DocumentCode :
1669631
Title :
Using smart devices for system-level management and control in the smart grid: A reinforcement learning framework
Author :
Kara, Emre Can ; Berges, Mario ; Krogh, Bruce ; Kar, Soummya
Author_Institution :
Civil & Environ. Eng, Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2012
Firstpage :
85
Lastpage :
90
Abstract :
This paper presents a stochastic modeling framework to employ adaptive control strategies in order to provide short term ancillary services to the power grid by using a population of heterogenous thermostatically controlled loads. The problem is cast anew as a classical Markov Decision Process (MDP) to leverage existing tools in the field of reinforcement learning. Initial considerations and possible reductions in the action and state spaces are described. A Q-learning approach is implemented in simulation to demonstrate how the performance of the new MDP representation is comparable to that of a Linear Time-Invariant (LTI) one on a reference tracking scenario.
Keywords :
Markov processes; learning (artificial intelligence); power system control; smart power grids; LTI; MDP representation; Q-learning approach; adaptive control strategies; classical Markov decision process; heterogenous thermostatically controlled loads; linear time-invariant; reference tracking scenario; reinforcement learning; reinforcement learning framework; short term ancillary services; smart devices; smart grid control; stochastic modeling framework; system-level management; Home appliances; Load modeling; Sociology; Statistics; Switches; Temperature control; Temperature distribution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Smart Grid Communications (SmartGridComm), 2012 IEEE Third International Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4673-0910-3
Electronic_ISBN :
978-1-4673-0909-7
Type :
conf
DOI :
10.1109/SmartGridComm.2012.6485964
Filename :
6485964
Link To Document :
بازگشت