with aerosol inputs to simulate the impacts of aerosols at various sites in Australia with short-‐term DNI forecasts. The RMSE calculated using ground-‐based observed and WRF predicted GHI and DNI over a 24-‐hour period is shown in the Table 7. The errors in GHI are reduced at two sites with addition of aerosols, whereas DNI errors improve at only one site. Notably, the Thomson-‐aerosol aware scheme simulates the aerosol indirect effects relating to cloud seeding, thus the actual dust storm is not simulated. More aerosol data from satellites and ground needs to be assimilated into WRF for better forecasts. Also, the spin-‐up time for aerosols and clouds needs to be optimised.

Table 7 – Comparison of observed and WRF predicted DNI and GHI. DNI related values are shaded grey

Sites RMSE-‐Aerosol OFF (Wm-‐2) RMSE-‐Aerosol ON (Wm-‐2)

Rockhampton 143.50 108.42

113.41 74.82

Melbourne 380.51 398.48

162.92 164.80

Adelaide 275.18 311.64

197.86 151.55


Short-‐term schemes based on on-‐site and peripheral met data and sky camera imaging

Research into short-‐term ground based solar forecasting as part of the ASEFS project has developed:

a) An advanced cloud classification model, able to distinguish between areas of cloud and sky in a sky-‐image using inexpensive off the shelf camera hardware (‘skycams’);

b) Algorithms for projecting cloud motion vectors across a calibrated fisheye lens distortion model estimate future cloud positions and; c) a model that can warn of large solar ramp events up to 30 minutes in the future, allowing pre-‐emptive mitigating actions to be taken

CSIRO Energy has been investigating the use of low-‐cost fisheye sky cameras for cloud tracking, ramp prediction and solar forecasting for several years. This technology has been improved and adapted in the ASEFS project for use as a ‘smart sensor’ which can be deployed to key locations, such as large solar farms, to provide signals to assist with very short-‐term solar forecasting. The aim of this research was to develop software that can use whole-‐sky images to provide a forecast signal to assist in prediction of solar power generation at 5 minute intervals, up to 20 minutes ahead, updated every 30 seconds. A new cloud classification model which is able to classify each pixel of a sky image as cloud or sky has been developed. This model employs a new hybrid approach which uses Random Forests, a supervised machine learning technique, alongside a traditional red-‐blue ratio thresholding approach. This combination allows for much improved classification in dark and uniform areas where there is little texture information, and better performance in bright areas near the sun

A new cloud motion vector projection algorithm using a dense optical flow technique was developed for ASEFS. Cloud motion vectors are extracted from a sequence of sky images taken at 10-‐second intervals and a calibrated fisheye lens distortion model is used to simulate the future position of each cloud at every time-‐step into the future, assuming the current velocity remains constant. The three figures below show typical examples of this process, forecasting the timing of a shade event with approaching clouds. These examples were chosen to show the performance of the system in several typical cloud conditions which cause intermittent solar generation – high cirrus cloud, relatively stable / low advection cumulus, and high advection (dissolution) cumulus clouds. The system was able to detect the upcoming shade events more than 10 minutes in advances in all cases, and shade-‐event forecasts were all forecast on or prior to the actual event. For the case of relatively stable / low advection cumulus Figure 11 shows the timing of a forecast cumulus cloud shading event. This cloud is detected 9 minutes in advance. In this example, the forecast time to shading is lower than the perfect forecast – this is caused by a small cloud that preceded the main cloud bank but disappeared 2 minutes before the actual shade event. While there was a small underestimate of the event time, a warning of the event was still given 9-‐10 minutes before the event.

Overall, research highlights for the sky camera imaging work include:

• Development of a novel cloud image classification system that, using inexpensive off the shelf camera hardware, was used to classify a 1-‐million pixel test set as cloud or sky correctly for 97% of the samples.

• Tests of the shade event timing prediction algorithms found them to accurately forecast future events for periods of up to 30 minutes in advance for slow high cloud conditions, while giving more than 5 minutes of warning for fast low clouds.

• Over a 30 day validation period of highly intermittent cloud conditions, the irradiance ramp warning system was found to correctly predict 99.96% of shading events. This is the equivalent of, on average, only missing a shade event once every 42 days of operation.


Figure 11 – Timing of a forecast cumulus cloud shading event.

Further research is planned that will extend this system to provide probabilistic ramp magnitude (as well as timing) forecasts by incorporating additional data streams about cloud opacity from satellite and LIDAR data.

The skycam forecasting system developed in this project is currently being trialled by CSIRO in the lab and in small scale in the field in conjunction with commercial partners, and is at a sufficient readiness level that the system could be quickly made operational at key locations on the national energy grid, such as in large solar fields; helping to mitigate the effects of solar intermittency in Australia’s renewable generation mix

Options for integration of skycam-‐based forecast into the operational ASEFS system include:

1. Cloud presence warnings. Providing a consistent real-‐time measurement of current and historical whole-‐sky cloud amount (in oktas or percentage of sky) as an additional input parameter to the existing statistical short-‐term solar forecasting models in ASEFS to improve their performance. This would require a skycam and embedded PC with data connection to be installed at participating solar farms.

2. Ramp event warnings. Providing forecasts/warnings of when ramp events will occur. This would employ the ramp-‐event forecast described earlier to provide advance warning of large ramp events in solar power output from a farm to be provided up to 15 minutes in advance of the event, allowing the 5-‐minute power forecasts to be adjusted accordingly. This would provide timing information on when these events would occur, but not the magnitude of the decrease in power output. This option would require a skycam and embedded PC with data connection to be installed at participating solar farms.

