Indexed on: 19 Mar '19Published on: 08 Mar '19Published in: Artificial Intelligence Review
Smart grids are considered a promising alternative to the existing power grid, combining intelligent energy management with green power generation. Decomposed further into microgrids, these small-scaled power systems increase control and management efficiency. With scattered renewable energy resources and loads, multi-agent systems are a viable tool for controlling and improving the operation of microgrids. They are autonomous systems, where agents interact together to optimize decisions and reach system objectives. This paper presents an overview of multi-agent systems for microgrid control and management. It discusses design elements and performance issues, whereby various performance indicators and optimization algorithms are summarized and compared in terms of convergence time and performance in achieving system objectives. It is found that Particle Swarm Optimization has a good convergence time, so it is combined with other algorithms to address optimization issues in microgrids. Further, information diffusion and consensus algorithms are explored, and according to the literature, many variants of average-consensus algorithm are used to asynchronously reach an equilibrium. Finally, multi-agent system for multi-microgrid service restoration is discussed. Throughout the paper, challenges and research gaps are highlighted in each section as an opportunity for future work.