Enhancing Rural Energy Resilience Through Combined Agrivoltaic and Bioenergy Systems: A Case Study of a Real Small-Scale Farm in Southern Italy

Abstract

Agrivoltaics (APV) mitigates land-use competition between photovoltaic installations and agricultural activities, thereby supporting multifaceted policy objectives in energy transition and sustainability. The availability of organic residuals from agrifood practices may also open the way to their energy valorization. This paper examines a small-scale farm in the Basilicata Region, southern Italy, to investigate the potential installation of an APV plant or a combined APV and bioenergy system to meet the electrical needs of the existing processing machinery. A dynamic numerical analysis is performed over an annual cycle to properly size the storage system under three distinct APV configurations. The panel shadowing effects on the underlying crops are quantified by evaluating the reduction in incident solar irradiance during daylight and the consequent agricultural yield differentials over the life period of each crop. The integration of APV and a biomass-powered cogenerator is then considered to explore the possible off-grid farm operation. In the sole APV case, the single-axis tracking configuration achieves the highest performance, with 45.83% self-consumption, a land equivalent ratio (LER) of 1.7, and a payback period of 2.77 years. For APV and bioenergy, integration with a 20 kW cogeneration unit achieves over 99% grid independence by utilizing a 97.57 kWh storage system. The CO₂ emission reduction is 49.6% for APV alone and 100% with biomass integration.

1. Introduction

The concurrent global imperatives of transitioning to renewable energy systems and ensuring food security have catalyzed significant interest in integrated approaches that address both challenges simultaneously. Among these, agrivoltaic (agrophotovoltaic, APV) systems have emerged as a particularly promising solution for land optimization, effectively reconciling the seemingly competing demands of energy generation and agricultural production.

The fundamental premise of APV technology, namely, the co-location of photovoltaic installations and agricultural activities on the same land parcel, represents a paradigm shift from conventional land-use strategies based on mutually exclusive utilization and has gained attention in regions characterized by limited available arable land and high solar radiation, especially where energy independence may be beneficial for more resilient and profitable agrifood businesses. The EMERA project [1,2], funded by the Basilicata Region through the European Regional Development Fund, addresses challenges specific to rural areas facing depopulation and aging [3] in Southern Italy and offers a model that enhances sustainability, increases territorial resilience, and reduces soil contamination. This public–private initiative focuses on developing micro-grids powered exclusively by renewable sources, combining solar energy production with biomass valorization in a whole system, properly managed by an advanced Internet of Things (IoT) platform for control and diagnostics, aimed at enhancing the prosumer role of farmers.

Renewable energy deployment is a cornerstone of EU policy, evolving from the initial “20-20-20” targets through RED I to the current 42.5% renewable energy target set for 2030 under REPowerEU [4]. The 2023 revision to RED II (RED III) introduced specific provisions supporting agrivoltaics, acknowledging their potential contribution to both energy generation and sustainable land use [5].

Indeed, among renewables, solar energy definitely plays a crucial role as an established and continuously rising leader in the global energy transition. The World Energy Outlook 2024 of the International Energy Agency provides compelling evidence of the central role of solar photovoltaic (PV) technology in the global energy transition [6]. According to the report, solar PV technology is projected to be the predominant contributor to clean power expansion, constituting more than half of the incremental clean power capacity between 2023 and 2030 under the so-called Stated Policies Scenario (STEPS). The IEA report documents extraordinary momentum in solar PV deployment worldwide, with global capacity additions surging by approximately 80% in 2023 to reach an unprecedented 425 GW. Concurrently, it is highlighted that manufacturing capacity expansion has led to significant cost reductions, with PV module prices declining by approximately 50% since December 2022, further accelerating market penetration. The geographical distribution of solar growth demonstrates notable patterns, with China experiencing a 2.5-fold increase in deployment. The IEA analysis further indicates that solar PV and wind generation must exhibit unprecedented growth rates to bridge the substantial gap between current policy trajectories and the Net-Zero Emission (NZE) Scenario requirements. Future projections indicate continued robust growth, with PV capacity additions anticipated to expand by nearly 60% by 2035 under the STEPS framework, double under the Announced Pledges Scenario (APS), and increase 2.5 times under the more ambitious NZE Scenario. The latter specifically identifies the necessity for an additional 7000 TWh of solar PV generation by 2035, a target justified by the technology’s ubiquitous availability, substantial policy support mechanisms, and increasingly favorable economic proposition.

In Europe, the solar market has expanded rapidly, according to recent data from the International Renewable Energy Agency (IRENA) [7]. By the end of 2023, the European Union reached approximately 260 GW of cumulative installed solar PV capacity, with Germany, Spain, Italy, the Netherlands, and France leading the deployment. In Southern European countries, solar PV presents especially favorable economics due to high solar radiation levels and declining installation costs. In Italy, according to TERNA, the territorial distribution of newly authorized photovoltaic power shows a significant concentration in the south [8].

Nevertheless, some factors still hinder the diffusion of photovoltaics. A main limiting issue is the availability of surfaces. Although the possibilities offered by buildings or infrastructure coverings (the best option from an environmental compatibility perspective) could be sufficient to satisfy the entire energy need, numerous constraints (artistic, landscape, physical, proprietary, financial, civil, administrative, condominium, etc.) hinder their implementation.

This circumstance, therefore, brings to light the agrivoltaic technology. APV diffusion has accelerated in recent years. As particularly emphasized by the European Commission in May 2022, its role is to reduce the EU’s dependency upon Russia’s fossil fuels. The entire REPowerEU directive encourages Member States to consider not only utility-scale solar and rooftop solar but also innovative forms of solar energy deployment, including agrivoltaics. However, the lack of a clear and EU-harmonized definition of APV plants still presents a significant obstacle, as the installation of these systems may lead to changes in land characterization, which could affect eligibility for agricultural subsidies and reduced taxation. Moreover, complex permitting and grid connection procedures, as well as increases in land prices, could jeopardize the welfare and security of farmers.

Engaging rural communities in the planning and decision-making process relevant to new APV installations is therefore essential for the successful expansion of the technology. This objective was considered within the EMERA project in one of its recent revisions. In particular, a case study on the small-farm Vito Nigro, located in the Basilicata Region, simultaneously analyzed the potential self-consumption of renewable energy harvested by an APV plant, both in a stand-alone configuration and in combination with a bioenergy plant just manufactured by a Company participating in the project and operating in that region.

Mediterranean countries are increasingly focused on integrating solar PV with agricultural practices since their interaction is particularly well-suited to their climate conditions. The optimization of APV systems, in any case, necessitates careful consideration of the complex interplay between energy yield, agricultural productivity, and economic viability. It also requires sophisticated modeling methodologies to quantify not only the energy generation but also the shadowing effects on underlying crops and to accurately predict the resultant impact on agricultural yields.

In this context, the present investigation aims to contribute to the evolving body of knowledge by developing and implementing a comprehensive numerical framework for the dynamic assessment of different APV configurations, with specific reference to the aforementioned case study, which can be considered as representative of the Mediterranean agricultural context.

Despite the growing body of research on APV systems, significant knowledge gaps remain regarding their integration with other renewable energy sources and their performance in Southern European agricultural contexts. Specifically, the following research questions remain unanswered and are therefore considered in this paper:

-

How do different APV configurations affect crop yields in areas characterized by high solar radiation?

-

What is the optimal integration strategy for combining APV with biomass cogeneration to achieve energy self-sufficiency?

-

How do evolving energy market conditions affect the economic viability of integrated APV+biomass systems?

-

What are the practical implementation challenges for small-scale farms, particularly in rural Southern Italy?

This study addresses these gaps through a comprehensive analysis of a real small-scale farm in the Basilicata Region, contributing novel insights on integrated renewable energy systems for rural resilience. This research incorporates electrical engineering principles, agricultural science, and economic analysis to derive actionable insights regarding the optimal configuration of APV systems under specific geographical, climatic, and agricultural conditions [9]. Moreover, the synergic use of APV and a biomass-powered cogenerator is discussed, as it fully exploits local resources for increased energy independence of rural communities and enhanced resilience.

1.1. APV Technology

The conceptual foundation of agrivoltaics was first articulated by Goetzberger and Zastrow [10], who proposed the simultaneous use of land for solar energy collection and plant cultivation. This pioneering work established the theoretical framework for what would later develop into a diverse field of research and application. The term “agrivoltaic” was subsequently coined by Dupraz et al. [11], who conducted early experimental studies demonstrating the viability of combined systems for certain crop types under specific conditions. The fundamental concept involves the strategic installation of PV panels in configurations that permit agricultural activities beneath or between the panel arrays, potentially creating beneficial microclimatic conditions for certain crops while simultaneously generating renewable electricity [12]. Valle et al. [13] expanded upon this foundation by developing comprehensive models for land productivity optimization, introducing the concept of the land equivalent ratio (LER) to quantify the relative efficiency of agrivoltaic systems compared to separate agricultural and PV installations. The LER has become a standard metric for evaluating APV system performance, with values exceeding 1.0 indicating superior land-use efficiency in combined systems.

Research by Trommsdorff et al. [14] identified multiple technical configurations for agrivoltaic systems, each with distinct implications for both agricultural and electrical productivity. These broadly fall into three categories:

• Fixed-mounted systems are characterized by elevated panels mounted at predetermined heights and tilt angles, providing partial shading to underlying crops.
• Vertical bifacial systems utilize bifacial PV modules installed in vertical east–west orientations, creating alternating patterns of light and shade throughout the day.
• Dynamic systems employ tracking technologies to optimize panel orientation relative to solar position, potentially adjustable based on crop requirements.