3. Ramp event and magnitude warnings. Providing forecasts/warnings of when ramp events and their magnitude. This will provide the forecast data as in option 2, but also supplying a probabilistic bound of the magnitude of the change in global horizontal irradiance, which can be used to estimate the level of generated solar power. Further research into forecasting ramp magnitude in addition to the ramp timing forecast system developed to date will be needed to supply these forecasts


The algorithms and software to realise options 1 and 2 exist, and could be deployed as part of a trial in a follow up of ASEFS. The software for these options is currently being trialled by CSIRO and is estimated to be at a Technology Readiness Level (TRL) of 5-‐6 at the time of writing. Option 3 needs further research into forecasting local cloud optical depth metrics, which will allow predictions of cloud opacity and therefore irradiance decrease from particular cloud layers during a ramp event. Currently, cloud optical depth information is difficult to determine using ground-‐based cameras alone, because cloud brightness is dependent on cloud composition and thickness, which is not easily measureable using a single sky camera. Additional data streams from LIDAR ceilometers and satellite measurements have been investigated as a means of providing the required measurements to augment the existing ramp timing forecast system.

CSIRO is currently constructing two city-‐wide networks of sky cameras and irradiance monitoring stations around Newcastle and Canberra. The capability this network will allow the forecasting system developed to be extended to tackle distributed solar power forecasting in Australia. There is an existing and growing need to provide predictions and warnings of sharp changes in rooftop solar generation in Australian cities, and the forecasting techniques developed in ASEFS phase 1 are equally applicable to this problem, though a range of practical and research challenges remain to be tackled.

In summary, we have developed a novel ground-‐based camera solar forecasting system, capable of providing localised, high temporal resolution forecasts and warnings of cloud shade events up to 30 minutes before the event. An accurate cloud/sky classification model was developed that can be trained on a sequence of sample images and will correctly differentiate cloud from sky in an image with an accuracy of 97%. A ramp event warning system was developed that detected 99.96% of the ramps in a 30 day validation sequence of 10 second sky images in a variety of intermittent conditions, this equates to a mean time between missed ramp forecasts of around 42 days.

This system is currently being trialled in the lab and in the field in small scale, and could be quickly made operational at key solar generation sites, such as large solar farms, for detecting ramp events in time to take mitigating actions at the farm or in the energy market.

Further research could adapt these forecasting algorithms to incorporate additional data streams for improvements in forecasting the size of ramp events, and for generating wide-‐area solar forecasts for distributed solar power application

CST power conversion models

A study to focus on the application of forecasts to concentrated solar thermal (CST) power plants was conducted. This study examined the value of forecasting CST plant output. CST power plants generate electricity by reflecting sunlight over a wide area onto a small absorber to create a lot of heat. This heat is used to drive a steam turbine and generate electricity. An advantage of CST power plants over PV power plants is that the heat can be stored to generate electricity later, such as after sunset. Currently, heat storage is cheaper and more efficient than battery storage.

The strength of sunlight changes as the sun moves across the sky and when clouds cover the sun. This will affect the amount of electricity that can be generated. The strength of sunlight, and hence the amount of electricity that can be generated, can be predicted by using forecast methods. There are different methods that can be used to forecast available sunlight for generating electricity. The methods can be similar or different to one another by how they forecast sunlight, how often they


can produce the forecast and how far ahead the forecast covers. CST plant output forecasts would be of interest to a CST plant operator for financial reasons and of interest to the AEMO for network reliability reasons. Both perspectives were considered in this study through the use of financial value metrics and network reliability metrics.

The value of forecast information was evaluated by using a CST plant model and a network model. The value to a CST plant owner was found by calculating how much money would be earned by using a forecast method (Figure 12). The value to AEMO was found by calculating numbers that describe network reliability. The scope of the study included 3 forecast methods and 5 different sizes for each of the CST plant solar field and storage components. The CST plant model was designed to resemble Andasol-‐1. The network model was created from a selection of generators from the Victoria region of the NEM. DNI data measured at Mildura airport in Victoria and electricity demand data for running simulations were obtained for 1 June to 30 November 2005. The evaluation was conducted for a range of solar field and thermal energy storage (TES) sizes.

Results showed that from the perspective of a CST plant operator, a forecast method with lower mean absolute error (MAE) or root mean square error (RMSE) is likely to be more valuable. If two forecast methods have similar MAE and RMSE, then the value of the forecast will depend on the mean bias error (MBE) and the size of the solar field and TES. A forecast method with negative MBE is likely more valuable for a CST plant with a small solar field or large TES. In contrast, a forecast method with a positive MBE is likely more valuable for a CST plant with a large solar field or small TES. From the perspective of AEMO, a forecast method with lower MAE and RMSE is also more valuable. However, if the MAE and RMSE are similar then the forecast method with the lower or negative MBE is likely to be more valuable regardless of solar field and TES sizes.

The central conclusion of this study is that the most valuable type of forecast may be the same for both AEMO and the operator of a CST plant with a small solar multiple or many hours of storage. To encourage CST plant operators to decide CST plant operation based on forecasts that are also beneficial to network reliability, AEMO may consider setting requirements for the design of CST plants that want to connect to the NEM.

Figure 12 – Summary of method to calculate financial value of DNI forecast method.


Basic forecasting schemes based on more complex NWP fields (cloud character, synoptic class)

Short-‐term forecasting of solar irradiance and associated PV power production is a key issue for the effective management of solar PV power installations. In particular, the ability to accurately forecast the timing and magnitude of ramp-‐down events caused by passing cloud cover can be of great benefit for smoothing the output to the grid and for taking optimum advantage of energy storage systems. Recent research into the use of sky cameras has led to advances in forecasting the timing of ramp-‐down or ramp-‐up events within the next 20 minutes, but these methods do not provide information about the magnitude of these events. Forecasts of the magnitude of ramp-‐down events, or equivalently the attenuation of irradiance during these events, are required in order to translate the forecasts into their effect on PV power output.

