# The Economic Potential of Agrivoltaic Systems in Apple Cultivation—A Hungarian Case Study

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Exploring Global Agrivoltaics: Case Studies and Research Perspectives

^{®}software, achieving an R

^{2}greater than 90%. The sensitivity analysis reveals a strong correlation between mutual row distance (5–20 m) and crop yield, demonstrating a doubling of yield within this range. The maximum oats and potato yield without shading effects were 5.0 t/ha and 6.9 t/ha, respectively.

#### 2.2. The European and Hungarian Electricity Market

_{2}emissions [29]. However, as there is currently not a sufficient incentive to reach the EU target due to different externalities between countries, Karakosta-Petropoulou (2022) [30] suggests that a re-introduction of mandatory national targets should be considered. Yi et al. [31] proposed a technical conversion coefficient system for each renewable energy source in China; for example, the technical conversion coefficients for hydropower, wind, and PV could be 1, 1.2, and 1.5, respectively.

#### 2.3. Key Facts of the European and Hungarian Apple Production

#### 2.4. Effect of AV System on Apple Production

#### 2.5. Economic Background

_{p}) and soil preparation (190–266 EUR), just like the maintenance, may increase the costs by up to 10%, which may vary to a higher extent depending on the height and distance between posts. In the past couple of years, there has been a high risk of change in steel prices. Module price depends on the light transmission ability and the design. Trommsdorff et al. [52] calculated a price between 240 and 440 EUR for semi-transparent and 326 Euro per kW

_{p}for double glass modules. The extra cost can be compensated for by the higher electricity production per installed capacity. The costs of the planting material and the irrigation system can be calculated as unchanged. There is also a lack of building, nature, and landscape conservation legislation, which may result in increased costs for any possible authorization processes depending on the given country [13,14,52,56].

## 3. Materials and Methods

#### 3.1. Project Description and Objectives

#### 3.2. Economic Indicators for Implementing Agrivoltaic Systems in Hungary

_{0}represents the cost of the initial investment (CAPEX), DCF

_{i}is the discounted cash flow in the ith year, and t is the time period of the analysis.

_{i}is the annual cash flow in the ith year, and DF

_{i}is the discount factor in the ith year.

_{i}= Rev

_{i}− OPEX

_{i}− Tax

_{i}

_{i}represents the total annual revenue from the PV system and apple production in the ith year, OPEX

_{i}is the total annual OPEX of the PV system and apple production in the ith year, and Tax

_{i}is the annual corporate tax.

_{a}) is calculated as follows:

_{c}is the annual yield of apples for consumption purposes (class I and II) per hectare, P

_{c}is the price of apples for consumption purposes, Y

_{i}is the annual yield of industrial (juice) apples per hectare, P

_{i}is the price of industrial (juice) apples, S is the annual subsidy per hectare, and A is the area of apple orchard.

_{e}) is determined by the following:

_{i}and OPEX

_{i}are the nominal value of the annual revenue and the nominal value of the OPEX in the ith year, Rev

_{i−1}and OPEX

_{i−1}are the nominal value of the annual revenue and the nominal value of the OPEX in the previous year, and IR is the inflation rate of the given year.

_{i}= (1 + r)

^{i}

_{i}is the total cost of PV system and apple production in the ith year, RevSe

_{i}is the share of electricity in total revenues (%) in the ith year, and Ye

_{i}is the annual yield of electricity in the ith year. The annual total cost is the sum of OPEX and the annual depreciation of the PV system and apple orchard.

_{i}is the share of apple production in total revenues (%) in the ith year, and Ya

_{i}is the annual yield of apples in the ith year.

