Econometric modeling of regional electricity spot prices in the Australian market
Michael Smith () and
Thomas S. Shively
Energy Economics, 2018, vol. 74, issue C, 886-903
Abstract:
Wholesale electricity markets are increasingly integrated via high voltage interconnectors, and inter-regional trade in electricity is growing. To model this, we consider a spatial equilibrium model of price formation, where constraints on inter-regional flows result in three distinct equilibria in prices. We use this to motivate an econometric model for the distribution of observed electricity spot prices that captures many of their unique empirical characteristics. The econometric model features supply and inter-regional trade cost functions, which are estimated using Bayesian monotonic regression smoothing methodology. A copula multivariate time series model is employed to capture additional dependence — both cross-sectional and serial — in regional prices. The marginal distributions are nonparametric, with means given by the regression means. The model has the advantage of preserving the heavy right-hand tail in the predictive densities of price. We fit the model to half-hourly spot price data in the five interconnected regions of the Australian national electricity market. The fitted model is then used to measure how both supply and price shocks in one region are transmitted to the distribution of prices in all regions in subsequent periods. Finally, to validate our econometric model, we show that prices forecast using the proposed model compares favorably with those from some benchmark alternatives.
Keywords: Bayesian monotonic function estimation; Intraday electricity prices; Copula time series model (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C53 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)
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Working Paper: Econometric Modeling of Regional Electricity Spot Prices in the Australian Market (2018) 
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:74:y:2018:i:c:p:886-903
DOI: 10.1016/j.eneco.2018.07.013
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