Conference Agenda

This study develops a representative farm profile for hilly areas of Central Italy to support the preliminary sizing and configurability of a Modular Hybrid Energy System during the early development phase of the HyAgroBox project. The system is conceived as an integrated and scalable on-farm energy architecture combining renewable generation, electrical storage, electrified loads, and potential hydrogen-based (H₂) functionalities within a flexible and staged implementation framework.

Rather than relying on farm-specific consumption data, the research translates recurring agronomic and operational conditions into engineering requirements suitable for early-stage system design. The representative profile was developed through an integrated approach combining official statistical sources, literature-based interpretation, and project-driven functional analysis.

The resulting reference case describes a family-run farm with 10–12 hectares of utilized agricultural area, fragmented land parcels, and mixed cropping systems including arable land, olive groves, and vineyards. Farm activities rely mainly on family labor, with seasonal peaks in workforce demand and mechanization centered on tractor-powered operations.

The analysis identifies recurring characteristics relevant to hybrid energy system design, including dispersed operational nodes, seasonal concentration of mechanized activities, modest structural scale, and interactions between crop management, irrigation, and processing chains.

Within the TEAPOTS project, a novel modular technical solution is offered to agri-food stakeholders for the efficient and eco-friendly utilization of agricultural waste by pyrolyzing it to simultaneously produce biochar and renewable energy. Key to this solution is a high-efficiency Organic Rankine Cycle (ORC) engine recovering heat from the pyrolysis flue gases. This work details an optimization procedure prioritizing high efficiency and flexibility under varying operating conditions. At design conditions—heat supply at 160 °C with 400 kWth nominal duty and 45 °C condensation to a dry cooler—a thermodynamic model evaluated several design degrees of freedom. Analysis identified a cascade layout using R1233zd(E) and R1234ze(E) as the optimal working fluids for the high and low ORC stages, respectively. Utilizing twin-screw volumetric expanders with 65% and 69% isentropic efficiencies at design conditions, the engine achieves a 10% nominal thermal efficiency, producing 40 kWel of renewable electricity. Sensitivity analysis for limited feedstock availability (200–300 kWth) demonstrated the design’s robustness, with only minor efficiency decreases predicted at off-design conditions. Following the successful design phase, the ORC engine has been manufactured and installed at a dedicated demonstration facility. It is currently undergoing experimental testing to validate its performance and reliability in a real-world environment.

Plant production in controlled environment agriculture (CEA) relies on supplemental lighting in Nordic and Central Europe. Although LED luminaires are more energy-efficient than HPS systems, lighting remains a major cost. In Northern Europe, electricity day-ahead market prices are determined by the NordPool electricity exchange at 15-minute resolution. Aligning lighting with low-price periods offers potential cost savings without compromising yield or quality.

We present a Python-based algorithm that optimizes greenhouse lighting schedules using NordPool day-ahead prices. The algorithm retrieves price data via the NordPool API and applies plant-specific constraints, including target Daily Light Integral (DLI), minimum and maximum light intensities, and a fixed photoperiod. All intervals are first assigned minimum intensity, after which time steps are ranked by price. Light intensity is then increased during the cheapest intervals until the target DLI is achieved without exceeding maximum limits.

The algorithm was integrated into a control system at Metropolia’s UrbanFarmLab using DALI-controlled LED luminaires, a Siemens PLC, and an edge-computing platform. Simulations and pilot implementation show that the target DLI can be maintained while shifting energy use to lower-price periods, enabling significant electricity cost savings and supporting flexible energy integration in CEA.

Farms often face high and seasonal electricity demand (irrigation, cold storage) and weak grid connection. This paper presents preliminary results of the HyAgroBox project, which targets a modular PV-driven storage system combining batteries (daily) and hydrogen (seasonal). Two farms in Central Italy were monitored through smart metering and field surveys to quantify hourly load profiles and agronomic drivers. Loads were disaggregated by end-use and classified by criticality and flexibility, identifying peak-demand events and demand-shifting windows. Results show pronounced seasonality: irrigation drives short high-power peaks and daytime summer demand, while cold-chain and auxiliary loads are more continuous. Compared with PV generation scenarios, batteries mainly mitigate short-term peaks and increase self-consumption, whereas hydrogen storage is relevant to capture prolonged PV surplus and supply energy during multi-day deficits, improving autonomy in off-grid/weak-grid conditions. The dataset provides key sizing indicators (peak power, load factor, cumulative demand curves) to support dynamic simulations and predictive energy management. Hybrid battery-hydrogen storage emerges as a promising option for resilient, low-carbon energy services in agriculture and as a platform for future power-to-X uses.

The objective of the study was to evaluate the effects of combining plant-growth-promoting bacteria on the development of strawberry plants grown in a controlled environment under different artificial light spectra.

The experiment was conducted indoors using a completely randomized design with four replicates. The treatments consisted of three microorganisms (bacterium B5.1, Waitea circinata, and control) and four LED light spectra (RBW, White, Blue, and Red). Biometric, productive, physiological, and qualitative parameters of the plants were evaluated.