The optimal panel density, height, and spacing represent critical design parameters that significantly influence productivity. Amaducci et al. [15] conducted parametric studies demonstrating how these variables affect light distribution patterns and consequently impact crop development. Their research established that panel densities exceeding 50% of the ground coverage typically result in substantial agricultural yield reductions for most crop types, though exceptions exist for shade-tolerant species. Indeed, one of the most significant aspects of APV is the capacity to modify microclimatic conditions beneath the PV array. Marrou et al. [16] documented substantial changes in soil moisture retention, ambient temperature fluctuations, and photosynthetically active radiation (PAR) distributions in APV installations compared to open-field cultivation. These modifications can produce both beneficial and detrimental effects depending on crop type, climate zone, and system design. In arid and semi-arid regions, the partial shading provided by PV panels was shown to reduce evapotranspiration rates, potentially decreasing irrigation requirements by 14–29% depending on crop type and climate conditions [12]. Adeh et al. [17] reported soil moisture levels approximately 15% higher under APV arrays compared to open-field conditions in Mediterranean-type climates, resulting in improved water use efficiency for certain crops.

Crop responses to APV conditions, indeed, vary significantly across species. Weselek et al. [18] categorized crops according to their shade tolerance, identifying three primary groups:

• Shade-tolerant crops, including many leafy vegetables, certain berry varieties, and shade-adapted specialty crops, demonstrate minimal yield reductions or even yield improvements under moderate shading.
• Moderately shade-tolerant crops, comprising many root vegetables, legumes, and certain cereal crops, can maintain acceptable yields with up to 30% reduction in incident solar radiation.
• Shade-intolerant crops, encompassing many fruiting vegetables, corn, and certain grain crops, exhibit substantial yield reductions under even moderate shading conditions.

Comprehensive work by Sekiyama and Nagashima [19], which focused on maize cultivation under varying shading intensities, demonstrated that even traditionally shade-intolerant crops might be viable in APV systems with appropriate design modifications and management practices.

The economic viability of agrivoltaic systems and their social impact have also been extensively analyzed in the literature. Dinesh and Pearce [20] developed comprehensive cost–benefit models incorporating both agricultural revenue streams and electricity generation. Their research indicated that APV systems can achieve payback periods comparable to conventional PV installations when appropriate crop selection and market conditions align. Pascaris et al. [21] conducted lifecycle assessments of APV systems, demonstrating potential reductions in greenhouse gas emissions of 30–65% compared to separate agricultural and PV systems occupying an equivalent land area. These environmental benefits derive from both the direct displacement of fossil fuel-based electricity generation and the reduced inputs required for certain agricultural practices under APV conditions. A comprehensive economic analysis by Schindele et al. [22], incorporating externalities such as ecosystem services, carbon sequestration, and land-use efficiency, suggested that APV systems may provide substantial societal value beyond direct economic returns, particularly when deployed at scale in appropriate regions.

The practical implementation of agrivoltaic systems has accelerated globally in recent years. Fraunhofer ISE’s APV installations in Germany have demonstrated successful integration with potato and celery cultivation, achieving combined land productivity increases of up to 60% compared to separate systems [14]. In Japan, Nagata and Ogata [23] documented long-term performance of the “Solar Sharing” program, which has successfully integrated PV arrays with diverse crop production systems since 2004, demonstrating both technical viability and farmer acceptance across multiple growing seasons and crop rotations. Mediterranean applications were extensively studied by Cossu et al. [24], who analyzed multiple APV installations across Italy, Spain, and Greece. Their findings indicated particularly favorable conditions for APV implementation in these regions due to high solar radiation, water conservation benefits, and suitable crop varieties.

The policy landscape governing agrivoltaic implementation varies substantially across jurisdictions. Ketzer et al. [25] conducted a comparative analysis of regulatory frameworks in Europe, identifying significant barriers related to land-use classification, agricultural subsidy eligibility, and electricity market access. Policy innovations in certain regions have accelerated APV adoption through adjusted feed-in tariffs, modified land-use regulations, and targeted incentive programs. Acceptance factors for the future acceptance of APV were examined by Pascaris et al. [26], who identified farmer perceptions, community engagement processes, and local economic impacts as critical determinants of successful APV implementation. Participatory designs and clear communication of benefits can substantially improve stakeholder acceptance. Emerging research from Xia et al. [27] examined large-scale APV implementation in China, where national policies increasingly promote dual-use systems as a strategy to maintain food security while expanding renewable energy capacity. Their analysis suggested that appropriate APV deployment could contribute significantly to China’s renewable energy targets while minimizing impacts on agricultural production.

Recent technological developments are expanding the potential applications of agrivoltaic systems. Lu et al. [28] demonstrated the integration of wavelength-selective PV materials that transmit specific light spectra beneficial for plant growth while converting others to electricity, potentially optimizing both energy generation and crop production simultaneously. Vélez et al. [29] explored the effects of overhead agrivoltaics on precision agricultural technologies, highlighting the importance of addressing signal interference to avoid hindering the integration of drones, robots, and other advanced technologies in APV management, and suggested alternative solutions to enhance performance.

Despite substantial progress, significant challenges remain in optimizing agrivoltaic systems across diverse geographical, agricultural, and economic contexts. Campana et al. [30] identified several critical research gaps requiring attention:

• Long-term studies of soil health and biodiversity impacts under APV systems;
• Standardized methodologies for economic assessment incorporating multiple value streams;
• Crop-specific optimization protocols for different climate zones and agricultural systems;
• Technical innovations addressing maintenance challenges in combined systems;
• Socio-economic research addressing implementation barriers and adoption patterns.

More recent advances in APV investigations were reported in the review paper by Soto-Gomez [31], which also presented a map of the land equivalent ratio (LER), derived from APV studies in the literature. As expected, a trend towards higher LERs is observed in the Mediterranean area, which is affected by more intense solar radiation and better weather conditions, where the benefits of shade on soil fertility, moisture preservation, and biodiversity are more evident.

A further very comprehensive paper, more focused on modeling approaches to APV, was conducted by Zainali et al. [32]. The variety of simulation methodologies across photovoltaic and farming domains, combined with a lack of uniform reference standards, makes it challenging to identify suitable frameworks for particular configurations and environments. Research priorities moving forward should focus on establishing standardized reference points to allow for systematic evaluation between different frameworks, enabling clearer insights into the balance between processing speed, comprehensibility, and precision.

1.2. Integration of APV with Biomass-Powered Cogeneration

Within the panorama of renewable energy sources, a versatile and flexible solution that can significantly contribute to achieving decarbonization objectives, especially in rural contexts, is bioenergy. This term refers to energy produced from biomass, defined as the biodegradable fraction of products, waste, and residues from agriculture (including plant and animal substances), forestry, and related industries, as well as the biodegradable fraction of industrial and municipal waste. For this study, biomass is considered to have a net-zero CO₂ impact, assuming sustainable sourcing, where the carbon dioxide released during combustion equals that previously absorbed during the plant growth cycle. Importantly, biomass is a renewable energy source that can make significant contributions to the electricity, heating, and transportation sectors [33].

In particular, biomass gasification for combined heat and power (CHP) generation has gained significant traction across Europe to effectively utilize agricultural and forestry residues while reducing greenhouse gas emissions. At the end of 2024, Europe housed approximately 141 biomass and waste gasification installations, with an additional 54 projects under development. Germany accounted for 61 installations, while France, Finland, and Italy emerged as significant contributors to this growing market. The waste streams used in gasification accounted for about 7%, while the remaining facilities utilized mixed feedstock sources, 75% of which came from forestry and agricultural residues [34]. The potential for gasification in Europe is indeed significant, with estimates indicating a production capacity of 37 billion cubic meters (bcm) by 2040. The technology landscape is diverse, ranging from large-scale fluidized bed gasifiers (>5 MWe) in Northern Europe to distributed small-scale, fixed-bed systems (50–500 kWe), which are more common in Central and Southern European countries [35].

In this European framework, Italy has emerged as a significant player in bioenergy. The Italian biomass gasification sector has been shaped by the country’s agricultural profile and energy policies, with installations predominantly concentrated in the northern regions of Lombardy, Veneto, and Emilia-Romagna, where agricultural and forestry residues are abundant [36,37]. Already in 2018, a total of 218 operational plants with a 43.5 MW electrical capacity were reported by the IEA Bioenergy Task 33, with the majority of installations (64.2%) in Northern Italy, particularly in Trentino-South Tyrol. These facilities predominantly employed commercial fixed-bed downdraft gasifiers in the micro- to small-scale range (20–200 kWe), with over 80% of the plants falling within this capacity category and achieving electrical efficiencies between 25 and 30% and overall energy efficiencies (including heat recovery) of 75–85%. The syngas produced by thermal conversion through gasification is typically utilized in internal combustion engines for combined heat and power generation, with all plants connected to the national electricity grid. This distributed generation model has been supported by government incentives promoting renewable energy development, particularly favoring smaller-scale installations with higher feed-in tariffs. An update of the current state of development can be found in the IEA Task 33 database [35].

Recently, biomass gasification has garnered renewed interest for biofuel and hydrogen production. Molino et al. [34] documented performance data from 18 commercial plants across Italy, demonstrating that advanced syngas cleaning systems have significantly improved engine durability and reduced maintenance requirements, a historical challenge for gasification technology. A paper by Rubinsin et al. [38] reviewed enhanced biomass gasification processes for hydrogen production, examining factors affecting the process, including catalyst types, reactor configurations, and operating parameters. The review compared different gasification reactors (fluidized bed and fixed bed), highlighting how fluidized bed gasifiers offer better fuel flexibility and efficiency despite higher capital costs. It was concluded that while biomass gasification for hydrogen shows promise and environmental benefits, economic feasibility remains challenging due to capital costs, operating expenses, and the need for further process optimization.

The integration of biomass-powered CHP with PV plants is of direct interest today in the framework of the Renewable Energy Communities (RECs) [39,40]. Following the EU directives (RED II and IEMD) [5,41], Italy has implemented a legal framework for RECs, which are non-profit entities that share renewable electricity among members [42]. While most Italian RECs focus on electricity production, biomass presents unique advantages when integrated with solar PV. Biomass CHP systems, in fact, provide stable, controllable energy generation that compensates for solar energy’s intermittent nature [43]. Wooden biomass, particularly available in mountainous areas, can result in significant CO₂ reduction [44]. High-efficiency cogeneration incentives can support the financial feasibility of combining RECs with biomass CHP systems.

This integration, therefore, optimally leverages local resources while creating synergies between production systems and local businesses [45], addressing a still-present regulatory gap while promoting sustainable energy transitions.

