Begin New Search
Proceed to Checkout

Search Results for All:
(Showing results 1 to 3 of 3)

It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices

Timothy Christensen, Stan Hurn and Kenneth Lindsay

Year: 2009
Volume: Volume 30
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol30-No1-2
View Abstract

During periods of market stress, electricity prices can rise dramatically. This paper treats these abnormal episodes or price spikes as count events and attempts to build a model of the spiking process. By contrast to the existing literature, which either ignores temporal dependence in the spiking process or attempts to model the dependence solely in terms of deterministic variables (like seasonal and day of the week effects), this paper argues that persistence in the spiking process is an important factor in building an effective model. A Poisson autoregressive framework is proposed in which price spikes occur as a result of the latent arrival and survival of system stresses. This formulation captures the salient features of the process adequately, and yields forecasts of price spikes that are superior to those obtained from na�ve models that do not account for persistence in the spiking process.

The Effect of Transmission Constraints on Electricity Prices

Adam E. Clements, A. Stan Hurn, and Zili Li

Year: 2017
Volume: Volume 38
Number: Number 4
DOI: 10.5547/01956574.38.4.acle
View Abstract

Electricity prices in an interconnected market are influenced by the occurrence of transmission constraints. Until relatively recently, however, the important effects of transmission constraints on both the trajectory and volatility of electricity prices have not played a large role in empirical models of prices. This paper explores the contribution to price volatility in the Queensland electricity market made by transmission constraints. It is found that robust estimation techniques are necessary to guard against incorrect inference in time series models using electricity price data in which severe price spikes occur. The main empirical lesson is that transmission constraints contribute significantly both to the level and variability of price and consequently the performance of a price forecasting model is likely to be improved by incorporating information on transmission constraints. While the general tenor of this conclusion will come as no surprise, the extent and the importance of these effects found in this paper for forecasting price and for computing summary measures like Value-at-Risk serve as a timely reminder to practitioners.

Modeling Multi-horizon Electricity Demand Forecasts in Australia: A Term Structure Approach

Stan Hurn, Vance Martin, and Jing Tian

Year: 2023
Volume: Volume 44
Number: Number 3
DOI: 10.5547/01956574.44.2.shur
View Abstract

The Australian Electricity Market Operator generates one-day ahead electricity demand forecasts for the National Electricity Market in Australia and updates these forecasts over time until the time of dispatch. Despite the fact that these forecasts play a crucial role in the decision-making process of market participants, little attention has been paid to their evaluation and interpretation. Using half-hourly data from 2011 to 2015 for New South Wales and Queensland, it is shown that the official half-hourly demand forecasts do not satisfy the econometric properties required of rational forecasts. Instead there is a relationship between forecasts and forecast horizon similar to a term structure model of interest rates. To study the term structure of demand forecasts, a factor analysis that uses a small set of latent factors to explain the common variation among multiple observables is implemented. A three-factor model is identified with the factors admitting interpretation as the level, slope and curvature of the term structure of forecasts. The validity of the model is reinforced by assessing the economic value of demand forecasts. It is demonstrated that simple adjustments to long-horizon electricity demand forecasts based on the three estimated factors can enhance the informational content of the official forecasts.

Begin New Search
Proceed to Checkout


© 2023 International Association for Energy Economics | Privacy Policy | Return Policy