Next Article in Journal
Learning from the Past: Lessons from the First United Nations Report on Problems of the Human-Environment
Previous Article in Journal
Spotlight: An Interview with NCCIH Director, Dr. Helene M. Langevin, on Whole Person Health
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Challenges
Volume 13
Issue 2
10.3390/challe13020043
challenges-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Agrivoltaic Engineering and Layout Optimization Approaches in the Transition to Renewable Energy Technologies: A Review

by
Meagan Reasoner
and
Aritra Ghosh
*
College of Engineering, Mathematics and Physical Sciences, Renewable Energy, University of Exeter, Exeter EX4 4PY, UK
*
Author to whom correspondence should be addressed.
Challenges 2022, 13(2), 43; https://doi.org/10.3390/challe13020043
Submission received: 7 June 2022 / Revised: 25 August 2022 / Accepted: 25 August 2022 / Published: 1 September 2022
Browse Figures
Versions Notes

Abstract

:
As more nations move towards net-zero emission goals by 2050, research into the coupling of photovoltaics (PV) and agriculture has increased into a new sector of agrivoltaics (AV). Measurement of the Land Equivalent Ratio (LER) has allowed researchers to develop methods for optimizing the agrivoltaic system. Studies on innovative engineering technologies related to photovoltaic tracking along with new generation PV cells were reviewed to determine the factors that influence optimization. This review also considered AV farm layouts and how different spacing, height, and density impact the shading under the panels. As panels block the light from hitting the plants, the photosynthetically active radiation (PAR) changes and alters plant growth. The shading, however, also creates micro-climates that have beneficial qualities in terms of water usage and PV efficiency. The overall review investigated the research of the last five years into AV optimization and the implications for future AV developments.

1. Introduction

As more nations sign on to the Paris Agreement and attempt to achieve net-zero emissions (NZEs) by 2050 [1], the utilization of renewable energy technologies (RETs) has increased. PVs are a major component of shifting energy production to RETs. This is mainly due to the low levelized cost of energy [2] in most regions compared to other RETs. However, with the increase in PV installation, there is a conflict with the United Nations’ sustainability development goals pertaining to land restoration [3]. The goals reflect the sustainable use of lands to promote food security and nourishment through sustainable agriculture [4]. This potential conflict of land use has led to an increased interest in AVs. In the dual-land use concept that integrates PV and agriculture, first proposed by [5], the AV system allows for both land restoration and NZE goals to be reached simultaneously. The interest has grown exponentially within the past decade, as illustrated in Figure 1. It also shows the growth in installed PV electricity generation by GWh. This article reviewed new research in the field of AVs, with a focus on design optimization of the coupled system and the impacts on both crop growth and PV efficiency. The review included both in situ experiments and computer simulations. By focusing on recent studies, this review will provide researchers with an overview of the current state of the industry and help direct new avenues of future work. In addition, reviewing the optimization of the AV farms will also motivate growth in the industry globally.

1.1. Land Use Optimization

While the advancements in PV technologies have improved the overall power density [7], land requirements are still a major concern for utility-scale PV power plants. The recent study conducted by [7] determined that the power density ( M W D C A c r e ) was 52% higher than the 2011 calculations for fixed-tilt plants and 42% higher for tracking plants. However, even with the vast improvements in power density, a small utility-scale solar system would require ~14.25 acres of land. This land requirement has helped drive the increase in AV research [8]. In a PV system, optimization is dependent on the tilt angle (θ), maximizing the solar irradiation on the panels [9], whereas the coupled system needs to account for inter-row shading to minimize the loss of photosynthetically active radiation (PAR) on the crops. This has led the way for optimization factors such as the land equivalent ratio (LER), which according to [10], is defined as
L E R = Y i e l d   o f   P l a n t s   A V Y i e l d   o f   P l a n t s   n o   A V + E l e c t r i c i t y   Y i e l d   A V E l e c t r i c i t y   Y i e l d   n o   A V  
to use as an economic parameter that considers both biomass and electricity yields. As shown in Figure 2, the LER compares the land utilization individually and combined in an AV system. The initial works of [5] considered module spacing as a key parameter in optimizing the AV system. Early works [11], based in Montpelier, France, analyzed the LER of a full-density and half-density AV set-up and compared the results to determine the impact of module shading on the overall system efficiency.
In addition to module spacing, modeling of different PV technologies, such as vertical bifacial solar modules [12,13] and single-axis tracking [14], were used as methods to optimize electricity generation. Experiments run by the University of Oregon [15,16] and by [17] examined in situ data on the effects of crops on the efficiency of PV modules. Several feasibility studies examined land availability [18] and different climates throughout Europe [19] to determine where an AV system would be most suitable. Studies looked into the impact of crops on the ambient temperature surrounding the solar system. Throughout this review, both numerical models and field tests were used to determine how AV systems contribute to the overall efficiency of the PV.

1.2. Mutually Beneficial Relationship

The optimization of an AV system requires an understanding of both the crops PAR, which is considered to be the entire visible light spectrum (400–700 nm) [21], and PV efficiency. Earlier studies analyzed the impacts of shading on crop yields (lettuce) [22] and the benefits of micro-climates created by under-panel crops [23]. These initial studies were limited in the variety of crop types and locations. However, with the growing need to move towards dual-land use options, numerous experimental and modeled studies were conducted in this region of interest. A substantial amount of current research revolves around identifying crops that would benefit from growing in an AV system. The determination of AV-compatible crops is vital to the success of a coupled system. Studies such as those conducted by [24,25] analyzed the irradiation under the PV modules and the overall effects on crop yields. Along with in situ experiments, researchers made use of simulation software for both PV electricity generation (PVSYST) and for crop modeling (STICS) to run optimization studies [9]. This review provides a comprehensive list of crops that were studied recently (post-2016) and how the shading from an AV system impacts crop yield.

