Application of Photovoltaic Systems for Agriculture: a Study On

Case Report Application of Photovoltaic Systems for Agriculture: A Study on the Relationship between Power Generation and Farming for the Improvement of Photovoltaic Applications in Agriculture

Jaiyoung Cho 1,* , Sung Min Park 2, A Reum Park 1, On Chan Lee 3, Geemoon Nam 3 and In-Ho Ra 4,*

1 Wongwang Electric Power Co., 243 Haenamhwasan-ro, Haenam-gun 59046, Jeollanamdo, Korea; [email protected] 2 Department of Horticulture, Kangwon National University, Chuncheon 24341, Jeollanamdo, Korea; [email protected] 3 SM Software, 1175, Seokhyeon-dong, Mokposi 58656, Jeollanamdo, Korea; [email protected] (O.C.L.); [email protected] (G.N.) 4 Department of Information and Communication Technology, Kunsan National University, Gunsan 54150, Jeollabuk-do, Korea * Correspondence: [email protected] (J.C.); [email protected] (I.-H.R.); Tel.: +82-62-384-9118 (J.C.); +82-63-469-4697(I.-H.R.) Received: 29 August 2020; Accepted: 11 September 2020; Published: 15 September 2020

Abstract: Agrivoltaic (agriculture–photovoltaic) or solar sharing has gained growing recognition as a promising means of integrating agriculture and solar-energy harvesting. Although this field offers great potential, data on the impact on crop growth and development are insufficient. As such, this study examines the impact of agriculture–photovoltaic farming on crops using energy information and communications technology (ICT). The researched crops were grapes, cultivated land was divided into six sections, photovoltaic panels were installed in three test areas, and not installed in the other three. A 1300 520 mm photovoltaic module was installed on a screen that was designed with a × shading rate of 30%. In addition, to collect farming-cultivation-environment data and to analyze power generation, sensors for growing environments and wireless-communication devices were used. As a result, normal modules generated 25.2 MWh, bifacial modules generated 21.6 MWh, and transparent modules generated 25.7 MWh over a five-month period. We could not find a difference in grape growth according to the difference of each module. However, a slight slowing of grape growth was found in the experiment group compared to the control group. Nevertheless, the sugar content of the test area of the grape fruit in the harvest season was 17.6 Brix on average, and the sugar content of the control area was measured at 17.2 Brix. Grape sugar-content level was shown to be at almost the same level as that in the control group by delaying the harvest time by about 10 days. In conclusion, this study shows that it is possible to produce renewable energy without any meaningful negative impact on normal grape farming.

Keywords: solar sharing; agriculture–photovoltaic; integrating agriculture; solar-energy harvesting

1. Introduction The Korean government’s Ministry of Trade, Industry, and Energy (MOTIE) recently announced the 2030 Energy New Industry Expansion Strategy Plan, which aims to supply 20% of electricity from renewable-energy sources by 2030, thereby increasing photovoltaic (PV) power-generation capacity

Energies 2020, 13, 4815; doi:10.3390/en13184815 www.mdpi.com/journal/energies Energies 2020, 13, 4815 2 of 18 from 5.7 to 35.5 GW. The availability of usable land, however, poses a challenge, since 63% of the existing PV plants are currently built in rural areas, encroaching on farmland and fields. Securing land six times larger than the current mass to implement extra PV plants is also impossible without consuming more farmland. Under these circumstances, the government, power producers, and farmers are all showing great interest in agrivoltaic farming, a method that combines PV generation and conventional agriculture. Accordingly, this maximizes land use by utilizing arable land for the coexistence of power generation and crop cultivation. However, since agrivoltaic systems are under-researched, current data on the effects on crops under an agrivoltaic system, correlations with power-generation levels, and economic feasibility are insufficient. Therefore, this study provides data on power generation and crop growth using information and communications technology (ICT), and analyses of the characteristics and economics of an agrivoltaic farm. As the number of PV power plants has increased globally, there have been concerns regarding their ability to cause environmental disruption. As an alternative, in 2004, Nagashima Akira introduced the concept of solar sharing, where PV power generation and crop cultivation are simultaneously performed. Solar sharing, also described as an agrivoltaic (agriculture–photovoltaic) system, is currently being actively researched as a new alternative to conventional PV plants. The effect of onion growth under the shadow of solar panels was studied. In this study, two types of PV panel distribution (checkerboard and straight-line) were tested, each occupying 12.9% of the roof area. As a result of the study, the generated electricity by the PV array of the two types was similar. The straight-line PV array, however, decreased the dry-matter weight (DW) and fresh weight (FW) of onions compared to the checkerboard PV array [1]. In a study where a solar module was installed over 50% of the roof area of a greenhouse, the result showed that solar radiation inside the greenhouse was reduced by 64% annually on average [2]. Auxiliary lighting that supplies electricity without exceeding the energy produced by the PV module was insufficient to produce onions, and profits were lower than the cost of heating and lighting [2]. Onion farms with 50% solar-module shade had a negative impact on agriculture, which led to the study of a shading rate of about 30%. Kuo YC studied the optical properties of greenhouses that had various types of PV modules (crystalline light-through, colorful, and see-through modules) installed, and reported that it is possible to make spectrum and light distributions ideal for plant growth through the selection of appropriate modules [3]. In modern precision farming, the need for real-time monitoring of crop-growth-parameter data, including microclimate, soil temperature, soil moisture, solar radiation, and leaf wetness, necessitates the use of wireless sensors and robot agents [4] on fields that operate on rechargeable batteries with solar panels. Such sensors have been successfully used in greenhouses for monitoring the comfort ratio of microclimate parameters [5], where artificial lights are available, and shadowing is not considered to be a limiting factor. In the checkerboard pattern, the presence of solar panels on the roof of a tomato greenhouse with 10% shade did not significantly affect tomato yield. Farmers in warm climates can produce electricity by installing solar panels on 10% of the roofs of tomato greenhouses without harming agricultural production during the spring–summer crop cycle [6]. On the basis of these research results, we designed a shading rate higher than 10%. Studies on farm-type photovoltaic-power-generation systems have so far focused on minimizing the negative effects of photovoltaic systems on the cultivation of crops by installing photovoltaic panels at a height of more than 4 m from the ground and a less than 30% shading rate. Figure1 is a farm-type solar-power plant installed by the Wongwang electric power company (WEPCO) in the city of Naju, Republic of Korea. Figure1 shows an example of installing solar power in a cabbage farm using a half module [7–16]. Energies 2020, 13, 4815 3 of 18 Energies 2020, 13, x FOR PEER REVIEW 3 of 19

Figure 1. Farm-type photovoltaic-power-generation system.

On the other hand, we studied how to use a photovoltaicphotovoltaic system as part of a crop-cultivationcrop-cultivation environment, which was different diﬀerent from the conventi conventionalonal approach whereby the negative effects eﬀects of a photovoltaic system on crop cultivation are minimized [[17–28].17–28]. Plants such as grapes and peppers increase the incidence of pests and deteriorate yi yieldeld and productivity when they are subjected to rainfall duringduring the the growth growth process. process. Therefore, Therefore, an umbrella-shapedan umbrella-shaped facility facili usingty using a photovoltaic a photovoltaic system wassystem installed was installed to prevent to prevent raindrops raindrops from hitting from thehitting crops, the shown crops, inshown Figure in2 [Figure29–32]. 2 [29–32].

Figure 2. Rain-hit-protection facility in a grape farm.

