Real-time digital twin machine learning-based cost minimization model for renewable-based microgrids considering uncertainty

This research study aims to investigate the microgrid operation for distributing energy including of a local user, a wind turbine, 5 photovoltaics (PV), and a battery, which is linked by a transformer to the external network. This paper examines a reinforcement learning (RL) method that uses 2steps-ahead to schedule the batteries, which is essential for achieving the objective of the users. There is an essential architecture to make multi-criteria de-cisions via an individual user to increase the battery's usage at peak times and increase the wind turbine's usage for local consumption. RL algorithms select the optimum battery planning measures based on forecasts of wind power and photovoltaic availability. Through the suggested learning, the user can better understand the opti-mum battery planning measures for various time-varying environment factors. By using the proposed archi-tecture, smart users are capable of learning the uncertain environment and selecting optimum energy management measures based on their experiences.