2. Optimization Studies

As discussed in Section 1, the need to understand the coupled efficiency of an AV system can help move nations toward their NZE goals. The amount of solar irradiance that is converted into electricity is dependent on panel tracking [14,26,27], tilt and azimuth angle [25], and the PV used in the system [21,28,29]. Along with the electrical efficiency, the AV layout, including inter-module spacing [30] or density [31,32], PV transparency [33], and the water usage change due to panel shading [34,35] are all variables that must be considered when optimizing an AV system. Initial AV farms, such as the one in Montpelier, France, acted as proof of concept [11], and the first AV farm in Japan [36] allowed for different methods of optimization to be studied. The optimization of these systems can be divided by which component of the AV system it belongs to. Table 1 lists the different methods that were studied in the last five years.

2.1. Engineering Optimization

2.1.1. Tracking

A PV array is designed to maximize the amount of solar irradiance striking the PV. In an effort to improve electricity production, researchers engineered different methods of tracking the sunlight [37]. While the tracking control system may differ between an active and passive method, the tracking is relative to the axis of rotation. A single-axis tracking system, Figure 3B, can be rotated either about the North/South axis, as shown, or the East/West axis. However, the dual-axis tracking system (Figure 3A) is controlled to rotate both axes. The AV system solar tracking was studied for both the increased electrical output and the effective shading factor [27,38]. The study conducted by [31] modeled the effects of different systems in Lahore, Pakistan. The study compared the effects that N/S and E/W tracking had on the PAR for lettuce and tomato plants. The conclusion showed that a vertical E/W tracking orientation provided the best power output, while the fixed E/W allowed for the most useful PAR. This same conclusion was drawn by [27] for AV systems modeled in both California and Texas, USA. A 2018 study by Amaducci et al. on the dual axis tracking AV system Agrovoltaico© (Figure 3A) analyzed the impact of sun tacking on the shading and growth of biogas maize. The study investigated the LER for full tracking systems compared to fixed arrays. The study compared arrays with 10 panels and 32 panels, both fixed (F1 and F2) and tracking (ST1 and ST2) to full sunlight maize growth and a ground-based sun-tracking PV system. The analysis showed the LER as 1.31, 1.31, 2.04, and 1.74 for the ST1, FT1, ST2, and FT2, respectively. The dual tracking 32-panel system (ST2) generated the highest level of electricity, and all four systems were modeled to produce more bio yield compared to full spectrum light. The modeling of tracking from the [38] was validated with field observations of a rice paddy with AVs.

2.1.2. PV Cell Type

Another area of engineering optimization that has been researched in the last five years centers around the PV cell. While silicon solar cells are the most commercially available [39], it has limitations in the AV market. The opaque characteristics of the silicon cells block the PAR from reaching the crops unless proper spacing is applied (Section 2.2). This led researchers to investigate the potential of using different generations of solar cells. Studies by [40] and [21] investigated the use of high-concentration PV as a method of optimizing crop and electrical output. A high concentration PV works by splitting the light into solar radiation and PAR for plant growth. Ref. [40] suggested the use of a bi-layer, as shown in Figure 4, to increase solar production and the ability to tune the spectral band that is transmitted, which is beneficial for agriculture. Ref. [41] studied the use of spectral separation with ultra-thin amorphous germanium (a-Ge:H) solar cells. Moreover, [42] experimented with semi-transparent PV cells and their effects on the growth of spinach and basil. While all studies showed viability in tuning the transmitted light for PAR, the electrical output efficiency typically does not reach above 5% [41].

2.2. Layout Optimization

2.2.1. Placement

The most promising area of research in AV optimization in the last five years is in the placement of solar modules [25]. Since the first AV farm in Montpelier, France [11], ten new AV research facilities have been developed [43]. These facilities allow for the in situ data on different spacing and density methods; different layouts are shown in Figure 5. The AV system needs to be optimized for both power and agriculture. Research into spacing methods was conducted to determine if there is an optimal method to reduce shading with limited power loss. The cost of tracking panels is a limiting factor for many solar projects [44] and has led to different studies on AV farm layouts both at research facilities and modeled.
Table 2 provides a list of different panel placement methods that have been studied in the last five years. Ref. [45] reviewed different impacts of panel spacing, height [25], arrangement [27], and density [26,46] were other factors in AV system layouts.

2.2.2. Orientation

Methods for optimization also included the orientation of the panels and the tilt angle. As shown in Figure 6, experiments were performed with vertical bifacial PV panels to measure shadow depth. This type of study was conducted in a variety of regions and both in situ and modeled. Table 2 lists some of the observations for the studies. Ref. [48], in particular, studied the optimization of an AV farm in terms of the LER and determined the optimum spacing of the bifacial panels to generate the highest amount of electricity without sacrificing plant growth. Studies such as those conducted by [25,31] researched the tilt angle of the panels to reduce shading while maintaining a high PV efficiency.

3. Potential Benefits of AV System

While AVs are seen as a method to reduce the land loss for traditional PV farms, research has shown that they can be both beneficial to crop growth [49] and PV efficiency [50]. The levels of PAR under the solar modules impact crop species in different ways. Understanding the PAR can be used to determine the most suitable crop to plant for each season [47]. Factors such as micro-climates and water requirements are also changed due to the set-up of each AV system [35]. Research into the benefits of plant growth from an AV farm will allow for better crop rotation and, ultimately, more efficient dual land use.

3.1. Crop Production

As installations of AV systems have increased in the last five years, the field of research has expanded into determining crops that are best suited for the coupled system. This review covered 23 studies that focused on the impact of PVs on the growth of different crops. The most common plants studied were lettuce, tomatoes, and wheat, covering four different countries and three states. The different studies were designed to make observations on different aspects of the AV systems. Figure 7 shows the set-up for a study on lettuce growth under two types of AV arrangements [24]. Some studies, such as those conducted by [49], investigated the relationship to crop production to the profitably of the AV farm, while other studies, such as the one conducted by [50], focused on crop yield. Table 3 reviews the different studies and notable observations.

