Title :
Distributed demand response algorithms against semi-honest adversaries
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
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;
Conference_Titel :
PES General Meeting | Conference & Exposition, 2014 IEEE
Conference_Location :
National Harbor, MD
DOI :
10.1109/PESGM.2014.6939191