In this study, thethe solar-power-generationsolar-power-generation system replaced the rain-hit-protection facility, facility, and a model was developed to use as a rain-hit-protectionrain-hit-protection construction to reduce maintenance costs and increase farmers’farmers’ revenuerevenue by by additional additional proﬁt profit from from photovoltaic photovoltaic generation. generation The. The main main contribution contribution of ofthis this research research was was to to examine examine the the e ﬀeffectsects of of agrivoltaic agrivoltaic farming farming on on power-generationpower-generation levellevel and crop growth above above 30% 30% shading, shading, as as shown shown in inFigure Figure 3.3 We. We studied studied whether whether there there was was any anyimpact impact on crop on productivity under shading rates greater than 12.9% and at 10% which were used from previous studies on onions and tomatoes, respectively [1,6].

Energies 2020, 13, 4815 4 of 18 crop productivity under shading rates greater than 12.9% and at 10% which were used from previous Energiesstudies 2020 on, onions13, x FOR and PEER tomatoes, REVIEW respectively [1,6]. 4 of 19

Figure 3. Rain-hit-protection solar facility in a grape farm.

Crop-growth monitoring and a smart-farming syst systemem were implemented to gather data using various sensors. The The collected collected data data were were used used as as a a basis basis to monitor and evaluate the impact of agrivoltaic generating on grape-crop growth and sugarsugar content.content.

2. Materials Materials and Methods

2.1. Solar-Generation System 2.1. Solar-Generation System To study the crop-growth environment in a rain-hit-protection facility, a test bed (37 15 54.9” N To study the crop-growth environment in a rain-hit-protection facility, a test bed (37°15◦ 0 ′54.9″N 126 29 15.8” E) was installed in Ongjin-Gun, Republic of Korea, where rain-shield cultivation is popular. 126°29◦ 0′15.8″E) was installed in Ongjin-Gun, Republic of Korea, where rain-shield cultivation is The structure of the photovoltaic system was designed to be similar to the existing rain-shield popular. system in order to compare the cultivation environment and product quality under homogeneous The structure of the photovoltaic system was designed to be similar to the existing rain-shield conditions. Structure span was 2400 mm, and height was 2000 mm above the ground. The upper system in order to compare the cultivation environment and product quality under homogeneous structure had a 15 angle to force the rain to drop in a specific direction and braces were implemented conditions. Structure◦ span was 2400 mm, and height was 2000 mm above the ground. The upper 570 mm above ground to control structure deflection and wind load. To use the photovoltaic system as structure had a 15° angle to force the rain to drop in a specific direction and braces were a rain shield, acryl panels were installed on top of the structure that formed the 15 angle. The shading implemented 570 mm above ground to control structure deflection and wind◦ load. To use the rate of the solar panel was designed to be 30% of the total roof area. The solar panels were fitted onto photovoltaic system as a rain shield, acryl panels were installed on top of the structure that formed the roof using a checkerboard pattern, as shown in Figure4. the 15° angle. The shading rate of the solar panel was designed to be 30% of the total roof area. The To analyze the difference in cultivation environment due to module shading, normal solar panels solar panels were fitted onto the roof using a checkerboard pattern, as shown in Figure 4. and transparent panels were installed. In addition, to increase power-generation efficiency, normal and bifacial modules were installed. Module sizes were 1476 666, 1524 686, and 1476 780 mm2, × × × which are approximately half the size of a general module. In the 1800 m2 test bed, 600 m2 was used to install each type of module, thus forming 3 types of test bed. Figures5 and6 shows aerial photographs and section views of a farm with three installed panels. The aerial photographs and drawings of the farm show that Section B faces south, Section D is southeast, and Section F is southwest.

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Energies 2020, 13, 4815 5 of 18 Energies 2020, 13, x FOR PEER REVIEW 5 of 19

Figure 4. Structure of photovoltaic system.

To analyze the difference in cultivation environment due to module shading, normal solar panels and transparent panels were installed. In addition, to increase power-generation efficiency, normal and bifacial modules were installed. Module sizes were 1476 × 666, 1524 × 686, and 1476 × 780 mm2, which are approximately half the size of a general module. In the 1800 m2 test bed, 600 m2 was used to install each type of module, thus forming 3 types of test bed. Figures 5 and 6 shows aerial photographs and section views of a farm with three installed panels. The aerial photographs and drawings of the farm show that Section B faces south, Section D is southeast, and Section F is southwest. FigureFigure 4. 4.Structure Structure ofof photovoltaic system. system.

Energies 2020, 13, x FOR PEERFigure REVIEWFigure 5. 5.Aerial Aerial photo photo of 3 types of of installed installed panels. panels. 6 of 19

Figure 5. Aerial photo of 3 types of installed panels.

FigureFigure 6. Sections 6. Sections of 3of types3 types of of installed installed panels. A, A, norm normalal control control site; site;B, norm B, normalal solar-panel solar-panel site; C, site; C, bifacialbifacial control control site;site; D,D, bifacialbifacial solar-panel site; site; E, E, transparent transparent control control site; site; and andF, transparent F, transparent solar-panel solar-panel site. site.

The installationThe installation capacity capacity of of each each type type of of modulemodule was was 33 33 kW, kW, and and the the total total solar-power solar-power capacity capacity of theof test the bedtest bed was was 99 kW.99 kW. Generally, Generally, photovoltaic photovoltaic modulesmodules were were installed installed facing facing southward, southward, but the but the direction of the modules for agrivoltaic systems changed depending on the shape of the farm ridge and furrow. Therefore, not all modules were facing in the same direction. Section B maintained a south direction by a turnable type of solar system on top of a rain-hit facility, but it is vulnerable to typhoons and was more costly than the solar systems used Sections D and F. Sections D and F were installed as roof built-in types to compare energy generation. Figure 7 shows the 3 types of solar-system structures in the experiment site.

Section B Section D Section F

Figure 7. Three types of solar panels. B, normal type; D, bifacial type; F, transparent type.

As described above, 3 types of photovoltaic power plant were constructed in the test bed. Then, cultivation environment and crop quality were compared with the control site (Sections A, C, and E).

2.2. Smart-Farming System Sensors in the facility were installed to monitor the growth environment in the solar-power-generation system for rain-hit-protection facilities. The sensors were an illuminance sensor (BH1750FVI, ROHM SEMICONDUCTOR, Kyoto, Japan), solar-irradiance sensor (NHFS15, Wuhan Ahongke Nenghui Technology Co., Wuhan, China), temperature sensor (DS18B20, Maxim Integrated, San Jose, CA, USA), CO2 sensor (MH-Z16, Zhengzhou Winsen Electronics Technology CO., Zhengzhou, China), and a temperature and humidity sensor (AM2305, AOSONG, Guangzhou, China). Sensors were installed in Sections C and D. The growth environment was monitored from April 2019 to December 2019. Grapes germinate in April, the coloration and growth of the fruit occur until July, and grapes are harvested in August. The growth-environment monitoring during germination,

Energies 2020, 13, x FOR PEER REVIEW 6 of 19

Figure 6. Sections of 3 types of installed panels. A, normal control site; B, normal solar-panel site; C, bifacial control site; D, bifacial solar-panel site; E, transparent control site; and F, transparent solar-panel site.

