The agrifood sector is characterized by high energy intensity; consequently, integrating renewable energy sources to replace fossil fuels can substantially contribute to global decarbonization. Among renewables, bioenergy from residual biomass emerges as a promising pathway to enhance the environmental sustainability of the agrifood sector. Nevertheless, the energy recovery potential of such residues is often insufficient to meet a relevant share of the producers’ energy demand, primarily due to the seasonal nature of production and the limited quantity of available feedstock. Therefore, this study emphasizes the relevance of coupling bioenergy with other renewable energy sources, selected according to local resource availability, to substantially reduce the environmental impact of energy consumption within the agrifood sector. The analysis is carried out by developing a comprehensive and dynamic mixed-integer linear programming model to optimize the operation of hybrid energy systems. To evaluate its applicability and robustness, the model is applied to simulate the hourly operation of multiple generation technologies using real load profiles from two real-world agrifood industries, for which integrated bioenergy–solar systems are designed. The results show that the proposed approach provides an effective tool for optimizing the operation of complex energy systems and for identifying optimal renewable-based system capacities while accounting for their temporal variability and fluctuations.
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