Multi-agent systems applications in energy optimization problems: a state-of-the-art review

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization
solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper
and it is compared with traditional approaches in the development of energy optimization solutions.
The different types of agent-based architectures are described, the role played by the environment is
analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it.
Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions
aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model
and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field,
and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore,
we can argue that MAS is a widespread approach in the field of energy optimization and that it is
commonly used due to its capacity for the communication, coordination, cooperation of agents and
the robustness that this methodology gives in assigning different tasks to agents. Finally, this article
considers how MASs can be used for various purposes, from capturing sensor data to decision-making.
We propose some research perspectives on the development of electrical optimization solutions
through their development using MASs. In conclusion, we argue that researchers in the field of
energy optimization should use multi-agent systems at those junctures where it is necessary to model
energy efficiency solutions that involve a wide range of factors, as well as context independence
that they can achieve through the addition of new agents or agent organizations, enabling the
development of energy-efficient solutions for smart cities and intelligent buildings.