Bayesian estimation of trend components within Markovian regime-switching models for wholesale electricity prices: an application to the South Australian wholesale electricity market

TL;DR

This paper develops a Bayesian approach for estimating trend components in Markovian regime-switching models for wholesale electricity prices, addressing issues in existing methods and applying it to the South Australian market.

Contribution

It introduces a model-based definition of extreme prices and implements exact Bayesian inference, improving model selection and fit assessment in electricity price modeling.

Findings

Effective Bayesian estimation of regime-switching models

Improved model selection using Bayesian criteria

Successful application to South Australian electricity market

Abstract

We discuss and extend methods for estimating Markovian-Regime-Switching (MRS) and trend models for wholesale electricity prices. We argue the existing methods of trend estimation used in the electricity price modelling literature either require an ambiguous definition of an extreme price, or lead to issues when implementing model selection [23]. The first main contribution of this paper is to design and infer a model which has a model-based definition of extreme prices and permits the use of model selection criteria. Due to the complexity of the MRS models inference is not straightforward. In the existing literature an approximate EM algorithm is used [26]. Another contribution of this paper is to implement exact inference in a Bayesian setting. This also allows the use of posterior predictive checks to assess model fit. We demonstrate the methodologies with South Australian electricity market.