Using extreme value theory to measure value-at-risk for daily electricity spot prices

The recent deregulation in electricity markets worldwide has heightened the importance of risk management in energy markets. Assessing Value-at-Risk (VaR) in electricity markets is arguably more difficult than in traditional financial markets because the distinctive features of the former result in a highly unusual distribution of returns-electricity returns are highly volatile, display seasonalities in both their mean and volatility, exhibit leverage effects and clustering in volatility, and feature extreme levels of skewness and kurtosis. With electricity applications in mind, this paper proposes a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of returns, as well as leverage effects via an EGARCH specification. In addition, extreme value theory (EVT) is adopted to explicitly model the tails of the return distribution. Compared to a number of other parametric models and simple historical simulation based approaches, the proposed EVT-based model performs well in forecasting out-of-sample VaR. In addition, statistical tests show that the proposed model provides appropriate interval coverage in both unconditional and, more importantly, conditional contexts. Overall, the results are encouraging in suggesting that the proposed EVT-based model is a useful technique in forecasting VaR in electricity markets. (c) 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Factors affecting hot spot development in bulk coal and associated gas evolution

There are many physical factors that can affect the self-heating rate of coal. The presence of seam gas has often been referred to as inhibiting coal self-heating due to the limited access of oxidation sites created by the presence of the gas adsorbed on the coal pores. Similarly, the presence of bed moisture in the coal also acts as an inhibitor of oxidation by blocking access of air into the pores. Gas drainage of a coal seam prior to mining removes both gas and moisture from the seam. Bulk coal self-heating tests in a two-metre column on both gassy, as-mined and gas-drained, dried high volatile bituminous coal show that removal of gas and moisture from the coal accelerates the rate of self-heating to thermal runaway from 8.5 days to 4.25 days, from a start temperature of 30°C, with an airflow of 0.25 L/min. The corresponding gas evolution pattern for each of these situations is different. Therefore, it is necessary to take this change in coal condition into consideration when developing a spontaneous combustion management plan.

Computer animation of hot spot development in bulk coal as an aid for training coal miners

The processes that take place during the development of a heating are difficult to visualise. Bulk coal self-heating tests at The University of Queensland (UQ) using a two-metre column are providing graphic evidence of the stages that occur during a heating. Data obtained from these tests, both temperature and corresponding off-gas evolution can be transformed into what is effectively a video-replay of the heating event. This is achieved by loading both sets of data into a newly developed animation package called Hotspot. The resulting animation is ideal for spontaneous combustion training purposes as the viewer can readily identify the different hot spot stages and corresponding off-gas signatures. Colour coding of the coal temperature, as the hot spot forms, highlights its location in the coal pile and shows its ability to migrate upwind. An added benefit of the package is that once a mine has been tested in the UQ two-metre column, there is a permanent record of that particular coals performance for mine personnel to view.