This work presents a comprehensive simulation and optimal control framework for the energy management of a biogas generation plant. Using the Modelon platform and the Optimica library, a dynamic model of the plant was developed, capturing the behavior of digesters, gas storage, renewable energy and cogeneration units. The optimization targets the real-time scheduling of energy production and consumption, aiming to minimize operational costs and maximize revenue from electricity trading.

A model predictive control (MPC) approach was implemented, utilizing the IPOPT solver to compute optimal control trajectories over a defined prediction horizon, updated every hour. The control variables include the partialization of cogenerators, the decision to buy or sell electricity from the grid, and the management of energy buffers, all while ensuring compliance with process constraints and system dynamics.

To enhance forecast accuracy and system responsiveness, real-time weather data and electricity price forecasts are integrated into the control algorithm. This allows the plant to proactively adapt to variations in ambient temperature (affecting biogas production) and market price volatility.

The implementation resulted in a 6% improvement in overall energy efficiency and increase in profitability compared to baseline rule-based control strategies. These results validate the use of predictive and optimization-based approaches for advanced energy management in renewable biogas systems.