Dynamic Energy Optimization and Lighting Flexibility Classification for Sustainable Vertical Farming: A Simulation-Based Multi-Scenario Analysis

<ns3:p> Background Vertical farming offers a promising solution to food production challenges in urban and climate-constrained regions, yet its high energy demand raises concerns about sustainability. Methods This study evaluates the performance and carbon footprint of a pilot vertical farming unit in Northern Greece using TRNSYS 18 simulations and high- resolution environmental data. Forty-eight cultivation scenarios were generated by varying photoperiods, humidity levels, and HVAC setpoints to reflect seasonal Mediterranean conditions. Each scenario was analyzed for energy consumption, grid reliance, photovoltaic sufficiency, and life cycle and CO <ns3:sub>2</ns3:sub> emissions. A dynamic crop cycle estimation model was applied to capture seasonal variability and align planting windows with solar energy availability. Performance benchmarking was conducted through a multi-criteria framework introducing the Grid Independence Index, Seasonal Resilience Score, and energy-per-cycle indicators. Results The median carbon footprint across scenarios was 3.67kg CO <ns3:sub>2</ns3:sub> per kilogram of lettuce (Lactuca Sativa), representing favorable performance compared to conventional methods such as greenhouse cultivation. The analysis demonstrated seasonal fluctuations in photovoltaic sufficiency, with certain scenarios achieving higher autonomy during spring and summer. A Lighting Flexibility Classification was developed to identify the maximum lighting reduction tolerance per scenario without increasing total energy demand, resulting in five flexibility levels and supporting adaptive climate-responsive strategies. Conclusions The findings highlight the potential of simulation-based design for optimizing energy use and minimizing environmental impacts in control-environment agriculture. The proposed metrics and classification provide practical tools for improving the resilience and sustainability of vertical farming under Mediterranean conditions. </ns3:p>

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Publication Details

Journal
Open Research Europe
Published
2025-10-16
DOI
https://doi.org/10.12688/openreseurope.20847.1
Primary Topic
Greenhouse Technology and Climate Control
Type
article
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article

Dynamic Energy Optimization and Lighting Flexibility Classification for Sustainable Vertical Farming: A Simulation-Based Multi-Scenario Analysis

Chrysovalantis Ketikidis, Petros Dallas, Panagiotis Grammelis, Aristotelis Triantafyllidis et al.
Open Research Europe
Greenhouse Technology and Climate Control
article

Dynamic Energy Optimization and Lighting Flexibility Classification for Sustainable Vertical Farming: A Simulation-Based Multi-Scenario Analysis

Chrysovalantis Ketikidis, Petros Dallas, Panagiotis Grammelis, Aristotelis Triantafyllidis, Christina Makri
article en

Abstract

<ns3:p> Background Vertical farming offers a promising solution to food production challenges in urban and climate-constrained regions, yet its high energy demand raises concerns about sustainability. Methods This study evaluates the performance and carbon footprint of a pilot vertical farming unit in Northern Greece using TRNSYS 18 simulations and high- resolution environmental data. Forty-eight cultivation scenarios were generated by varying photoperiods, humidity levels, and HVAC setpoints to reflect seasonal Mediterranean conditions. Each scenario was analyzed for energy consumption, grid reliance, photovoltaic sufficiency, and life cycle and CO <ns3:sub>2</ns3:sub> emissions. A dynamic crop cycle estimation model was applied to capture seasonal variability and align planting windows with solar energy availability. Performance benchmarking was conducted through a multi-criteria framework introducing the Grid Independence Index, Seasonal Resilience Score, and energy-per-cycle indicators. Results The median carbon footprint across scenarios was 3.67kg CO <ns3:sub>2</ns3:sub> per kilogram of lettuce (Lactuca Sativa), representing favorable performance compared to conventional methods such as greenhouse cultivation. The analysis demonstrated seasonal fluctuations in photovoltaic sufficiency, with certain scenarios achieving higher autonomy during spring and summer. A Lighting Flexibility Classification was developed to identify the maximum lighting reduction tolerance per scenario without increasing total energy demand, resulting in five flexibility levels and supporting adaptive climate-responsive strategies. Conclusions The findings highlight the potential of simulation-based design for optimizing energy use and minimizing environmental impacts in control-environment agriculture. The proposed metrics and classification provide practical tools for improving the resilience and sustainability of vertical farming under Mediterranean conditions. </ns3:p>

Open Research EuropeVol. 5
Openalex Percentile: Top 78%
Greenhouse Technology and Climate Control
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