Optimization and Shannon entropy multi-criteria decision-making method for implementing modern renewable energies in stand-alone greenhouses

• The Shannon entropy MCDM method is integrated with the TRNSYS-HOMER optimization tool. • Highest weights assigned to energy source diversity, energy cost, and emission factors. • Cost of energy in the agrivoltaic-based system is less than 0.12 $/kWh. • More than 51% of total agricultural demand supplied by solar-wind renewable resources. • The proposed energy system is affordable for distances > 12 km from the power grid. Commercial optimization tools often prioritize economic outcomes at the expense of sustainability and environmental performance. This limitation is particularly evident when evaluating renewable technologies with high capital costs, such as transparent agrivoltaic systems, compared to conventional photovoltaic alternatives. The lack of objective multi-criteria decision-making frameworks integrated with these tools presents a gap in supporting sustainable energy development, especially for policymakers. This study introduces a hybrid MATLAB/HOMER framework to optimize the energy supply of stand-alone greenhouse systems equipped with water treatment units in tropical climates. The framework integrates a Shannon Entropy-based TOPSIS method, implemented in MATLAB, to objectively rank system configurations using HOMER’s economic, technical, environmental, and energy security outputs. For an average daily load of about 100–110 kWh, the optimal configuration comprises 1.5 kW wind turbines, 4 kW transparent agrivoltaic panels, 14.5 kW conventional photovoltaic panels, a 10 kW diesel generator, 12 kWh of battery storage, and a 13.1 kW converter. This system achieves a levelized cost of electricity of $0.119 per kWh, a 51.2 % renewable energy share, and a lifetime CO 2 reduction of 13,240 kg, while maintaining 100 % supply reliability. Additionally, the results show that the Shannon Entropy method provided a more decisive identification of the optimal scenario compared to the subjective Analytic Hierarchy Process (AHP). These findings demonstrate the effectiveness of entropy-weighted decision-making in identifying high-performance, sustainable energy solutions that extend beyond purely economic criteria, with zero unmet load.