Postdoctoral Research Fellow, Clean Energy
Email: Dr Phillip Wild
Dr Phillip Wild brings agent based modelling capability to project 1 'Control Methodologies of Distributed Generation' and project 2 'Market and Economic Modelling of the impacts of Distributed Generation' at the Global Change Institute. Phillip's previous research experience has been in the areas of econometric modelling of National Energy Market (NEM) spot price and load time series data, 'levelised cost' and 'agent based' modelling of the NEM. Dr Wild has a PhD from The University of Queensland specialising in the field of macro-economic modelling.
Two papers published in the proceedings of the 2016 Asia-Pacific Solar Research Conference.
The Levelised Cost Of Energy Of Three Solar PV Technologies Installed At UQ Gatton Campus
Economic assessment of the viability of different types of solar PV tracking technologies suitable for installation in utility scale solar farms centers on assessment of whether annual production of the different tracking technologies is increased enough to compensate for the higher cost of installation and operation incurred by the tracking systems. To investigate this, the levelised cost of energy of three representative solar PV systems installed at the University of Queensland’s Gatton Campus is calculated. These solar array technologies are Fixed Tilt, Single Axis Tracking and Dual Axis Tracking arrays. These calculations depend crucially on assumptions made about ($/kW) construction costs and annual capacity factors of the three solar technologies being considered. A key finding was that the Single Axis Tracking technology was the most competitive, followed by a Fixed Tilt system. The Dual Axis Tracking system was the least competitive technology of those considered. It is also demonstrated how LCOE can underpin a ‘Contract-for-Difference’ feed-in tariff scheme applicable to supporting investment in utility-scale solar PV.
Projecting Solar PV Yield Of The Solar Array Installed At UQ Gatton Campus
The viability of utility scale solar PV farms will depend critically upon the annual production of such farms. A crucial determinant of solar PV yield will be prevailing solar irradiance and weather conditions. In Australia, the combined effects of weather relating to solar irradiance, temperature and rainfall on PV yield is likely to be closely linked to the El Niño–Southern Oscillation ENSO cycle. To investigate this we use NREL’s SAM model to simulate electricity production from a 3.275 megawatt pilot solar PV plant at the University of Queensland’s Gatton Campus. A key finding was that the best simulated PV yields were obtained during 2013 and 2014 when ENSO neutral conditions but with an El Nino bias prevailed. The worst years were 2010 and 2011 which were characterised by moderate and weak La Nina phases of ENSO. All other years considered had average PV yield outcomes including 2015 which experienced a very strong El Nino event.