Non-Commercial Crops

The studies reviewed also considered the use of AV for grazing grounds for different animals. An in situ study conducted in Oregon, USA, [61] determined that despite a reduction in herbage (38%), the AV produced higher quality; thus, spring lamb growth was not affected. In a modeled study regarding rabbits, Ref. [62] described how AV could also be used as a protective fence against predators while allowing the rabbits to graze on the grass under the panel, which has the potential to increase the revenue of a PV farm. The conclusion was reached by a reduction in operation and maintenance costs associated with grass mowing. Another new area of research for non-commercial crops is based on what is referred to as pollinators. Ref. [63] investigated the impact the solar canopy had on the bloom time of habitats for pollinating insects. They showed that the AV system increased the floral yield and delayed bloom time, which can help late-season pollinators.

3.2. Micro-Climates

The effects of a PV on the shaded area underneath tend to create a micro-climate. These micro-climates generally have lower soil and crop temperatures [23] and impact water requirements and PV efficiency. The panel layout, as discussed in Section 2.2.1, also impacts the micro-climate. Ref. [64] evaluated a Python model using a checkerboard panel placement. The method was not able to increase soil temperatures enough to prevent frost.

3.2.1. Water Usage

Several studies were conducted on the micro-climate that is created under AV systems. As indicated in Table 3, [50] monitored both crop yield but also water efficiency. The transpiration rate is slower under the PV due to the shading. This both lowers the soil temperature [35] and increases soil moisture [50], leading to a reduction in water usage. It was concluded by both [50,65] that hot arid climates would benefit from the creation of these AV micro-climates. The study by [52] saw a reduction in crop yield but also observed an increase in irrigation savings for all crops ranging from 9 to 14% savings. Another study in Oregon by [34] found a staggering 328% increase in water efficiency at their Rabbit Hills site (Figure 8) for areas observed under the AV system. This savings in water is yet another benefit that farmers would gain with an AV system.

3.2.2. Increased PV Efficiency

There is a correlation between increasing temperatures and decreasing efficiencies of PV panels [66]. The micro-climates of the AV systems were shown to reduce ambient temperatures [67]. In a study conducted on Bok Choy in Thailand, it was demonstrated that while plant growth was reduced due to shading, the panel efficiency increased by ~1% [51]. While this increase is not a significant improvement over the life-cycle of the panels depending on the size of the array could lead to a surmountable amount of energy production.

4. Discussion

Agrivoltaics were demonstrated as a viable option for a dual-land use solution to the climate change crisis. Early systems such as those in Germany [11] and Japan [36] helped push research around the world. Countries such as the United States of America determined that an adaption of AVs can generate ~20% of domestic energy needs [68]. Research into the methods of optimization has shown the effects of shading on plant growth. In the majority of studies, the use of a half-density or patterned array allowed for higher production in plant growth. This was most often associated with the amount of PAR that the plants were able to receive throughout the day. The use of the LER has become a method of finding an optimal reduction in plant growth. There is an understanding that the shading from PV modules impacts the overall yield, but economically the coupling with electricity production should have an LER of greater than 1. Governmental policies are beginning to reflect the viability of coupling the two systems. In Japan, 80% of crop production must be maintained with the installation of PVs [59]. This is why optimization research plays a vital role in the installation of AV systems. In addition to governmental policies, social impact studies in Japan found support for the AV system among farmers, workers, and local residents [69], signaling that the industry can expect to see continued growth. Ref. [70] recently reported on a new vertical AV system sharing land use with livestock grazing. It was reported that Japan has over 200 MW grid-connected AV farms operational.
The engineering optimization of an AV farm has shown several areas of improvement. Most notably the tracking methods. The dual-axis tracking studies were shown to produce the highest electricity without sacrificing crop yields. However, these studies are limited, and the cost implications of a dual-axis system might be a limiting factor. When determining the feasibility of an AV system, economics plays a role in the decision process [71,72]. Simpler and most cost-effective tracking systems are more practical for small-scale AV farms. A South facing panel that is tracked along the East/West axis was shown to provide the best power production at a minimal loss to plant production. This was a result shared across a variety of studies conducted in different countries, both in situ and modeled.
Cost restrictions and efficiency are also factors when considering novel PV cells designed in an AV system. While spectral selective cells are capable of providing plants with the PAR spectrum required for photosynthesis, in the current phase of research, they only reach a 5% efficiency. This aspect of the cell would need to be improved if they were to be considered a viable option. The concentrated PV cells showed potential but would be better suited in a greenhouse application. There was research conducted in this field that was not covered in the scope of this review.
The optimization of the AV has the potential to be beneficial to both crop yields and PV efficiencies. Modeling studies that are then validated by in situ data are invaluable for the future of AV. They allow for crop rotation to be analyzed before implementation and can help boost the production of both crops and energy. Measurements of PAR under the AV system are another area that has shown value in crop rotation and placement. Collecting data on the PAR under the arrays allows farmers to select specific crops that thrive under those conditions. According to [73], the required PAR for crops varies and is considered when farmers are planted. By monitoring the PAR, crop rotation can be optimized to encourage the largest yields. This helps to prevent unnecessary crop loss and improve the overall efficiency of the entire system.
Micro-climates created under the arrays are another benefit that the coupled system provides. Hot arid regions, such as those studied in Arizona, USA [50], will see water usage decrease for the AV system. As climate change continues, the water supply will be closely monitored, and any method that allows for efficient water usage will need to be explored. As an established system, AV systems might begin to see more installations in water scarcity regions.