2. The Case Study

In the context of the EMERA project, proposed by the paper authors in collaboration with other industrial and research partners, Vito Nigro’s Farm was assumed as a case study. It is located in Sant’Arcangelo, whose geographic coordinates are reported in

Table 1. Geographic coordinates of the farm assumed as case study of the EMERA project.

	Value	Unit
Location	Sant’ Arcangelo (PZ)
Latitude	40°15′24.16″ N
Longitude	16°15′34.06″ E
Altitude	388	m a.s.l.
Climatic Characteristics *
Climate type	Continental Mediterranean
Annual mean temperature	13.5	°C
Annual precipitation	687	mm
Solar radiation (annual)	~1545	kWh/m2
Cultivated area	782	m2

* Sources: Climate data derived from the Potenza meteorological station (845 m a.s.l.) with altitude correction for Sant’Arcangelo (388 m a.s.l.). Solar radiation estimated from regional data for Southern Italy at latitude 40° N.

, in the province of Potenza, in the Basilicata Region, Italy. The region is rural, and both the availability of solar energy and biomass can be considered for energy purposes.

The electricity consumptions of the farm were provided by the owner in annual aggregate form, based on the utility bill. The hourly load profile was reconstructed using a bottom-up approach based on individual equipment specifications and operational schedules.

Table 2. Farm energy consumption: (a) aggregated data in the time bands; (b) characterization of the farm equipment and operation timetable.

(a)
Consumption by time slot		kWh			%
F1		8887.7			24
F2		10,369			28
F3		17,775.3			48
Total		37,032
(b)
Qty	Machinery	kW	[h/day]	Period		months
1–2	Dryers	7.8	7	July–October (at night)		5
1	Dryer	6.3	7	November		1
2	Storage cells	1.5	24	June–October		5
1	Cured meat aging cell	3	8	2 + 3 h over the year		12
1	Steam cooking oven	10	2	Entire year (from 12 a.m. to 1 p.m.)		12
	External lighting	0.3	10	Night hours vary seasonally		~7
	Estimated auxiliary services	0.9–1.2	12	January–December		12

a shows the total yearly consumption of 37,032 kWh subdivided into time bands of absorption, according to Italian electricity tariffs. Given the global consumption for one year of operation, an hourly load curve was estimated based on the knowledge of the machinery used by the farm and other users, where the characteristics are reported in Table 2b. The assumed cultivated area is located nearby and is of 782 m².

The energy consumptions presented in Table 2a were reconciled with the values calculated from the equipment specifications given in Table 2b by applying operational adjustments that more accurately reflect the actual farm practices.

Starting from the above information, the farm electricity consumption was reconstructed in detail over a whole year period. In Figure 1, this is represented on an hourly basis in the two months of January and July. January represents peak winter operations with continuous heating and processing loads, while July shows summer operations with drying activities concentrated during nighttime hours to avoid peak electricity tariffs. These contrasting months provide representative boundary conditions essential for proper sizing of the APV system and storage capacity, ensuring the system can meet energy demands across the full range of seasonal operational requirements.

Regarding the local powering of the assumed cogeneration system, generally speaking, it was considered that resources that can be utilized for energy generation are highly heterogeneous in their physical and chemical characteristics and could be categorized based on their origin sectors:

(a)

The forestry and agroforestry sector, including residues from forest management, agroforestry activities, silvicultural operations, and related industries;

(b)

Wood and paper industries, comprising untreated wood processing residues and waste products from the paper industry (including sludge from water treatment);

(c)

The agricultural and agro-industrial sector, encompassing crop residues (prunings, trimmings), dedicated woody (willow, poplar, black locust) or herbaceous (miscanthus, sorghum, common reed) crops, oil-producing plants (sunflower, rapeseed), sugar crops (sugar beet, sweet sorghum), and residues from food processing industries (dairy, slaughterhouse, fish processing, sugar, and beverage industries);

(d)

The livestock sector, including animal manure (primarily used for biogas production);

(e)

Municipal waste, comprising residues from urban green space maintenance and the organic fraction of municipal solid waste.

Within the present work, biomass was assumed to derive only from category c of the above list, as crop residues, with possible integration with woodchips, depending on the availability and the time of operation of the bioenergy plant.

The assumed sequence of crops was bean, wheat, and potato, so as to cover a whole solar year over the same land area and to assume cultivations that react differently to the presence of the APV, namely, to the consequent shading by the panels. Due to the limited extension of the cultivation for the specific case, and to ensure the feasibility of the proposed 20 kW biomass-powered cogeneration system, a comprehensive biomass resource assessment was conducted for the Sant’Arcangelo area. Based on local agricultural practices and land-use patterns within a 15 km radius of the farm, the biomass resources reported in

Table 3. Estimated available biomass resources in the case study area.

Biomass Source	Annual Availability (Tonnes/Year)	Seasonal Availability	Calorific Value (MJ/kg)	Moisture Content (%)
Wheat straw	320	June–July	14.5	10–15
Bean residues	85	July–August	13.2	20–30
Potato crop residues	65	October–November	12.0	30–40
Olive prunings	130	January–March	17.5	30–35
Vineyard prunings	95	December–February	16.8	25–35
Forestry residues	220	Year-round (variable)	18.5	20–40

were identified as potentially available. The assumed biomass cogeneration system requires approximately 175 tonnes of biomass annually (based on 24 kg/h consumption at 7300 h of operation). The assessment confirmed sufficient biomass availability within the region to sustain continuous operation, with seasonal variations addressed through the appropriate storage and mixture of different biomass types. It was determined that the diversity of available feedstocks also provides operational flexibility and resilience against potential supply disruptions.

3. Methodology

In the first part of the present work, the possible installation of a 10 kW APV plant was considered to serve the farm, while keeping the existing connection to the national grid. Three geometrical configurators were analyzed, all mounting bifacial panels: a vertical configuration, a fixed configuration with inclined panels at 30° in the south direction, and a one-axis tracking system capturing solar radiation by rotating panels around the north–south axis during the course of the day. A parametric study was performed under the different configurations to size the electrical energy storage system, which is assumed to be a Li-Ion battery, and to find the optimal solution for profitability. The best geometrical configuration between the three configurations, in the second part of the work, was then studied with a 20 kW bioenergy system, manufactured by the Italian Company Costruzioni Motori Diesel, S.p.A., Caserta, Italy [2], to evaluate the possible exploitation of residual materials from agrifood practices. The two scenarios, APV only and APV+biomass CHP, clearly differ in investment cost, but both intend to supply the farm with locally available renewables and to assess conditions for independence from the national grid. To this aim, the severe framework of the 2022–2023 energy crisis was first assumed to carry out economic evaluations; then, we switched to the current (2025) tariffs of energy and vegetable selling prices to realize a proper comparison and evaluate how changing the time of the contingent conditions of small farmers’ operation may affect decisions about the considered opportunities of technology installation.

3.1. APV Shading Influence over Crop Yields

The present study intends to evaluate how the installation of agrivoltaic systems may impact small farm activities in terms of energy dependency and the related economic outcome. The ecosystem beneath the structure, depending on the specific plant configuration, is affected by shade from the APV panels. The primary and most evident consequence is reducing incident solar radiation available to crops, although other microclimatic factors are also altered. Regarding water balance, a substantial change concerns evapotranspiration—the amount of water (per unit of time) transferred from the soil to the air as vapor through the combined effect of plant transpiration and direct soil evaporation—which is reduced under photovoltaic arrays due to decreased light. During the warmer periods of the year, the panels protect crops in daylight hours by maintaining lower soil temperatures, allowing the retention of more moisture. Conversely, at night, the panels prevent the soil from reaching excessively low temperatures, reducing plant stress related to temperature fluctuations. The impact of APV on crops can therefore be optimized through appropriate adjustment of panel inclination [46].

The height and tilt of the modules can be selected according to the light, moisture, and temperature requirements of plants for germination. Depending on the panel arrangement, three distinct zones can be identified, as shown in Figure 2:

• Zone 1, characterized by low irradiation and high humidity levels;
• Zone 2, characterized by regular light exposure and sufficiently moist soil;
• Zone 3, characterized by the highest irradiation and lowest humidity.

Figure 2. Three different zones affected by an APV panel. Yellow represents a sun beam, red represents Zone 1, orange represents Zone 2, and blue represents Zone 3.

Figure 2. Three different zones affected by an APV panel. Yellow represents a sun beam, red represents Zone 1, orange represents Zone 2, and blue represents Zone 3.

A flowchart summarizing the steps implemented in the numerical study conducted to evaluate the different zones and the related impact of the PV panel presence on crop yield is shown in Figure 3, from the input data (in blue) up to the results in terms of yield (in green). A numerical model by the authors was used within the Matlab^® environment to first evaluate the reduction in the incident irradiance due to the shading effect of the APV solar panels on the soil and to quantify its influence over the underlying crop yield [46]. The computational procedure employed an hourly temporal resolution to capture diurnal variations in solar positions and irradiance patterns, which is essential for accurate assessment of crop-specific shading effects throughout different growth stages.

With the aim of estimating the actual crop yield in the three assumed APV configurations, the variation in radiation due to the presence of the PV panels during the whole solar year was evaluated. For the sake of clarity, for each considered panel, a condition like the one schematized in Figure 4a was assumed: the shadows projected on the ground were calculated as they moved in time during each day of the year, and their intersection with the cultivated area was evaluated to derive the shaded area that actually covers the area of interest. Once the shaded area was obtained, the direct shading factor for each hour of the day and the single diffuse shading factor were derived, assuming the anisotropy of the sky. In Figure 4b, showing an example relevant to June 21st, the PV panel projection on the cultivated area is shown for the day on which it is possible to observe the shortest shadows of the year. The consequent radiation reduction was thus evaluated. This calculation was repeated for each day during each crop lifecycle duration.

