By Topic

Adaptive Multiagent Model Based on Reinforcement Learning for Distributed Generation Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Divenyi, D. ; Dept. of Electr. Power Eng., Budapest Univ. of Technol. & Econ., Budapest, Hungary ; Dan, A.

Distributed generation have been widely spread in the last decades raising a lot of questions regarding the safe and high-quality operation of the power systems. The investigation of these questions requires a proper model considering the different technical, economical and legal aspects. The goal of our research was to develop a multiagent system where rational agents control each distributed generation unit. Based on intelligent agent-program the agents are able to optimize their operations taking several viewpoints into account, like fulfilling the contractual obligations, considering the technical constraints and maximizing the realized profit in a continuously varying market environment. This paper describes a simple reinforcement learning method resulting in an adaptive agent-program. The agents are informed about their realized profits and they apply this information to evaluate their former decisions and to adjust the parameters of their agent-program. The verification of the model proved that the developed agent-program provides acceptable results compared to the real productions.

Published in:

Database and Expert Systems Applications (DEXA), 2012 23rd International Workshop on

Date of Conference:

3-7 Sept. 2012