High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems

Stefanos Delikaraoglou, Pierre Pinson

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

234 Downloads (Pure)

Abstract

The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model.
Original languageEnglish
Title of host publicationProceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks
Number of pages6
PublisherIEEE
Publication date2014
Publication statusPublished - 2014
Event13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power (WIW 2014) - Berlin, Germany
Duration: 11 Nov 201413 Nov 2014
Conference number: 13

Conference

Conference13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power (WIW 2014)
Number13
CountryGermany
CityBerlin
Period11/11/201413/11/2014

Cite this

Delikaraoglou, S., & Pinson, P. (2014). High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems. In Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks IEEE.
Delikaraoglou, Stefanos ; Pinson, Pierre. / High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems. Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks. IEEE, 2014.
@inproceedings{7518e2fd8da24ed892661b95ce57c94d,
title = "High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems",
abstract = "The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model.",
author = "Stefanos Delikaraoglou and Pierre Pinson",
year = "2014",
language = "English",
booktitle = "Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks",
publisher = "IEEE",
address = "United States",

}

Delikaraoglou, S & Pinson, P 2014, High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems. in Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks. IEEE, 13th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power (WIW 2014), Berlin, Germany, 11/11/2014.

High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems. / Delikaraoglou, Stefanos; Pinson, Pierre.

Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks. IEEE, 2014.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems

AU - Delikaraoglou, Stefanos

AU - Pinson, Pierre

PY - 2014

Y1 - 2014

N2 - The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model.

AB - The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model.

M3 - Article in proceedings

BT - Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks

PB - IEEE

ER -

Delikaraoglou S, Pinson P. High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems. In Proceedings of 13th International Workshop on Large-Scale Integration of Wind Power and Transmission Networks. IEEE. 2014