Understanding the ability of crops to tolerate shade, or benefit from it, is becoming increasingly crucial for the development of agrivoltaic systems. Despite extensive studies on agricultural growth characteristics, comprehensive assessments of how the yields of various crops react to various shading levels are still lacking. Laub et al. [47] carried out a meta-analysis based on experimental data gathered from the literature studies about agrivoltaic fields to close this gap. Their goal was to quantitatively estimate the susceptibility of various crop varieties to tolerate increasing amounts of shading. Figure 5 depicts yield response curves for crops built as a function of radiation reduction, as derived from the meta-analysis of ref. [47], based on experimental data from multiple agrivoltaic studies.

The green curve describes the mean reaction of crops that benefit from shading. The yellow and the red curves, respectively, show the average reactions of crops that are tolerant and susceptible to radiation decreases. Specifically, forages, leafy vegetables, and cereals first show a less than proportional drop, while berries, fruits, and vegetables benefit from a radiation reduction of up to 30% before suffering a less than proportional loss. On the other hand, even in areas with low shadow, maize and legumes show yield decreases. The equations for each of these curves are listed below:

$$Y_b=\left( 0.003·x^3\right)-\left( 0.128·x^2\right)+\left(3.135·x\right)+99.052$$

(1)

$$Y_t=-\left( 0.010·x^2\right)-\left(0.265·x\right)+99.327$$

(2)

$$Y_s=-\left( 0.004·x^3\right)+\left( 0.224·x^2\right)-\left(6.496·x\right)+100.620$$

(3)

where Y_b, Y_t, and Y_s stand for the anticipated yields for crops that benefit from, tolerate, and are vulnerable to the presence of shade, respectively. These equations are essential for the accurate assessment of a crop yield in accordance with the variation in radiation on the ground over the course of a crop’s whole lifecycle.

Several studies have shown that crop variability leads to higher agricultural productivity and benefits for the soil [48,49]. Therefore, the study conducted in the present work assumed a crop rotation for a whole year following the principle of soil preparation, where a depleting crop was alternated with an improving crop, including potatoes, beans, and winter wheat. Regarding the agrivoltaic application, the first two crops belong to families that benefit from shading, while the last does not tolerate shading. This rotation was chosen so that the lifecycles of the crops were spread throughout a whole solar year, as shown in

Table 4. Chosen crops with their relative lifecycles.

Crop	Lifecycle
Bean	June–July
Potato	August–October
Winter Wheat	November–May

.

The evaluation of the impact of shade on crop production using the aforementioned equations is a main innovative aspect of this study. The reduction in solar radiation (x) is calculated in Equations (4)–(6) as follows:

$$x= {\displaystyle \frac{G_{tot}-G_{shaded}}{G_{tot}}}$$

(4)

where G_tot is the global incident radiation to the ground during the crop lifecycle and G_shaded represents the value of the incident radiation, during the same period, decreased due to the shading effect. The latter was evaluated through a properly developed Matlab^® model, whose features are described in the next sections.

Once the radiation reduction to the ground and the expected yield of the crops is known, it is possible to analyze the profits (P_{veg_sale}) obtained from the sale of the vegetables.

$$P_{veg\_sale}=A_G·{Yield}_{crop}·{Sale}_{price}$$

(5)

where A_G represents the portion of soil committed to the crop [m₂], Yield_crop is the expected yield for that crop [kg/m²], and Sale_price is the selling price [EUR/kg].

The global horizontal irradiance data were obtained from Meteonorm software, whose database was obtained from 8325 weather stations placed worldwide and 5 geostationary satellites. Interpolation models provided hourly values of this quantity with high accuracy. Subsequently, the beam and diffuse irradiance at ground level were obtained through the relationships proposed by Erbs et al. [50] and Perez et al. [51]. A more comprehensive description of this approach, along with a detailed reference to each of the needed input data types, is given in reference [46].

The study of each of the assumed APV configurations was therefore conducted in terms of electrical energy production and crop yield. The land equivalent ratio (LER) parameter was considered, as expressed by the following relationship [52]:

$$LER={\displaystyle \frac{Y_{crop,APV}}{Y_{crop,only}}}+{\displaystyle \frac{Y_{electricity,APV}}{Y_{electricity,only}}}$$

(6)

where Y_crop,only and Y_{electricity,only} are the agricultural and electrical yields in the case that the whole field has a single use. Y_crop,APV and Y_{electricity,APV} are the yields in the case of combined production. This parameter allows for comparing the performance of the land when it is used for a combined agricultural and PV (agrivoltaic) use and when it independently has PV or agricultural production.

The economic performances of each agrivoltaic layout were evaluated in terms of cost savings through the relation:

$$Savings= {\mathsf{\Delta }C}_{el\_taken}+ P_{el\_sold}+P_{veg\_sales}$$

(7)

in which the profits earned from the sale of fruits and vegetables (P_{veg_sales}), whose yield is impacted by the presence of the APV plant, were considered along with the expenses and profits linked to electricity taken and sold to the grid (ΔC_{el_taken} and P_{el_sold}).

Table 5. Electricity costs for purchasing and selling from/to the grid and assumed vegetable costs.

Cost of Energy from the Grid	2022/2023 [EUR/kWh]	2025 [EUR/kWh]	Change
F1 (Mon–Fri 8:00–19:00)	0.52100	0.27900	−46.4%
F2 (Mon–Fri 7:00–8:00, 19:00–23:00; Sat 7:00–23:00)	0.49200	0.26800	−45.5%
F3 (Mon–Sat 23:00–7:00; Sun and holidays)	0.49200	0.22400	−54.5%
Profit from energy sold to the grid	2022/2023 [EUR/kWh]	2025 [EUR/kWh]
F1 (Mon–Fri 8:00–19:00)	0.04520	0.13050	+188.7%
F2 Mon–Fri 7:00–8:00, 19:00–23:00; Sat 7:00–23:00)	0.05306	0.13560	+155.6%
F3 (Mon–Sat 23:00–7:00; Sun and holidays)	0.03305	0.10590	+220.4%
Profit from sales of vegetables [EUR/kg]
Bean	1.10	1.85	+68.2%
Wheat	0.72	0.34	−52.8%
Potato	0.50	0.45	−10%

summarizes the different electricity and vegetable cost values assumed in this study: the first column contains data relevant to the period between the end of 2022 and the beginning of 2023, and the second column contains data relevant to the current scenario of 2025. These are representative of a time of uncertainty for energy prices and a time of certain relative stability, respectively, which were considered to show how the APV solution or the APV + bioenergy integration can represent viable options for more resilient rural communities and independence from the national electricity grid. Compared to other European countries, many Italian farmers suffer from oscillations in energy prices and are reluctant to adopt renewables due to insufficient knowledge about their benefits. The present work is intended to help fill this gap in a predominantly rural region of the south of Italy: the Basilicata Region. It is also important to note that electricity prices and agricultural commodity prices follow fundamentally independent market dynamics, driven by different economic factors and supply chains. Electricity prices are primarily influenced by energy market volatility, fuel costs, grid infrastructure, and energy policies, while crop prices depend on agricultural commodity markets, weather conditions, harvest yields, and global food supply–demand balances. This independence of price evolution strengthens the economic resilience argument for APV systems, as diversification across uncorrelated revenue streams (energy and agricultural products) provides natural hedging against market volatility in either sector.

A significant shift in the time of both energy market parameters and agricultural product prices is evident in Table 5: in the recent past, the energy sector underwent a substantial transformation, with electricity purchase costs from the grid decreasing markedly across all time bands. The F1 peak period (weekdays, 8:00–19:00) shows a 46.4% reduction from 0.52100 EUR/kWh to 0.27900 EUR/kWh, while similar substantial reductions are observed for the F2 (−45.5%) and F3 (−54.5%) time bands. Concurrently, the revenue potential from energy sold to the grid has increased, with profits in the F1 band rising by 188.7% (from 0.04520 EUR/kWh to 0.13050 EUR/kWh), the F2 band by 155.6%, and the F3 band by an even more substantial 220.4%. This dual transformation in energy economics—lower purchase costs combined with higher selling prices—fundamentally alters the value proposition for distributed generation systems like agrivoltaics. As a general trend, the spread between purchase and selling prices has narrowed considerably, enhancing the economic benefits of self-consumption while simultaneously increasing the value of excess production fed into the grid, thus overwhelming the crisis period that immediately occurred after the start of the Ukraine war.

Agricultural product pricing demonstrates heterogeneous trends that significantly impact crop selection strategies for agrivoltaic implementations. Fresh bean prices have increased by 68.2% (from 1.10 to 1.85 EUR/kg), substantially enhancing the economic viability of this crop, which shows a positive response to partial shading. In contrast, wheat prices have decreased by 52.8% (from 0.72 to 0.34 EUR/kg), which, while representing a market challenge for conventional producers, actually mitigates the economic penalty associated with yield reductions for this shade-intolerant crop under agrivoltaic systems. Potato prices have experienced a modest 10.0% decrease (from 0.50 to 0.45 EUR/kg). These price variations collectively create a more favorable economic environment for agrivoltaic implementations in 2025 compared to the 2022–2023 period. The evolution particularly benefits systems incorporating shade-tolerant, high-value crops, like beans, while diminishing the financial impact of including rotation crops, such as wheat, that may experience yield reductions under partial shading conditions.

A further important parameter also taken into consideration within the present study is the “electricity mix emission factor” (f_e), enabling an assessment of the favorable environmental impact of the solar plant. In the considered period between 2022 and 2023, the factor was equivalent to 0.48 kg CO₂/kWh, indicating an average value of CO₂ emissions due to electricity production in Italy [53]. For 2025, based on projections and Italy’s decarbonization targets under the National Energy and Climate Plan (PNIEC), the electricity mix emission factor is expected to decrease to approximately 0.28–0.32 kg CO₂/kWh. A value of 0.30 kg CO₂/kWh was assumed in this study. This reduction is due to the increasing share of renewables in the Italian electricity mix and the progressive phase-out of coal-fired power plants. Therefore, the reduction in greenhouse gas emissions that the proposed system brings about compared to the reference system (in which the demand is met by drawing energy only from the electricity grid) was calculated using the equation:

$${{CO}_2}_{savings}= f_e\ast ({WE}_{ref}- {WE}_{prop})$$

(8)

where f_e is the abovementioned electricity mix emission factor and WE_ref and WE_prop are the energy withdrawn from the reference system and the energy withdrawn from the proposed system, respectively. The reference system is defined as a conventional grid-connected farm with no on-site generation, consuming the same annual electricity profile as our case study, with emissions calculated according to the Italian national grid mix.