5. Conclusions

In the last five years, the agrivoltaic industry has begun to see exponential growth. A once novel concept, AV, has established its feasibility, and new research has shifted to optimizing the coupled system. The complexity of trying to boost energy production without sacrificing crop yields has led researchers to use the LER parameter to improve the dynamic system. Thus far, single-axis tracking along the East/West axis has produced the best results in a variety of regions around the world. The dual-axis, while effective, comes with a higher installation and operating cost and, at this time, would be considered less practical than small-scale farming. Increased costs and PV efficiency are other drawbacks of spectral selective cells in their current state. While their applications could be used in other sectors of AV, such as greenhouses, concentrators, and spectral selective PVs are still underdeveloped at this time. The inter-row spacing and height of the PV arrays were reviewed to determine the impact of shading on crop yields. Each region had different results, but overall having a lower density of panels allowed for more PAR to reach the crops and reduce shading loss. It was shown that understanding the PAR levels would improve crop growth by allowing for the best-suited crop to be planted. The creation of a micro-climate is both beneficial in terms of water usage but also has a positive impact on PV efficiency. Overall, agrivoltaics are considered a viable dual-land use option for the future to provide renewable energy and still allow for sustainable use to promote food security.

Author Contributions

Writing—original draft preparation, M.R.; review and editing, A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors wish to the Environment and Sustainability Institute (ESI) and the College of Engineering, Mathematical and Physical Sciences (CEMPS) at the University of Exeter, Penryn Campus for allowing us to explore this topic and prepare this paper.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AVagrivoltaic
LERland equivalent ratio
NZEnet-zero emission
PARphotosynthetically active radiation
PVphotovoltaic
RETrenewable energy technology
STICSSimulateur mulTIdisciplinaire les Cultures Standard

