TY - GEN
T1 - Distributed demand response algorithms against semi-honest adversaries
AU - Zhu, Minghui
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/29
Y1 - 2014/10/29
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.
UR - http://www.scopus.com/inward/record.url?scp=84931003407&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84931003407&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2014.6939191
DO - 10.1109/PESGM.2014.6939191
M3 - Conference contribution
AN - SCOPUS:84931003407
T3 - IEEE Power and Energy Society General Meeting
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 -