Optimized Self‐Consumption of Renewable Energies With Forecast‐Based Energy Management for Agricultural Farms

ABSTRACT This work introduces an optimally controlled forecast‐based energy management system (EMS) that integrates energy demand and production forecast with optimal control techniques to optimize the management of renewable energy sources. Although controlling the energy generation is not feasible for weather‐dependent power plants, it is possible to predict the amount of energy produced. Self‐sufficiency can be improved by regulating individual devices and storage units based on anticipated energy production and demand. For electricity tariffs much larger than feed‐in tariffs, such demand‐side management can notably improve the profitability of (private) renewable energy systems. The EMS is based on long‐term predictions that enable high‐level control decisions and specific controls for the next controlling steps on a low level. Weather forecasts and forecasts of the power demand and state behavior of each device are considered to evaluate both cost function and constraints for the next prediction horizon. The system's performance is evaluated through a case study, demonstrating its ability to control the energy distribution successfully. The proposed EMS presents a promising approach to smart energy management that can contribute to a more sustainable future.