3.2. Numerical Model for the Assessment of Dynamic Operations of the APV Plant

The dynamic operation of the APV plant was studied within the TRNSYS 16 environment, a program employed for the assessment of each of the suggested solutions against energetic loads belonging to the real farm considered as a case study. This software was selected for this analysis due to its proven capabilities in modeling complex renewable energy systems with multiple interconnected components. Figure 6 illustrates the schematization of the model arrangement for the farm’s electrical requirements.

Each energetic component of the plant was modeled through proper Types. Type 48c, which can control energy flows from the generation system, was used to mimic the existence of the inverter, while Type 47b was used to model the grid and the storage. The farm load curve, whose samples from January and July are represented in Figure 1, is to be fulfilled by the agrivoltaic system. A lithium–iron–phosphate (LiFePO₄) storage system is assumed, whose capacity ranges from a minimum of 1.5 kWh to a maximum of 152 kWh, depending on the number of cells in series from 6 to 60, with a step of six, and the number of cells in parallel from 7 to 70, with a step of seven. Storage capacity is the decision variable to fulfill the farm’s overall energy requirement of 37,032 kWh. The hourly data relative to APV electrical production were obtained by employing PVSyst^® software, considering the actual plant configuration: vertical, fixed, or one-axis tracking.

The capacity of the electrical storage system varied from one dynamic simulation to another to carry out a techno-economic and environmental analysis of the APV layout performances. When determining the optimal storage size for each solution, the economic aspect was also considered by balancing investment costs and savings. More specifically, the optimal size was chosen to be the one that guaranteed a Simple PayBack period (SPB) of less than 7 years, since the useful life of the system is approximately 10 years:

$$SPB={\displaystyle \frac{{Investment}_{cost}}{Savings}}<7 \mathit{years}.$$

(9)

The chosen photovoltaic module was the Canadian Solar’s bifacial CS3W-440MB AG model in monocrystalline silicon [54]. The choice of the bifacial module was guided by the increase in production that this configuration guarantees by reducing the total number of modules required and optimizing the occupation of the ground. The direct current (DC) produced was assumed to be subsequently converted into alternating current (AC) thanks to Canadian Solar’s CSI-20KTL-GI-FL inverter. The module and inverter technical characteristics are summarized in

Table 6. PV module and inverter technical characteristics.

Bifacial Module CS3W-440MB AG
Cell type	monocrystalline silicon
Number of cells	144
Efficiency [%]	19.70
	STC	NOCT
Maximum Power Wp [W]	440.00	329.00
Maximum Voltage Vmp [V]	40.40	37.50
Maximum Current Imp [A]	10.82	8.78
Open Circuit Voltage Voc [V]	48.70	45.40
Shortcut Voltage Isc [A]	11.48	9.30
Inverter CSI-20KTL-GI-FL
Maximum efficiency [%]	98.70
Vmax [V]	1000.00
Imax MPPT [A]	18.00
Icc MPPT [A]	28.10
Starting Voltage [V]	350.00
Operating temperatures	−25 °C$~$60 °C

[54,55]. Each battery pack consisted of lithium–iron–phosphate (LiFePO₄) cells with a capacity of 16.5 Ah and a voltage of 2.2 V.

Figure 7 shows schematized representations of the three assumed APV structures, modeled in the Matlab^® environment. The ground is shown in green, and the panel sheds are shown in blue. The first configuration involves the use of a structure with vertically placed modules; the second involves mounting the modules on a fixed structure at the same height above the ground. The third proposed configuration, finally, is for a system of single-axis trackers facing south to maximize electricity production (modules rotate with respect to the horizontal axis at an angle ranging from −30° to 80°). The height of the support structures is defined so that the free space between the ground and the modules, at maximum inclination, is more than 3.5 m, to facilitate the use of the land for agricultural activities. The first two structures are cheaper than the tracking solution. As the power of each module is 0.440 kW and the total power of the system is 10 kW, 24 photovoltaic modules are used. The design parameters for the three configurations in the Matlab^® numerical model are reported in

Table 7. Design features for the three considered APV configurations.

Parameter	Unit	Vertical	Fixed	One-Axis Tracking
Module width	m	0.99	0.99	0.99
Module length	m	2.08	2.08	2.08
Number of modules	-	24	24	24
Total module area	m2	49.42	49.42	49.42
Total system power	kW	10.56	10.56	10.56
System Layout
Shed width	m	1.98	11.88	1.98
Shed length	l	24.96	4.16	24.96
Crop area	m2	782	782	782
Height	m	1.5	4	4
Module-to-surface ratio	%	6.32	6.32	6.32
Panel Configuration
Fixed tilt angle [°]		−90	−30	-
Maximum tilt angle [°]		-	-	−30
Minimum tilt angle [°]		-	-	80
Azimuth angle [°]		0	0	0

.

The investment costs for the various system components, presented comparatively in

Table 8. Investment costs.

	2022–2023 Costs	2025 Costs	Change
440 W Bifacial modules	500 [EUR/kW]	250 [EUR/kW]	−50%
Inverter	4000 [EUR]	2800 [EUR]	−30%
Storage	950 [EUR/kWh]	650 [EUR/kWh]	−31.6%
Vertical	94 [EUR/kW]	120 [EUR/kW]	+27.7%
Fixed	300 [EUR/kW]	220 [EUR/kW]	−26.7%
One-axis tracking	730 [EUR/kW]	480 [EUR/kW]	−34.2%
Administrative costs	10,800 [EUR]	8500 [EUR]	−21.3%

, demonstrate substantial changes between the 2022–2023 period and the current 2025 market conditions [54,55]. PV module costs have experienced the most dramatic reduction, decreasing by 50% from 500 EUR/kW to 250 EUR/kW. This significant decline reflects the continued maturation of PV manufacturing technologies, increased production capacity (particularly in China), and enhanced economies of scale. Similarly, inverter costs have decreased by 30% (from EUR 4000 to EUR 2800), while energy storage systems have become 31.6% more affordable (from 950 EUR/kWh to 650 EUR/kWh), primarily due to advancements in battery technology and expanded manufacturing capacity for lithium-ion systems. Mounting structure costs show divergent trends across different configurations. While the vertical structure costs have increased by 27.7% (from 94 EUR/kW to 120 EUR/kW), likely reflecting rising material costs and more robust engineering requirements, both the fixed and tracking systems have become more economical. Fixed structure costs have decreased by 26.7% (from 300 EUR/kW to 220 EUR/kW), and one-axis tracking systems have experienced an even more substantial cost reduction of 34.2% (from 730 EUR/kW to 480 EUR/kW). This significant decrease in tracking system costs is particularly noteworthy as it improves the economic viability of the most technically efficient configuration identified in our analysis. Administrative costs have also decreased by 21.3% (from EUR 10,800 to EUR 8500), indicating streamlined permitting processes and increased regulatory familiarity with agrivoltaic installations. These capital expenditure reductions substantially enhance the economic feasibility of all agrivoltaic configurations, but they particularly benefit the tracking system option due to its disproportionate cost reduction. When combined with the favorable changes in operating economics illustrated in Table 5, these investment cost reductions considerably strengthen the business case for agrivoltaic implementations in the current market environment [56].

3.3. APV Integration with a Biomass-Powered Cogeneration Unit

The assumed biomass-powered combined heat and power (CHP) system is the CMD ECO20X manufactured by the Company Costruzioni Motori Diesel, S.p.A. (CMD), selected for its commercial level of development. It can generate up to 20 kW of energy and 40 kW of thermal energy using 24 kg/h of biomass. The system combines a downdraft gasifier, synthesis gas cleaning devices, a spark-ignition engine, and an electric generator in a single integrated layout. A schematization of the entire system is shown in Figure 8 [57]. In particular, the red arrow highlights the path of the synthesis gas, which is produced by the gasification of biomass in a downdraft reactor, using air as a gasifying agent (biomass equivalence ratio in the range of 0.3), and is characterized at the outlet by a temperature of about 600–700 °C. Subsequently, the syngas is directed to different cleaning systems made of a cyclone, for the removal of dust and particulates; a heat exchanger, through which the syngas is cooled to remove the moisture content, increase the volumetric efficiency of the heat engine, and condense the TARs; a biological filter, filled with the same biomass used as raw material, to remove carbon and water vapor particles; and a further cyclone, to eliminate the last part of ash and particulates. Following the cleaning phase, the syngas is mixed with air in a stoichiometric ratio and sent to the combustion engine, a GM Vortec 4.3 L, 6 cylinders, naturally aspirated with spark ignition, operating in cogeneration mode for the simultaneous production of electricity and thermal energy. Heat recovery is carried out by means of special heat exchangers placed along the engine cooling circuit and along the exhaust gas line. The main features of the system are summarized in

Table 9. Micro-cogeneration system main features.

	Micro-Cogeneration System Data Sheet
Electrical power	20.00	[kW]
Thermal power	40.00	[kW]
Electrical efficiency	22.5	[%]
Thermal efficiency	56.2	[%]
Biomass feeding	24.00	[kg/h]

.

The synergic modeling and experimental characterization of the CHP unit resulted in a proper engine map suitable for modeling purposes without describing the details of the whole biomass conversion process. Its operation, as integrated with the APV system, therefore, can be simulated within the TRNSYS environment by referring to the scheme in Figure 9. The possibility of using the ECO20X system is considered to maximize the exploitation of residual feedstocks as biomass in the farm area. In detail, the system has a cost of around EUR 200,000, while, for the biomass cost, a value of 35 EUR/kg is assumed.

The battery management strategy employs a rule-based control algorithm prioritizing self-consumption. The control hierarchy is as follows:

• Direct consumption of generated power;
• Battery charging when generation exceeds demand;
• Battery discharging when demand exceeds generation;
• Grid interaction only when the battery state-of-charge reaches operational limits (10–90%).