Current methods employed by ASEFS for short timescales of up to 30 minutes use statistical techniques such as autoregressive integrated moving average (ARIMA) models, which are based on past values of the quantity being forecast. In the case of forecasting solar energy, there is potential to improve upon the forecasting skill of these statistical methods by incorporating information on clouds.

Two sources of local, real-‐time cloud information are explored in this work. A laser ceilometer has the potential to provide some information on the height and density of clouds, which is related to attenuation of irradiance. The ceilometer returns data on the vertical profile of aerosol concentration, based on the timing of scattered light received back at the lidar from laser pulses sent vertically through the atmosphere.

The other available source of cloud information is provided by a sky camera (‘skycam’) whose images are processed by classifying clouds and projecting their movement in order to forecast the fraction of cloud covering the sun (West et al, 2014).

This study assessed the potential value of these data sources for forecasting solar irradiance, including predicting the attenuation of irradiance in ramp events caused by clouds.

Table 8 shows the error statistics for a selection of models, and a range of lead times from 10 to 30 minutes. The results are compared with a persistence forecast which uses past values of the clear-‐sky index itself, filtered to average only over cloudy periods within the hour up to the lead time ahead of the forecast time. Although reasonably good predictions were made using backscatter data, the results show that better results can be obtained using the persistence forecast.

Analysis of data from a ceilometer at CSIRO’s Solar lab in Canberra has shown that there is a clear and reasonably strong relationship between backscatter data from the ceilometer and the GHI clear-‐sky index. Knowledge of the GHI clear-‐sky index is an important step in determining the reduction in PV power output due to clouds. Predictive models of clear-‐sky index, using backscatter intensities during previous cloudy periods and split into four height bands, have been shown to have useful skill in forecasting the attenuation of global irradiance due to clouds.

However, results have shown that backscatter data does not add forecasting skill to that which can be achieved using past values of the predictand, clear-‐sky index. Forecasts of the fraction of the sun covered by cloud obtained through analysis of skycam images do, however, add some skill. These forecasts provide extra information by estimating when clouds will pass over the sun and cause potentially rapid ramps in the solar irradiance and power output. However, the uncertainty of these forecasts limits their usefulness, and model results showed only a 3% reduction in error due to their


introduction as predictors. There is uncertainty both in the timing of cloud passing the sun estimated by cloud motion vectors, as well as the forward path of clouds. Further uncertainty is caused by the inability to predict clouds when they develop or dissipate near to the sun. Another complicating factor is that clouds can sometimes enhance global irradiance by increasing diffuse irradiance due to reflectivity especially when thin, bright clouds pass close to or partially over the sun. The skycam forecasting is unable to distinguish between different types of cloud.

It has been noted that the ceilometer is limited to providing information on clouds which are vertically above the instrument. The possibility of incorporating knowledge from the skycam motion vectors of the position in the sky and direction and speed of movement of clouds which are set to intersect the sun could be investigated. This could enable ceilometer data for the most relevant area of cloud to be used for forecasting whenever possible.

This work has so far only considered a co-‐located ceilometer and irradiance forecast site. An option for further work would be to investigate remote positioning of one or more ceilometers in order for them to be able to anticipate more frequently the clouds which are due to intercept the sun. This would consider the prevailing direction of cloud ramp events, and would make use of other sites in the Canberra solar monitoring network.

Table 8 – Error statistics for a selection of predictive models of GHI clear-‐sky index

Model Lead time (mins) Predictors RMSE MAE Bias Correlation

Persistence 10 CI 0.158 0.119 -‐0.007 0.69

Decision Tree 10 Backscatter 0.194 0.157 0.001 0.40

Random Forest 10 Backscatter 0.188 0.153 -‐0.007 0.46

Persistence 20 CI 0.172 0.130 -‐0.005 0.63

Decision Tree 20 Backscatter 0.194 0.157 -‐0.002 0.40


Persistence 30 CI 0.184 0.139 -‐0.002 0.58

Decision Tree 30 Backscatter 0.197 0.160 -‐0.001 0.37


Development of basic intermittency prediction schemes at all time scales

Solar irradiance received at or near ground is highly variable in nature, which in turn leads to the variability of the power out of solar PV. Given the trend of the increasing grid penetration of solar power, this has significant impacts on the operation of power systems across a range of time scales. At the time scale of seconds, solar variability can influence the resulting power quality (e.g. voltage flicker and power frequency fluctuations); at minutes, regulation needs to balance the random variations in total power generation; at the scale of minutes to hours, actions need to be taken to


follow the changes in load within the day; finally at hours to days, power units need to be scheduled in advance for maintenance and/or to meet individual load requests.

To alleviate the adverse effects of solar variability on power stability, developments in forecasting solar irradiance and solar power output have been proliferating. The appropriate forecasting techniques depend on the forecast horizon. For day ahead forecasts, numerical weather prediction (NWP) is the best tool despite there are significant biases associated with its irradiance estimates. Despite the intense research attention on solar irradiance forecasting, there are not enough research efforts devoted directly to the quantification and prediction of temporal solar variability.

Using GHI time series, a simple and robust metric (called daily variability index, or DVI) to quantify the daily variability of solar irradiance was adopted. One-‐minute GHI time series measured by the Bureau of Meteorology at Wagga-‐Wagga is used and the DVI series is calculated correspondingly. Random forest and multiple linear regression, which respectively represent techniques of nonlinear and linear regression, are used to build empirical models between DVI and large-‐scale meteorological fields, such as cloud cover, wind velocity and boundary-‐layer characteristics. And their corresponding performances are compared to reveal the differences of performance using linear and nonlinear approaches.

