Report
Resources
Attachment Size
apo-nid40631.pdf 1.01 MB
Description

The final report has been published and is available here.

Executive Summary

1        Introduction

This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  The subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant. 

2        Literature review

The literature review discusses the four drivers for yield for LFR technology:

  • DNI (Direct Normal Irradiance)
  • Temperature
  • Humidity
  • Pressure

Collinsville lacks complete historical datasets of the four drivers to develop yield projects but its three nearby neighbours do possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville.

We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology.

We also introduce a modified TMY technique to overcome technology specific Typical Metrological Year (TMY).  We discuss the effect of climate change and the El Nino Southern Oscillation (ENSO) on yield and their implications for a TMY.

2.1     Research questions

Research question arising from the literature review include:

The overarching research question:

Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites?

This overarching question has a number of smaller supporting research questions:

  • Is BoM’s DNI satellite dataset adequately adjusted for cloud cover at Collinsville?
  • Given the dust and dew effects, is using raw satellite data sufficient to model yield?
  • Does elevation between Collinsville and nearby sites affect yield?
  • How does the ENSO affect yield?
  • Given the 2007-2012 constraint, will the TMY process provide a “Typical” year over the ENSO cycle?
  • How does climate change affect yield?

A further research question arises in the methodology but is included here for completeness.

  • What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler?

3        Methodology

In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield.

4        Results and analysis

In the results section we present the four driver models selected and the process that was undertaken to arrive at the models.

5        Discussion

We analyse the extent to which the research questions are informed by the results.

6        Conclusion

In this report, we have identified the key research questions and established a methodology to address these questions.  The models for the four drivers have been established allowing the calculation of the yield projections for Collinsville.

Publication Details