## 4. Results and Discussion

#### 4.1. Investment Analysis

#### 4.2. Unit Cost of Electricity and Apple Production

#### 4.2.1. Unit Cost of Apple Production

#### 4.2.2. Unit Cost of Electricity

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AVS | Agrivoltaic systems |

CAPEX | Capital expenditure |

CDCF | Cumulative discounted cash flow (DCF) |

ConAPS | Conventional apple production system |

DF | Discount factor |

FIT | Feed-in-tariff price |

GCR | Ground–coverage ratio |

GM-PV | Ground-mounted photovoltaic system |

GWp | Gigawatt-peak |

HN | Hail net |

IRR | Internal rate of return |

JPY | Japanese Yen |

KRW | South Korean Won |

kW | Kilowatt |

kWac | Kilowatt alternating current |

kWh | Kilowatt-hour |

kWh/a | Kilowatt-hours per annum (per year) |

kWh/ha | kilowatt-hours per hectare per year |

kWp | Kilowatt-peak |

LCOE | Levelized cost of electricity |

MW | Megawatt |

MWp | Megawatt peak |

NPV | Net present value |

OPEX | Operating Expenses |

PI | Profitability index |

PV | Photovoltaic |

SPV | Solar photovoltaic |

UCa2027 | Unit cost of apples in 2027 (first harvest year) |

UCe2025 | Unit cost of electricity in 2025 (first year of operation) |

## Appendix A

#### Appendix A.1. Agrivoltaics in Asia

#### Appendix A.1.1. Japan

#### Appendix A.1.2. China

#### Appendix A.1.3. South Korea

^{2}for efficient photovoltaic systems at 33° latitude, representing 18.3% efficiency. However, AVS implementation only commenced in 2017 for research purposes, with plans to scale up [87]. Policymakers in South Korea envision 100,000 AVS projects, each with 100 kWp, by 2030, targeting a market size of 10 GWp [13].

#### Appendix A.1.4. India

#### Appendix A.2. Agrivoltaics in Europe

^{2}) is eligible for AVS installation. The distribution of eligible land varies significantly, with most countries in Europe having between 12% and 29% of their land suitable for AVS, while some countries have as low as from 1% to 9%. However, Hungary (58.6%), Denmark (53.9%), and Ireland (63.9%) have larger percentages of eligible agrivoltaics areas [91]. Moreover, the disparities in land eligibility emphasize the need for strategic planning and policy frameworks to optimize agrivoltaic systems deployment across diverse European landscapes.

#### Appendix A.2.1. Germany

#### Appendix A.2.2. Italy

^{®}, was developed by REM Tec in collaboration with the University of Piacenza. It assesses its integration with maize crop production and evaluates its economic and environmental performance. Installed in Castelvetro Piacentino and Monticelli d’Ongina in 2012, these systems cover 7 ha and 20 ha, respectively, showcasing their scalability in the northern region of Italy [93].

#### Appendix A.2.3. France

#### Appendix A.3. Agrivoltaics in the United States

Crop | Shading Rate Range | Influence on Crop Yield | Electricity Production | Location | Capacity of AVS | Investment Cost | Ref. |
---|---|---|---|---|---|---|---|

Rice | 27% to 39% | Sustains at least 80% of yield | 28% density: 284 million MWh/yr. (29% of Japanese electricity demand, 2018) | Japan | 231 million kW | NA | [83] |

Corn | NA | Control: 3.35 kg/m^{2}Low Density: 3.54 kg/m ^{2}High Density: 3.23 kg/m ^{2} | HD: 2974 kWh/a LD: 1487 kWh/a | Ichihara City, Chiba Prefecture, Japan | 4.5 kW | NA | [103] |

Soybeans | NA | Crop production decreased by less than 20%. | LAOR: 35% generate 17.8 GWh/year | Kyoto Prefecture, Japan | 50 kWac | 320,000 JPY/kWac | [104] |

Lettuce | 70% 50% | No significant effect on crop yield Significant effect on crop yield | 6.5 to18 kWh/m^{2} | Montpellier, France | NA | NA | [46,105,106] |

Lettuces and cucumbers | Lettuce: 32% in FD and 48% in HD Cucumber: 37% in FD and 62% in HD | NA | Montpellier, France | NA | NA | [46] | |

Potatoes | 50% | Potato plants beneath the PV modules had more leaves than those in the reference area. | 2447 kWh | Belgium | NA | NA | [107] |

Winter wheat, potatoes, celeriac | NA | Winter wheat yields increased by 3%. Potato yields increased by 11%. Celeriac yields increased by 12%. | 246 MWh | Germany | 194.4 kW | NA | [108] |

Apple | NA | NA | 996 kWh/kWp/a | Germany | 700 kWp | 1387 k EUR/ha | [14] |

Tomato transplants (Solanum lycopersicum var. Legend) | NA | Control Fully Irrigated (a): 88.42 (kg/row) Control Fully Irrigated (b): 68.13 (kg/row) Row Full Irrigated (a): 53.59 (kg/row) Row Full Irrigated (b): 32.76 (kg/row) Panel Full Irrigated (a): 33.61(kg/row) Panel Full Irrigated (b): 21.64 (kg/row) | NA | Oregon State University Vegetable Farm (Corvallis, OR, USA) | 482 kW | NA | [109] |