References

  1. IEA. Net Zero by 2050; Technical Report; IEA: Paris, France, 2021; All Rights Reserved. [Google Scholar]
  2. Mancini, F.; Nastasi, B. Solar energy data analytics: PV deployment and land use. Energies 2020, 13, 417. [Google Scholar] [CrossRef]
  3. Herrick, J.E.; Abrahamse, T. Land Restoration for Achieving the Sustainable Development Goals; A think piece of the International Resource Panel; United Nations Environment Programme: Nairobi, Kenya, 2019. [Google Scholar]
  4. Fritsche, U.R.; Berndes, B.; Cowie, A.L.; Dale, V.H.; Kline, K.L.; Johnson, F.X.; Langeveld, H.; Sharma, N.; Watson, H.; Woods, J. Sustainable Energy Options and Implications for Land Use; Technical Report; IEA: Paris, France, 2017. [Google Scholar]
  5. Goetzberger, A.; Zastrow, A. On the Coexistence of Solar-Energy Conversion and Plant Cultivation. Int. J. Sol. Energy 1982, 1, 55–69. [Google Scholar] [CrossRef]
  6. IEA. Solar PV—Analysis—IEA. 2021. All Rights Reserved. Available online: https://www.iea.org/reports/solar-pv (accessed on 6 June 2022).
  7. Bolinger, M.; Bolinger, G. Land Requirements for Utility-Scale PV: An Empirical Update on Power and Energy Density. IEEE J. Photovolt. 2022, 12, 589–594. [Google Scholar] [CrossRef]
  8. Dias, L.; Gouveia, J.P.; Lourenço, P.; Seixas, J. Interplay between the potential of photovoltaic systems and agricultural land use. Land Use Policy 2018, 81, 725–735. [Google Scholar] [CrossRef]
  9. Dinesh, H.; Pearce, J.M. The Potential of Agrivoltaic Systems. Renew. Sustain. Energy Rev. 2016, 54, 299–308. [Google Scholar] [CrossRef]
  10. Beck, M.; Bopp, G.; Goetzberger, A.; Obergfell, T.; Reise, C.; Schindele, S. Combining PV and Food Crops to Agrophotovoltaic? Optimization of Orientation and Harvest. In Proceedings of the 27th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC, Frankfurt, Germany, 24–28 September 2012. [Google Scholar] [CrossRef]
  11. Dupraz, C.; Marrou, H.; Talbot, G.; Dufour, L.; Nogier, A.; Ferard, Y. Combining solar photovoltaic panels and food crops for optimising land use: Towards new agrivoltaic schemes. Renew. Energy 2011, 36, 2725–2732. [Google Scholar] [CrossRef]
  12. Riaz, M.H.; Imran, H.; Younas, R.; Butt, N.Z. The optimization of vertical bifacial photovoltaic farms for efficient agrivoltaic systems. Sol. Energy 2021, 230, 1004–1012. [Google Scholar] [CrossRef]
  13. Imran, H.; Riaz, M.H. Investigating the potential of east/west vertical bifacial photovoltaic farm for agrivoltaic systems. J. Renew. Sustain. Energy 2021, 13, 033502. [Google Scholar] [CrossRef]
  14. Imran, H.; Riaz, M.H.; Butt, N.Z. Optimization of Single-Axis Tracking of Photovoltaic Modules for Agrivoltaic Systems. In Proceedings of the 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, AB, Canada, 15 June–21 August 2020; IEEE: New York, NY, USA, 2020; pp. 1353–1356. [Google Scholar] [CrossRef]
  15. Adeh, E.H.; Good, S.P.; Calaf, M.; Higgins, C.W. Solar PV Power Potential is Greatest Over Croplands. Sci. Rep. 2019, 9, 1–6. [Google Scholar] [CrossRef]
  16. Al-Agele, H.A.; Nackley, L.; Higgins, C.W. A pathway for sustainable agriculture. Sustainability 2021, 13, 4328. [Google Scholar] [CrossRef]
  17. Weselek, A.; Bauerle, A.; Zikeli, S.; Lewandowski, I.; Högy, P. Effects on Crop Development, Yields and Chemical Composition of Celeriac (Apium graveolens L. var. rapaceum) Cultivated Underneath an Agrivoltaic System. Agronomy 2021, 11, 733. [Google Scholar] [CrossRef]
  18. Cos¸gun, A.E. The potential of Agrivoltaic systems in TURKEY. Energy Rep. 2021, 7, 105–111. [Google Scholar] [CrossRef]
  19. Willockx, B.; Herteleer, B.; Cappelle, J. Theoretical potential of agrovoltaic systems in Europe: A preliminary study with winter wheat. In Proceedings of the Conference Record of the IEEE Photovoltaic Specialists Conference, Calgary, AB, Canada, 15 June–21 August 2020. [Google Scholar] [CrossRef]
  20. MetSolar. What Is Agrivoltaics? How Can Solar Energy and Agriculture Work Together. 2021. Available online: https://metsolar.eu/blog/what-is-agrivoltaics-how-can-solar-energy-and-agriculture-work-together/ (accessed on 6 June 2022).
  21. Apostoleris, H.; Chiesa, M. High-concentration photovoltaics for dual-use with agriculture. In Proceedings of the AIP Conference Proceedings, Fes, Morocco, 25–27 March 2019; American Institute of Physics Inc.: College Park, MD, USA, 2019; Volume 2149. [Google Scholar] [CrossRef]
  22. Marrou, H.; Wery, J.; Dufour, L.; Dupraz, C. Productivity and radiation use efficiency of lettuces grown in the partial shade of photovoltaic panels. Eur. J. Agron. 2013, 44, 54–66. [Google Scholar] [CrossRef]
  23. Marrou, H.; Guilioni, L.; Dufour, L.; Dupraz, C.; Wery, J. Microclimate under agrivoltaic systems: Is crop growth rate af-fected in the partial shade of solar panels? Agric. For. Meteorol. 2013, 177, 117–132. [Google Scholar] [CrossRef]
  24. Carreño-Ortega, A.; do Paço, T.A.; Díaz-Pérez, M.; Gómez-Galán, M. Lettuce Production under Mini-PV Modules Arranged in Patterned Designs. Agronomy 2021, 11, 2554. [Google Scholar] [CrossRef]
  25. Kwon, O.H.; Lee, K.S. Agrophotovoltaic Designs: Irradiation Analysis on and under PV Modules. J. Korean Sol. Energy Soc. 2021, 41, 9–23. [Google Scholar] [CrossRef]
  26. Amaducci, S.; Yin, X.; Colauzzi, M. Agrivoltaic systems to optimise land use for electric energy production. Appl. Energy 2018, 220, 545–561. [Google Scholar] [CrossRef]
  27. Perna, A.; Grubbs, E.K.; Agrawal, R.; Bermel, P. Design Considerations for Agrophotovoltaic Systems: Maintaining PV Area with Increased Crop Yield. In Proceedings of the 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC), Chicago, IL, USA, 16–21 June 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 668–672. [Google Scholar] [CrossRef]