EnergiesThe2020 installation, 13, 4815 capacity of each type of module was 33 kW, and the total solar-power capacity6 of 18 of the test bed was 99 kW. Generally, photovoltaic modules were installed facing southward, but the direction ofof thethe modulesmodules for for agrivoltaic agrivoltaic systems systems changed changed depending depending on on the the shape shape of the of farmthe farm ridge ridge and furrow.and furrow. Therefore, Therefore, not allnot modules all modules were were facing facing in the in same the direction.same direction. Section Section B maintained B maintained a south a directionsouth direction by a turnable by a turnable type of type solar of system solar onsystem top of on a top rain-hit of a facility,rain-hit but facility, it is vulnerable but it is vulnerable to typhoons to andtyphoons was more and was costly more than costly the solar than systems the solar used systems Sections used D Sections and F. Sections D and DF. andSections F were D and installed F were as roofinstalled built-in as typesroof tobuilt-in compare types energy to compare generation. en Figureergy generation.7 shows the 3Figure types of7 solar-systemshows the 3 structures types of insolar-system the experiment structures site. in the experiment site.

Section B Section D Section F

Figure 7. Three types of solar panels. B,, normal normal type; type; D,, bifacial type; F, transparent type.

As described above, 3 types of photovoltaic powerpower plant were constructed in the test bed. Then, Then, cultivation environment and crop quality were comparedcompared with the control site (Sections A, C, and E). 2.2. Smart-Farming System 2.2. Smart-Farming System Sensors in the facility were installed to monitor the growth environment in the solar-power- Sensors in the facility were installed to monitor the growth environment in the generation system for rain-hit-protection facilities. The sensors were an illuminance sensor (BH1750FVI, solar-power-generation system for rain-hit-protection facilities. The sensors were an illuminance ROHM SEMICONDUCTOR, Kyoto, Japan), solar-irradiance sensor (NHFS15, Wuhan Ahongke sensor (BH1750FVI, ROHM SEMICONDUCTOR, Kyoto, Japan), solar-irradiance sensor (NHFS15, Nenghui Technology Co., Wuhan, China), temperature sensor (DS18B20, Maxim Integrated, San Jose, Wuhan Ahongke Nenghui Technology Co., Wuhan, China), temperature sensor (DS18B20, Maxim CA, USA), CO sensor (MH-Z16, Zhengzhou Winsen Electronics Technology CO., Zhengzhou, China), Integrated, San2 Jose, CA, USA), CO2 sensor (MH-Z16, Zhengzhou Winsen Electronics Technology and a temperature and humidity sensor (AM2305, AOSONG, Guangzhou, China). CO., Zhengzhou, China), and a temperature and humidity sensor (AM2305, AOSONG, Guangzhou, Sensors were installed in Sections C and D. The growth environment was monitored from April China). 2019 to December 2019. Grapes germinate in April, the coloration and growth of the fruit occur until EnergiesSensors 2020, 13 were, x FOR installed PEER REVIEW in Sections C and D. The growth environment was monitored from April7 of 19 July, and grapes are harvested in August. The growth-environment monitoring during germination, 2019 to December 2019. Grapes germinate in April, the coloration and growth of the fruit occur until fruiting, coloration, and harvesting is crucial, as changes in the growthgrowth environmentenvironment duringduring these July, and grapes are harvested in August. The growth-environment monitoring during germination, periods could cause major changeschanges inin cropcrop quality.quality. Figure8 8 showsshows sensorsensor locationslocations inin thethe experimentalexperimental site,site, SectionSection D.D. DataData measurementsmeasurements byby thethe sensors were stored on thethe serverserver everyevery minute.minute. Da Datata with electric values from other sensors were acquired from the solar inverter.inverter.

Figure 8. Data-collection process. Figure 8. Data-collection process.

The measured data by each sensor and inverter were stored in a web server by TCP/IP communication, as illustrated in Figure 9. Smart farming system, in turn, is enabled by the growth environment data from the web server, which provides an automated schedule to control the amount of water supply, as shown in Figure 10. Figure 11 represents the web page for monitoring the integrated management system. Overall, these systems allow us to check the amount of power generated by the inverter, the crop environment, and cumulative history on power generation.

Figure 9. Flow chart for storage in a web server by TCP/IP communication.

Energies 2020, 13, x FOR PEER REVIEW 7 of 19 fruiting, coloration, and harvesting is crucial, as changes in the growth environment during these periods could cause major changes in crop quality. Figure 8 shows sensor locations in the experimental site, Section D. Data measurements by the sensors were stored on the server every minute. Data with electric values from other sensors were acquired from the solar inverter.

Energies 13 2020, , 4815 Figure 8. Data-collection process. 7 of 18

The measured data by each sensor and inverter were stored in a web server by TCP/IP The measured data by each sensor and inverter were stored in a web server by TCP/IP communication, as illustrated in Figure 9. Smart farming system, in turn, is enabled by the growth communication, as illustrated in Figure9. Smart farming system, in turn, is enabled by the growth environment data from the web server, which provides an automated schedule to control the environment data from the web server, which provides an automated schedule to control the amount of amount of water supply, as shown in Figure 10. Figure 11 represents the web page for monitoring water supply, as shown in Figure 10. Figure 11 represents the web page for monitoring the integrated the integrated management system. Overall, these systems allow us to check the amount of power management system. Overall, these systems allow us to check the amount of power generated by the generated by the inverter, the crop environment, and cumulative history on power generation. inverter, the crop environment, and cumulative history on power generation.

Energies 2020, 13, x FOR PEER REVIEW 8 of 19 Figure 9. Flow chart for storage in a web server by TCP/IP communication. Figure 9. Flow chart for storage in a web server by TCP/IP communication.

Figure 10. Smart-farming-system diagram. Figure 10. Smart-farming-system diagram.

Figure 11. Integrated management system for agrivoltaics.

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Energies 2020, 13, 4815 8 of 18 Figure 10. Smart-farming-system diagram.

Energies 2020, 13, x FOR PEER REVIEW 9 of 19 Energies 2020, 13, x FOR PEERFigureFigure REVIEW 11. 11.Integrated Integrated managementmanagement system system for for agrivoltaics. agrivoltaics. 9 of 19 Figures 12–14 show detailed characteristics of three types of installed photovoltaic (PV) panels. Figures Figures 12–14 12–14 show show detailed detailed characteristics characteristics ofof threethree types types of of installed installed phot photovoltaicovoltaic (PV) (PV) panels. panels.

Figure 12. Normal-panel specification (Section B). FigureFigure 12. 12.Normal-panel Normal-panel specification speciﬁcation (Section (Section B). B).

FigureFigure 13. 13.Bifacial-panel Bifacial-panel specification speciﬁcation (Section (Section D). D). Figure 13. Bifacial-panel specification (Section D).

Figure 14. Transparent-panel specification (Section F). Figure 14. Transparent-panel specification (Section F).

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Figures 12–14 show detailed characteristics of three types of installed photovoltaic (PV) panels.

Figure 12. Normal-panel specification (Section B).

Energies 2020, 13, 4815 Figure 13. Bifacial-panel specification (Section D). 9 of 18

Figure 14. Transparent-panel speciﬁcation (Section F). Figure 14. Transparent-panel specification (Section F). 3. Results and Discussion

3.1. Power-Generation Analysis

The normal, bifacial, and transparent solar-panel modules were connected to three inverters, and real-time power-generation data were uploaded to the server. The photovoltaic system was a grid-connected type, and the inverter was 34 kW. Photovoltaic-generation data during the five months were collected, and results are shown in Table1. Bifacial modules were expected to show higher power-generation efficiency than that of normal modules, but power generation was higher in the normal modules installed in the south direction than that in bifacial modules installed in the southeast direction. In addition, the power generation of normal and transparent modules installed in the south direction was similar, but the generation peak time was different due to the difference in the direction of module installation. Figure 15 shows the electricity-generation pattern of each type of panel on 18 July 2019. It shows that the power-generation efficiency was determined by the module installation direction rather than the module performance in this study. The reason for the different peak times of generation was that Section B was facing south, D southeast, and F southwest, as aerial photographs and drawings of the entire vineyard show.

