The integration of photovoltaic power generation with agricultural production, known as agrivoltaics, presents a compelling pathway for sustainable land use and clean energy adoption. However, the conventional approach of deploying solar panel arrays at maximum density creates a fundamental conflict: the need for solar panels to capture sunlight directly opposes the photosynthetic requirements of crops growing beneath them. This “light competition” often leads to suboptimal crop yields, undermining the agricultural viability of such projects and contributing to perceptions of land-use change. My research addresses this core challenge by investigating how strategic modifications to solar panel density can modulate the underlying microclimate, seeking a balance that supports both energy generation and plant growth.
This study was conceived and conducted in a typical agrivoltaic setting within the Jiangsu province. The foundation was a standard, single-span fixed-tilt solar panel array with a density of 100%—meaning the mounting structure was fully covered with panels. From this baseline, I systematically created two modified array configurations by physically removing a specific, regular pattern of panels. This resulted in two additional test sites with effective solar panel densities of 75% and 50%, while keeping all other structural parameters—such as tilt angle (24°), clearance height (2.5m), and support material—constant. The primary objective was to quantify and analyze the variations in the photothermal environment (light and heat) beneath these arrays to establish empirical relationships between solar panel density and microclimate conditions.
The heart of any agrivoltaic system is the solar panel itself, a device that converts photons into electricity while inevitably casting shade. The spatial arrangement and packing density of these solar panels are thus critical design variables. To capture the spatial heterogeneity of the environment under the arrays, I divided a single representative span (6.8m wide) into three distinct zones along the north-south axis: the southern edge, the central region, and the northern edge. A comprehensive monitoring system was deployed to record key parameters both inside the array and in an adjacent open field as a control. Data loggers measured solar irradiance (W/m²) and air temperature (°C) at a height of 1.0 m (approximating crop canopy level), and soil temperature (°C) at a depth of 0.15 m (approximating root zone). Measurements were taken at 10-minute intervals over a continuous 20-day period during July, encompassing varied weather conditions.
Methodological Framework and Data Synthesis
The experimental setup allowed for a direct comparison of environmental parameters across the three solar panel densities. The key relationships can be summarized conceptually. The solar irradiance at a point beneath the array, \( I_{array} \), is a function of the direct, diffuse, and reflected components of sunlight, all filtered by the geometric configuration of the solar panels. A simplified model for the average irradiance under an array can be expressed as:
$$ I_{array}(d) = I_{open} \cdot \tau(d) $$
where \( I_{open} \) is the irradiance in the open field, \( d \) is the solar panel density (ranging from 0 to 1), and \( \tau(d) \) is a light transmission coefficient that decreases with increasing density \( d \). My hypothesis was that \( \tau(50\%) > \tau(75\%) > \tau(100\%) \).
Similarly, the thermal environment is governed by energy balance. The net radiation, convective heat transfer, and conductive heat flow into the soil are all altered by the presence of the solar panel array. The solar panels absorb a significant portion of incoming radiation, converting some to electricity and dissipating the rest as heat, which influences the air temperature below. A basic heat balance for the air layer under the array can be framed as:
$$ \rho c_p \frac{\partial T_a}{\partial t} = R_{n, array} – H – LE – G $$
where \( T_a \) is air temperature, \( R_{n, array} \) is the net radiation under the array (much reduced compared to open field), \( H \) is sensible heat flux, \( LE \) is latent heat flux, and \( G \) is soil heat flux. The shading from the solar panels dramatically reduces \( R_{n,array} \), leading to a moderated temperature regime.
Analysis of Solar Radiation Intensity Under Different Solar Panel Densities
The data unequivocally demonstrates that solar panel density is the dominant factor controlling light availability beneath the array. As anticipated, the open field received the highest cumulative and average irradiance. The reduction under the arrays followed a clear ordinal pattern based on the density of the solar panel cover.
| Condition | Open Field Avg. (W/m²) | Under 50% Solar Panel Density (W/m²) | Under 75% Solar Panel Density (W/m²) | Under 100% Solar Panel Density (W/m²) |
|---|---|---|---|---|
| All Days (20-day Avg.) | 336 | 208 | 168 | 125 |
| Clear Day Peak | ~986 | ~903 (Central Zone) | ~908 (Central Zone) | ~667 (Central Zone) |
| Overcast Day Avg. | ~200 | ~130 | ~103 | ~81 |
The relationship is precisely ordered: 50% solar panel density > 75% solar panel density > 100% solar panel density. The solar irradiance under the 50% density array was significantly higher than under the 75% and 100% configurations. This quantitative result validates the initial light transmission model, showing that each incremental decrease in solar panel density allows a substantially greater amount of photosynthetic photon flux density (PPFD) to reach the ground level.
