Day-Ahead Market Self-Scheduling of a Virtual Power Plant under Uncertainties

Obada Ghassan Al-Zibak, Khalid Sulaiman Al-Jibreen, Fahad Saleh Al-Ismail

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, the problem of a Virtual Power Plant participating in the Day-Ahead energy market was addressed with the aim of maximizing its profits. The problem considered the uncertainties in the wind and prices of the day-ahead market. The uncertainties were addressed by forecasting and by applying the confidence interval statistical theory. Machine learning that is based on the Gaussian Processes was employed for estimating and forecasting the uncertain variables. Moreover, the forecasting algorithms used the exponential kernel in the regression process. The self-scheduling problem was modeled using the MILP Robust optimization model. The proposed models were tested on an illustrative case study that comprises a conventional power plant, a wind power farm, and a flexible demand.

Original languageEnglish
Title of host publication2021 International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728156811
DOIs
StatePublished - 21 Jan 2021

Publication series

Name2021 International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Gaussian Process with exponential Kernel
  • Machine learning
  • Uncertainty
  • confidence intervals
  • robust optimization

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Transportation

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