Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks

A.N.M.M. Haque; T.H. Vo; H.P. Nguyen

doi:10.1109/ICSET.2016.7811819

Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks

A.N.M.M. Haque
, T.H. Vo
, H.P. Nguyen

Electrical Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

1 Downloads (Pure)

Abstract

Electrical distribution networks worldwide are facing frequent capacity challenges due to the widespread roll out of various distributed energy resources (DERs). A number of demand response (DR) mechanisms have been developed in order to circumvent the problems and enhance the flexibility of the distribution network. While the existing centralized control system remains its crucial role for reliable and secure grid operation, distributed intelligence is a complement technology with a focus on dividing the control task into a number of simpler problems and solve them with minimum exchange of information. Based on the recent developments of distributed intelligence, this paper discusses a decentralized approach to enable demand response for managing the congestions more efficiently. The approach is validated with simulations for representative Dutch low-voltage (LV) networks.

Original language	English
Title of host publication	2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	407-413
Number of pages	7
ISBN (Electronic)	978-1-5090-5200-4
ISBN (Print)	978-1-5090-5201-1
DOIs	https://doi.org/10.1109/ICSET.2016.7811819
Publication status	Published - 16 Jan 2017
Event	2016 IEEE International Conference on Sustainable Energy Technologies - Melia Hanoi, Hanoi, Viet Nam Duration: 14 Nov 2016 → 16 Nov 2016

Conference

Conference	2016 IEEE International Conference on Sustainable Energy Technologies
Abbreviated title	ICSET 2016
Country/Territory	Viet Nam
City	Hanoi
Period	14/11/16 → 16/11/16

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Demand response
congestion management
distributed intelligence
flexibility

Access to Document

10.1109/ICSET.2016.7811819

Cite this

Haque, A. N. M. M., Vo, T. H., & Nguyen, H. P. (2017). Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks. In 2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam (pp. 407-413). Article 7811819 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICSET.2016.7811819

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abstract = " Electrical distribution networks worldwide are facing frequent capacity challenges due to the widespread roll out of various distributed energy resources (DERs). A number of demand response (DR) mechanisms have been developed in order to circumvent the problems and enhance the flexibility of the distribution network. While the existing centralized control system remains its crucial role for reliable and secure grid operation, distributed intelligence is a complement technology with a focus on dividing the control task into a number of simpler problems and solve them with minimum exchange of information. Based on the recent developments of distributed intelligence, this paper discusses a decentralized approach to enable demand response for managing the congestions more efficiently. The approach is validated with simulations for representative Dutch low-voltage (LV) networks.",

keywords = "Demand response, congestion management, distributed intelligence, flexibility",

author = "A.N.M.M. Haque and T.H. Vo and H.P. Nguyen",

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booktitle = "2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam",

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Haque, ANMM, Vo, TH & Nguyen, HP 2017, Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks. in 2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam., 7811819, Institute of Electrical and Electronics Engineers, Piscataway, pp. 407-413, 2016 IEEE International Conference on Sustainable Energy Technologies, Hanoi, Viet Nam, 14/11/16. https://doi.org/10.1109/ICSET.2016.7811819

Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks. / Haque, A.N.M.M.; Vo, T.H.; Nguyen, H.P.
2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam. Piscataway: Institute of Electrical and Electronics Engineers, 2017. p. 407-413 7811819.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks

AU - Haque, A.N.M.M.

AU - Vo, T.H.

AU - Nguyen, H.P.

PY - 2017/1/16

Y1 - 2017/1/16

N2 - Electrical distribution networks worldwide are facing frequent capacity challenges due to the widespread roll out of various distributed energy resources (DERs). A number of demand response (DR) mechanisms have been developed in order to circumvent the problems and enhance the flexibility of the distribution network. While the existing centralized control system remains its crucial role for reliable and secure grid operation, distributed intelligence is a complement technology with a focus on dividing the control task into a number of simpler problems and solve them with minimum exchange of information. Based on the recent developments of distributed intelligence, this paper discusses a decentralized approach to enable demand response for managing the congestions more efficiently. The approach is validated with simulations for representative Dutch low-voltage (LV) networks.

AB - Electrical distribution networks worldwide are facing frequent capacity challenges due to the widespread roll out of various distributed energy resources (DERs). A number of demand response (DR) mechanisms have been developed in order to circumvent the problems and enhance the flexibility of the distribution network. While the existing centralized control system remains its crucial role for reliable and secure grid operation, distributed intelligence is a complement technology with a focus on dividing the control task into a number of simpler problems and solve them with minimum exchange of information. Based on the recent developments of distributed intelligence, this paper discusses a decentralized approach to enable demand response for managing the congestions more efficiently. The approach is validated with simulations for representative Dutch low-voltage (LV) networks.

KW - Demand response

KW - congestion management

KW - distributed intelligence

KW - flexibility

UR - https://www.scopus.com/pages/publications/85011928635

U2 - 10.1109/ICSET.2016.7811819

DO - 10.1109/ICSET.2016.7811819

M3 - Conference contribution

SN - 978-1-5090-5201-1

SP - 407

EP - 413

BT - 2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2016 IEEE International Conference on Sustainable Energy Technologies

Y2 - 14 November 2016 through 16 November 2016

ER -

Haque ANMM, Vo TH, Nguyen HP. Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks. In 2016 IEEE International Conference on Sustainable Energy Technologies, 14-16 November 2016, Hanoi, Vietnam. Piscataway: Institute of Electrical and Electronics Engineers. 2017. p. 407-413. 7811819 doi: 10.1109/ICSET.2016.7811819

Distributed intelligence : unleashing flexibilities for congestion management in smart distribution networks

Abstract

Conference

UN SDGs

Keywords

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Demand response for real-time congestion management incorporating dynamic thermal overloading cost

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