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: Contribution to journal › Conference article › peer-review

8 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)
Article number	6939191
Journal	IEEE Power and Energy Society General Meeting
Volume	2014-October
Issue number	October
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

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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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.",

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