  28. Frick, K.; Talbot, P.; Wendt, D.; Boardman, R.; Rabiti, C.; Bragg-Sitton, S.; Ruth, M.; Levie, D.; Frew, B.; Elgowainy, A.; et al. Evaluation of Hydrogen Production Feasibility for a Light Water Reactor in the Midwest. 2019. Available online: https://www.osti.gov/biblio/1569271 (accessed on 6 July 2022). [CrossRef]
  29. Pascaris, A.S.; Schelly, C.; Pearce, J.M. A first investigation of agriculture sector perspectives on the opportunities and barriers for agrivoltaics. Agronomy 2020, 10, 1885. [Google Scholar] [CrossRef]
  30. Sargentis, G.F.; Siamparina, P.; Sakki, G.K.; Efstratiadis, A.; Chiotinis, M.; Koutsoyiannis, D. Agricultural land or photovoltaic parks? The water—Energy—Food nexus and land development perspectives in the thessaly plain, Greece. Sustainability 2021, 13, 8935. [Google Scholar] [CrossRef]
  31. Tahir, Z.; Zafar Butt, N. Implications of Spatial-Temporal Shading Under Mobile & Fixed Tilt Bifacial Agrivoltaic Panels & Implications for the Cropping Practices. Renew. Energy 2022, 190, 167–176. [Google Scholar] [CrossRef]
  32. Martín-Chivelet, N. Photovoltaic potential and land-use estimation methodology. Energy 2016, 94, 233–242. [Google Scholar] [CrossRef]
  33. Willockx, B.; Herteleer, B.; Cappelle, J. Techno-Economic Study of Agrovoltaic Systems Focusing on Orchard Crops. In Proceedings of the 37th EU PVSEC 2020, Virtual, 7–11 September 2020. [Google Scholar]
  34. Hassanpour Adeh, E.; Selker, J.S.; Higgins, C.W. Remarkable agrivoltaic influence on soil moisture, micrometeorology and water-use efficiency. PLoS ONE 2018, 13, e0203256. [Google Scholar] [CrossRef]
  35. Weselek, A.; Bauerle, A.; Zikeli, S.; Lewandowski, I.; Högy, P. Agrivoltaic system impacts on microclimate and yield of different crops within an organic crop rotation in a temperate climate. Agron. Sustain. Dev. 2021, 41, 59. [Google Scholar] [CrossRef]
  36. Tajima, M.; Iida, T. Evolution of agrivoltaic farms in Japan. AIP Conf. Proc. 2021, 2361, 030002. [Google Scholar] [CrossRef]
  37. Awasthi, A.; Shukla, A.K.; Murali Manohar, S.R.; Dondariya, C.; Shukla, K.N.; Porwal, D.; Richhariya, G. Review on sun tracking technology in solar PV system. Energy Rep. 2020, 6, 392–405. [Google Scholar] [CrossRef]
  38. Takahashi, N.; Uchida, Y. Prediction of Effects of Solar Panel Control Methods on Solar Radiation on the Ground under the Panels and Power Generation in an Agrivoltaic System with Rotatable Panels. Kagaku Kogaku Ronbunshu 2021, 47, 184–190. [Google Scholar] [CrossRef]
  39. Majewski, P.; Al-shammari, W.; Dudley, M.; Jit, J.; Lee, S.H.; Myoung-Kug, K.; Sung-Jim, K. Recycling of solar PV panels- product stewardship and regulatory approaches. Energy Policy 2021, 149, 112062. [Google Scholar] [CrossRef]
  40. Keil, J.; Liu, Y.; Kortshagen, U.; Ferry, V.E. Bilayer Luminescent Solar Concentrators with Enhanced Absorption and Efficiency for Agrivoltaic Applications. ACS Appl. Energy Mater. 2021, 4, 14102–14110. [Google Scholar] [CrossRef]
  41. Osterthun, N.; Neugebohrn, N.; Gehrke, K.; Vehse, M.; Agert, C. Spectral engineering of ultrathin germanium solar cells for combined photovoltaic and photosynthesis. Opt. Express 2021, 29, 938–950. [Google Scholar] [CrossRef]
  42. Thompson, E.P.; Bombelli, E.L.; Shubham, S.; Watson, H.; Everard, A.; D’Ardes, V.; Schievano, A.; Bocchi, S.; Zand, N.; Howe, C.J.; et al. Tinted Semi-Transparent Solar Panels Allow Concurrent Production of Crops and Electricity on the Same Cropland. Adv. Energy Mater. 2020, 10, 2001189. [Google Scholar] [CrossRef]
  43. Toledo, C.; Scognamiglio, A. Agrivoltaic systems design and assessment: A critical review, and a descriptive model towards a sustainable landscape vision (three-dimensional agrivoltaic patterns). Sustainability 2021, 13, 6871. [Google Scholar] [CrossRef]
  44. Racharla, S.; Rajan, K. Solar tracking system—A review. Int. J. Sustain. Eng. 2017, 10, 72–81. [Google Scholar] [CrossRef]
  45. Oleskewicz, K. The Effect of Gap Spacing Between Solar Panel Clusters on Crop The Effect of Gap Spacing Between Solar Panel Clusters on Crop Biomass Yields, Nutrients, and the Microenvironment in a Dual-Biomass Yields, Nutrients, and the Microenvironment in a Dual-Use Agrivoltaic System Use Agrivoltaic System. Master’s Thesis, University of Massachusetts Amherst, Amherst, MA, USA, 2020. [Google Scholar] [CrossRef]
  46. Elamri, Y.; Cheviron, B.; Lopez, J.M.; Dejean, C.; Belaud, G. Water budget and crop modelling for agrivoltaic systems: Application to irrigated lettuces. Agric. Water Manag. 2018, 208, 440–453. [Google Scholar] [CrossRef]
  47. Santra, P.; Meena, H.M.; Yadav, O.P. Spatial and temporal variation of photosynthetic photon flux density within agrivoltaic system in hot arid region of India. Biosyst. Eng. 2021, 209, 74–93. [Google Scholar] [CrossRef]
  48. Campana, P.E.; Stridh, B.; Amaducci, S.; Colauzzi, M. Optimisation of vertically mounted agrivoltaic systems. J. Clean. Prod. 2021, 325, 129091. [Google Scholar] [CrossRef]
  49. Cuppari, R.I.; Higgins, C.W.; Characklis, G.W. Agrivoltaics and weather risk: A diversification strategy for landowners. Appl. Energy 2021, 291, 116809. [Google Scholar] [CrossRef]
  50. Barron-Gafford, G.A.; Pavao-Zuckerman, M.A.; Minor, R.L.; Sutter, L.F.; Barnett-Moreno, I.; Blackett, D.T.; Thompson, M.; Dimond, K.; Gerlak, A.K.; Nabhan, G.P.; et al. Agrivoltaics provide mutual benefits across the food-energy-water nexus in drylands. Nat. Sustain. 2019, 2, 848–855. [Google Scholar] [CrossRef]
  51. Kumpanalaisatit, M.; Setthapun, W.; Sintuya, H.; Jansri, S.N. Efficiency improvement of ground-mounted solar power generation in agrivoltaic system by cultivation of bok choy (Brassica rapa subsp. chinensis L.) under the panels. Int. J. Renew. Energy Dev. 2021, 11, 103–110. [Google Scholar] [CrossRef]
