Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids. (1st June 2018)

• Record Type: Journal Article
• Authors: Kofinas, P.; Dounis, A.I.; Vouros, G.A.
• Is Part Of: Applied energy. Volume 219(2018)
• Page Start: 53
• Page End: 67
• Publication Date: 2018-06-01
• Subjects: Energy management -- Reinforcement learning (RL) -- Fuzzy Q-Learning -- Multi-agent system (MAS) -- Microgrid; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.03.017
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Power balancing with a fully decentralized framework. MAS with modified Independent Learners approach for energy management of microgrid. MAS and Fuzzy Q-Learning for continuous states and actions space. Reinforcement Learning (Q-learning) for Collaborative MAS. Abstract: This study proposes a cooperative multi-agent system for managing the energy of a stand-alone microgrid. The multi-agent system learns to control the components of the microgrid so as this to achieve its purposes and operate effectively, by means of a distributed, collaborative reinforcement learning method in continuous actions-states space. Stand-alone microgrids present challenges regarding guaranteeing electricity supply and increasing the reliability of the system under the uncertainties introduced by the renewable power sources and the stochastic demand of the consumers. In this article we consider a microgrid that consists of power production, power consumption and power storage units: the power production group includes a Photovoltaic source, a fuel cell and a diesel generator; the power consumption group includes an electrolyzer unit, a desalination plant and a variable electrical load that represent the power consumption of a building; the power storage group includes only the Battery bank. We conjecture that a distributed multi-agent system presents specific advantages to control the microgrid components which operate in a continuous states and actions space: For this purpose we propose the use of fuzzy Q-Learning methods for agents representing microgrid components to act as independent learners, while sharing state variables to coordinate their behavior. Experimental results highlight both the effectiveness of individual agents to control system components, as well as the effectiveness of the multi-agent system to guarantee electricity supply and increase the reliability of the microgrid.