Soybean | AV1 = 27%, AV2 = 16%, AV3 = 9%, AV4 = 18% | Total pod number decreased by 13% on average in all AV conditions compared to open field conditions. | NA | Monticelli d’Ongina, Italy | NA | NA | [110] |

Turmeric (Curcuma longa) | 70–75% shading of SPV | Crop production decreased by approximately 15% due to underneath cultivation. | 1120 kWh | Jatni campus, Odisha, India | 0.675 kWp | 742.92 USD | [89] |

Apple | 50–55% | Reductions in yield by 32% and 27% in 2019 and 2020. | NA | La Pugère, France | NA | NA | [10] |

Maize (Zea mays L.) | 29.5% and 13.4% for double-density and single-density, respectively. | NA | Po Valley, Northern Italy | NA | NA | [15] |

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**Figure 1.**Basic layout of a single solar PV shed on an agrivoltaic farm. Source: The result of the authors’ calculations using SOLIDWORKS

^{®}2022 and Table 1 (2024) data.

**Figure 2.**Solar PV sheds on an agrivoltaic apple farm. Source: The result of the authors’ calculations using SOLIDWORKS

^{®}2022 and Table 1 (2024) data.

**Figure 3.**Change in output means across range of input values, regarding NPV. Source: own calculation (2024).

**Figure 4.**Correlation coefficients (Spearman rank) of the influence of analyzed input and output data on the NPV. Source: own calculation (2024).

**Figure 5.**Regression coefficients of the influence of analyzed input and output data on the NPV. Source: own calculation (2024).

**Figure 6.**The summary of the Monte Carlo simulations regarding the value of NPV. Source: own calculation (2024).

**Figure 7.**Change in outputs means across a range of input values, regarding the unit cost of apple production. Source: own calculation (2024).

**Figure 8.**Correlation coefficients (Spearman rank) of the effects of the analyzed input and output data on the unit cost of apple production. Source: own calculation (2024).

**Figure 9.**Regression coefficients of the effects of the analyzed input and output data on the unit cost of apple production. Source: own calculation (2024).

**Figure 10.**The summary of the Monte Carlo simulations regarding the value of unit cost. Source: own calculation (2024).

**Figure 11.**Mean change in outputs across a range of input values regarding unit cost (EUR/Nm

^{3}). Source: own calculations (2024).

**Figure 12.**Correlation coefficients (Spearman rank) of the effects of analyzed input and output data on the unit cost. Source: own calculations (2024).

**Figure 13.**Regression coefficients of the effects of analyzed input and output data on the unit cost. Source: own calculations (2023).

**Figure 14.**The summary of the Monte Carlo simulations regarding the value of unit cost. Source: own calculations (2023).

APV Shed | Basic APV Shed | ||
---|---|---|---|

Area (ha) | 42 * | Basic APV Shed (kWp) | 251.12 * |

Length (m) | 1.64 * | Basic APV Shed (width) | 26 ^{1} |

Width (m) | 1 ^{1} | Basic APV Shed (length) | 106 ^{1} |

Weight (kg) of PV | 18.2 ^{1} | Basic APV Shed (Area ha) | 0.2756 * |

Area of each panel (m^{2}) | 1.64 * | Total N° APV Shed | 239 * |

Design of apple tree | kWp/ha | 911.16 * | |

Height of structures (m) | 5 ^{1} | PV capacity installed in each of the seven 6 ha plots (kWp/plots) | 5467 * |

AVS width over the apple row (m) | 1.7 ^{1} | Number of PV models | 866 * |

Space within rows (m) | 1 ^{1} | The overall module surface area (m^{2}) | 866 * |