  52. Moreda, G.P.; Muñoz-García, M.A.; Alonso-García, M.C.; Hernández-Callejo, L. Techno-economic viability of agro-photovoltaic irrigated arable lands in the eu-med region: A case-study in southwestern spain. Agronomy 2021, 11, 593. [Google Scholar] [CrossRef]
  53. Trommsdorff, M.; Kang, J.; Reise, C.; Schindele, S.; Bopp, G.; Ehmann, A.; Weselek, A.; Högy, P.; Obergfell, T. Combining food and energy production: Design of an agrivoltaic system applied in arable and vegetable farming in Germany. Renew. Sustain. Energy Rev. 2021, 140, 110694. [Google Scholar] [CrossRef]
  54. Sekiyama, T.; Nagashima, A. Solar Sharing for Both Food and Clean Energy Production: Performance of Agrivoltaic Systems for Corn, A Typical Shade-Intolerant Crop. Environments 2019, 6, 65. [Google Scholar] [CrossRef]
  55. Cho, J.; Park, S.M.; Park, A.R.; Lee, O.C.; Nam, G.; Ra, I.H. Application of Photovoltaic Systems for Agriculture: A Study on the Relationship between Power Generation and Farming for the Improvement of Photovoltaic Applications in Agriculture. Energies 2020, 13, 4815. [Google Scholar] [CrossRef]
  56. Malu, P.R.; Sharma, U.S.; Pearce, J.M. Agrivoltaic potential on grape farms in India. Sustain. Energy Technol. Assess. 2017, 23, 104–110. [Google Scholar] [CrossRef] [Green Version]
  57. Valle, B.; Simonneau, T.; Sourd, F.; Pechier, P.; Hamard, P.; Frisson, T.; Ryckewaert, M.; Christophe, A. Increasing the total productivity of a land by combining mobile photovoltaic panels and food crops. Appl. Energy 2017, 206, 1495–1507. [Google Scholar] [CrossRef]
  58. Othman, N.F.; Yaacob, M.E.; Su, A.S.M.; Jaafar, J.N.; Hizam, H.; Shahidan, M.F.; Jamaluddin, A.H.; Chen, G.; Jalaludin, A. Modeling of stochastic temperature and heat stress directly underneath agrivoltaic conditions with orthosiphon Stamineus Crop Cultivation. Agronomy 2020, 10, 1472. [Google Scholar] [CrossRef]
  59. Gonocruz, R.A.; Nakamura, R.; Yoshino, K.; Homma, M.; Doi, T.; Yoshida, Y.; Tani, A. Analysis of the Rice Yield under an Agrivoltaic System: A Case Study in Japan. Environments 2021, 8, 65. [Google Scholar] [CrossRef]
  60. AL-agele, H.A.; Proctor, K.; Murthy, G.; Higgins, C. A Case Study of Tomato (Solanum lycopersicon var. Legend) Production and Water Productivity in Agrivoltaic Systems. Sustainability 2021, 13, 2850. [Google Scholar] [CrossRef]
  61. Andrew, A.C.; Higgins, C.W.; Smallman, M.A.; Graham, M.; Ates, S. Herbage Yield, Lamb Growth and Foraging Behavior in Agrivoltaic Production System. Front. Sustain. Food Syst. 2021, 5, 659175. [Google Scholar] [CrossRef]
  62. Lytle, W.; Meyer, T.K.; Tanikella, N.G.; Burnham, L.; Engel, J.; Schelly, C.; Pearce, J.M. Conceptual Design and Rationale for a New Agrivoltaics Concept: Pasture-Raised Rabbits and Solar Farming. J. Clean. Prod. 2020, 282, 124476. [Google Scholar] [CrossRef]
  63. Graham, M.; Ates, S.; Melathopoulos, A.P.; Moldenke, A.R.; DeBano, S.J.; Best, L.R.; Higgins, C.W. Partial shading by solar panels delays bloom, increases floral abundance during the late-season for pollinators in a dryland, agrivoltaic ecosystem. Sci. Rep. 2021, 11, 1–13. [Google Scholar] [CrossRef]
  64. Willockx, B.; Herteleer, B.; Cappelle, J. Combining photovoltaic modules and food crops: First agrovoltaic prototype in belgium. Renew. Energy Power Qual. J. 2020, 18, 266–271. [Google Scholar] [CrossRef]
  65. Weselek, A.; Ehmann, A.; Zikeli, S.; Lewandowski, I.; Schindele, S.; Högy, P. Agrophotovoltaic systems: Applications, challenges, and opportunities. A review. Agron. Sustain. Dev. 2019, 39, 35. [Google Scholar] [CrossRef]
  66. Dubey, S.; Sarvaiya, J.N.; Seshadri, B. Temperature Dependent Photovoltaic (PV) Efficiency and Its Effect on PV Production in the World—A Review. Energy Procedia 2013, 33, 311–321. [Google Scholar] [CrossRef] [Green Version]
  67. Othman, N.F.; Yap, S.; Ya’Acob, M.E.; Hizam, H.; Su, A.S.; Iskandar, N. Performance evaluation for agrovoltaic DC generation in tropical climatic conditions. AIP Conf. Proc. 2019, 2129, 020006. [Google Scholar] [CrossRef]
  68. Proctor, K.W.; Murthy, G.S.; Higgins, C.W. Agrivoltaics align with green new deal goals while supporting investment in the us’ rural economy. Sustainability 2021, 13, 137. [Google Scholar] [CrossRef]
  69. Irie, N.; Kawahara, N.; Esteves, A.M. Sector-wide social impact scoping of agrivoltaic systems: A case study in Japan. Renew. Energy 2019, 139, 1463–1476. [Google Scholar] [CrossRef]
  70. Bellini, E. Japan’s First Vertical Agrivoltaic Project—PV Magazine International. PV Magazine. 2022. Available online: https://www.pv-magazine.com/2022/04/26/japans-first-vertical-agrivoltaic-project/ (accessed on 6 June 2022).
  71. Feuerbacher, A.; Laub, M.; Högy, P.; Lippert, C.; Pataczek, L.; Schindele, S.; Wieck, C.; Zikeli, S. An analytical framework to estimate the economics and adoption potential of dual land-use systems: The case of agrivoltaics. Agric. Syst. 2021, 192, 103193. [Google Scholar] [CrossRef]
  72. Guerrero Hernández, A.S.; Ramos de Arruda, L.V. Technical–economic potential of agrivoltaic for the production of clean energy and industrial cassava in the Colombian intertropical zone. Environ. Qual. Manag. 2021, 31, 267–281. [Google Scholar] [CrossRef]
  73. Kalaji, H.M.; Jajoo, A.; Oukarroum, A.; Brestic, M.; Zivcak, M.; Samborska, I.A.; Cetner, M.D.; Łukasik, I.; Goltsev, V.; Ladle, R.J.; et al. The Use of Chlorophyll Fluorescence Kinetics Analysis to Study the Performance of Photosynthetic Machinery in Plants. Emerg. Technol. Manag. Crop Stress Toler. 2014, 2, 347–384. [Google Scholar] [CrossRef]
Challenges 13 00043 g001 550
Figure 1. Increasing number of scholarly articles relating to AVs in the past 10 years. Sources: [6] All Rights Reserved.
Figure 1. Increasing number of scholarly articles relating to AVs in the past 10 years. Sources: [6] All Rights Reserved.
Challenges 13 00043 g001
Challenges 13 00043 g002 550
