MIBEL prices: parameter estimation and pattern simulation

Abstract

EDP Group is an Energy Solutions Operator which operates in the business areas of generation, supply and distribution of electricity and supply and distribution of gas.

The challenge proposed by EDP consists in simulating electricity prices not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. Data concerning hourly electricity prices from 2008 to 2016 was provided.

Numerous methods to deal with Electricity Price Forecasting (EPF) have been proposed and can be classified as: (i) multi-agent models, (ii) fundamental models, (iii) reduced-form models, (iv) statistical models and (v) computational intelligence models. A recent an exaustive review is presentes in [13].

During this study group different promising Statistical techniques were propose by the study group contributors: ARIMA, sARIMA, Longitudinal Models, Generalized Linear Models and Vector Autoregressive Models. In this report a GLM and a vector autoregressive model are presented and their predictive power is discussed.

In the GLM framework two different transformations were consider and for both the season of the year, month or winter/summer period revealed significant explanatory variables in the different estimated models.
On the other hand, the multivariate approach using VAR considering as exogenous variables the meteorologic season and the type of day yield a multivariate model that explains the intra-day and intra-hour dynamics of the hourly prices. Although the forecast do not exactly replicate the real price they are quite similar.

In both of the approaches here reported a more extensive work would certainly improve the proposed models.
In conclusions, EPF is a growing area that groups multiple different approaches that can be applied. In fact, other approaches from multi-agent models, fundamental models, reduced-form models and computational intelligence models, also present a great space for EPF.