The results indicated that inoculation with bacterium B5.1 promoted greater root growth and greater dry mass of the aerial part, reaching 33.1 cm and 2.6 g, respectively. The White spectrum showed a higher number of leaves (10.2), root length (33.7 cm), and fresh and dry aboveground biomass production (13.1 g and 3.0 g). Regarding productive characteristics, the RBW spectrum yielded a higher average fruit weight (8.9 g). Furthermore, the combination of B5.1 and White light resulted in up to 6.5 fruits per plant and higher soluble solids content (10.05 °Brix). It is concluded that the White and RBW spectra, combined with inoculation with the B5.1 bacterium, promote growth, productivity, and fruit quality in vertical farming systems.

Agrivoltaics combines renewable energy production with agricultural land use. In northern Europe, where arable land is limited and solar deployment is increasing, assessing crop performance under agrivoltaic systems is essential before large-scale implementation. This study evaluates three crop growth models, DSSAT, APSIM, and PCSE, to simulate spring wheat in Finland and estimate agrivoltaic shading impacts on development and yield.

Field experiments were conducted at the Viikki Research Farm, University of Helsinki, Finland, in summer 2025. Weather, soil, and crop data, including biomass, leaf area index, crop height, and chlorophyll concentration, were collected. These data were used to calibrate and validate the models. Model performance (phenology, biomass, and yield) was compared with measurements.

The models were used to simulate agrivoltaic scenarios by modifying incoming radiation to represent five irradiance zones (8–27% shading) in a vertical agrivoltaic setup. As no shaded field data were available, impacts were evaluated through model-based estimations.

Results indicate that all models capture general growth dynamics but differ in sensitivity to radiation reduction. Simulated shading alters phenology and yield with varying magnitude among models. The study highlights the importance of multi-model comparison for assessing agrivoltaic impacts in high-latitude environments and provides a basis for evaluating Nordic agrivoltaic systems.

The Archimedes turbine (the so-called Archimedes screw) is one of the most interesting technologies in the field of low-fall hydropower. The last decade has seen a significant increase in its applications in Europe, especially in areas with a large number of historic weirs and finished falls after water mills. In the context of Poland - a country with a dense network of watercourses and numerous damming facilities - this technology can play an important role in the development of small hydropower plants. Small hydropower plants, in addition to distributed generation of electricity (which has a good impact on the quality of electricity in remote locations of the power system), can contribute to the regulation of water flow which has a significant impact on water retention. The so-called small retention is of great importance especially in the case of existing large water shortages in Poland. The article analyzes the energetic and ecological possibilities of implementing the Archimedes turbine in Polish hydrological conditions, taking into account the latest literature reports.

This study aimed to quantify carbon use efficiency (CUE) in a closed vertical farm by coupling a three-dimensional transient computational fluid dynamics (CFD) model with a leaf-level photosynthesis–stomatal conductance–energy balance model. Gas exchange measurements of romaine lettuce were conducted in a multi-layer vertical farm to parameterize a Farquhar-based biochemical photosynthesis model combined with the Medlyn stomatal model. The calibrated model was embedded into the CFD framework as a dynamic CO₂ sink using leaf area density, enabling two-way coupling between local microclimate (air velocity, temperature, humidity, and CO₂ concentration) and plant physiological responses. Model validation was performed against measured photosynthetic rates and spatial CO₂ distributions. Scenario analyses compared uniform enrichment and localized CO₂ supply under different airflow configurations. Results showed that neglecting plant CO₂ uptake led to overestimation of internal CO₂ concentration and spatial uniformity. The coupled model revealed that airflow-induced heterogeneity significantly affected local assimilation rates, resulting in differences in CUE across cultivation layers. Localized enrichment combined with improved airflow distribution enhanced CUE and reduced CO₂ consumption while maintaining comparable assimilation. The proposed framework provides a quantitative basis for optimizing CO₂ management and energy-efficient operation in high-density vertical farming systems.

Optimal greenhouse environment management aims to minimize energy consumption while enhancing crop growth and quality, and greenhouse energy simulation models have been employed. Accurate energy load prediction requires accounting for dynamic crop-environment interactions, whereby transpiration affects the temperature and humidity of the microclimate, and these environmental changes in turn drive physiological responses in the crop. In this study, a dynamic simulation model was developed using the EnergyPlus Python API by bi-directionally coupling a building energy model with a crop growth model for a tomato greenhouse in the Daegwallyeong region of South Korea. The model transfers microclimate calculated by EnergyPlus to the crop model and feeds the sensible and latent heat responses back into EnergyPlus, enabling precise energy load prediction. Validation against operational data from the greenhouse in May showed that the model excluding the crop model yielded Cv(RMSE) of 18.24% for temperature and 32.38% for relative humidity. In contrast, the proposed integrated model reduced the humidity prediction error to 18.75%, demonstrating that incorporating dynamic crop-environment interactions improve microclimate prediction accuracy. By incorporating dynamic crop-environment interactions, the proposed model enhances the accuracy of greenhouse microclimate and energy load predictions, providing a foundation for the development of efficient greenhouse energy management strategies.