The biomass gasifier operates with the following constraints:

-

Minimum load factor: 30% (6 kW electrical output);

-

Start-up time: at least 30 min.

-

Shutdown time: 15 min;

-

Maintenance intervals: 400 operating hours.

4. Results and Discussion

4.1. Effect of APV on Crop Yields

A comprehensive economic analysis of the expected crop yields derived from the presence of the APV plant is given in

Table 10. Expected yields due to the shading effect and relative profits for the (a) vertical, (b) fixed, and (c) one-axis tracking APV layouts.

(a)
	Yield [kg/m2]	Radiation Reduction	Vertical APV Δ Yield	Expected Yield [kg/year]	2022–2023 Sale Price [EUR/kg]	2022–2023 Farmer Earnings [EUR]	2025 Sale Price [EUR/kg]	2025 Farmer Earnings [EUR]	Variation
Bean	9.90	10.3%	20.6%	9332.74	1.10	3079.80	1.85	5179.67	68.2%
Wheat	8.30	12.4%	−52.1%	3112.24	0.72	672.24	0.34	317.45	−52.8%
Potato	59.40	12.6%	23.1%	57,162.35	0.50	8574.35	0.45	7716.92	−10.0%
Total						12,326.40		13,214.04	7.2%
Farmer Earnings no APV						10,924.38		11,229.60	2.8%
Difference						1402.02		1984.44	41.5%
(b)
	Yield [kg/m2]	Radiation Reduction	Vertical APV Δ Yield	Expected Yield [kg/year]	2022–2023 Sale price [EUR/kg]	2022–2023 Farmer Earnings [EUR]	2025 Sale price [EUR/kg]	2025 Farmer Earnings [EUR]	Variation
Bean	9.90	10.3%	24.2%	9618.39	1.10	3174.07	1.85	5338.21	68.2%
Wheat	8.30	12.4%	−57.7%	2746.53	0.72	593.25	0.34	280.15	−52.8%
Potato	59.40	12.6%	24.6%	57,893.48	0.50	8684.02	0.45	7815.62	−10.0%
Total						12,451.34		13,433.97	7.9%
Farmer Earnings no APV						10,924.38		11,229.60	2.8%
Difference						1526.96		2204.37	44.4%
(c)
	Yield [kg/m2]	Radiation Reduction	Vertical APVΔYield	Expected Yield [kg/year]	2022–2023 Sale price [EUR/kg]	2022–2023 Farmer Earnings [EUR]	2025 Sale price [EUR/kg]	2025 Farmer Earnings [EUR]	Variation
Bean	9.90	10.3%	24.3%	9626.02	1.10	3176.59	1.85	5342.44	68.2%
Wheat	8.30	12.4%	−55.5%	2889.93	0.72	624.22	0.34	294.77	−52.8%
Potato	59.40	12.6%	24.2%	57,687.60	0.50	8653.14	0.45	7787.83	−10.0%
Total						12,453.95		13,425.04	7.8%
Farmer Earnings no APV						10,924.38		11,229.60	2.8%
Difference						1529.57		2195.44	43.5%

a under the vertical agrivoltaic configuration and the hypothesized alternate bean, wheat, and potato cultivations during a year. A comparison of outcomes in the 2022–2023 period and in 2025 is shown, based on the respective market prices given above. In both assumed timeframes, the earnings for farmers are assumed to be equal to 30% of the vegetable selling price. The data reveal not only the change in the profitability of the crops due to APV, but also, when maintaining the physical parameters (radiation reduction percentages and corresponding yield effects are unchanged), the economic implications shift significantly due to evolving market prices for agricultural products.

In light of the change that occurred from the 2022–2023 period to 2025, bean production demonstrates the most favorable economic transformation due to APV: for a 20.6% yield enhancement under partial shading (resulting in 9332.74 kg/year), the increase in market price from EUR 1.10/kg to EUR 1.85/kg generates a 68.2% improvement in economic returns from EUR 3079.80 to EUR 5179.67. This substantial increase in profitability for a crop benefitting from the APV environment represents a particularly advantageous synergy between biological response and market conditions. Conversely, wheat production, which experiences a 52.1% yield reduction under shading conditions and a decrease in sale price, suffers a contraction in the economic returns by 52.8%, with earnings decreasing from EUR 672.24 to EUR 317.45. This diminishes the financial viability of including a shade-intolerant crop in the rotation. However, the overall economic resilience of the agrivoltaic system remains intact due to the performance of other crops, while the advantages of soil regeneration due to crop alternation are maintained. Potato cultivation, which benefits from a 23.1% yield enhancement under partial shading, experiences a modest 10.0% reduction in financial returns due to the corresponding decrease in market price from EUR 0.50/kg to EUR 0.45/kg, with earnings declining from EUR 8574.35 to EUR 7716.92. Despite this slight reduction, potato production remains economically advantageous within the agrivoltaic context.

In 2025, the aggregate economic impact across all three crops in rotation demonstrates a 41.5% improvement. This enhancement, when integrated with the favorable changes in both energy economics and reduced investment costs, contributes to the change in crop yield over a year and an improved value proposition in 2025 related to the 2022–2023 period. Table 10b,c reports the analogous data for the other two assumed APV configurations, which show an even higher potential benefit over agricultural profits.

Beyond energy production and their effects on crop yields, APV systems provide other environmental and economic benefits, especially in Mediterranean climates, as significant water conservation can be achieved through reduced evapotranspiration rates [16,17]. An average 18% reduction can be considered for the three assumed crop types. The bean–potato–wheat rotation, on the other hand, creates diverse habitats supporting greater biodiversity than monocultures, while vegetation corridors between panels may support crucial pollinators. Reduced soil temperatures (3–5 °C lower in summer) improve microbial activity and organic matter preservation, particularly beneficial in Basilicata and Southern Italy’s semi-arid conditions, where soil degradation is a serious problem. These water and biodiversity benefits, however, are not quantified here due to insufficient data, preventing accurate evaluations.

4.2. Dynamic Simulation of the APV Installation

Concerning the dynamic operations of the three APV configurations, as previously said, a parametric analysis was carried out by varying the size of the storage system to increase the rate of self-consumed energy while keeping the SPB below 7 years. Figure 10 presents the main results derived from the dynamic simulations under the three plant configurations, while

Table 11. Parametric analysis results relevant to the (a) vertical, (b) fixed, and (c) one-axis tracking APV configurations. The optimal battery capacity (highlighted in grey) minimizes the simple payback period while maximizing self-consumption.

(a)
Vertical APV
	Battery Capacity [kWh]	Withdrawn Energy Cost [EUR/Year]	Earnings from Energy Sale [EUR/Year]	CAPEX [EUR]	Earnings from Crops [EUR/Year]	SPB
1	1.52	14,649.99	103.51	22,188.37	1402.02	4.16
2	6.10	14,235.92	63.92	26,533.48	1402.02	4.65
3	13.72	13,841.94	26.70	33,775.33	1402.02	5.57
4	24.39	13,671.20	8.49	43,913.92	1402.02	7.06
5	38.12	13,655.81	7.47	56,949.25	1402.02	9.14
6	54.89	13,673.38	7.48	72,881.32	1402.02	11.73
7	74.71	13,649.69	7.43	91,710.13	1402.02	14.70
8	97.57	13,634.24	7.42	113,435.68	1402.02	18.14
9	123.49	13,633.98	7.45	138,057.97	1402.02	22.08
10	152.46	13,625.09	7.46	165,577.00	1402.02	26.44
(b)
Fixed APV
	Battery capacity [kWh]	Withdrawn energy cost [EUR/year]	Earnings from energy sale [EUR/year]	CAPEX [EUR]	Earnings from crops [EUR/year]	SPB
1	1.52	12,648.17	215.50	27,248.37	1526.96	3.60
2	6.10	11,928.48	144.92	31,593.48	1526.96	3.84
3	13.72	11,115.33	66.60	38,835.33	1526.96	4.34
4	24.39	10,585.61	16.21	48,973.92	1526.96	5.19
5	38.12	10,565.42	9.43	62,009.25	1526.96	6.56
6	54.89	10,568.21	9.55	77,941.32	1526.96	8.25
7	74.71	10,552.38	9.56	96,770.13	1526.96	10.23
8	97.57	10,534.78	9.46	115,495.68	1526.96	12.18
9	123.49	10,525.55	9.49	140,117.97	1526.96	14.77
10	152.46	10,507.11	9.49	167,637.00	1526.96	17.63
(c)
One-axis tracking APV
	Battery capacity [kWh]	Withdrawn energy cost [EUR/year]	Earnings from energy sale [EUR/year]	CAPEX [EUR]	Earnings from crops [EUR/year]	SPB
1	1.52	12,118.90	316.63	28,548.37	1529.57	3.48
2	6.10	11,324.22	237.93	32,893.48	1529.57	3.69
3	13.72	10,275.69	134.66	40,135.33	1529.57	4.07
4	24.39	9469.82	52.41	50,273.92	1529.57	4.75
5	38.12	9340.64	39.57	63,309.25	1529.57	5.91
6	54.89	9323.55	35.13	79,241.32	1529.57	7.39
7	74.71	9263.54	29.86	98,070.13	1529.57	9.10
8	97.57	9189.03	24.77	119,795.68	1529.57	11.05
9	123.49	9123.67	21.37	144,417.97	1529.57	13.24
10	152.46	9093.17	19.72	171,937.00	1529.57	15.73

reports the corresponding economic evaluations, as derived by accounting for the energy cost in the 2022–2023 period and the related earnings from the vegetable sale.

Table 12. Results for optimal solutions under different tariffs for 2022–2023 and 2025.

