Bayesian Predictive Distributions for Imbalance Prices With Time-Varying Factor Impacts

A dynamic Bayesian model is developed to estimate the time-varying nature of the drivers of the system imbalance prices in the British electricity market. We find that the key exogenous factors that significantly influence prices have impacts that evolve substantially over time. Thus, by modeling th...

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Bibliographic Details
Published in:IEEE transactions on power systems 2023-01, Vol.38 (1), p.349-357
Main Authors: Lima, Luana Marangon, Damien, Paul, Bunn, Derek W.
Format: Article
Language:English
Subjects:
Autoregressive models
Balancing market
Bayes methods
Bayesian analysis
Bayesian inference
Data models
Forecasting
Mathematical models
Parameter estimation
pinball loss
Power system dynamics
predictive distributions
Predictive models
Pricing
Quantiles
time-varying parameters
Time-varying systems
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Summary:A dynamic Bayesian model is developed to estimate the time-varying nature of the drivers of the system imbalance prices in the British electricity market. We find that the key exogenous factors that significantly influence prices have impacts that evolve substantially over time. Thus, by modeling their evolution with time varying parameter estimation and making conditional forecasts on the latest estimates, more accurate forecasts are produced. Furthermore, using a Bayesian approach allows predictive distributions to be developed, as would be required for value-at-risk compliance purposes. These densities are also found to be more accurate at the extreme quantiles than a conventional GARCH model with static parameters. We validated the superior performance of this Bayesian time varying predictive density method with the same data as in a previously published benchmark model.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2022.3165149