Multi-objective optimization of photovoltaic greenhouses with modelling and field validation

Photovoltaic greenhouses (PVGHs) offer a sustainable solution for food and energy production, particularly in off-grid and water-scarce regions. However, optimizing their operation requires managing competing objectives across energy generation, irrigation efficiency, crop quality, and economic viability. This study presents a field-validated, first-principles-based multi-objective optimization framework tailored for PVGHs. The model concurrently maximizes four objectives: (i) photovoltaic energy efficiency, (ii) water-use-informed crop yield, (iii) composite fruit quality, and (iv) economic return. Unlike prior work, the framework integrates a dynamic quality index—combining °Brix, firmness, acidity, and size—into the optimization process and embeds real-time cost–revenue analysis into control logic. Empirical validation is conducted over a 97-day tomato crop cycle in a semi-arid, off-grid PVGH. The model achieves strong predictive agreement across key performance indicators (R² > 0.92; deviation < 7 %) for yield, water use, energy generation, and quality. Sensitivity and scenario-based analysis demonstrate system robustness under environmental and resource constraints. Implemented as a low-cost digital twin, the framework supports adaptive, quality-aware greenhouse control and holds strong potential for deployment in climate-sensitive, resource-limited agriculture. It also contributes to food-energy-water security and sustainable development in vulnerable rural regions.