Development and implementation of a hybrid renewable energy system in a pilot cheese production facility in Beja, Portugal
DOI:
https://doi.org/10.18686/cest713Keywords:
hybrid renewable energy system; pilot plant; cheese production; energy efficiency; sustainable agro-food processingAbstract
The increasing demand for renewable energies, encouraged by the European Union (EU), aims to position Europe as a leader in clean energy production while reducing its carbon footprint. The application of renewable energy in industry is crucial, given the high energy consumption associated with it, particularly in the production and preservation of food. This article presents the development and implementation of an integrated energy system applied to the production and ripening of traditional Alentejo cheese. During the production phase, which includes pasteurization and coagulation processes that rely on hot water, heating is provided by a pellet boiler, capable of using alternative biofuels, and by solar thermal collectors. For the curing of cheese, conducted in a refrigerated chamber with humidity control, a refrigeration system using R744 (carbon dioxide) was considered, complemented by a heat exchanger to assist in water heating. The electrical supply is ensured by photovoltaic (PV) panels, a wind turbine, and batteries, allowing for storage and energy autonomy during periods without production. The electrical supply is ensured by PV panels, a wind turbine, and batteries, allowing for storage and energy autonomy during periods without production. Monthly analysis indicates that photovoltaic energy is the dominant source, supplying 3,391 kWh annually and covering, on average, 87% of the monthly demand. The system also integrates passive heating technologies and phase change materials (PCM) with the aim of optimizing performance and reducing equipment operating hours. Upon completion of the installation, it will be possible to evaluate cheese production under real conditions and collect data on thermal and electrical consumption, allowing for future system optimization.
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Copyright (c) 2026 João Garcia, Arian Semedo, Francisco Calvo, João Dias

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