Sample data are extracted for the nearest grid point of the Wagga Wagga site for the two NWP models. Using the year of 2012 as the training period and the year of 2013 as the test period, the DVI is forecasted by the two NWP models, respectively. The main results are demonstrated in Figure 13. As shown in the left column, both GFS and CCAM (the CSIRO’s Conformal Cubical Atmospheric Model) forecast the 3 hour averaged GHI well evidenced by the small value of MAE. In terms of DVI forecasting (the middle column), CCAM performs slightly better than GFS as reflected by the comparison of the metrics. In addition, it is only slightly worse to use CCAM forecast than to use the ERA-‐Interim reanalysis data. Note that in making the plots in Figure 13, another machine learning technique, gradient boosting, is used instead of random forest as gradient boosting normally results in similar or better performance than random forests for regression problems.

Regarding the implementation of the DVI model (presumably using gradient boosting), it only requires available NWP variables at the nearest grid point for each solar farm to be forecasted. The resulting performance of the model will vary from site to site and is commensurate with the length of the available training data. With an approximately 2 years training period and 240 predictors from the CCAM model for a single site, the training phase takes about 3 seconds on a 2.2GHz Intel i7 MacBook Pro. With a longer training period and more sites to apply, the computing time will add up accordingly. As such, it is recommended to use extra computing resource for the training phase of this module. The computation time of the operational phase is small and extra computing source is not needed.


Figure 13 – Performance of the GFS model (top row) and the CCAM model (bottom row) for 3 hour average GHI (left), DVI forecasting (middle), and relative influence of predictors (right) at Wagga Wagga

Probabilistic forecasting and participation in GEFCom2014 solar power forecasting

Having demonstrated the performance of using NWP models to forecast DVI, it is beneficial to add the information of error distribution to the deterministic forecast. This is tackled by identifying similar situations in the past and using them to form the error distribution, i.e., the analogue approach. More specifically, the basic steps are:

1. First use gradient boosting to perform the deterministic forecast.

2. Then estimate the probabilistic distribution of the forecast error, i.e. the observed DVI minus the deterministic forecast: for each point in the test dataset, find the nearest k points in the training dataset based on the deterministic forecast values, and use them to form the PDF of the error for the point.

Figure 14 demonstrates the main results using the analogue approach. The left plot illustrates the positive correlation between the amplitude of error and the forecasted DVI value. The right plot depicts the range of the probabilistic forecast of DVI superimposed by the observation time series for January 2013. It is shown that the observation time series falls in the 0.1-‐0.9 quantile probabilistic forecasts for most of the period.

The proposed approach for solar variability forecasting has been adapted in our participation in the solar track of Global Energy Forecasting Competition 2014 (GEFCom2014). The main task of the competition is to forecast the solar power generation of three farms using the output of ECMWF and historical training data. Our team ranked first among more than 250 participants all over the world.


Figure 14 – The relationship between the amplitude of the error of DVI modelling and the modelled DVI (left); Forecasted DVI quantiles (0.1, 0.2, … ,0.9) for a sample period of one month (right)

Investigation and testing of distributed PV generation data sets, upscaling-‐schemes for distributed PV, testing and further development of distributed PV power prediction techniques

Installations of residential solar PV panels have grown rapidly in several countries, mainly spurred by government incentives, increasing energy prices and reductions in the cost of solar power. Latest estimates indicate about 4 GW in installed small scale PV power for Australia. With progressively lower PV production costs and improving system quality and reliability, growth in installations in the near future is projected to be even stronger.

Prediction of solar radiation and PV-‐produced power at the residential and business level is therefore key to allowing a smoother integration of power into the electricity grid. Ideally, one would collect all of the relevant variables from each individual installation to accurately describe the specific system parameters and hence attempt a detailed solar power prediction for each system. However, this would clearly be a very expensive, time consuming and essentially impractical approach since PV installations are characterized by a variety of features: i) PV technology, ii) inverter type and technology, iii) panel orientation (including accounting for tracking devices), iv) amount of shading (which can depend on variables such as solar zenith angle, but also on the changing nature of obstructions), v) efficiency of the PV panels (dependent on the type of installations, whether free standing or roof integrated systems, as well as on weather conditions, such as air temperature and wind speed).

It is apparent therefore that a deterministic approach to urban or regional PV power forecasting is impractical. Practical approaches to predicting solar power at increasing level of approximation are therefore sought. Such approaches by necessity will have to consider PV system aggregation to differing degrees. Sometimes these approaches are called upscaling: prediction is derived for a small sample of PV systems, which is then used to infer the behavior of analogous PV systems over a broader area.

In this work, we start from the underlying assumption that, because the ultimate driver of PV systems and their outputs is global irradiance, accurate meteorological observations are key to accurate power predictions. At the same time, and with the view to limit the amount and cost of instrumentation required for accurate forecasts, we also assess the type and number of


meteorological observations required to achieve accurate forecasts. The irradiance forecasts are then used to produce power forecasts for a target (generic) system.

This work relies on a number of high-‐frequency monitoring stations installed, and regularly maintained, around Canberra (Figure 15). Specifically we use measurements from two stations to produce forecasts for a, third, target station, for which we have all measurements. Where the geometry of the PV system is known, as in our case, we derive the global irradiance on the PV plane by means of statistical relationship between the three irradiance components (global, diffuse and direct). In the absence of PV system specifications, one would need to make standard assumptions about system performance, tilt and orientation angles.

The prediction lead (or horizon) time extends from 5 minute to 3 hours ahead. Such time frames are particularly useful for regulation reserves, and enhanced system reliability and security and, potentially, for load shifting, at the high-‐end of this horizon time. At these lead times, it is generally accepted that statistical techniques offer the most appropriate and practical approach.

Figure 16 shows the forecast results of the PV power prediction. When local data are not used/available, the approach presented in this section provides an improvement with respect to using GHI at short lead times (under 30 minutes). This is valid for both winter and summer.