Table 1. Agricultural-solar-plant proceeds.

Contents Unit April May June July August Total Days Days 30 31 30 31 31 153 PV energy kWh 8978 11,454 12,393 10,426 12,631 55,882 Electric price (SMP) USD/kWh 0.117 0.094 0.093 0.094 0.1 0.1 Sales income USD 1057 1081 1153 985 1270 5551 PV exchange time hours 3.02 3.73 4.17 3.40 4.12 3.69

In the case of plant agrivoltaic systems, like grapes, most crops have a fixed cultivation direction. Thus, the priority needs to be decided at the design stage between the furrow cultivation direction and power-generation efficiency. Figure 16 shows the power-generation data of three different PV types. The normal module generated 25.2 MWh, the bifacial module generated 21.6 MWh, and the transparent module generated 25.7 MWh over seven months. The power generation of the transparent module was the best, but this was due to the direction of the module. Therefore, it cannot be concluded that the power-generation performance of the transparent module was superior to that of other modules. Energies 2020, 13, x FOR PEER REVIEW 10 of 19

3. Results and Discussion

3.1. Power-Generation Analysis The normal, bifacial, and transparent solar-panel modules were connected to three inverters, and real-time power-generation data were uploaded to the server. The photovoltaic system was a grid-connected type, and the inverter was 34 kW. Photovoltaic-generation data during the five months were collected, and results are shown in Table 1. Bifacial modules were expected to show higher power-generation efficiency than that of normal modules, but power generation was higher in the normal modules installed in the south direction than that in bifacial modules installed in the southeast direction. In addition, the power generation of normal and transparent modules installed in the south direction was similar, but the generation peak time was different due to the difference in the direction of module installation. Figure 15 shows the electricity-generation pattern of each type of panel on 18 July 2019. It shows that the power-generation efficiency was determined by the module installation direction rather than the module performance in this study. The reason for the different peak times of generation was that Section B was facing south, D southeast, and F southwest, as aerial photographs and drawings of the entire vineyard show.

Table 1. Agricultural-solar-plant proceeds.

Contents Unit April May June July August Total Days Days 30 31 30 31 31 153 PV energy kWh 8978 11,454 12,393 10,426 12,631 55,882 Electric price (SMP) USD/kWh 0.117 0.094 0.093 0.094 0.1 0.1 Sales income USD 1057 1081 1153 985 1270 5551 Energies 2020, 13, 4815 10 of 18 PV exchange time hours 3.02 3.73 4.17 3.40 4.12 3.69

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best, but this was due to the direction of the module. Therefore, it cannot be concluded that the power-generation performance of the transparent module was superior to that of other modules. Figure 15. AC power-generation monitoring graph. Figure 15. AC power-generation monitoring graph.

In the case of plant agrivoltaic systems, like grapes, most crops have a fixed cultivation direction. Thus, the priority needs to be decided at the design stage between the furrow cultivation direction and power-generation efficiency. Figure 16 shows the power-generation data of three different PV types. The normal module generated 25.2 MWh, the bifacial module generated 21.6 MWh, and the transparent module generated 25.7 MWh over seven months. The power generation of the transparent module was the

Figure 16. Generated energy of each module per month. The commercial operation of the agrivoltaic plant started in April 2019. Table1 shows the daily The commercial operation of the agrivoltaic plant started in April 2019. Table 1 shows the daily average and monthly generation amount (KWh) and electricity-price (system marginal price, SMP) average and monthly generation amount (KWh) and electricity-price (system marginal price, SMP) income-to-power purchase agreement during the five months. However, this power plant was built income-to-power purchase agreement during the five months. However, this power plant was built for research purposes, so there was no renewable-energy-certificate (REC) profit. for research purposes, so there was no renewable-energy-certificate (REC) profit. 3.2. Growth-Environment Analysis 3.2. Growth-Environment Analysis The data collected from the crop-growth environment sensor included insolation, temperature The data collected from the crop-growth environment sensor included insolation, temperature of the top and bottom layers of the module, soil temperature, and soil-moisture level. We compared of the top and bottom layers of the module, soil temperature, and soil-moisture level. We compared the growth environment (carbon dioxide, illuminance, and soil temperature) of the farmland with the growth environment (carbon dioxide, illuminance, and soil temperature) of the farmland with installed solar panels and without solar panels using the collected data. The growth-environment data installed solar panels and without solar panels using the collected data. The growth-environment of Figures 17–19 were collected from Section D. data of Figures 17–19 were collected from Section D. Carbon dioxide (CO2) level data obtained from the experiment site with the solar panels and the control site without the panels were compared. The average CO2 level from the control site was 0.09%, and the maximum was 0.17% in July and August. On the other hand, the CO2 level of the experiment site was 0.05% on average and 0.07% at its peak throughout the year. Moreover, CO2 is used as fuel for photosynthesis, and plants generally have a 0.003–0.01% CO2 compensation point and 0.21–0.3% CO2 saturation point. The minimum CO2 level is represented by the CO2 compensation point, where the generation speed of organic matter from photosynthesis matches the consumption speed of organic matter from breathing. Figure 17 shows the change in monthly CO2 levels. The difference in CO2 level between the control and experiment sites was observed, with a minimum CO2 level of 0.05% detected at the experiment site.

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Figure 17. Carbon dioxide level comparison.

Another environmental factor is the comparison of illuminance between the control and experiment sites. In the control site, the highest illuminance was observed in May at an average of 12,252 lx, with a maximum value of 38,368 lx. The lowest illuminance was in December at an average of 1349 lx, reaching a maximum value of 6203 lx. In the agrivoltaic system, on the other hand, highest illuminance was observed in May at an average of 3905 lx with a maximum value of 19,799 lx. Further, the lowest illuminance was in December at an average of 295 lx with a maximum value of 1946 lx. Lastly, grape luminosity for maximum photosynthesis is 30,000–40,000 lx, meaning that the control site provided enough illuminance for maximum photosynthesis from April to September. However, in the agrivoltaic system, only 50% of maximum photosynthesis occurred from April to September. This lack of photosynthesis slowed grape growth by about 10 days. Figure 17. Carbon dioxide level comparison.

Figure 18. Illuminance comparison.

Soil temperature is one of the most important factors affecting soil nutrients, and soil-moisture density and viscosity. As shown in Figure 11, the soil temperature of the control and experiment sites only had minor difference. Figure 19 compares the soil characteristics between the experiment site with solar panels and the control site without panels. On this basis, farmland-soil temperature is an important factor for crop growth, as the dissolutionFigureFigure level 19. 19.of TemperaturelandTemperature nourishment comparison. accordingly changes, directly affecting crop growth. The reason that the temperature was measured as higher in the test site than in the controlledThe soil site temperature is due to the of temperature the control risesite in was the at panel its peak during in powerAugust generation at an average and theof 23.94 fact that°C, maximum of 24.87 °C, and minimum of 23.06 °C, and at its lowest in December at an average of 4.16 °C, maximum of 4.75 °C, and minimum of 3.60 °C. In contrast, the soil temperature of the experiment site was at its peak in August, showing an average of 23.94 °C, maximum of 24.48 °C, and minimum of 23.06 °C, and at its lowest in December showing an average of 6.38 °C, maximum of 6.90 °C, and minimum of 5.88 °C. The maximum temperature of the control and experiment sites was similar; however, the minimum temperature of the experiment site was higher than that of the control site by approximately 2 °C. The optimal soil temperature for grape-root generation is in the range of 23–28 °C, indicating that the soil temperature of the control and experiment sites was suitable—no significant difference in grape root generation was observed for temperatures within this range. However, in April, when germination occurs, the soil temperature of the control site was at an average of 12.52 °C, maximum of 14.70 °C, and minimum of 10.31 °C, and the soil temperature of the experiment site was at an average of 14.78 °C, maximum of 16.52 °C, and minimum of 13.54 °C, meaning that the soil temperature of the experiment site was approximately 2 °C higher than that of the control site. Grape roots become active when the soil temperature reaches 10–14 °C, and start to germinate when the air temperature reaches 10 °C. The experiment site, having a higher soil temperature by 2 °C in April (germination period), could result in faster grape germination compared to the control site. The actual grape-germination-period data showed a 1–2-day faster germination of grapes at the experiment site.