	Vertical		Fixed		One-Axis Tracking
	2022–2023	2025	2022–2023	2025	2022–2023	2025
Battery capacity [kWh]	13.72	13.72	38.12	38.12	38.12	38.12
Self-consumed energy [%]	25.24	25.25	40.40	40.40	45.83	45.83
Withdrawn from the grid [%]	74.33	74.33	59.35	59.35	51.56	51.56
Given to the grid [%]	6.60	6.60	1.52	1.52	2.60	2.60
∆MCO2 [%]	23.54	23.54	42.65	42.65	49.60	49.60
Investment cost [EUR]	33,775.33	23,918.91	62,009.25	42,974.75	63,309.25	43,374.75
Economic savings [EUR/year]	6064.27	13,327.62	9448.46	15,019.97	10,705.99	15,673.31
SPB [years]	5.57	1.79	6.56	2.86	5.91	2.77
Radiation reduction [%] (during bean lifecycle)	10.32		13.84		10.32
Radiation reduction [%] (during wheat lifecycle)	12.41		15.04		12.41
Radiation reduction [%] (during potato lifecycle)	12.56		14.32		12.56
Bean expected yield [kg/m2]	20.55		24.24		24.34
Wheat expected yield [kg/m2]	−52.05		−57.68		−55.48
Potato expected yield [kg/m2]	23.06		24.63		24.19
LER	1.4		1.6		1.7

, finally, represents data relevant to the 2022–2023 and 2025 periods to compare the economic viability of the APV installation in the small-scale farm and its evolution from a scenario of energy crisis to a moderately more stable situation. The energy plant could ensure greater independence of agrifood activities in a context such as that of Southern Italy, where radiation over a solar year is high.

Analyzing the trend of the self-consumed energy (shown in yellow in Figure 10) as the capacity of the battery pack increases from simulation N. 1 to 10, one may note that the self-consumed energy also increases, with a consequent decrease in the amount withdrawn and sold to the grid. However, a threshold is reached, beyond which the advantages remain moderate. Once this information is obtained, the cost of the electricity withdrawn and sold to the grid, the investment cost, the economic savings, and the SPB for the combinations considered are evaluated. The optimum point, evaluated in the 2022–2023 framework of prices, is highlighted as a grey line. The corresponding capacity of the battery pack differs for the three configurations, as the related SPB. Table 12 compares the optimum of the three APV configurations. The reduction in CO₂ emissions, compared to the reference system, is 8.8 t/year (49.60% less).

The fixed layout configuration with a 38.12 kWh storage system demonstrates superior technical performance, with 40.40% self-consumed energy and 42.65% CO₂ reduction. Its economic parameters similarly improve, with investment costs decreasing from EUR 62,009.25 to EUR 42,974.75 and annual savings increasing from EUR 9448.46 to EUR 15,019.97. Consequently, the SPB decreases from 6.56 to 2.86 years.

The one-axis tracking system, also characterized by a storage system size of 38.12 kWh, exhibits the highest technical performance among all configurations, with 45.83% self-consumed energy and the highest CO₂ emission reduction. Its economic evolution is similarly impressive, with investment costs decreasing from EUR 63,309.25 to EUR 43,374.75 and annual savings increasing from EUR 10,705.99 to EUR 15,673.31. These changes reduce the SPB from 5.91 to 2.77 years, an improvement that positions this configuration as also an economically attractive option.

Finally, it can be seen that the magnitude of economic improvement is relatively uniform across all configurations, with SPB reductions higher than 50% in all cases, indicating that market evolution benefits all APV implementations rather than favoring specific technical approaches.

These results underscore the increasingly compelling business case for agrivoltaic implementations, particularly the tracking configuration, which combines optimal technical performance with the most attractive economic returns under current market conditions.

The interaction between crop selection, shading tolerance, and overall system profitability is critical for optimal APV implementations. The analysis demonstrates that strategic crop selection can transform APV from a compromise solution to a synergistic enhancement of both agricultural and energy outcomes. Shade-tolerant crops, as the chosen bean, represent the ideal crop for APV integration, while including wheat in rotation does not correspond to yield improvement but serves multiple purposes, especially for maintaining soil health through the completion of the nitrogen fixation cycle. The overall cycle, therefore, demonstrates that even unfavorable crops can remain economically viable within properly integrated systems.

To assess the robustness of the aforementioned techno-economic findings, a sensitivity analysis was also conducted, examining the impact of ±20% variations in critical parameters on the optimal one-axis tracking configuration. The analysis focused on four key variables, namely, PV module costs, electricity purchase prices, and crop selling prices. The main results are shown in

Table 13. Results of the sensitivity analysis.

Parameter Variation	SPB	LER	Annual Savings [EUR]	Self-Consumption [%]
Base case (2025)	2.77	1.7	15,673.31	45.83
PV costs +20%	3.08	1.7	15,673.31	45.83
PV costs −20%	2.46	1.7	15,673.31	45.83
Electricity price +20%	2.24	1.7	18,591.22	45.83
Electricity price −20%	3.52	1.7	12,755.40	45.83
Crop prices +20%	2.64	1.7	16,357.64	45.83
Crop prices −20%	2.90	1.7	14,988.98	45.83

Results for combined APV+biomass configuration.

. Electricity prices exert the strongest influence on system economics, with a ±20% variation causing SPB changes of −19% to +27%. PV module cost variations show a moderate impact (SPB range: 2.46–3.08 years), while crop price fluctuations have minimal effects on overall economic viability.

These results confirm the robustness of the APV implementation across realistic parameter ranges, with all scenarios maintaining SPB below 4 years, well within the acceptable threshold for renewable energy investments.

4.3. Combined Operation of APV and Biomass-Powered CHP

The objective of this analysis stems from the aim of evaluating a plant configuration, powered by both biomass and solar energy, that can operate as an island system, completely disconnected from the electrical grid. The configuration couples the CMD ECO20X bioenergy unit with the one-axis tracking APV configuration selected by the previous analysis, with a battery pack of 38.12 kWh. The whole system was modeled in the TRNSYS environment, as shown in Figure 9, to assess the interactions between the generation system and the loads to be served. The developed model has the same structure as in the previously analyzed scenarios, with the introduction of the IC_Engine type appropriately modeled with the sizing characteristics of the ECO20x system, and a calculation unit named Produzione_tot, where the interaction between energy flows from the two generators is implemented. The annual full-load operating hours of the bioenergy system are 7500 due to the necessary maintenance operations scheduled every 400 h [57]. The annual required biomass quantity is therefore given by:

Biomass quantity = (7500 h/year × 24 kg/h)/(1000 kg/t) = 180.00 t/year

(10)

In the gasifier, the conversion of wood chips into syngas occurs with the following energy flows:

• ▪ The thermal power associated with the biomass input to the gasifier is equal to 124.87 kW;
• ▪ The thermal power associated with the air flow input to the gasifier is equal to 19.86 kW;
• ▪ The thermal power dissipated through the reactor walls is equal to 42.55 kW;
• ▪ The thermal power related to conversion losses is equal to 4.00 kW;
• ▪ The thermal power of ash sensible heat is equal to 0.18 kW;
• ▪ The thermal power related to the vaporization of moisture in the biomass is equal to 2.75 kW;
• ▪ The thermal power of syngas sensible heat is equal to 12.18 kW;
• ▪ The useful thermal power of syngas is equal to 83.07 kW.

Given that the peak potential of the CHP-APV system is 30 kW, it is expected that serving only the small-scale agricultural farm, Vito Nigro, would result in a large amount of surplus energy fed into the grid. Therefore, the assumption was made that the integrated energy plant would serve two farms with the same consumption of electrical energy. As a result of the simulation considering two farm loads and characterized by a battery pack capacity of 30 kWh, the energy balances are summarized in

Table 14. Preliminary energy balance of the small-scale farm load fulfilment by the integrated CHP-APV energy system.

Self-Consumed Energy [%]	22.06
Energy taken from the grid [%]	0
Energy fed into the grid [%]	77.94

. The self-consumed energy equals 22.06% and completely satisfies the needs of the two hypothesized business cases, while the amount of energy fed into the grid equals 77.94% of the produced energy, for a value of 193,774 kWh. Figure 11 represents the energy that can be transferred monthly to the grid. Given these findings, in order to minimize interactions with the national power grid, it was deemed appropriate to first analyze the system behavior by modulating the cogenerator power and then realizing the battery pack optimization, with the aim of entirely eliminating these interactions.

An analysis of the demand profile and the power generated by the integrated system was performed to determine the degree of modulation that would allow minimal interaction with the grid. In this specific case, the degree of modulation of the cogeneration engine was varied to follow the demand curve. First, the portion of the requested energy covered by the cogenerator was defined through the proposed relationship:

$${Frac}_{COG}=Load-P_{PV}$$

(11)

This approach considered the power generated by the PV system to be utilized when available. Having determined the demand that the cogenerator must meet, the degree of modulation of the cogenerator was calculated using the following relationship:

$$f_c={\displaystyle \frac{{Frac}_{COG}}{{Pow}_{COG}}}$$

(12)

where Frac_COG and Pow_COG represent the load to be covered by the cogenerator and the maximum power made available by the cogenerator, respectively. Varying the latter between a minimum value of 0.3 and a maximum unitary value with a step of 0.1, the hourly values obtained from Equation (4) were post-processed in an Excel spreadsheet. In particular, they were rounded up, and conditional formatting ensured that values below the minimum and those exceeding unity were set to the maximum and minimum possible values, respectively. As an example, the values for the first 5 operating hours of the cogenerator are reported in

Table 15. Degree of modulation of the cogenerator in the first 5 operating hours of the CHP unit.

PPV	Load	FracCOG	fccalcolated	fcprocessed
0.9361	11.2	10.2639	0.6	0.6
0.5681	1.2	0.6319	0.1	0.3
0.2068	1.2	0.9932	0.1	0.3
0.0498	1.2	1.1502	0.1	0.3
0	1.2	1.2	0.1	0.3

. The hourly data thus determined were provided to the cogenerator through a Type9c (data reader).

By performing this further analysis, a sharp decrease in surplus energy was observed, and the amount of energy drawn from the grid was also obtained, as shown in Figure 12 (4661 kWh/year is fed into the grid and 2314.6 kWh/year is drawn).

The next step consisted of the optimization of the size of the storage system, starting from a storage system size of 38.12 kWh (30 cells in series and 35 in parallel) and increasing it up to 152.46 kWh (60 cells in series and 70 in parallel), by varying the number of cells in series with a step of 6 and those in parallel with a step of 7. The simulation results are reported in

Table 16. Decision variables and simulation results of the APV+CHP scenario.