Figure 2. Comparison of land use for separate and combined agriculture and PV systems. Source: Reprinted with permission [20]. Copyright {2021}.
Figure 2. Comparison of land use for separate and combined agriculture and PV systems. Source: Reprinted with permission [20]. Copyright {2021}.
Challenges 13 00043 g002
Challenges 13 00043 g003 550
Figure 3. Different options for PV array tracking. (A) source: [26], (B) source: [37].
Figure 3. Different options for PV array tracking. (A) source: [26], (B) source: [37].
Challenges 13 00043 g003
Challenges 13 00043 g004 550
Figure 4. High Concentrator PV with diffused light used for plant growth; source: Reprinted with permission [40]. Copyright {2021}.
Figure 4. High Concentrator PV with diffused light used for plant growth; source: Reprinted with permission [40]. Copyright {2021}.
Challenges 13 00043 g004
Challenges 13 00043 g005 550
Figure 5. Different array patterns were studied in situ. Source: [43].
Figure 5. Different array patterns were studied in situ. Source: [43].
Challenges 13 00043 g005
Challenges 13 00043 g006 550
Figure 6. Bifacial vertical installation in Västerås, Sweden. Source: [48].
Figure 6. Bifacial vertical installation in Västerås, Sweden. Source: [48].
Challenges 13 00043 g006
Challenges 13 00043 g007 550
Figure 7. Lettuce experiment conducted by [24].
Figure 7. Lettuce experiment conducted by [24].
Challenges 13 00043 g007
Challenges 13 00043 g008 550
Figure 8. Rabbit Hill site set-up for micro-climate observation Source: [34].
Figure 8. Rabbit Hill site set-up for micro-climate observation Source: [34].
Challenges 13 00043 g008
Table
Table 1. Optimization methods modeled or field tested in the last 5 years.
Table 1. Optimization methods modeled or field tested in the last 5 years.
Engineering
TrackingDual Axis
North/South
West/East
PVBifacial
Concentrated
Thin Film
Transparent
Spectral Selective
System Layout
PlacementDensity
Spacing
Patterned (i.e., Checker-board)
Height
OrientationVertical Panels
Tilt Angle
Table
Table 2. Comparison of different layout methods tested: IS—In situ, M—Model.
Table 2. Comparison of different layout methods tested: IS—In situ, M—Model.
Testing Facility or Model LocationMethodObservationsSource
Montpelier, FranceDensity (IS)Half density produced more than full density in summer only.[46]
ICAR-Central Arid Reseach Insitute, IndiaVertical and Spacing (IS)PAR measured, and shading under PV was 18–58%.[47]
Lahore, PakistanSpacing (M)Inter-cropping increases production. Half density was ∼24% more productive.[31]
Vertical Bifacial DensitySpacial uniformity of sunlight better and increased LER.[12]
Incheon, KoreaHeight/Spacing (M)2–3 m height saw the largest change. Spacing between panels, not just rows, decreased shading.[25]
Fresno, CaCheckerboard Arrangement (M)No significant shadow difference from 15 m × 15 m vs. 3 m × 3 m Checkerboard reduces irradiation losses by 6%.[27]
R.E.M Tech Energy, ItalyPanel Density (M)Higher density tracking saw increased maize yield. Non-tracking saw decrease in yield.[26]
Västerås, SwedenBifacial Spacing (IS)Optimal spacing for oats is 9.7 m. Optimal distance for potatoes is 9.7 m.[48]
Table
Table 3. Impacts of AV on crop development in field tests.
Table 3. Impacts of AV on crop development in field tests.
CropLocationObservationsSource
AlfalfaOR, USA $2.623 per acre improvement [49]
BasilItaly−15% in market yield +2.5% financial gain[42]
Bok ChoyThailandSignificant growth loss, ↑ panel efficiency[51]
CanolaSpain−20% yield[52]
CarrotsSpain−10% yield[52]
CeleriacGermany+12% (’18) harvest yield, 1.76 LER[53]
−19% (’17) + 12% (’18) harvestable yield[17]
Chiltepin PepperAK, USAProduction 3x greater, same water efficiency[50]
Clover GrassGermany−8% (’17) − 5% (’18) harvest yield[53]
CornJapan+5% low density, −3% high density[54]
Fava BeanSpainNo change yield[52]
GrapesKoreaNo growth difference, 10 delay for harvest[55]
India∼same yield, 15x economic gains[56]
JalapeñoAZ, USAProduction equal with 157% water efficiency[50]
LettuceSpainPattern Array: +68.8% (sp) + 87.6% (sum), Single Array: +15.3% (sp) + 16.4% (sum)[24]
LER:1.51 (sp), 1.57 (sum), 1.53 (fall)[57]
MelonSpain−17% yield[52]
Misai KuciyMalaysiaNo yield change, +14.27% PV efficiency[58]
Onion Spain6% decrease in crop yield[52]
PotatoGermany−10% (’17) + 11 (’18)% harvest yield, 1.76 LER[53]
Spain23% decrease in crop yield[52]
RiceJapanShading limit range 27–39%[59]
SoybeansNC, USA$2.473 per acre improvement[49]
SpinachItaly−26% marketable yield, +35% financial gain[42]
StrawberriesNC, USAIncrease from $656/acre to $2.162/acre[49]
OR, USAIncrease from $149/acre to $1.884/acre
TomatoAZ, USA2x greater production 65% water efficiency[50]
OR, USAUnder Panel: −51%, Between Panel: −39%[60]
Spain5% decrease in crop yield[52]
WheatGermany−19% (’17) + 3% (’18) harvest yield, 1.71 LER[53]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Reasoner, M.; Ghosh, A. Agrivoltaic Engineering and Layout Optimization Approaches in the Transition to Renewable Energy Technologies: A Review. Challenges 2022, 13, 43. https://doi.org/10.3390/challe13020043

AMA Style

Reasoner M, Ghosh A. Agrivoltaic Engineering and Layout Optimization Approaches in the Transition to Renewable Energy Technologies: A Review. Challenges. 2022; 13(2):43. https://doi.org/10.3390/challe13020043

Chicago/Turabian Style

Reasoner, Meagan, and Aritra Ghosh. 2022. "Agrivoltaic Engineering and Layout Optimization Approaches in the Transition to Renewable Energy Technologies: A Review" Challenges 13, no. 2: 43. https://doi.org/10.3390/challe13020043

APA Style

Reasoner, M., & Ghosh, A. (2022). Agrivoltaic Engineering and Layout Optimization Approaches in the Transition to Renewable Energy Technologies: A Review. Challenges, 13(2), 43. https://doi.org/10.3390/challe13020043

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop