Energy Management in Agricultural Microgrids: A Comparative Study of Deterministic and Stochastic Model Predictive Control

Abstract

This paper compares deterministic and stochastic Model Predictive Control (MPC) strategies for optimal energy management in an agricultural microgrid integrating renewable sources, battery storage, and controllable loads. The deterministic MPC assumes perfect knowledge of future disturbances, whereas the stochastic MPC accounts for uncertainties in weather and renewable generation through probabilistic forecasting. Both approaches are evaluated in terms of tracking performance, storage utilization, and grid interaction. The study highlights the value of uncertainty-aware control in enhancing the sustainability and resilience of agricultural microgrids under variable operating conditions.