Tri-objective optimization of spectrally selective absorber in semitransparent PV greenhouses in arid climates

There is an urgent need to design energy-autonomous and spectrally optimized greenhouses capable of sustaining year-round crop production in arid and semi-arid regions. These regions face extreme solar conditions, and high cooling demands, making conventional greenhouse systems inefficient and often economically unfeasible. This study presents an enhanced configuration of semi-transparent photovoltaic (STPV) greenhouses integrated with spectrally selective absorber (SSA) coatings, employing a tri-objective optimization framework. The proposed model simultaneously maximizes net energy generation and a spectral utility index (SUI), a novel metric introduced to evaluate crop-light usability, while minimizing total cost. Comprehensive solar, thermal, and optical simulations were performed for six greenhouse envelope sections under various material configurations, and optimization was carried out using the Equilibrium Optimizer (EO) algorithm to capture trade-offs among performance indicators. Results show that the energy-driven configuration achieves the highest effective net energy benefit (4.4 MWh/yr) and significant cooling demand reduction (2.7 MWh/yr) when STPV and SSA coatings are applied, though at a moderate increase in cost and reduced transmitted light. The cost-driven design, using only plastic cladding, provides the lowest investment cost (1.43 k $) and maximum light availability (SUI ≈ 0.9), while the balanced configuration delivers the most robust overall performance, ensuring adequate daily light integral (DLI) for diverse crop requirements. Overall, the study introduces a decision-support framework integrating energy, optical, and economic objectives. The SSA–STPV combination, evaluated through SUI-based optimization, represents a promising pathway toward net-positive, climate-adaptive greenhouse systems for hot-arid environments.