Comparison of Deep Reinforcement Learning Algorithms in Enhancing Energy Trading in Microgrids

This paper aims to introduce a solution to Sudan’s inadequate electricity supply; focusing on current unconnected rural areas and the high cost of connecting these areas to Sudan’s national grid. Microgrids were introduced as a viable option to create small scale distributed grids that depend solely on renewable energy to generate sufficient electricity to satisfy their loads. The paper also aims to enhance the usability of Microgrids by introducing a Machine learning technique to their secondary control that uses energy trading to ensure that all loads in islanded Microgrids are secured. The algorithm uses Reinforcement learning as control for the trading procedure. Data was extracted from a Matlab simulation and was then used to enhance the design of the Reinforcement learning environment. A generic environment for microgrids was designed and implemented which can be further used in Reinforcement learning.

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