This project explores the development of an Energy Management System (EMS) designed to optimize power generation from wind turbines, battery storage, and load management using Reinforcement Learning (RL). By leveraging load profile and wind speed data, we create an RL agent that makes optimal decisions in a fluctuating energy landscape. The EMS incorporates key cost parameters, including USD 0.20 per kWh for imported energy, USD 0.05 per kWh for exported energy, and USD 0.10 per kWh for battery usage. The main objective of the agent is to maximize rewards by minimizing costs associated with energy consumption while improving the efficiency of both energy generation and storage.

Through extensive training and simulation, the RL agent adapts to varying conditions, effectively balancing energy supply and demand in response to changes in wind energy generation. Preliminary results indicate that the EMS not only enhances cost efficiency but also improves overall energy utilization. This demonstrates the viability of applying RL techniques in the management of renewable energy resources.

The findings of this research significantly contribute to the advancement of smart energy systems and the integration of sustainable energy sources, providing a framework for developing more efficient and resilient energy networks. By showcasing the potential of RL in optimizing energy management, this project paves the way for future innovations in renewable energy applications.