DocumentCode
135242
Title
Distributed demand response algorithms against semi-honest adversaries
Author
Minghui Zhu
Author_Institution
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
fYear
2014
fDate
27-31 July 2014
Firstpage
1
Lastpage
5
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.
Keywords
demand side management; learning (artificial intelligence); numerical analysis; power markets; stochastic games; demand allocation market; demand shedding market; distributed demand response algorithms; multiparty computation security; numerical simulation; reinforcement learning; stochastic approximation; Approximation algorithms; Games; Load management; Nash equilibrium; Pricing; Privacy; Resource management;
fLanguage
English
Publisher
ieee
Conference_Titel
PES General Meeting | Conference & Exposition, 2014 IEEE
Conference_Location
National Harbor, MD
Type
conf
DOI
10.1109/PESGM.2014.6939191
Filename
6939191
Link To Document