A Survey of Multi-Agent Systems for Smartgrids

Yusuf Izmirlioglu, Loc Pham, Tran Cao Son and Enrico Pontelli ()
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Yusuf Izmirlioglu: Department of Computer Science, University of Roehampton, London SW15 5PH, UK
Loc Pham: Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
Tran Cao Son: Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
Enrico Pontelli: Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA

Energies, 2024, vol. 17, issue 15, 1-62

Abstract: This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems.

Keywords: Smartgrid; multi-agent systems; electrical infrastructure; renewable energy; Artificial Intelligence; knowledge reasoning (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: 2024
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