Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

Ruiqiu Yao, Yukun Hu () and Liz Varga
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Ruiqiu Yao: Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK
Yukun Hu: Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK
Liz Varga: Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK

Energies, 2023, vol. 16, issue 5, 1-36

Abstract: The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process.

Keywords: integrated energy system; multi-energy system; multi-agent system; agent-based modeling; systematic literature review; optimization; muti-agent reinforcement learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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