3.3. Crop-Impact Analysis Grape germination under the three types of solar modules occurred on approximately 20 April. The difference in the germination period owing to solar-panel implementation was not visually observable, and it occurred during a similar period. The bud-sprouting period differed, between 1 and 2 days, as compared to in the control site, showing almost no difference between the control site (which had no solar panels) and the site with the solar panels. Figure 20 shows the state of a flower cluster positioned on a branch after 20 days of bud germination. Each branch had 2 to 3 flower clusters, and the nutritional state of the branch caused

Energies 2020, 13, 4815 12 of 18

Carbon dioxide (CO2) level data obtained from the experiment site with the solar panels and the control site without the panels were compared. The average CO2 level from the control site was 0.09%, and the maximum was 0.17% in July and August. On the other hand, the CO2 level of the experiment site was 0.05% on average and 0.07% at its peak throughout the year. Moreover, CO2 is used as fuel for photosynthesis, and plants generally have a 0.003–0.01% CO2 compensation point and 0.21–0.3% CO2 saturation point. The minimum CO2 level is represented by the CO2 compensation point, where the generation speed of organic matter from photosynthesis matches the consumption speed of organic matter from breathing. Figure 17 shows the change in monthly CO2 levels. The difference in CO2 level between the control and experiment sites was observed, with a minimum CO2 level of 0.05% detected at the experiment site. Another environmental factor is the comparison of illuminance between the control and experiment sites. In the control site, the highest illuminance was observed in May at an average of 12,252 lx, with a maximum value of 38,368 lx. The lowest illuminance was in December at an average of 1349 lx, reaching a maximum value of 6203 lx. In the agrivoltaic system, on the other hand, highest illuminance was observed in May at an average of 3905 lx with a maximum value of 19,799 lx. Further, the lowest illuminance was in December at an average of 295 lx with a maximum value of 1946 lx. Lastly, grape luminosity for maximum photosynthesis is 30,000–40,000 lx, meaning that the control site provided enough illuminance for maximum photosynthesis from April to September. However, in the agrivoltaic system, only 50% of maximum photosynthesis occurred from April to September. This lack of photosynthesis slowed grape growth by about 10 days. Soil temperature is one of the most important factors affecting soil nutrients, and soil-moisture density and viscosity. As shown in Figure 11, the soil temperature of the control and experiment sites only had minor difference. Figure 19 compares the soil characteristics between the experiment site with solar panels and the control site without panels. On this basis, farmland-soil temperature is an important factor for crop growth, as the dissolution level of land nourishment accordingly changes, directly affecting crop growth. The reason that the temperature was measured as higher in the test site than in the controlled site is due to the temperature rise in the panel during power generation and the fact that it blocks cold air. Despite the shade created by the panel, the temperature did not decrease, but rather rose. The panel is expected to be applied more effectively in cold climates where a temperature increase is required. The soil temperature of the control site was at its peak in August at an average of 23.94 ◦C, maximum of 24.87 ◦C, and minimum of 23.06 ◦C, and at its lowest in December at an average of 4.16 ◦C, maximum of 4.75 ◦C, and minimum of 3.60 ◦C. In contrast, the soil temperature of the experiment site was at its peak in August, showing an average of 23.94 ◦C, maximum of 24.48 ◦C, and minimum of 23.06 ◦C, and at its lowest in December showing an average of 6.38 ◦C, maximum of 6.90 ◦C, and minimum of 5.88 ◦C. The maximum temperature of the control and experiment sites was similar; however, the minimum temperature of the experiment site was higher than that of the control site by approximately 2 ◦C. The optimal soil temperature for grape-root generation is in the range of 23–28 ◦C, indicating that the soil temperature of the control and experiment sites was suitable—no significant difference in grape root generation was observed for temperatures within this range. However, in April, when germination occurs, the soil temperature of the control site was at an average of 12.52 ◦C, maximum of 14.70 ◦C, and minimum of 10.31 ◦C, and the soil temperature of the experiment site was at an average of 14.78 ◦C, maximum of 16.52 ◦C, and minimum of 13.54 ◦C, meaning that the soil temperature of the experiment site was approximately 2 ◦C higher than that of the control site. Grape roots become active when the soil temperature reaches 10–14 ◦C, and start to germinate when the air temperature reaches 10 ◦C. The experiment site, having a higher soil temperature by 2 ◦C in April (germination period), could result in faster grape germination compared Energies 2020, 13, 4815 13 of 18 to the control site. The actual grape-germination-period data showed a 1–2-day faster germination of grapes at the experiment site.

3.3. Crop-Impact Analysis Grape germination under the three types of solar modules occurred on approximately 20 April. The difference in the germination period owing to solar-panel implementation was not visually observable, and it occurred during a similar period. The bud-sprouting period differed, between 1 and 2 days, as compared to in the control site, showing almost no difference between the control site (which had no solar panels) and the site with the solar panels. Figure 20 shows the state of a flower cluster positioned on a branch after 20 days of bud Energies 2020, 13, x FOR PEER REVIEW 14 of 19 germination. Each branch had 2 to 3 flower clusters, and the nutritional state of the branch caused any dianyfference difference in the in numberthe number of flower of flower clusters. clusters. The The number number and and growth growth of flower of flower clusters clusters between between the solar-panel-implementedthe solar-panel-implemented and and control contro sitesl sites did notdid shownot show any any difference. difference.

Figure 20. GrapeGrape germination germination (20 (20 April April 2019). 2019). (A ()A Normal) Normal control control site, site, (B) (normalB) normal solar-panel solar-panel site, site, (C) (bifacialC) bifacial control control site, site, (D) ( Dbifacial) bifacial solar-panel solar-panel site, site, (E ()E )transparent transparent control control site, site, and and ( F) transparenttransparent solar-panel site.

We researchedresearched thethe impactimpact onon thethe growthgrowth andand qualityquality ofof grapes,grapes, andand thethe soil-pollutionsoil-pollution level of the grape farm due to implementing the the normal normal (Typ (Typee 1), bifacial bifacial (Type (Type 2), and transparent (Type (Type 3) solar panels. Figures Figures 2121 andand 22 compare grape growthgrowth in the controlcontrol andand solar-panel-experimentsolar-panel-experiment plots. The top-left picture picture (A) (A) is is the the Type 1 controlcontrol plot, and top-right (B) is the Type 1 experimentexperiment plot. The middle-left imageimage (C)(C) isis thethe TypeType 22 controlcontrol plot, plot, and and the the middle-right middle-right image image (D) (D) is is the the Type Type 2 experiment2 experiment plot. plot. The The bottom-left bottom-left image image (E) (E) is is the the TypeType 3 3 controlcontrol plot,plot, andand thethe bottom-rightbottom-right (F) is the Type 33 experimentexperiment plot. plot. Grape Grape berries berries observed observed 17 17 days days after after pollination pollination showed showed normal normal growth, growth, and thereand there was nowas di noﬀerence difference in growth in growth between between the control the control and solar-panel-experimentand solar-panel-experiment plots. plots. Figure 23 shows grapes harvested on 28 August to examine pericarp coloration. As compared to the control site, pericarp coloration under the solar-panel module showed a 7–10 days delay in coloration. As the coloration of Campbell Early is related to light, the delay in grape coloration under the experiment site, as compared to the control site, may have occurred because of the diﬀerence in luminous intensity.

