Optimal Energy Management of Battery and Telecommunication Mast Systems

This paper proposes a mathematical framework using mixed-integer linear programming (MILP) for optimizing energy management in systems with a grid, battery, and telecommunication mast. Incorporating real-world data, it minimizes energy costs while respecting constraints like battery state-of-charge, system reliability, and no grid injection. A forecasting approach predicts energy demand, guiding battery and grid energy use, while Model Predictive Control (MPC) adapts to forecast updates. Results show a 10% cost reduction and 18% CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission decrease, highlighting MILP's effectiveness in integrated energy management.