Modelling the Evolution of Wind and Solar Power Infeed Forecasts

Journal of Commodity Markets

28 Pages Posted: 9 Jun 2020 Last revised: 7 Apr 2021

See all articles by Wei Li

Wei Li

affiliation not provided to SSRN

Florentina Paraschiv

Zeppelin University, Chair of Finance; Norwegian University of Science and Technology, Faculty of Economics and Management, NTNU Business School; University of St. Gallen, Institute for Operations Research and Computational Finance

Date Written: May 14, 2020

Abstract

With the increasing integration of wind and photovoltaic power in the whole European power system, there is a longing for detecting how to trade energy in the ever-changing intraday market from electric power industries. Intraday trading becomes even more relevant in the wake of the European Cross-Border Intraday (XBID) project, which aims at integrating electricity trading across Europe. Therefore, optimal trading strategies to address forecast fluctuations in renewables output are growingly required to be designed. In this study, we model, simulate and predict the evolution of wind and PV infeed forecasting errors over eight days preceding the start of a given quarter-hourly delivery period and updated in 15-minute steps. We test comparatively the performance of several stochastic and probabilistic models, and recommend their complementary use, depending on the frequency in which forecast values are adjusted. Since ex-ante updated forecasting errors of renewables infeed are usually not available to researchers, simulations based on our proposed models break the ground for further applications to intraday pricing and optimization.

Keywords: Wind/Photovoltaic forecasting errors, intraday market, GMM, stochastic models

JEL Classification: C22; C53; C55; G10; Q20; Q40; Q41; Q42

Suggested Citation

Li, Wei and Paraschiv, Florentina, Modelling the Evolution of Wind and Solar Power Infeed Forecasts (May 14, 2020). Journal of Commodity Markets, Available at SSRN: https://ssrn.com/abstract=3600775 or http://dx.doi.org/10.2139/ssrn.3600775

Wei Li (Contact Author)

affiliation not provided to SSRN

Florentina Paraschiv

Zeppelin University, Chair of Finance ( email )

Am Seemooser Horn 20
Friedrichshafen, 88045
Germany

Norwegian University of Science and Technology, Faculty of Economics and Management, NTNU Business School ( email )

Klæbuveien 72
Trondheim, NO-7030
Norway

University of St. Gallen, Institute for Operations Research and Computational Finance ( email )

Bodanstrasse 6
St. Gallen, 9000
Switzerland

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