Figure 21. Development changes of grape flower gardens (6 May 2019). (A) Normal control site, (B) normal solar-panel site, (C) bifacial control site, (D) bifacial solar-panel site, (E) transparent control site, and (F) transparent solar-panel site.

Energies 2020, 13, x FOR PEER REVIEW 14 of 19

any difference in the number of flower clusters. The number and growth of flower clusters between the solar-panel-implemented and control sites did not show any difference.

Figure 20. Grape germination (20 April 2019). (A) Normal control site, (B) normal solar-panel site, (C) bifacial control site, (D) bifacial solar-panel site, (E) transparent control site, and (F) transparent solar-panel site.

We researched the impact on the growth and quality of grapes, and the soil-pollution level of the grape farm due to implementing the normal (Type 1), bifacial (Type 2), and transparent (Type 3) solar panels. Figures 21 and 22 compare grape growth in the control and solar-panel-experiment plots. The top-left picture (A) is the Type 1 control plot, and top-right (B) is the Type 1 experiment plot. The middle-left image (C) is the Type 2 control plot, and the middle-right image (D) is the Type 2 experiment plot. The bottom-left image (E) is the Type 3 control plot, and the bottom-right (F) is the EnergiesType2020 ,313 experiment, 4815 plot. Grape berries observed 17 days after pollination showed normal growth,14 of 18 and there was no difference in growth between the control and solar-panel-experiment plots.

Energies 2020, 13, x FOR PEER REVIEW 15 of 19

FigureFigure 21. Development21. Development changes changes ofof grape flower ﬂower gardens gardens (6 May (6 May 2019). 2019). (A) Normal (A) Normal control site, control (B) site, (B) normalnormal solar-panel solar-panel site,site, (C) bifacial control control site, site, (D (D) bifacial) bifacial solar-panel solar-panel site, site, (E) transparent (E) transparent control control site, and (F) transparent solar-panel site. site,Energies and 2020 (F,) 13 transparent, x FOR PEER solar-panelREVIEW site. 15 of 19

Figure 22. Development status of grape granules according to solar-module installation. (A) Normal control site, (B) normal solar-panel site, (C) bifacial control site, (D) bifacial solar-panel site, (E) transparent control site, and (F) transparent solar-panel site.

Figure 23 shows grapes harvested on 28 August to examine pericarp coloration. As compared

to the control site, pericarp coloration under the solar-panel module showed a 7–10 days delay in Figurecoloration.Figure 22. Development As22. Developmentthe coloration status status of ofCampbell of grape grape granules granules Early is accordingacrelatedcording to to light, to solar-module solar-module the delay installation. in installation. grape coloration (A) Normal (A) Normal under controlthe experimentcontrol site, site, (B )site,(B normal) normalas compared solar-panel solar-panel to the site, site, control (C (C) )bifacial si bifacialte, may control controlhave site, occurred site,(D) bifacial (D because) bifacial solar-panel of the solar-panel differencesite, (E) site, in (luminousE) transparenttransparent intensity. controlcontrol site, site, and and (F ()F transparent) transparent solar-panel solar-panel site. site.

Figure 23 shows grapes harvested on 28 August to examine pericarp coloration. As compared to the control site, pericarp coloration under the solar-panel module showed a 7–10 days delay in coloration. As the coloration of Campbell Early is related to light, the delay in grape coloration under the experiment site, as compared to the control site, may have occurred because of the difference in luminous intensity.

FigureFigure 23. Changes 23. Changes in grapein grape color color with with PV PV module module sections B/D/F—delay B/D/F—delay in incoloration. coloration. (A) Normal (A) Normal controlcontrol site, site, (B )(B normal) normal solar-panel solar-panel site, site, (C (C) )bifacial bifacial control control site, site,(D) bifacial (D) bifacial solar-panel solar-panel site, (E) site, (E) transparenttransparent controlcontrol site, and and (F (F) )transparent transparent solar-panel solar-panel site. site.

Table 2 compares the characteristics of grapes from the control and experiment sites, harvested

on 28 August. Grape samples were collected from 1 grape to 10 granules, and from 1 tree to 10 grapes;Figure three 23. trees Changes were in targetedgrape color in witheach PV section. module The sections average B/D/F—delay sugar content in coloration. of 300 granules (A) Normal in each sectioncontrol was site, compared. (B) normal Letter solar-panel a~d means site, (C )in bifacial a row control by different site, (D) bifacialsuperscripts solar-panel are significantlysite, (E) differenttransparent at 5% controlsignificance site, and level (F) transparentby Duncan’s solar-panel multiple site. range test. Letter a is significant to b, c but "a" and "ab" is not significant. Table 2 compares the characteristics of grapes from the control and experiment sites, harvested

on 28 August. Grape samples were collected from 1 grape to 10 granules, and from 1 tree to 10 grapes; three trees were targeted in each section. The average sugar content of 300 granules in each section was compared. Letter a~d means in a row by different superscripts are significantly different at 5% significance level by Duncan’s multiple range test. Letter a is significant to b, c but "a" and "ab" is not significant.

Energies 2020, 13, 4815 15 of 18

Table2 compares the characteristics of grapes from the control and experiment sites, harvested on 28 August. Grape samples were collected from 1 grape to 10 granules, and from 1 tree to 10 grapes; three trees were targeted in each section. The average sugar content of 300 granules in each section was compared. Letter a~d means in a row by different superscripts are significantly different at 5% significanceEnergies 2020, 13 level, x FOR by PEER Duncan’s REVIEW multiple range test. Letter a is significant to b, c but “a” and “ab”16 of is19 not significant. Table 2. Grape-growth results by photovoltaic (PV) module type.

Table 2. Grape-growthGranule Sugar results Content by photovoltaic (PV) module type.Length Width PV Module Type Granule Quantity GranuleWeight (Brix) (mm)Length (mm)Width PV Module Type Sugar Content (Brix) Granule Quantity Without module (Section A)Weight 5.7 ± 0.9 a 1 13.5 ± 0.8 b 66.4 ± 11.0 bc 21.4(mm) ± 1.3 a 20.2 (mm)± 1.3 a WithoutNormal module (Section A) B) 5.7 5.40.9 ± a 0.81 c 13.5 12.9 ±0.8 1.8 b bc 66.4 58 ± 11.013.1 bc c 20.9 21.4 ± 1.01.3 ab a 19.9 20.2 ± 1.21.3 ab a ± ± ± ± ± Normal (Section B) 5.4 0.8 c 12.9 1.8 bc 58 13.1 c 20.9 1.0 ab 19.9 1.2 ab Without module (Section C) 5.1± ± 0.6 ab 13.4± ± 0.9 bc 68.4± ± 5.2 bc 20.7 ±± 0.9 bc 19.6 ± ±0.8 abc Without module (Section C) 5.1 0.6 ab 13.4 0.9 bc 68.4 5.2 bc 20.7 0.9 bc 19.6 0.8 abc ± ± ± ± ± BifacialBifacial (Section (Section D) D) 5.0 5.00.5 ± bc0.5 bc 12.7 12.7 ±0.8 0.8 c c 73 ± 5.45.4 b b 20.7 20.7 ± 0.70.7 bcbc 19.3 19.3 ± 0.70.7 bc bc ± ± ± ± ± WithoutWithout module module (Section (Section E) E) 4.9 4.90.8 ± ab0.8 ab 15.4 15.4 ±0.9 0.9 a a 70.8 70.8 ± 12.312.3 bc bc 20. 20.00 ± 1.11.1 abab 19.0 19.0 ± 1.21.2 c c ± ± ± ± ± Transparent (Section F) 4.2 0.6 ab 11.3 0.9 d 89.8 6.9 a 19.5 1.1 a 18.1 1.0 d Transparent (Section F) 4.2± ± 0.6 ab 11.3± ± 0.9 d 89.8± ± 6.9 a 19.5 ± 1.1 a 18.1 ±± 1.0 d 1 a 1, amean, mean separation separation within within each each columncolumn by by Duncan’s Duncan’s multiple multiple range range test, 5%test, level. 5% level.

