Multi agent systems and reinforcement learning for advanced energy management of smart micro grids

Abstract

The power sector is undergoing a profound change depletion of fossil fuels and environmental considerations have made it embrace renewable energy resources such as solar and wind. A micro-grid is a building block of a smart grid and is poised to play a major role in enabling the widespread adoption of renewable distributed energy resources. However, as the power generated from renewable resources is intermittent in nature, it impacts the dynamics and stability of the micro-grid, and hence their integration into the micro-grid necessitates new approaches to coordination and control. The existing systems lack run-time adaptive behaviour and suffer from communication overhead. To meet these challenges and to achieve an newlineoptimal balance between generation, energy storage and load demands, we need to incorporate efficient communication and control strategies into microgrid monitoring. Agent oriented programing is the latest paradigm of computer programming, used for complex and distributed systems. It has autonomic and proactive characteristics with higher level abstraction. Multi-Agent System (MAS) is emerging as an integrated solution approach to distributed computing, communication, and data integration needs for smart grid application. A Multi-Agent System (MAS) is a distributed system consisting of multiple software agents, forming a loosely coupled network and working together to solve problems that are beyond their individual capabilities. Distributed and heterogeneous information can be efficiently processed locally, but utilized globally to coordinate distributed knowledge networks, resulting in reduction of information processing time and network bandwidth, compared to centralized schemes. The MAS approach makes decision making in simulation platforms flexible and versatile from technical and economic point of view. newline newline