AI 中文总结
本研究提出一种基于代理的混合整数线性规划框架，用于垂直农场光照调度优化，通过将作物-能源数据转换为数学规划关系，使生菜种植的电能消耗与成本分别最多降低15.9%和17.8%。
AI 中文摘要
垂直农场可在可控条件下实现高产量与稳定的作物生产，但其经济可行性仍受限于人工光照与气候控制的电力需求。本研究开发了一种基于代理的优化框架，用于水培垂直农场中成本最小化的光照管理。核心贡献在于提出了一种将作物-能源响应数据转换为适用于数学规划的关系的方法，该方法可应用于实验数据集或经验证的动态模型输出，前提是需涵盖控制变量、作物阶段及能源响应的相关范围。该框架被应用于意大利北部一座工业规模垂直农场的生菜种植，利用经验证的农业-能源模型生成的合成数据，推导出生鲜生物量增长、叶面积指数及照明、加热、冷却用电需求的代理关系，这些关系取决于光合光子通量密度、作物阶段与室外温度。研究检验了不同时间聚合水平，以评估代理精度、调度灵活性与计算紧凑性之间的权衡，代理模型以优化所需的足够精度复现了参考模型的作物与能源趋势。与固定光照基准相比，优化后的调度方案在达到目标收获重量的同时，最多可减少15.9%的电能消耗与17.8%的电力成本；结果表明，在研究条件下，中等光照强度结合灵活光周期比高强度光照更具成本效益，所提出的框架为将作物生长、能源需求与电价波动性整合到垂直农场光照策略中提供了一种易处理且可推广的方法。
英文摘要
Vertical farming enables high productivity and stable crop production under controlled conditions, but its economic feasibility remains limited by electricity demand for artificial lighting and climate control. This study develops a surrogate-based optimization framework for cost-minimizing lighting management in hydroponic vertical farms. A key contribution is a procedure for converting crop-energy response data into relationships suitable for mathematical programming. The method can be applied to experimental datasets or outputs from validated dynamic models, provided that relevant ranges of control variables, crop stages, and energy responses are represented. The framework is applied to lettuce cultivation in an industrial-scale vertical farm in northern Italy. Synthetic data generated by a verified agri-energy model are used to derive surrogate relationships for fresh biomass growth, leaf area index, and electricity demand for lighting, heating, and cooling. These relationships depend on photosynthetic photon flux density, crop stage, and outdoor temperature. Different temporal aggregation levels are examined to evaluate the trade-off among surrogate accuracy, scheduling flexibility, and computational compactness. The surrogate model reproduced crop and energy trends of the reference model with sufficient accuracy for optimization. Compared with fixed-lighting benchmarks, optimized schedules reduced electric energy consumption by up to 15.9% and electricity cost by up to 17.8%, while achieving the target harvest weight. Results indicate that moderate light intensities combined with flexible photoperiods are more cost-effective than high-intensity lighting under the investigated conditions. The proposed framework offers a tractable and generalizable approach for integrating crop growth, energy demand, and electricity price variability into vertical farming lighting strategies.
