Multi-agent reinforcement learning for microgrids

Dimeas, AL; Hatziargyriou, ND

dc.contributor.author	Dimeas, AL	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T02:46:53Z
dc.date.available	2014-03-01T02:46:53Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32912
dc.subject	Distributed generation	en
dc.subject	Island operation	en
dc.subject	Layered learning	en
dc.subject	Microgrids	en
dc.subject	Multi agent system	en
dc.subject	Reinforcement learning	en
dc.subject.other	Distributed Generation	en
dc.subject.other	Island operation	en
dc.subject.other	Layered learning	en
dc.subject.other	Micro grid	en
dc.subject.other	Multi-agent reinforcement learning	en
dc.subject.other	Proposed architectures	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Reinforcement learning	en
dc.subject.other	Multi agent systems	en
dc.title	Multi-agent reinforcement learning for microgrids	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/PES.2010.5589633	en
heal.identifier.secondary	5589633	en
heal.identifier.secondary	http://dx.doi.org/10.1109/PES.2010.5589633	en
heal.publicationDate	2010	en
heal.abstract	This paper presents a general framework for Microgrids control based on Multi Agent System Technology. The proposed architecture is capable to integrate several functionalities, adaptable to the complexity and the size of the Microgrid. To achieve this, the idea of layered learning is used, where the various controls and actions of the agents are grouped depending on their effect on the environment. Moreover this paper, focus on how the agent will cooperate in order to achieve their goals. The core of the cooperation is a Multi Agent Reinforcement Learning Algorithm that allows the system to operate autonomously in island mode. ©2010 IEEE.	en
heal.journalName	IEEE PES General Meeting, PES 2010	en
dc.identifier.doi	10.1109/PES.2010.5589633	en