Spatially, within each array, the central zone consistently received more radiation than the southern and northern edge zones. This is particularly pronounced in the 75% density design, where the central zone’s irradiance was comparable to that of the 50% array’s central zone on a clear day, while its edge zones were deeply shaded. This pattern arises from the sun’s path and the specific geometry of the panel gaps. The design of the solar panel layout, therefore, not only determines the average light level but also creates distinct light gradients that could be leveraged for zone-specific crop planting strategies.
Modulation of Air and Soil Temperature by Solar Panel Arrays
The impact on temperature was equally systematic and significant. The solar panel array acts as a dynamic thermal buffer. During daytime hours, the panels intercept radiant energy, cooling the sub-array environment. At night, they likely reduce radiative heat loss to the cold sky, providing a slight insulating effect. The data from the 20-day period clearly illustrates this.
| Thermal Parameter | Open Field | Under 50% Solar Panels | Under 75% Solar Panels | Under 100% Solar Panels |
|---|---|---|---|---|
| Avg. Daytime Air Temp. | 33.7 °C | 32.2 °C | 31.8 °C | 31.2 °C |
| Avg. Nighttime Air Temp. | 26.8 °C | ~27.0 °C | ~27.0 °C | ~27.1 °C |
| Avg. Daytime Soil Temp. | 28.7 °C | 27.9 °C | 27.8 °C | 27.4 °C |
The trend for daytime air and soil cooling is inversely related to solar panel density: 100% solar panel density > 75% solar panel density > 50% solar panel density. In other words, a higher density of solar panels provides a greater cooling effect. The array with full solar panel coverage (100%) created the coolest microclimate during the hot summer days, lowering daytime air temperatures by about 2.5 °C on average compared to the open field. The 75% and 50% solar panel arrays provided progressively less cooling. This aligns with the energy balance principle; a denser solar panel canopy absorbs and reradiates more energy away from the ground zone, while also creating more extensive shade that reduces the net radiation (\(R_{n,array}\)) term in the heat balance equation.
The nighttime data suggests a very minimal insulating effect, with temperatures under the arrays being nearly identical or fractions of a degree higher than in the open. The spatial variation of temperature within a given array was minor compared to the variation caused by changing the solar panel density itself. The soil temperature followed the same pattern as air temperature, showing that the moderating influence of the solar panel cover extends through the surface layer of the agro-ecosystem.
Synthesis and Implications for Agrivoltaic System Design
The findings of this study provide a robust, data-driven framework for designing agrivoltaic systems. The core trade-off is visually and quantitatively clear: reducing the density of solar panels increases the light available for crops but reduces the cooling benefit (which could be an advantage or disadvantage depending on the regional climate and crop). The choice of optimal solar panel density becomes a site-specific and crop-specific optimization problem.
For regions with high solar insolation and heat stress, like the test location, a higher density of solar panels (e.g., 75%) could be advantageous. It would generate substantial electricity while providing significant cooling to shade-tolerant crops, potentially protecting them from extreme heat and reducing water loss through evapotranspiration. The lower light levels would need to be matched with appropriate crop selections, such as leafy greens, herbs, or certain berries.
Conversely, for crops requiring more light or in cooler climates, a lower density of solar panels (e.g., 50%) might be optimal. This configuration allows a much greater proportion of sunlight to reach the ground, supporting a wider variety of crops, including some fruiting vegetables, while still generating a valuable amount of solar power. The spatial light gradient—with brighter centers and shadier edges—can be actively managed through companion planting, placing light-demanding crops in the central zones and shade-tolerant ones along the edges.
The overarching conclusion is that moving away from the traditional 100% solar panel coverage is not merely an option but a necessity for productive and sustainable agrivoltaics. By treating solar panel density as a primary design variable, we can engineer microclimates that cater to agricultural needs. Future work should focus on coupling these environmental models with crop growth models and economic analyses for different solar panel layouts. This will enable the creation of decision-support tools to find the sweet spot—the solar panel array density that maximizes the synergistic benefit of both food and energy production from a single parcel of land, transforming the “competition for light” into a “collaboration for productivity.”