Number of Cells in Series	Number of Cells in Parallel	Battery Capacity [kWh]	Energy Sold [kWh/Year]	Energy Withdrawn [kWh/Year]	Self-Consumed Energy [kWh/Year]
30.00	35.00	38.12	4661.14	2314.65	40,430.35
36.00	42.00	54.89	3790.42	2012.41	40,732.59
42.00	49.00	74.71	3129.23	756.44	41,988.56
48.00	56.00	97.57	2923.59	0.00	42,745.00
54.00	63.00	123.49	2706.21	0.00	42,745.00
60.00	70.00	152.46	2600.97	0.00	42,745.00

and represented in Figure 13.

The optimal point, highlighted in grey in the table, corresponds to a battery pack capacity of approximately 100 kWh. Analyzing the graph shown in Figure 13, beyond that value, the benefit obtained from increasing the size does not justify the increase in investment cost. An economic analysis yielded the results in

Table 17. Summary framework for the APV+CHP scenario in the 2022–2023 tariffs scenario.

Battery Capacity [kWh]	Cost of Energy Withdrawn [EUR/year]	Cost of Energy Sold [EUR/Year]	Costo di Investimento [EUR]	Risparmio [EUR/Anno]	SPB [Years]
38.12	1157.33	186.45	275,609.25	14,408.86	19.1
54.89	1006.21	151.62	291,541.32	14,374.03	20.3
74.71	378.22	125.17	310,370.13	14,347.58	21.6
97.57	0.00	116.94	332,095.68	14,339.35	23.2
123.49	0.00	108.25	356,717.97	14,330.66	24.9
152.46	0.00	104.04	384,237.00	14,326.45	26.8

, considering the 2022–2023 tariffs.

Figure 14 presents a comparison between the annual interaction patterns for the chosen solution, which guarantees savings of EUR 14,339.35 and an SPB of 23.2 years, and the grid exchanges obtained by only modulating the cogeneration engine. Specifically, the energy values drawn and fed into the modulated system are represented in orange and blue, respectively, while the energy values drawn and fed into the optimized scenario are represented in red and light blue. The reduction in CO₂ emissions compared to the reference system is approximately 20.52 t/year (equivalent to a 100% reduction).

To contextualize our findings within the existing literature, the results are compared with similar APV studies in Mediterranean or similar agroclimatic contexts. The one-axis tracking configuration achieved superior performance across key indicators compared to the published studies.

The LER of 1.7 exceeds the values reported in similar Mediterranean installations: Cossu et al. [24] achieved 1.45 in Italy with tomato/basil systems, while Campana et al. [30] reported 1.52 with lettuce/spinach rotations.

The achieved self-consumption rate of 45.83% represents the highest among Mediterranean APV studies, attributed to optimized battery sizing and favorable load–generation matching. The 2.77-year payback period significantly outperforms comparable studies (typically 3.8–4.1 years), reflecting strategic crop selection and favorable 2025 economic conditions.

The CO₂ reduction of 49.6% surpasses most Mediterranean installations (typically 42–46%), demonstrating superior environmental benefits through this integrated approach.

Table 18. Consolidated results.

Performance Indicator	Units	Vertical APV	Fixed APV	One-Axis Tracking APV (OAT APV)	OAT APV + Biomass CHP
TECHNICAL PERFORMANCE
System Size	kW	10	10	10	30 (10 PV + 20 CHP)
Optimal Battery Capacity	kWh	13.72	38.12	38.12	97.57
Annual Energy Generation	kWh/year	14,234	16,847	18,926	42,745
Sel-Consumption Rate	%	25.24	40.40	45.83	99.1
Grid Withdrawal	%	74.33	59.35	51.56	0
Grid Supply	%	6.60	1.52	2.60	0
AGRICULTURAL PERFORMANCE
Land Equivalence Ratio (LER)	-	1.4	1.6	1.7	1.7
Average Radiation Reduction	%	11.76	14.40	11.76	11.76
Bean Yield Change	%	+20.55	+24.24	+24.34	+24.34
Potato Yield Change	%	+23.06	+24.63	+24.19	+24.19
Wheat Yield Change	%	−52.05	−57.68	−55.48	−55.48
Total Agricultural Revenue	EUR/year	13,213.99	13,609.80	13,633.46	13,633.46
ECONOMIC PERFORMANCE (2025)
Investment Cost	EUR	23,918.91	42,974.75	43,374.75	332,095.68
Annual Savings	EUR/year	13,327.62	15,019.97	15,673.31	14,339.35
Simple Payback Period	years	1.79	2.86	2.77	23.2
NPV (10 years, 5% discount)	EUR	79,042	73,113	77,867	−184,427
ENVIRONMENTAL PERFORMANCE
CO2 Emission Reduction	kg/year	4.275	7.326	8.741	20,520
CO2 Reduction Percentage	%	23.54	42.65	49.60	100.0
Water Use Reduction	m3/year	71	83	94	94
Water Savings	%	18	18	18	18
SYSTEM RESILIENCE
Energy Independence	%	25.24	40.40	45.83	99.1
Biomass Requirement	t/year	-	-	-	180
Critical Resource Dependency	-	Grid Electricity	Grid Electricity	Grid Electricity	Local Biomass

is a consolidated table summarizing all the main results of this study. These should be interpreted within the context of several limitations and assumptions. The crop yield model relies on empirical relationships derived from Laub et al. [47], which may not fully capture the specific agronomic conditions of Southern Italy. Local validation studies are needed to refine these relationships for Mediterranean crops. Additionally, the assumption that yield variations are solely attributable to shading oversimplifies the complex interactions between microclimate modifications, soil moisture retention, and plant physiology under APV systems.

Moreover, the case study farm (782 m²) represents a specific scale that may not linearly translate to larger agricultural operations. The integration of biomass cogeneration assumes local availability of agricultural residues, which may vary significantly across regions. Future research should investigate economies of scale and develop decision support tools for site-specific feasibility assessments. The findings of 45.83% self-consumption for the tracking configuration align with similar studies in Mediterranean regions. Trommsdorff et al. [14] reported self-consumption rates of 40–50% for comparable system sizes in Germany. However, the LER value of 1.7 found in this study exceeds the typical ranges (1.3–1.6) reported in the literature, potentially due to the selection of shade-tolerant crops and favorable climate conditions.

5. Conclusions

Farmers across Europe remain hesitant to transition to renewable energy sources despite EU policy incentives, primarily due to high initial investment costs and concerns about production stability.

Recent reports, such as the one by The European House Ambrosetti [58], highlight persistent electricity price pressures despite market stabilization, significantly impacting production costs across the food and beverage value chain. Local resource utilization, therefore, emerges as a critical strategy for building resilience against energy price fluctuations and reducing dependency on external energy sources.

Regional supply chain integration and energy community development could potentially mitigate farmers’ transition barriers while advancing sustainability goals. Targeted financial instruments and knowledge-sharing can accelerate renewable adoption rates among agricultural producers facing economic uncertainty.

In the present paper, the opportunity offered by the Italian EMERA project, funded by the Basilicata Region, was studied by the authors to uncover information about a real farm in the South of Italy. The authors evaluated the potential of installing an APV in the province of Potenza to serve a real operating small-scale farm, which also has biomass at its disposal for possible energetic valorization. The first goal of this study was to investigate the best possible geometrical configuration of the APV plant for the specific geographical area and the considered crops. Numerical studies allowed for predicting crop productivity and yield at the design stage, so as to provide clear information and therefore incentivize the adoption of agrivoltaic technology.

This study demonstrates the technical and economic viability of integrated APV–biomass systems for enhancing energy resilience in rural Mediterranean contexts. The key findings include the following:

The single-axis tracking APV configuration achieved optimal performance with 45.83% self-consumption and an LER of 1.7, confirming the efficiency of dual land use in high-radiation environments.

Economic viability improved dramatically between 2022–2023 and 2025, with payback periods decreasing from 5.91 to 2.77 years for the optimal configuration, highlighting the importance of market timing for renewable energy investments.

Integration with biomass cogeneration enabled near-complete grid independence (99%), demonstrating the synergistic potential of combined renewable systems.

Crop-specific responses to shading varied significantly, with beans showing a 24% yield increase, while wheat experienced a 55% reduction, emphasizing the importance of strategic crop selection for APV systems.

These findings have important policy implications for rural development strategies in Southern Italy and similar Mediterranean regions. The demonstrated economic viability also suggests that targeted incentive programs could accelerate APV adoption among small-scale farmers. However, successful implementation requires adequate technical support for system design and crop selection, financial instruments to address high initial investment costs, and regulatory frameworks that recognize the multiple benefits of APV systems.

The proposed APV approach also shows transferability potential across Mediterranean and semi-arid regions with similar climatic conditions (Southern Spain, Greece, Southern France, California), requiring minimal technical adaptations.

Critical factors for replicability include local solar resource assessment, region-appropriate crop selection, economic framework adjustment to local market conditions, and integration with existing agricultural policies.

The key transferability factors for various regions can be roughly summarized as follows:

High transferability regions: Areas with >1400 kWh/m²/year solar irradiance and moderate precipitation (400–800 mm/year) can directly implement the approach with minor adjustments to panel tilt angles and locally appropriate shade-tolerant crops.

Moderate transferability regions: Continental European climates may require system oversizing, increased storage capacity, and modified crop rotations, emphasizing cool-season species.

Limited transferability regions: Northern European contexts with <1000 kWh/m²/year irradiance may face economic barriers, requiring policy incentives and focus on high-value specialty crops.

The EMERA project framework, therefore, provides a valuable model for advancing rural energy transition.

Future research should focus on long-term field studies to validate crop yield models under Mediterranean conditions and develop advanced control algorithms integrating weather forecasting with energy market signals. System improvements should prioritize adaptive panel positioning and precision agriculture integration, including soil moisture sensors and automated irrigation systems.

In the future, economic optimization through dynamic energy trading and renewable energy community participation represents a key profitability enhancement, while scalability assessments across different farm sizes will provide essential deployment data.