Seasonal Aspects of Australian Electricity Market

Electricity spot price movement is influenced by many factors, such as demand variations, plant outages, reserve, network constraints and gaming strategies by generators. The objective of this paper is to explore the key factors that influence electricity spot price. The paper also highlights the elasticity and volatility nature of the spot price and presents some analysis on spot price volatility in several market regions.

Applied Economics, 2011

This paper documents seasonal patterns and other characteristics of electricity spot prices in the Australian National Electricity Market (NEM), over a seven-year sample period. The goal is to investigate more specifically the influence of seasonalities and outliers noted in the body of literature on electricity prices. The results confirm that electricity prices exhibit significant time-of-day and day-of-week effects and that monthly and yearly effects are significant to a lesser degree. Extremely high spikes in the price series are an important characteristic of electricity prices and are shown to be a highly significant component of returns behaviour. Negative prices are unusual in financial time series data but occur in Australian electricity prices and are found to be influential on returns. The implications of these finding are that seasonal and outlier effects should not be ignored in efforts to model electricity prices.

Energy Economics, 2008

It is commonly known that wholesale spot electricity markets exhibit high price volatility, strong meanreversion and frequent extreme price spikes. This paper employs a basic stochastic model, a mean-reverting model and a regime-switching model to capture these features in the Australian national electricity market (NEM), comprising the interconnected markets of New South Wales, Queensland, South Australia and Victoria. Daily spot prices from 1 January 1999 to 31 December 2004 are employed. The results show that the regimeswitching model outperforms the basic stochastic and mean-reverting models. Electricity prices are also found to exhibit stronger mean-reversion after a price spike than in the normal period, and price volatility is more than fourteen times higher in spike periods than in normal periods. The probability of a spike on any given day ranges between 5.16 percent in NSW to 9.44 percent in Victoria.

Quantitative Finance, 2004

In this article, we analyze the evolution of prices in deregulated electricity markets. We present a general model that simultaneously takes into account the following features: seasonal patterns, price spikes, mean reversion, price dependent volatilities and long-term non-stationarity. We estimate the parameters of the model using historical data from the European Energy Exchange. Finally, it is demonstrated how it can be used for pricing derivatives via Monte Carlo simulation.

2000

The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives. This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. Ornstein-Uhlenbeck processes are described as the basic m...

Journal of Energy - Energija

The price on the electricity market is constantly changing. Owing to the characteristics of electricity market the future movement of the price is relatively hard to predict. For this reason the following three processes are applied: the process of the future electricity price movement according to the geometric Brownian motion, the mean reversion process, and the price spikes process. The paper discusses all three processes, including their definitions, formulas and applications, as well as their upsides and downsides.

2004

This paper examines the relationship between futures and spot electricity prices for two of the Australian electricity regions in the National Electricity Market (NEM): namely, New South Wales and Victoria. A generalized autoregressive conditional heteroskedasticity (GARCH) model is used to identify the magnitude and significance of mean and volatility spillovers from the futures market to the spot market. The results indicate the presence of positive mean spillovers in the NSW market for peak and off-peak (base load) futures contracts and mean spillovers for the off-peak Victorian futures market. The large number of significant innovation and volatility spillovers between the futures and spot markets indicates the presence of strong ARCH and GARCH effects. Contrary to evidence from studies in North American electricity markets, the results also indicate that Australian electricity spot and futures prices are stationary.

The Application of Econophysics, 2004

In this paper we address the issue of modeling spot electricity prices. After analyzing factors leading to the unobservable in other financial or commodity markets price dynamics we propose a mean reverting jump diffusion model. We fit the model to data from the Nord Pool power exchange and find that it nearly duplicates the spot price's main characteristics. The model can thus be used for risk management and pricing derivatives written on the spot electricity price.

SSRN Electronic Journal, 2000