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Grid dependency minimization of a microgrid using Single and Multi agent Reinforcement Learning

Distributed generation has become an emerging concept with the people’s concern towards the options for environmentally friendly electricity generation. However, the penetration of renewable-based distributed generation resources into the main power network is challenging due to their continuous controlling and monitoring requirements. Therefore, microgrids have been identified as suitable integrating platforms for distributed generation resources and loads. As a novel approach, these microgrids come up with distributed control systems as multi-agent-based systems instead of the conventional centralized control architecture. These control agents are social, reactive, proactive, and autonomous in nature. These agents can be advanced by introducing intelligence through machine learning-based approaches. Therefore, this study focuses on applying Reinforcement Learning as an experimental machine learning method to optimize the energy generation function of a grid-connected microgrid such that the grid dependency of the microgrid is minimized as the agent learns. The performance of the proposed microgrid is evaluated under single and multi-agent Reinforcement learning models in Python to identify the appropriate approach for system optimization. For the completion of the study, hardware implementation of the proposed microgrid with Reinforcement Learning applicability is entitled.