Optimal power demand management by aggregators using matching theory

Hiroto Ikegami, Toru Namerikawa

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

Abstract

This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.

Original languageEnglish
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4584-4589
Number of pages6
Volume2018-June
ISBN (Print)9781538654286
DOIs
Publication statusPublished - 2018 Aug 9
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: 2018 Jun 272018 Jun 29

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period18/6/2718/6/29

Fingerprint

Profitability
Power generation
Power markets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Ikegami, H., & Namerikawa, T. (2018). Optimal power demand management by aggregators using matching theory. In 2018 Annual American Control Conference, ACC 2018 (Vol. 2018-June, pp. 4584-4589). [8431856] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2018.8431856

Optimal power demand management by aggregators using matching theory. / Ikegami, Hiroto; Namerikawa, Toru.

2018 Annual American Control Conference, ACC 2018. Vol. 2018-June Institute of Electrical and Electronics Engineers Inc., 2018. p. 4584-4589 8431856.

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

Ikegami, H & Namerikawa, T 2018, Optimal power demand management by aggregators using matching theory. in 2018 Annual American Control Conference, ACC 2018. vol. 2018-June, 8431856, Institute of Electrical and Electronics Engineers Inc., pp. 4584-4589, 2018 Annual American Control Conference, ACC 2018, Milwauke, United States, 18/6/27. https://doi.org/10.23919/ACC.2018.8431856
Ikegami H, Namerikawa T. Optimal power demand management by aggregators using matching theory. In 2018 Annual American Control Conference, ACC 2018. Vol. 2018-June. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4584-4589. 8431856 https://doi.org/10.23919/ACC.2018.8431856
Ikegami, Hiroto ; Namerikawa, Toru. / Optimal power demand management by aggregators using matching theory. 2018 Annual American Control Conference, ACC 2018. Vol. 2018-June Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4584-4589
@inproceedings{62afa076cc744f0b9061fb99e8d746d2,
title = "Optimal power demand management by aggregators using matching theory",
abstract = "This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.",
author = "Hiroto Ikegami and Toru Namerikawa",
year = "2018",
month = "8",
day = "9",
doi = "10.23919/ACC.2018.8431856",
language = "English",
isbn = "9781538654286",
volume = "2018-June",
pages = "4584--4589",
booktitle = "2018 Annual American Control Conference, ACC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Optimal power demand management by aggregators using matching theory

AU - Ikegami, Hiroto

AU - Namerikawa, Toru

PY - 2018/8/9

Y1 - 2018/8/9

N2 - This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.

AB - This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.

UR - http://www.scopus.com/inward/record.url?scp=85052593949&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052593949&partnerID=8YFLogxK

U2 - 10.23919/ACC.2018.8431856

DO - 10.23919/ACC.2018.8431856

M3 - Conference contribution

SN - 9781538654286

VL - 2018-June

SP - 4584

EP - 4589

BT - 2018 Annual American Control Conference, ACC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -