Risk-limiting power grid control with an ARMA-based prediction model

Masahiro Ono, Ufuk Topcu, Masaki Yo, Shuichi Adachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper is concerned with the risk-limiting operation of electric power grids with stochastic uncertainties due to, for example, demand and integration of renewable generation. The main contribution is incorporating autoregressive- moving-average (ARMA) type prediction models for the underlying uncertainties into chance-constrained, finitehorizon optimal control. This uncertainty model leads to a more (compared to existing work in literature) careful treatment of correlation in time which is significant especially in renewable generation yet has attracted limited attention. The paper first discusses how the resulting chance-constrained optimization problems can be solved computationally and demonstrates the effects of the use of the proposed prediction models through simulation-based case studies with realistic data.

Original languageEnglish
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4949-4956
Number of pages8
ISBN (Print)9781467357173
DOIs
Publication statusPublished - 2013 Jan 1
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: 2013 Dec 102013 Dec 13

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
CountryItaly
CityFlorence
Period13/12/1013/12/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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  • Cite this

    Ono, M., Topcu, U., Yo, M., & Adachi, S. (2013). Risk-limiting power grid control with an ARMA-based prediction model. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 4949-4956). [6760666] (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6760666