Phillip Wild

The Western Australian Power Dilemma

From 1984 gas-fired power generation had been gradually increasing its share of the electricity market in Western Australia (WA) starting at 1 per cent and rising to about 50 per cent by 2008. Had it continued on this trajectory, the WA power system would have made great advances in terms of cost and environmental efficiencies given the looming commencement of the Carbon Pollution Reduction Scheme in Australia from 2011. However, more recently the cost of natural gas has increased from

Detecting finite bandwidth periodic signals in stationary noise using the signal coherence spectrum

3/GJ to

IDENTIFYING NONLINEAR SERIAL DEPENDENCE IN VOLATILE, HIGH-FREQUENCY TIME SERIES AND ITS IMPLICATIONS FOR VOLATILITY MODELING

7/GJ following the sudden collapse of the East Spar gas field in the North West Shelf. In this article, we analyse the impact of the gas price increase and demonstrate that despite being the most environmentally efficient conventional technology, natural gas combined cycle plant has been squeezed out of the market which in turn will increase forward electricity price risks to WA consumers through greater exposure to CO2 pricing in the long run.

Impact of module soiling on the productive performance of three solar PV technologies at gatton

All signals that appear to be periodic have some sort of variability from period to period regardless of how stable they appear to be in a data plot. A true sinusoidal time series is a deterministic function of time that never changes and thus has zero bandwidth around the sinusoids frequency. A zero bandwidth is impossible in nature since all signals have some intrinsic variability over time. Deterministic sinusoids are used to model cycles as a mathematical convenience. Hinich [IEEE J. Oceanic Eng. 25 (2) (2000) 256-261] introduced a parametric statistical model, called the randomly modulated periodicity (RMP) that allows one to capture the intrinsic variability of a cycle. As with a deterministic periodic signal the RMP can have a number of harmonics. The likelihood ratio test for this model when the amplitudes and phases are known is given in [M.J. Hinich, Signal Processing 83 (2003) 1349-1352]. A method for detecting a RMP whose amplitudes and phases are unknown random process plus a stationary noise process is addressed in this paper. The only assumption on the additive noise is that it has finite dependence and finite moments. Using simulations based on a simple RMP model we show a case where the new method can detect the signal when the signal is not detectable in a standard waterfall spectrogram display.

A spectral-based cusum test of evolutionary change

In this article, we show how tests of nonlinear serial dependence can be applied to high-frequency time series data that exhibit high volatility, strong mean reversion, and leptokurtotis. Portmanteau correlation, bicorrelation, and tricorrelation tests are used to detect nonlinear serial dependence in the data. Trimming is used to control for the presence of outliers in the data. The data that are employed are 161,786 half-hourly spot electricity price observations recorded over nearly a decade in the wholesale electricity market in New South Wales, Australia. Strong evidence of nonlinear serial dependence is found and its implications for time series modeling are discussed.

Comparative assessment of the productive performance of three solar PV technologies at gatton

After solar irradiance and air temperature, module soiling is generally accepted as the next most crucial issue affecting solar PV yield. Economic assessment of the viability of different solar PV tracking technologies centers on assessment of comparative production outcomes and relative costs. Module soiling will affect project viability through its adverse effect on PV yield. We use the NREL SAM model to simulate PV yield for three representative solar PV systems installed at Gatton. In these simulations we use three different module soiling regimes encompassing low, medium and high rates of module soiling. A key finding is that the medium and high soiling rates considered can reduce PV yield between 1.1 and 1.4 and 3.1 to 3.8 per cent, respectively, depending upon array technology.

Australian National Electricity Market Model - version 1.10

We develop a test of evolutionary change that incorporates a null hypothesis of homogeneity, which encompasses time invariance in the variance and autocovariance structure of residuals from estimated econometric relationships. The test framework is based on examining whether shifts in spectral decomposition between two frames of data are significant. Rejection of the null hypothesis will point not only to weak nonstationarity but to shifts in the structure of the second-order moments of the limiting distribution of the random process. This would indicate that the second-order properties of any underlying attractor set has changed in a statistically significant way, pointing to the presence of evolutionary change. A demonstration of the tests applicability to a real-world macroeconomic problem is accomplished by applying the test to the Australian Building Society Deposits (ABSD) model.

The Effect of Increasing the Number of Wind Turbine Generators on Generator Energy in the Australian National Electricity Market from 2014 to 2025

Economic assessment of the viability of different types of solar PV tracking technologies centers on assessment of comparative production outcomes of the different tracking technologies and there relative costs. We use the NREL SAM model to simulate PV yield for three representative solar PV systems installed at Gatton. In these simulations we use hourly solar irradiance, weather and surface albedo data, technical data relating to module and inverter characteristics and impacts associated with module soiling and near-object shading. A key finding is that over the period 2007 to 2015, average increases in annual production of between 23.9 and 24.3 per cent and 38.0 and 39.1 per cent were obtained for Single Axis and Dual Axis tracking systems relative to the Fixed Tilt system, depending upon module soiling rates.

The Effect of Increasing the Number of Wind Turbine Generators on Carbon Dioxide Emissions in the Australian National Electricity Market from 2014 to 2025

This working paper provides details of the Australian National Electricity Market (ANEM) model version 1.10 used in the research project titled: An investigation of the impacts of increased power supply to the national grid by wind generators on the Australian electricity industry. The paper provides a comprehensive reference of the ANEM model for the other project publications that use the ANEM model to analysis the sensitivity of four factors to increasing wind power penetration. The four factors include (1) transmission line congestion, (2) wholesale spot prices, (3) carbon dioxide emissions and (4) energy dispatch. The sensitivity of the four factors to wind power penetration is considered in conjunction with sensitivity to weather conditions, electricity demand growth and a major augmentation of the transmission grid of the Australian National Electricity Market (NEM) called NEMLink (AEMO 2010a, 2010b, 2011a, 2011b).The sensitivity analyses use 5 levels of wind power penetration from zero wind power penetration to enough wind power to meet the original 2020 41TWh Large-scale Renewable Energy Target. The sensitivity to weather is developed by using half hourly electricity demand profiles by node from three calendar years 2010, 2011 and 2012. The sensitivity to growth is developed by incrementing the nodal demand profiles over the projection years 2014 to 2025.

Discrete fourier transform filters: Cycle extraction and Gibbs effect considerations

This report investigates the effect of increasing the number of wind turbine generators on energy generation in the Australian National Electricity Market’s (NEM) existing transmission grid from 2014 to 2025. This report answers urgent questions concerning the capability of the existing transmission grid to cope with significant increases in wind power and aid emissions reductions. The report findings will help develop a coherent government policy to phase in renewable energy in a cost effective manner.We use a sensitivity analysis to evaluate the effect of five different levels of wind penetration on energy generation. The five levels of wind penetration span Scenarios A to E where Scenario A represents ‘no wind’ and Scenario E includes all the existing and planned wind power sufficient to meet Australia’s 2020 41TWh Large Renewable Energy Target (LRET). We compare the relative effect of five different levels of wind penetration on energy generation to the effect on emissions. We also use sensitivity analysis to evaluate the effect on energy generation of growth in electricity demand over the projections years 2014 to 2015 and weather over the years 2010 to 2012. The sensitivity analysis uses simulations from the ‘Australian National Electricity Market (ANEM) model version 1.10’ (Wild et al. 2015).