Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience

Kapil Deshpande (), Philipp Möhl, Alexander Hämmerle, Georg Weichhart, Helmut Zörrer and Andreas Pichler
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Kapil Deshpande: Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria
Philipp Möhl: Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria
Alexander Hämmerle: Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria
Georg Weichhart: Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria
Helmut Zörrer: Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria
Andreas Pichler: Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria

Energies, 2022, vol. 15, issue 19, 1-35

Abstract: The share of energy produced by small-scale renewable energy sources, including photovoltaic panels and wind turbines, will significantly increase in the near future. These systems will be integrated in microgrids to strengthen the independence of energy consumers. This work deals with energy management in microgrids, taking into account the volatile nature of renewable energy sources. In the developed approach, Multi-Agent Reinforcement Learning is applied, where agents represent microgrid components. The individual agents are trained to make good decisions with respect to adapting to the energy load in the grid. Training of agents leverages the historic energy profile data for energy consumption and renewable energy production. The implemented energy management simulation shows good performance and balances the energy flows. The quantitative performance evaluation includes comparisons with the exact solutions from a linear program. The computational results demonstrate good generalisation capabilities of the trained agents and the impact of these capabilities on the reliability and resilience of energy management in microgrids.

Keywords: energy management; multi-agent reinforcement learning; renewable energy systems; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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