In this study we used observations from an urban solar network based in Canberra, Australia, with the aim to predict both solar irradiance and solar power at a (generic) target station. Our target station, Namadgi School, is located in between, and at a few tens of kilometres from, two other monitoring stations, Black Mountain and Wombat Hill. All three stations, therefore including Namadgi School, have been collecting meteorological and power observations: this allows us to assess the predictions performance at the target station. The sensitivity of two statistical methods, random forest and multi-‐linear, for i) different meteorological and power variables as predictors, ii) different combinations of stations, iii) winter and summer seasons and iv) different sky conditions, is an integral part of this work.

A number of variables observed at our monitoring stations were selected as our predictors for the GHI predictors – two global irradiances (GHI and on the plane of the PV panels), temperature, pressure and humidity. Clear sky radiation was also used as an additional predictor. Aside from the importance of historical values of GHI, the other important predictors are air temperature and humidity in summer and pressure and humidity in winter. As a benchmark for the GHI prediction, a modified (or gap) persistence, whereby GHI values were simply modified by adding the next time step increment provided by the diurnal cycle (clear sky radiation), was used.

Compared to when only data from the two stations, Black Mountain and Wombat Hill, are used for GHI prediction, gap persistence yields better results up to about 15 minutes ahead in summer. However, this clearly implies availability of data at the target station. Of the two statistical models, random forest is more skilful than the linear method in summer. In winter, the performance of the two statistical methods is reversed compared to summer, with the multi-‐linear method superior to random forest. The fact that the performance of these two methods displays a strong seasonality is a reflection of the prevalent climate conditions in Canberra in the two seasons. In winter, when clear sky conditions dominate, solar irradiance is better predicted by a less elaborate multi-‐linear method, whereas in variable, non-‐linear, summer conditions the random forest method captures better the GHI variability.


Figure 15 – Map of Canberra with the position and names of our five monitoring stations. Highlighted with circles are the three stations used for our solar forecasting algorithms, with Namadgi School taken as the target station.

Figure 16 – rMAE of modified Power predictions based on the conversion presented in Section 5 and using data from different stations (a) in summer (Method: Random Forest; Predictors: SP-‐Solar, PV panel temperature, Absolute Humidity); (b) in winter (Method: Multi-‐Linear; Predictors: SP-‐Solar, PV panel temperature, Absolute Humidity).

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For power output prediction, geometry and other specifications of the PV systems also play important roles, particularly at short lead times. This is because the local real-‐time tilted solar irradiance is roughly proportional to the real-‐time power output from solar panels (regardless of the negative efficiency effect due to increasing solar panel temperature). However, as the lead-‐time becomes longer, the positive effect of tilted solar irradiance as a predictor diminishes. Thus choosing GHI as a predictor instead of the solar irradiance on tilted surface when local data is not used seems to be acceptable as GHI is less site-‐specific. As for other variables such as solar panels temperature, which in principle is an important variable as it influences the solar panels efficiency, in practice it did not make a marked impact in the power prediction skill.

In terms of the relative importance of stations, Black Mountain typically has a larger impact on the skill of GHI than Wombat Hill. However, for power prediction in summer the reverse seems to be true. In general, using both stations yields better results than using either.

In terms of predicting power output for a single site, global irradiance on tilted surface should be selected as a predictor if available. However, as this variable is site-‐specific, we demonstrated that by deriving it via a GHI conversion, with GHI observations at remote sites, a satisfactory prediction skill is obtained. Also, the prediction skill is higher under high clear-‐sky index conditions. This is especially the case in winter.

Possible future developments of this work, aimed at improving the prediction skill, may be:

• The use of a predictor obtained from sky camera images; this would be most useful to improve predictions at the short range, up to about 20-‐30 minutes;

• The use of a number of predictors from Numerical Weather Prediction output; these would be useful to improve the longer range, say 2-‐3 hours (and beyond), prediction skill.

Skill of direct solar radiation predicted by the ECMWF global atmospheric model over Australia

The need for deriving or predicting direct solar radiation is a burgeoning topic of research. For instance, electricity production from CSP, for which direct solar radiation is a critical input, is steadily increasing. However, to date most studies have targeted global solar irradiance, namely the sum of the two separate components: direct solar radiation (or direct beam, or, more formally, direct irradiance) and diffuse radiation. Deficiencies in the representation of cloud cover, a notoriously difficult variable to simulate, are present at varying degrees in all weather models. Uncertainty in the modelled cloud cover is what makes solar radiation difficult to predict even a few hours ahead. Under clear sky conditions, however, NWP models can simulate solar radiation reasonably well. The direct solar radiation component produced by the ECMWF model is the focus of our investigation, including its dependency on different cloud cover conditions.

Even when direct beam forecast is considered this variable is derived from the global irradiance rather than being directly computed by the meteorological model. The reason direct beam has not been readily available is possibly due to the fact that only recently has the CSP industry started to advocate for improved direct beam products. As a consequence meteorological models were not programmed to output this variable, even if it is routinely internally computed. With this study direct beam predicted by two versions of the ECMWF model is compared to solar observations collected at four ground stations in Australia. The stations were chosen for their different climatic conditions.


In this study the performance of direct irradiance forecast by a widely used NWP model, the ECMWF model, has been assessed against high-‐quality ground station observations over Australia. Three-‐hourly average forecasts out to five days (120 hours) have been evaluated using standard statistical measures, the relative mean absolute error (rMAE) and the linear correlation coefficient, averaged over all sky conditions as well as separated into a number of clear sky index categories.

Two versions of the model, developed a few years apart, have been assessed. Both versions represent reasonably well the monthly mean value of DHI and GHI. By applying a relatively simple bias correction approach, based on average errors over a limited numbers of clear sky index and solar zenith angle categories, their performance can be markedly enhanced. The improvement in rMAE and correlation coefficient due to the bias correction is at least an order of magnitude larger than the difference between the two model versions. Specifically, rMAEs for DHI are reduced by at least 10% for most of the lead times after bias correction, reaching values of around 10-‐15% on average. Improvements in correlation are even more marked, with increases of up to 0.5 after bias correction, reaching average values of around 0.9 for all four stations.