TheThe distinctivedistinctive factorfactor isis thatthat fruitsfruits underunder solar-panelsolar-panel modulesmodules showedshowed slowerslower growthgrowth thanthan thosethose atat thethe controlcontrol site.site. TheThe relevancerelevance ofof sugar-concentrationsugar-concentration levellevel waswas observableobservable wherewhere fruitsfruits inin thethe experimentexperiment sitesite hadhad lowerlower sugarsugar concentrationsconcentrations thanthan thosethose atat thethe controlcontrol site.site. AsAs shownshown inin FigureFigure 2323,, thisthis result result concurs concurs with with the factthe thatfact lowerthat lower sugar concentrationsugar concentration was observed was observed in grapes underin grapes solar-panel under modules,solar-panel which modules, had a which delay inhad coloration. a delay in coloration. FigureFigure 2424 showsshows thethe resultsresults ofof examiningexamining changeschanges inin skinskin colorationcoloration accordingaccording toto eacheach solarsolar modulemodule byby second-harvest second-harvest grapes, grapes, 10 10 days days after after the firstthe harvest.first harvest. Thecoloration The coloration of grape of skinsgrape grown skins ingrown each in solar each module solar module site was site not was diff erentnot different compared compared to that into thethat control in the site.control In thesite. earlyIn the stages early ofstages harvest, of harvest, bark coloration bark coloration was less was well-formed less well-for thanmed that than in that the controlin the control site, but site, there but wasthere no was color no dicolorfference difference in the maturityin the maturity period. period.

Figure 24. Changes in grape color with PV modules—no diﬀerence in coloration in all sections. Figure 24. Changes in grape color with PV modules—no difference in coloration in all sections. (A) (A) Normal control site, (B) normal solar-panel site, (C) bifacial control site, (D) bifacial solar-panel site, Normal control site, (B) normal solar-panel site, (C) bifacial control site, (D) bifacial solar-panel site, (E) transparent control site, and (F) transparent solar-panel site. (E) transparent control site, and (F) transparent solar-panel site. Table3 shows the sugar-concentration levels of grapes harvested during the full ripe period in Table 3 shows the sugar-concentration levels of grapes harvested during the full ripe period in the control and experiment groups. Measurement of the Brix degree of late-harvest grapes showed the control and experiment groups. Measurement of the Brix degree of late-harvest grapes showed similar values between the control group and those under normal, bifacial, and transparent solar-panel similar values between the control group and those under normal, bifacial, and transparent solar-panel modules. This implies that the grapes under solar panels had slower growth, but their quality could be maintained by controlling the harvesting period. Further research is planned for cyclical testing in 2020.

Energies 2020, 13, 4815 16 of 18 modules. This implies that the grapes under solar panels had slower growth, but their quality could be maintained by controlling the harvesting period. Further research is planned for cyclical testing in 2020.

Table 3. Changes in sugar content according to PV module in ripe grapes.

PV Module Type Division Sugar Content (Brix) Without module (A) 17.5 Normal With module (B) 18.6 Without module (C) 16.7 Bifacial With module (D) 17.1 Without module (E) 17.3 Transparent With module (F) 17.1

4. Conclusions This study compared the crop-growth environment in a rain-hit-protection facility with a solar-power system to a facility without solar panels. Then, effects on growth and power-production profit were analyzed. Soil temperature in the test site was higher in spring and winter compared to in the control site. In particular, the test-site soil temperature in April was 14.78 ◦C on average, 2.26 ◦C higher than that in the control site, and the germination of the test-site grapes in April was one to two days faster than that of grapes in the control site. On the other hand, the coloring and growth of the test-site grapes was delayed compared to those of grapes in the control site. This was due to a combination of solar-radiation reduction and temperature changes caused by the installation of solar modules. Grapes of the first harvested test site showed lower quality than those of the control site with regard to weight and sugar content. Considering the delayed growth of the grapes in the test site, the second harvest was performed 10 days after the first harvest. The quality of the second harvested test-site grapes was similar to that of the first harvested control-site grapes. This means that it is possible to cultivate high-quality crops by only changing harvest time at the solar-power facility. Three types of solar panels (normal, bifacial, and transparent panels) were used for the study, but no significant difference was found in their effect on power generation and grape growth. There was a profit of USD 5551 related to the power transaction, and 55 MWh was produced during the five months that grape growth was monitored. This study confirmed that the mutual coexistence of agriculture and renewable energy is possible by utilizing solar-power systems as a crop-cultivation environment. We will conduct it again for the verification of the relationship between solar power and grape growth in 2020. Other crop studies, such as for cabbages and onions, are also being conducted. This study may serve as a basis for further research on the coexistence of renewable energy and agriculture.

Author Contributions: Conceptualization, J.C. and I.-H.R.; methodology, J.C. and S.M.P.; software, O.C.L.; investigation, A.R.P.; writing—original draft preparation, J.C. and G.N.; writing—review and editing, J.C. and A.R.P.; supervision, I.-H.R. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Korean government (MOTIE) [20173010013390]. Acknowledgments: This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), grant funded by the Korean government (MOTIE) (20173010013390, development and field testing of 100 kW agro-PV systems) and this work was supported by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00508) development of blockchain-based embedded devices and platform for MG security and operational efficiency. Conflicts of Interest: The authors declare no conflict of interest. Energies 2020, 13, 4815 17 of 18