Agrivoltaics is a promising research and business field, that enables the co-generation of crops and electrical power on the same land. Its main drawback is the competition for light between the PV panels and the crops, as the panel-induced shading can decrease the crop yield. Optimising the number and placement of receivers is therefore essential to ensure both good power production and agricultural performance. In collaboration with LangaPonica s.r.l, this study investigates the integration of a novel concentrating photovoltaic collector into an aquaponics greenhouse located in Bra, Piedmont (Italy). The system employs a compound parabolic concentrator to focus the sunlight onto compact PV receivers, enabling more efficient light utilization while eliminating the need for a tracking system. The proposed design is sized according to the greenhouse’s energy demand, and a general framework is developed to adapt concentrating PV systems to greenhouse operating requirements. By varying the panel mounting angle, the peak power production can be shifted to different times of the day to better match the greenhouse load profile. The optimized system integration reduces reliance on expensive energy storage solutions and improves overall system efficiency

Horticultural and fruit processing buildings located in Mediterranean coastal regions commonly rely on natural ventilation to manage air renewal while limiting energy consumption. These agro-industrial facilities operate under warm and humid climatic conditions, where ventilation performance is strongly influenced by external wind variability, large internal volumes, and open architectural layouts designed for handling, storage, and pre-processing of fresh produce. This work reviews current knowledge on airflow behavior in naturally ventilated horticultural and fruit processing buildings, with a specific focus on the role of computational fluid dynamics (CFD) as a tool for analysis and design. Studies show that conventional ventilation design approaches often fail to capture the complex three-dimensional airflow patterns that develop inside these structures, leading to spatially heterogeneous air movement and reduced robustness under changing boundary conditions. CFD modelling enables a detailed assessment of air distribution, identification of poorly ventilated zones, and evaluation of key design parameters such as opening geometry, roof typology, and building orientation. The paper summarizes the potential of CFD-assisted design to support ventilation strategies. Future research directions are discussed, exposing the need to integrate airflow modelling into practical design workflows for this kind of processing facilities.

Vineyard pruning residues represent a relevant source of lignocellulosic biomass in Mediterranean regions, where viticulture is widely distributed and geographically concentrated. Although their energy potential is well recognized, practical implementation is often limited by technical constraints along the supply chain. This paper provides a structured technical assessment of vineyard pruning residues as a bioenergy resource, focusing on harvesting performance, logistics, storage behaviour, fuel quality and combustion-related implications. Reported recoverable yields are approximately 1 oven-dry tonne per hectare, with significant inter-annual variability. Mechanical harvesting efficiency is influenced by inter-row spacing, pruning technique and residue management, with substantial losses under suboptimal conditions. Fresh residues typically exhibit moisture contents around 40–45%, and comminuted material often fails to comply with residential chip standards due to non-uniform particle-size distribution. Ash content is generally higher than that of conventional forest fuels, and trace elements may accumulate due to plant protection treatments and soil contamination during collection. These factors restrict the suitability of vineyard residues for small domestic appliances but support their use in appropriately designed medium-scale automated systems. The study identifies the main structural bottlenecks along the pruning-to-energy chain and discusses their implications for system design in Mediterranean contexts.

This study focuses on the thermal simulation of a closed vertical composting facility (CVCF), which is widely used in Japan for the treatment of various organic wastes. A CVCF composts feedstock by agitating and aerating it within a sealed vessel. Feedstock is generally input to the CVCF daily, making it necessary to analyze daily changes in operating conditions. However, because the system is enclosed, the fermentation process functions as a black box, and it is difficult to observe internal conditions directly from the outside.

Therefore, this study aimed to estimate the internal conditions of a CVCF by simulating temperature changes over the 24-hour period following feedstock input, and to evaluate temperature dynamics under different aeration conditions. The simulation was conducted for a closed vertical composting facility with a volume of 30 m³, into which cattle manure compost was added as the feedstock.

The results showed that the time required for the compost temperature to reach 70 °C was 405 min. under winter conditions with an ambient temperature of 0 °C, 299 min. under summer conditions with an ambient temperature of 20 °C, and 264 min. when the supplied air was heated to 45 °C using heat recovery.

This study develops and validates a TRNSYS–TRNFlow building energy simulation model to analyze thermal and humidity environments and seasonal energy loads of a single-span plastic greenhouse in Gimje, Korea under naturally ventilated operation. Field measurements (temperature, humidity, and light) and crop-related parameters were incorporated, and CFD-derived wind pressure coefficients were embedded in the airflow network to improve ventilation-rate estimation. Model validation showed strong agreement with measurements (R² = 0.89; d = 0.92), and the natural-ventilation implementation reduced summer cooling energy demand by up to 25% while showing negligible impact on heating loads. Using the validated model, the vent opening ratio was varied from 0.0 to 1.0 to quantify load sensitivity and uncertainty. As opening increased, winter heating load rose markedly (up to 2.8×), summer cooling load increased, and spring/autumn cooling responses varied depending on ventilation conditions. ANOVA and Tukey HSD tests confirmed statistically significant differences among ventilation cases by month and season, and uncertainty quantification indicated that neglecting temporal variability in opening ratio substantially degrades load-prediction reliability. These results support season-specific ventilation strategies for energy-efficient greenhouse operation.