Antonio Bello

Medium-Term Probabilistic Forecasting of Electricity Prices: A Hybrid Approach

This paper provides a focus upon forecasting electricity prices in the medium term (from a few weeks to several months ahead) in which accurate estimates of tail risks, e.g., at the 1%, 5%, 95%, and 99%, are important. Medium term forecasting and risk analysis are important for operational scheduling, fuel purchasing, trading, and profit management. We extend the research on hybrid forecasting methods, which link detailed fundamental price formation models, using optimization techniques and market equilibrium considerations, with econometric recalibration to the time series data. This paper is innovative in its use of quantile regression to undertake the recalibration and provide accurate risk estimates. It is shown that probabilistic outputs from the fundamental model add value over expected value inputs to the quantile regressions and that if the fundamental model is itself well specified to diurnal variation through the inclusion of relevant explanatory variables such as demand or climatic conditions, then it is not necessary to undertake the quantile regressions separately for each hour of the day. A real application of the proposed methodology is successfully tested on the Spanish electric power system, in which the high penetration of intermittent wind generation creates extreme price risks. The hybrid method outperforms the more conventional fundamental model, making particular use of wind generation data in the quantile recalibrations.

Long-term Spanish electricity market price forecasting with cointegration and VEC models

Commodity and electricity price models are motivated by the several unexpected evolutions that commodity prices have shown over the previous decades. Several models are based on the classic Black-Scholes model, which was one of the first to simulate the stochastic behaviour of commodity prices. However, as of today, these forecasting models show poor performance when tested in long-term horizons, especially when applied to electricity market prices. This work attempts to determine a way to provide a decent accuracy in long-term (one year or more) forecasts of the Spanish electricity market price using cointegration and vector error correction (VEC) models, alongside other variables, such as fuel spot prices and futures prices. These variables have been assessed in order to determine which factors contribute to this works purpose.

Market equilibrium in the European electricity market. A methodology to reduce the network configurations

Market equilibrium models with conjectural variations have been used to planning generation in the medium term during the last years. When different countries or big markets try to function as unique markets equilibrium models encounter the problem of how to model the conjectures of the agents depending on the congestion of the different transmission network. This paper proposes a methodology to reduce the number of possible scenarios in the network. Then, a case of study applying the methodology to the Central Western Europe countries is presented.

Short-term forecasting of electricity prices with a computationally efficient hybrid approach

Electricity price forecasting models are of great importance for market participants due to their considerable volatility, especially in deregulated and competitive contexts. As a result, these models are highly demanded, especially in day-today applications, which require not only accurate results, but also fast responsiveness. Taking these needs into account, this work proposes a novel short-term electricity forecasting approach by means of a hybrid model, combining econometric and fundamental methods. In order to validate this works proposed method under complex price dynamics, the model has been tested for the Iberian electricity market case, and further verified by comparing its performance with other, more traditional, forecasting models.