Smart Microgrid Optimization using Deep Reinforcement Learning by utilizing the Energy Storage Systems

The need for power is expanding quickly, and as fossil fuels are depleted, alternatives based on renewable energy are emerging. In recent years, microgrids, which are based on distributed generation and storage systems, have been on an increasing trend. This generates various new opportunities, one of which is the capacity to monitor, control, and regulate the energy flow inside and outside of the microgrid. By making microgrid’s distributed energy resources (DERs) and energy storage components economically viable with artificial intelligence and machine learning incorporated into the cost-optimization process, the demand for and growth of these technologies will be accelerated significantly. This paper focuses on employing reinforcement learning (RL) algorithms to control energy flow in an AC microgrid. By incorporating artificial intelligence and machine learning into the energy management system (EMS), the paper aims to optimize costs and facilitate the integration of renewable energy sources. The RL agent is designed to trade energy with the main grid, taking advantage of the energy storage system and achieving cost savings. The RL agent is tested using real spot prices data in Norway from a simulation model that combines Python and MATLAB-Simulink software programs for efficient co-simulation technique. Results show significant cost savings of around 14% for a simple model and 7.5% for a complex dynamic model.