It is worth noting that while aerosols are likely to be responsible for a portion of the surface radiation errors, the fact that the two model versions adopt the same monthly mean aerosol climatology, and that the removal of the bias is essentially independent of the aerosols, indicate that other factors, particularly cloud cover, are likely to play a dominant role in the model bias.

There appears to be a distinct dependency of the forecast performance on their background climatic conditions. In particular, Wagga Wagga and Broome, which are characterized by predominantly low-‐cloud cover to clear-‐sky conditions, also reasonably well captured by the model, are the locations displaying the overall best performance. For Adelaide and Rockhampton where cloudier conditions are more prevalent, and for which the model is less skilful at capturing these varying conditions, there is more room for model improvement.

The ECMWF forecast has also been tested in an operational-‐type setting, by targeting three quantiles, forecast smaller than the 25% of its distribution, larger than 50% and larger than 75% (Figure 17). While the bias correction applied to half of the data set improves the scores only marginally over the remaining half, the forecast especially for the higher two quantiles (> 50% and > 75%) display values which could potentially be considered for operational use. Moreover, it was shown that by applying the bias correction to the whole period, so as to mimic a longer temporal coverage, the score could potentially be markedly improved.

The results of our analyses provide an indication of the potential practical use of direct irradiance forecast for solar power operations, especially for concentrating solar power farms for which direct irradiance is crucial. Our quantification of error growth for direct irradiance, also in relation to global irradiance, should allow solar power plant operators to take better informed decisions about the use of direct irradiance forecast. It may also assist forecast model developers to better target future model improvements. However, if improvements continue to be gradual, as was the case with the two versions assessed in this work, refined bias correction approaches will provide a more effective short-‐term solution to delivering improved direct radiation forecasts out to several days.

One of the reason for the gradual improvement in the NWP forecast skill for surface solar radiation is that, up to until very recently, comparison with detailed surface radiation measurements on a daily basis has not been the main focus of NWP development evaluations. Particularly with the


growing interest coming from the solar power industry, who would benefit from a much better representation of the solar radiation components, the situation is now changing. This paper has provided an indication of the skill achievable with a renowned NWP model and, via the bias correction, what would be some areas of focus for model development. Given the impact of different model versions on surface solar radiation over Australia, analogous evaluations could now be part of the acceptance tests to upgrade experimental versions of NWP models to operational status (Troccoli and Morcrette 2014).

Figure 17 – Three-‐hourly forecast scores expressed as percentage of correct forecasts for GHI (left panels) and DHI (right) for three different distribution quantiles: forecast < 25% (top panels), >50% (middle) and >75% (bottom) at Adelaide. The target period is the second half of 2006. The black lines are for non-‐corrected model output, the green lines for bias corrected model output over the entire 2006 and the red lines mimic a practical forecasting situation, whereby the first half of 2006 has been used to compute the bias which is then applied to the second half of 2006. The cyan lines show the persistence forecast (using same time-‐of-‐day, one to 5 days ahead), whereas the grey lines provide another reference score, based on the null-‐hypothesis of random forecasts.

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Global to Diffuse to Direct Normal Radiation

This work focused first on the development of models for diffuse solar radiation and then moved to discuss how to best obtain estimated hourly direct normal solar radiation. First, a logistic model for direct normal solar radiation using multiple location data was constructed. Then, the use of the logistic and Perez models in four different locations was compared. The results of four error analyses show that the logistic model performed arguably better than the Perez model.

Spatial-‐Temporal Forecasting

This research details the extension from single to multi-‐site solar forecasting. The interconnections between sites improve the forecasting skill on an hourly but not on a ten minute time scale. The forecast for a single site for time 𝑡+1, performed at time 𝑡, is performed first using the CARDS (Huang et al 2013) forecasting tool. Subsequently the errors at the three sites were tested for cross correlation, i.e. at time 𝑡, and for each site at the one step lag for the other sites, i.e. at time 𝑡−1. From finding that significant cross correlation existed, the performance of the single site forecast for time 𝑡+1 was improved by the connection of the error at site 𝑖, with the errors at sites 𝑗,𝑘 at time 𝑡, and the forecast of the errors at sites 𝑗,𝑘 for time 𝑡+1, by a small but significant amount. The procedure for constructing prediction intervals for the forecast is presented, using a Correlated Autoregressive Conditional Heteroscedastic (Corr ARCH) model for forecasting the variance. Note that the CARDS model is being rewritten into Python programming language by staff at NREL in Colorado.

Researchers Access

One of the planned key outputs of ASEFS was the researcher access, analogously to what done with AWEFS. However, the lack of operational ASEFS data, up to the time of completion of ASEFS, implied that researchers could not access the ASEFS system directly (as instead done with AWEFS). To alleviate the lack of direct connectivity, AEMO was to extract solar forecasting data from the test system so that ASEFS partners could assess their developments against these forecast data. Such data would have been key to demonstrating the value of the improved solar forecasting techniques developed under the ASEFS R&D activities. Technical issues prevented AEMO from delivering these data. This issue is being looked into further as it is hoped that funding sources for research work will be available in the near future to allow the benchmarking of solar forecasting techniques against the AEMO’s ASEFS system.

Meetings and Stakeholder Engagement

Fortnightly project catch-‐up calls, six-‐monthly meetings and workshops as well as stakeholder workshops, including industry advisory committee meetings, were integral to the execution and success of the project.


Transferability The ASEFS operational system provides an opportunity, as it was for AWEFS, to showcase the reliability of the system and its effectiveness at enabling the smooth integration of solar power into the national grid. In this sense, the expertise gained in developing and installing ASEFS could be transferred to other energy markets, including Western Australia, as well as overseas.

