Optimal power demand management among consumers with aggregator considering state and control constraints

Yoshihiro Okawa, Toru Namerikawa

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

2 Citations (Scopus)

Abstract

This paper presents a novel optimal power demand management method for power consumers with an aggregator considering state and control constraints. The aggregator is an organization who manages some consumers to achieve demand response efficiently. This paper proposes a power demand adjustment method for the aggregator to allocate power reduction among consumers in a distributed manner. Specifically, this method simultaneously derives the optimal power reduction of each consumer using a gradient method and determines the control inputs by solving a quadratic programming problem with state and input constraint conditions. This paper also shows the convergence of the proposed algorithm, and finally, numerical simulation results illustrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages801-806
Number of pages6
ISBN (Electronic)9781509018376
DOIs
Publication statusPublished - 2016 Dec 27
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: 2016 Dec 122016 Dec 14

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period16/12/1216/12/14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

Fingerprint Dive into the research topics of 'Optimal power demand management among consumers with aggregator considering state and control constraints'. Together they form a unique fingerprint.

  • Cite this

    Okawa, Y., & Namerikawa, T. (2016). Optimal power demand management among consumers with aggregator considering state and control constraints. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 801-806). [7798366] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798366