References

1. Kadowaki, M.; Yano, A.; Tanaka Ishizu, F.; Tanaka, T.; Noda, S. Effects of greenhouse photovoltaic array shading on welsh onion growth. Biosyst. Eng. 2012, 111, 290–297. [CrossRef] 2. Cossu, M.; Murgia, L.; Ledda, L.; Deligios, P.A.; Sirigu, A.; Chessa, F.; Pazzona, A. Solar radiation distribution inside a greenhouse with south-oriented photovoltaic roofs and effects on crop productivity. Appl. Energy 2014, 133, 89–100. [CrossRef] 3. Kuo, Y.-C.; Chiang, C.-M.; Chou, P.-C.; Chen, H.-J.; Lee, C.-Y.; Chan, C.-C. Applications of building integrated photovoltaic modules in a greenhouse of Northern Taiwan. J. Biobased Mater. Bioenergy 2012, 6, 721–727. [CrossRef] 4. Shamshiri, R.R.; Weltzien, C.; Hameed, I.A.; Yule, I.J.; Grift, T.E.; Balasundram, S.K.; Pitonakova, L.; Ahmad, D.; Chowdhary, G. Research and development in agricultural robotics: A perspective of digital farming. Int. J. Agric. Biol. Eng. 2018, 11, 1–14. [CrossRef] 5. Shamshiri, R.R.; Bojic, I.; van Henten, E.; Balasundram, S.K.; Dworak, V.; Sultan, M.; Weltzien, C. Model-based evaluation of greenhouse microclimate using IoT-Sensor data fusion for energy efficient crop production. J. Clean. Prod. 2020, 121303. [CrossRef] 6. Ezzaeri, K.; Fatnassi, H.; Bouharroud, R.; Gourdo, L.; Bazgaou, A.; Wifaya, A.; Demrati, H.; Bekkaoui, A.; Aharoune, A.; Poncet, C.; et al. The effect of photovoltaic panels on the microclimate and the tomato production under photovoltaic Canarian greenhouses. Sol. Energy 2018, 173, 1126–1134. [CrossRef] 7. Kim, D.; Kim, C.; Park, J.; Kim, C.; Nam, J.; Cho, J.; Lim, C. Computer simulation of lower farmland by the composition of an agrophotovoltaic system. New Renew. Energy 2020, 16, 41–46. [CrossRef] 8. Gray, M.K.; Morsi, W.G. On the role of prosumers owning rooftop solar photovoltaic in reducing the impact on transformer’s aging due to plug-in electric vehicles charging. Electr. Power Energy Syst. 2017, 143, 563–572. [CrossRef] 9. Luz, T.; Moura, P.; Almeida, A. Multi-objective power generation expansion planning with high penetration of renewables, Renew. Sustain. Energy Rev. 2018, 81, 2637–2643. [CrossRef] 10. Shang, C.; Srinivasan, D.; Reindl, T. An improved particle swarm optimization algorithm applied to battery sizing for stand-alone hybrid power systems. Int. J. Electr. Power Energy Syst. 2016, 74, 104–117. [CrossRef] 11. Aroca-Delgado, R.; Pérez-Alonso, J.; Callejón-Ferre, Á.-J.; Díaz-Pérez, M. Morphology, yield and quality of greenhouse tomato cultivation with flexible photovoltaic rooftop panels. Sci. Hortic. 2019, 257, 108768. [CrossRef] 12. Tsao, T.-F.; Chang, H.-C. Comparative case studies for value-based distribution system reliability planning. Electric Power Syst. Res. 2004, 68, 229–237. [CrossRef] 13. Dalei, J.; Mohanty, K.B. Fault classification in SEIG system using Hilbert-Huang transform and least square support vector machine. Int. J. Electr. Power Energy Syst. 2016, 76, 11–22. [CrossRef] 14. Eri¸sti,H.; Yıldırım, Ö.; Eri¸sti,B.; Demir, Y. Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines. Int. J. Electr. Power Energy Syst. 2013, 49, 95–103. [CrossRef] 15. ElNozahy, M.S.; Salama, M.M.A. Studying the feasibility of charging plug-in hybrid electric vehicles using photovoltaic electricity in residential distribution systems. Electr. Power Syst. Res. 2014, 110, 133–143. [CrossRef] 16. Kim, K.; Yoon, J.-Y.; Kwon, H.-J.; Han, J.-H.; Son, J.E.; Nam, S.-W.; Giacomelli, G.A.; Lee, I.-B. 3-D CFD analysis of relative humidity distribution in greenhouse with a fog cooling system and refrigerative dehumidifiers. Biosyst. Eng. 2008, 100, 245–255. [CrossRef] 17. Klaring, H.-P.;Klopotek, Y.; Schmidt, U.; Tantau, H.-J. Screening a cucumber crop during leaf area development reduces yields. Ann. Appl. Biol. 2012, 161, 161–168. [CrossRef] 18. Park, D.; Kim, M.; So, W.; Oh, S.-Y.; Park, H.; Jang, S.; Park, S.-H.; Kim, W.K. Evaluation of bifacial si solar module with different albedo conditions. Curr. Photovolt. Res. 2018, 6, 62–67. [CrossRef] 19. Kim, M.; Ji, S.; Oh, S.-Y.; Jung, J.H. Prediction study of solar modules considering the shadow effect. Curr. Photovolt. Res. 2016, 4, 80–86. [CrossRef] 20. Lee, S.-H.; Oh, H.K.; Lee, K.S. PV System Output Analysis Based on Weather Conditions, Azimuth, and Tilt Angl. Curr. Photovolt. Res. 2017, 5, 38–42. [CrossRef] Energies 2020, 13, 4815 18 of 18

21. Jeong, J.-H.; Lim, C.-M.; Jo, J.-H.; Kim, J.-H.; Kim, S.-H.; Lee, K.-Y.; Lee, S.-S. A study on the monitoring system of growing environment department for smart farm. J. Korea Inst. Inform. Electron. Commun. Technol. 2019, 12, 290–298. [CrossRef] 22. Yang, Q. Development Strategy of Plant Factory. Sci. Technol. Rev. 2014, 32, 20–24. [CrossRef] 23. Ureña-Sánchez, R.; Callejón-Ferre, Á.J.; Pérez-Alonso, J.; Carreño-Ortega, Á. Greenhouse tomato production with electricity generation by roof-mounted flexible solar panels. Sci. Agric. 2012, 69, 233–239. [CrossRef] 24. Nugraha, G.D.; Musa, A.; Cho, J.; Park, K.; Choi, D. Lambda-based data processing architecture for Two-Level load forecasting in residential buildings. Energies 2018, 11, 772. [CrossRef] 25. Sacchelli, S.; Garegnani, G.; Geri, F.; Grilli, G.; Paletto, A.; Zambelli, P.; Ciolli, M.; Vettorato, D. Trade-off between photovoltaic systems installation and agricultural practices on arable lands: An environmental and socio-economic impact analysis for Italy. Land Use Policy 2016, 56, 90–99. [CrossRef] 26. Dinesh, H.; Pearce, J.M. The potential of agrivoltaic systems. Renew. Sustain. Energy Rev. 2016, 54, 299–308. [CrossRef] 27. Cossu, M.; Cossu, A.; Deligios, P. Assessment and comparison of the solar radiation distribution inside the main commercial photovoltaic greenhouse types in Europe. Renew. Sustain. Energy Rev. 2018, 94, 822–834. [CrossRef] 28. Fukatsu, T.; Kiura, T.; Hirafuji, M. A web-based sensor network system with distributed data processing approach via web application. Comput. Stand. Interfaces 2011, 33, 565–573. [CrossRef] 29. Ge, J.; Cai, C.; Liu, Y.; Gong, X. Effect of Irrigation Time on the growth rate and indoor environment of greenhouse eggplant. J. Inst. Eng. (India) Ser. A 2018, 99, 647–651. [CrossRef] 30. Ganguly, A.; Ghosh, S. Model development and experimental validation of a floriculture greenhouse under natural ventilation. Energy Build. 2009, 41, 521–527. [CrossRef] 31. Yano, A.; Kadowaki, M.; Furue, A.; Tamaki, N.; Tanaka, T.; Hiraki, E.; Kato, Y.; Ishizu, F.; Nodad, S. Shading and electrical features of a photovoltaic array mounted inside the roof of an east-west oriented greenhouse. Biosyst. Eng. 2010, 106, 367–377. [CrossRef] 32. Gaddam, A. Designing a wireless sensors network for monitoring and predicting droughts. In Proceedings of the 8th International Conference on Sensing Technology, Liverpool, UK, 2–4 September 2014.

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