Seasonal Aspects of Australian Electricity Market

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.

Energy Pricing Models, 2014

We examine the impact of explanatory variables such as load, weather and capacity constraints on the occurrence and magnitude of price spikes in regional Australian electricity markets. We apply the so-called Heckman correction, a two-stage estimation procedure that allows us to investigate the impact of the considered variables on extreme price observations only, while correcting for a selection bias due to non-random sampling in the analysis. The framework is applied to four regional electricity markets in Australia and it is found that for these markets, load, relative air temperature and reserve margins are significant variables for the occurrence of price spikes, while electricity loads and relative air temperature are significant variables to impact on the magnitude of a price spike. The Heckman selection model is also found to outperform standard OLS regression models with respect to forecasting the magnitude of electricity price spikes.

Energy Policy, 2019

Against the backdrop of numerous evidence that variable renewable generation decreases electricity prices and increases price volatility, this paper assesses the drivers of electricity prices in spot and futures markets, focusing on the German electricity markets. We take into account nonlinearities in electricity prices by means of structural breaks and threshold regressions. We find that short-run and medium/long-run price drivers differ and, more importantly, that they vary over time. In the case of the spot market, the determinants of prices are renewable infeed and electricity demand, while in the futures market the main drivers are natural gas, coal and carbon prices. Our results give relevant insights for market participants who seek to optimize procurement/ selling strategies in the spot market, and use the futures market to hedge against spot price volatility, which has increased due to a higher renewable generation.

Energy Economics and Management Group Working Papers, 2010

This paper aims to analyze the issue of price spikes in electricity markets through the lens of noncooperative game theory. The case we consider is Australia's long established National Electricity Market (NEM). Specifically, we adapt von der Fehr and Harbord's [26] multi-unit auction model to settings that more closely reflect the structure of the NEM, showing that price spikes can be related to a specifiable threshold in demand.

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...

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.

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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, 2013

Price spikes are of particular importance due to their severe impacts on consumers, businesses and industry. They constitute a major source of price risk to market participants, e.g., electricity retailers with commitments to meet customers' daily electricity demands. To those trading in several electricity markets simultaneously, the probability of simultaneous price spikes termed as tail dependence is of great importance when computing risks. For this purpose, the problem of modeling joint price spikes in the Australian electricity market is considered. A common measure of tail dependence measure is the so-called tail dependence coefficient (TDC). We present a nonparametric estimator of the tail dependence and further, point estimation is complemented with an hypotheses test. We find significant tail dependence in electricity prices that cannot be ignored. Accurate characterization of this tail dependence is important for a variety of risk management purposes. These include the hedging activities of market participants.