In terms of ASEFS R&D activities, although these were mainly aimed at improving the forecasting produced by the operational ASEFS, they have yielded notable advances in several areas, as illustrated in the previous section and in the technical milestone reports. As such, these techniques could also be applied as a stand-‐alone (namely not linked to the AEMO ASEFS) modular prediction system.

The techniques developed with ASEFS R&D should also be further developed, including through a blending of multiple data sources for solar forecasting of distributed photovoltaic power generation on a city-‐wide scale. Indeed, the rapid expansion of rooftop solar PV, which has reached a overall capacity of over 4 GW in Australia, is demanding immediate attention in regards to its forecasting (AEMO has already commissioned a system to complement the current ASEFS operational system to deal with rooftop solar PV generation).

Other applications, that have started to be explored, are the combination of solar forecast with control algorithms with a view to optimise use of batteries/electricity generation/GHG emission/cost of electricity. Yet another area which would benefit from the ASEFS R&D outputs is the development of advanced ways to generate and communicate probabilistic forecasts. Indeed, being highly variable, solar power prediction would best be expressed by probabilistic information, which could be generated by combining the various techniques developed by ASEFS R&D.

Scientific advances in solar forecasting through ASEFS have also allowed Australian scientists to strengthen their knowledge and skills in the burgeoning solar forecasting area. This has led to international recognitions as in the case of an invitation to an ASEFS participant to author a chapter on solar forecasting in a book contributed by international experts in the area of renewable energy forecasting.

Conclusion and next steps A system to produce solar power forecasts was successfully developed and implemented at AEMO. This Australian Solar Energy Forecasting System (ASEFS) is essential for the operations of large-‐scale solar farms. As with AWEFS, ASEFS is amongst the most advanced solar forecasting systems worldwide.

ASEFS provides a system that uses basic forecasting techniques to cover all the AEMO-‐required forecasting timeframes, which range from five minutes to two years. Also, while the system was intended to feed large-‐scale photovoltaic and solar-‐thermal plants no large-‐scale solar generator was commissioned during the development and testing phase of the ASEFS system. In the absence of registered large-‐scale solar generators in ASEFS, the solution was to run the solar forecasts in a non-‐production environment using two small-‐scale test solar farms to exercise the forecasting models. The Black Mountain (Canberra) and the Norwest (Sydney) test solar farms replicated (scaled) fixed, non-‐tracking solar generators with scaled energy conversion models, providing scaled


“MW” output and onsite weather data to ASEFS. The normalised mean accuracy error for the different time horizons were tested against the required system specifications and the results were within the ASEFS agreed accuracy targets.

In parallel to the implementation of the ASEFS system, an advanced and varied R&D program has been developed which has allowed to develop skills and techniques in important future areas such as cloud tracking using sky cameras or satellite images, improvements of NWP models, better use of atmospheric models, methods to derive solar forecasts at the distributed level. The R&D developments have been documented in international journal papers, and presented at many public forums, thus allowing the rest of the Australian and worldwide research and industry communities to benefit from such acquired knowledge.

Conversations have already started around extending the R&D work by combining the various techniques which have thus far been developed in isolation. For instance, tracking of clouds from sky cameras and satellite could be merged to provide a more comprehensive picture of cloud evolution. Such combinations could be developed into modular software which can be commercialised for large solar farms as well as PV roof-‐top use.

In the meantime AEMO has already identified areas of further developments of their ASEFS operational system, particularly with the commissioned expansion of a system able to cope with distributed solar power, which has turned out to be a source of substantial aggregate power, much bigger than the currently available large-‐scale solar farms. Indeed, the surge in uptake of roof-‐top PV has been creating a growing problem for creating a growing problem for the balancing of supply-‐demand: forecast errors have already been experienced in regions like South Australia or South East Queensland, and these may increasingly contribute to severe power quality (frequency) issues.

The use of solar forecasting in combination of control algorithms for battery storage is another area of development. Such algorithms would allow the optimisation of battery longevity/electricity generation/GHG emission/cost of electricity. Solar forecasting would provide a key input in the development of such control algorithms. This topic was extensively discussed at the recent Solar Forecasting & Storage Stakeholder Workshop held in Melbourne on 10 August 2015. This stakeholder workshop, attended by around 40 experts from industry, government, and research institutions, aimed to:

1. Strengthen the link with industry around the issue of solar forecasting and electrical storage 2. Potentially co-‐develop a proposal for a feasibility study on the role and value of solar

forecasting in relation to various electrical storage scenarios

We are already working on a proposal for such a feasibility study. The proposal development has been benefiting from conversations with industry and research institutions, as well as with ARENA staff, including its CEO. The discussions at the workshop are also informing the way in which the feasibility study is being shaped. Specifically, it is apparent that the projected larger use of storage, particularly combined with PV, will make the role of forecasting both more important and more diverse, also due to the variety of battery technologies available. There are some useful prospects to provide supporting information such as solar irradiance and forecasting to also complement e.g. the APVI solar PV web portal, and which could be developed in collaboration with the AREMI project (http://www.nationalmap.gov.au/renewables/).


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classification using artificial neural network. Solar Energy, 85(11), 2856–2870. Chow, W. C., Urquhart, B., Lave, M., Dominguez, A., Kleissl, J., Shields, J., Washom, B., 2011. Intra-‐

hour forecasting with a total sky imager at the UC San Diego solar energy test bed. Solar Energy, 85(11), 2881–2893.

Crispim, E. M., Ferreira, P. M., Ruano, A. E., 2008. Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices. Int. J. of Innovative Computing, Information and Control, 4(5), 1121–1133.

Huang J, Korolkiewicz M, Agrawal M and Boland J, (2013) Forecasting solar radiation on an hourly time scale using a coupled autoregressive and dynamical system (CARDS) model, Solar Energy, 87, pp. 136-‐149.

