Modeling and forecasting electricity prices with input/output hidden Markov models

In competitive electricity markets, in addition to the uncertainty of exogenous variables such as energy demand, water inflows, and availability of generation units and fuel costs, participants are faced with the uncertainty of their competitors' behavior. The analysis of electricity price time series reflects a switching nature, related to discrete changes in competitors' strategies, which can be represented by a set of dynamic models sequenced together by a Markov chain. An input-output hidden Markov model (IOHMM) is proposed for analyzing and forecasting electricity spot prices. The model provides both good predictions in terms of accuracy as well as dynamic information about the market. In this way, different market states are identified and characterized by their more relevant explanatory variables. Moreover, a conditional probability transition matrix governs the probabilities of remaining in the same state, or changing to another, whenever a new market session is opened. The model has been successfully applied to real clearing prices in the Spanish electricity market.

I. Introduction

During the last decade, the electric industries of many countries all around the world have suffered profound regulatory changes, which gave rise to many international experiences. In Spain, the overall electricity business is organized as a sequence of markets. The day-ahead spot market consists of 24 hourly auctions that take place simultaneously one day in advance. The clearing of this market provides the provisional energy schedule of each bidding unit, and the hourly marginal price is found as the intersection of supply and demand curves. After the spot market, where the major part of the total energy is traded, subsequent short-term market mechanisms (intraday markets, ancillary reserves, and real-time markets) are available in order to guarantee the final balance between power generation and consumers' demand.