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

Masahiro Ono; Ufuk Topcu; Masaki Yo; Shuichi Adachi

doi:10.1109/CDC.2013.6760666

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

Masahiro Ono, Ufuk Topcu, Masaki Yo, Shuichi Adachi

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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 language	English
Title of host publication	2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4949-4956
Number of pages	8
ISBN (Print)	9781467357173
DOIs	https://doi.org/10.1109/CDC.2013.6760666
Publication status	Published - 2013
Event	52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy Duration: 2013 Dec 10 → 2013 Dec 13

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Other

Other	52nd IEEE Conference on Decision and Control, CDC 2013
Country/Territory	Italy
City	Florence
Period	13/12/10 → 13/12/13

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CDC.2013.6760666

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). Article 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

Risk-limiting power grid control with an ARMA-based prediction model. / Ono, Masahiro; Topcu, Ufuk; Yo, Masaki et al.
2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 4949-4956 6760666 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 6760666, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 4949-4956, 52nd IEEE Conference on Decision and Control, CDC 2013, Florence, Italy, 13/12/10. https://doi.org/10.1109/CDC.2013.6760666

@inproceedings{7459d5d9bd6d430cbc23bdb8801e0128,

title = "Risk-limiting power grid control with an ARMA-based prediction model",

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.",

author = "Masahiro Ono and Ufuk Topcu and Masaki Yo and Shuichi Adachi",

year = "2013",

doi = "10.1109/CDC.2013.6760666",

language = "English",

isbn = "9781467357173",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4949--4956",

booktitle = "2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013",

note = "52nd IEEE Conference on Decision and Control, CDC 2013 ; Conference date: 10-12-2013 Through 13-12-2013",

}

TY - GEN

T1 - Risk-limiting power grid control with an ARMA-based prediction model

AU - Ono, Masahiro

AU - Topcu, Ufuk

AU - Yo, Masaki

AU - Adachi, Shuichi

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84902352169&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902352169&partnerID=8YFLogxK

U2 - 10.1109/CDC.2013.6760666

DO - 10.1109/CDC.2013.6760666

M3 - Conference contribution

AN - SCOPUS:84902352169

SN - 9781467357173

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 4949

EP - 4956

BT - 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 52nd IEEE Conference on Decision and Control, CDC 2013

Y2 - 10 December 2013 through 13 December 2013

ER -

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

Abstract

Publication series

Other

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this