Huang J, Troccoli A and Coppin P (2014) “An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records”. Renewable Energy, 72, doi: 195–202, 10.1016/j.renene.2014.07.015

Lorenz E, Kühnert J and Heinemann D (2014) Overview of Irradiance and Photovoltaic Power Prediction. In Troccoli A., Dubus L and Haupt SE (eds.), Weather matters for energy, Springer, New York, USA, pp 429-‐454, DOI: 10.1007/978-‐1-‐4614-‐9221-‐4_21

Mathiesen, P., Kleissl, J., 2011. Evaluation of numerical weather prediction for intra-‐day solar forecasting in the continental united states. Solar Energy, 85(5), 967–977.

Marquez, R., Coimbra, C. F. M., 2011. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy, 85(5), 746–756.

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Pelland, S., Remund, J., Kleissl, J., Oozeki, T., Brabandere, K. D., 2013. Photovoltaic and Solar Forecasting: State of the Art. Technical report, IEA PVPS T14-‐01:2013.

Perez, R., Moore, K., Wilcox, S., Renné, D., Zelenka, A., 2007. Forecasting solar radiation—Preliminary evaluation of an approach based upon the National Forecast Database. Solar Energy, 81(6), 809–812.

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Lessons Learnt

Lessons Learnt Report: Delays with Solar Flagship program Project Name: Australian Solar Energy Forecasting System

Knowledge Category: Regulatory Knowledge Type: Planning & Development approvals Technology Type: Solar PV State/Territory: National

Key learning In spite of the Solar Flagships plans and commitments, large-‐scale solar farms (> 30 MW) were only installed towards the end of the ASEFS project. This externality was very difficult to anticipate. It affected the outcome of the project only to the extent that the ASEFS operational system could not be tested on large-‐scale solar farms during the duration of the ASEFS project. It will be however tested shortly by AEMO (beyond the ASEFS project), if not done so already.

Implications for future projects Greater assurance that large-‐solar farms are being deployed should be sought before commencing a project which relies on the running of such solar farms. While a system like ASEFS was predicated on the existence of large generators (> 30MW) which have a requirement to produce and provide a forecast, an enabling technology such as solar forecasting is not tied to the size of solar generators. Therefore future projects could be written in a more generic way, namely without putting too much emphasis on specific sizes of solar generators.

Knowledge gap None

Background

Objectives or project requirements

The ASEFS operational system was meant to provide solar forecasts for large-‐scale generators.

Process undertaken

Development of the ASEFS system was carried out anyway, but test solar installations had to be relatively quickly set up in order to test the system. Lack of large-‐scale solar farms was also linked to the unavailability of solar forecasts through the proposed researcher access.

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Lessons Learnt Report: Unexpected rapid increase in rooftop solar installations Project Name: Australian Solar Energy Forecasting System

Knowledge Category: Regulatory Knowledge Type: Planning & Development approvals Technology Type: Solar PV State/Territory: National

Key learning An unexpected rapid increase in the uptake of roof-‐top PV has been creating a growing problem for the balancing of supply-‐demand: forecast errors have already been experienced in regions like South Australia or South East Queensland, and these may increasingly contribute to severe power quality (frequency) issues. This externality was difficult to anticipate. However, it only affected the project to the extent that more emphasis could have been placed on targeting this issue during ASEFS, perhaps through a re-‐planning of the project.

Implications for future projects In a fast moving industry like solar, projects need to have the flexibility to adapt their targets to unexpected emerging issues.

Knowledge gap Better knowledge and forecasting tools to target distributed solar power could be developed.

Background


The development of forecasting tools for distributed solar power was set as a small portion of the project as at the time of the writing of the proposal roof-‐top PV installations were at a much lower level than they currently are.

Process undertaken

The planned work on forecasting tools for distributed solar power was delivered as planned. However, given the surge in roof-‐top PV a project re-‐planning to focus on this aspect might have been useful.

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Lessons Learnt Report: Lack of solar forecast data thorough the Researcher Access Project Name: Australian Solar Energy Forecasting System

Knowledge Category: Technical Knowledge Type: Technology Technology Type: Solar PV State/Territory: National

Key learning Lack of large-‐scale solar farms led to the unavailability of solar forecast data through the proposed researcher access. Attempts were made to obtain these data anyway, but unsuccessfully.

Implications for future projects The way in which solar forecast data are produced and stored at AEMO may be done in a more flexible way and also independently of the size of the solar farm

Knowledge gap A cost-‐benefit analysis for the more advanced forecasting techniques, for which the ASEFS data was essential, was not possible but should still be carried out

Background


Solar forecast data through Researcher Access were essential for testing the potential improvements of the advanced solar forecasting techniques developed during the project, by means of a cost-‐benefit analysis.

Process undertaken

Repeated attempts to obtain the solar forecast data offline (namely without going through the unavailable Researcher Access) were made but unsuccessful (see also main text)

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Lessons Learnt Report: Delays in signing the agreement between CSIRO and NREL Project Name: Australian Solar Energy Forecasting System

Knowledge Category: Technical Knowledge Type: Human Resources Technology Type: Solar PV State/Territory: Non-‐state specific

Key learning The 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 projects It 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 gap None

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 ASEFS project commenced on 7th January 2013 and it took over a year for this agreement to be signed.

Process undertaken

Many email and phone communications, including lengthy negotiations had been necessary in order to reach an agreement between CSIRO and NREL. As of June 2014, however, NREL consistently contributed to ASEFS, as have all other partners. Due to these delays, the project finished in June 2015, hence six months later than originally planned.

AustralianSolarEnergy ForecastingSystem* …...3! ExecutiveSummary*...

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AustralianSolarEnergy ForecastingSystem* …...3! ExecutiveSummary*...

Transcript of AustralianSolarEnergy ForecastingSystem* …...3! ExecutiveSummary*...