Distributed demand response algorithms against semi-honest adversaries

Minghui Zhu

doi:10.1109/PESGM.2014.6939191

Distributed demand response algorithms against semi-honest adversaries

Minghui Zhu

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

10 Scopus citations

Abstract

This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.

Original language	English (US)
Title of host publication	2014 IEEE PES General Meeting / Conference and Exposition
Publisher	IEEE Computer Society
Edition	October
ISBN (Electronic)	9781479964154
DOIs	https://doi.org/10.1109/PESGM.2014.6939191
State	Published - Oct 29 2014
Event	2014 IEEE Power and Energy Society General Meeting - National Harbor, United States Duration: Jul 27 2014 → Jul 31 2014

Publication series

Name	IEEE Power and Energy Society General Meeting
Number	October
Volume	2014-October
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Other

Other	2014 IEEE Power and Energy Society General Meeting
Country/Territory	United States
City	National Harbor
Period	7/27/14 → 7/31/14

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

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10.1109/PESGM.2014.6939191

Cite this

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title = "Distributed demand response algorithms against semi-honest adversaries",

abstract = "This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.",

author = "Minghui Zhu",

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Zhu, M 2014, Distributed demand response algorithms against semi-honest adversaries. in 2014 IEEE PES General Meeting / Conference and Exposition. October edn, 6939191, IEEE Power and Energy Society General Meeting, no. October, vol. 2014-October, IEEE Computer Society, 2014 IEEE Power and Energy Society General Meeting, National Harbor, United States, 7/27/14. https://doi.org/10.1109/PESGM.2014.6939191

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PY - 2014/10/29

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N2 - This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.

AB - This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.

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M3 - Conference contribution

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BT - 2014 IEEE PES General Meeting / Conference and Exposition

PB - IEEE Computer Society

T2 - 2014 IEEE Power and Energy Society General Meeting

Y2 - 27 July 2014 through 31 July 2014

ER -

Distributed demand response algorithms against semi-honest adversaries

Abstract

Publication series

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All Science Journal Classification (ASJC) codes

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