Row to row distance (m) | 3.6 ^{1} | GCR | 31% * |

Available space for PV system within rows | 2.6 * | kW plant capacity | 5467 * |

Indicators | Value |
---|---|

Investment cost (CAPEX) (EUR) | 23,386,364 |

FIT price (EUR/kWh) | 0.087 |

Sunshine hours per year | 2000 |

Efficiency compared to the previous year (%) | 99 |

Annual maintenance and repair (EUR/kW/year) | 2.1 |

Annual insurance and video surveillance (EUR/kW/year) | 1.8 |

Annual internet fee (EUR/kW/year) | 0.8 |

Indicators | Value |
---|---|

Investment cost (CAPEX) (EUR/ha) | 41,885 |

Cash flow in the 2nd year (revenue − cost) (EUR/ha) | −1047 |

Cash flow in the 3rd year (revenue − cost) (EUR/ha) | −262 |

Average yield in the mature state (from the 4th year) (t/ha/year) | 57.5 |

Ratio of apple for consumption purposes (%) | 90 |

Yield (class I and II) (t/ha/year) | 51.75 |

Yield (industrial, juice apple) (t/ha/year) | 5.75 |

Price (class I and II) (EUR/t) | 288 |

Price (industrial, juice apple) (EUR/t) | 105 |

Subsidy (Single area payment scheme) (EUR/ha/year) | 183 |

Direct production cost (EUR/ha/year) | 11,518 |

OPEX (direct production cost without depreciation) (EUR/ha/year) | 7853 |

Indicators | Intervals Used in the Simulation | A Short Explanation of the Value | Type of Distribution |
---|---|---|---|

Investment analysis | |||

Net investment cost of PV system (million EUR) | 14–23.4 | Subsidized (40%): EUR 14 million (by the current Hungarian tender), non-subsidized: EUR 23.4 million. | Discrete uniform distribution |

FIT prices (EUR/kWh) | 0–0.08–0.087–0.16 | Electricity prices may drop to zero or negative due to factors like surplus renewable energy, changing demand or supply conditions, grid constraints, and government policies. Large-scale storage of electricity cannot be economically stored. | Discrete uniform distribution |

Sunshine hours per year | 1700–2000–2300 | Hungarian geographical conditions [67,68] | Triangle distribution |

Discount rate (%) | 0–6.8–8 | Hungary’s discount rate range (0% to 6.8% to 8%) is influenced by the current 6.8% yield on 20-year government debt. The upper limit of 8% is considered as a ceiling. | Triangle distribution |

Inflation rate (%) | 3–4–6 | Current core inflation in Hungary (6%) is expected to decrease significantly to 4% in the short term and around 3% in the long term [69]. | Triangle distribution |

Yield of apple in mature state (t/ha) | 45–57.5–65 | sourced from expert opinions and references [40,44] | Truncated normal distribution |

Ratio of apple for consumption purposes (%) | 80–90–95 | sourced from expert opinions and references [40,44] | Truncated normal distribution |

Financial Planning: Expenditures and Revenues (Unit of Measurement: Thousand EUR): | Investment Year | Operational Years | ||||||
---|---|---|---|---|---|---|---|---|

2023 | 2024 | 2026 | 2038 | 2039 | 2046 | 2053 | ||

1. Capital Expenditure (CAPEX) for PV System | 23,386 | |||||||

2. CAPEX for Apple Plantation | 1759 | |||||||

3. CAPEX after 15 Years for New Apple Plantation | - | 3168 | ||||||

4. Annual Operating Expenses (OPEX) | ||||||||

Operation and Maintenance costs | PV System | - | 180 | 198 | 354 | 375 | 567 | 889 |

Apple | - | 44 | 357 | 571 | 0 | 782 | 845 | |

Total Annual OPEX | - | 224 | 555 | 925 | 375 | 1348 | 1735 | |

5. Annual Revenues | ||||||||

Outputs and revenues | PV Energy Generated | - | 6592 | 6988 | 9917 | 10,211 | 12,524 | 15,361 |

Apple | - | 0 | 713 | 1490 | 0 | 2374 | 2668 | |

Total Annual Revenues | - | 6592 | 7701 | 11,407 | 10,211 | 14,898 | 18,029 | |

Corporate Tax | 492 | 498 | 729 | 519 | 925 | 1146 | ||

Annual CF (after taxpaying) | −25,146 | 5876 | 5935 | 8263 | 6149 | 10,526 | 12,481 | |

(Cumulative) Discount Factors (DF) | 1.068 | 1.219 | 2.694 | 2.878 | 4.570 | 7.258 | ||

Discounted Cash Flow (DCF) | −25,146 | 5500 | 4868 | 3067 | 2136 | 2,243 | 1720 | |

Cumulative Discounted Cash Flow (CDCF) | −25,146 | −19,645 | −9455 | 36,464 | 38,601 | 56,648 | 70,157 |

Output Variables | Mean | Variance | Standard Deviation | Coefficient of Variation (%) |
---|---|---|---|---|

NPV | 102.6 (Million EUR) | 11,114,267,648.2 | 105.4 (Million EUR) | 102.77 |

UCa2027 | 170.58 (EUR/t) | 49,927.84 | 223.45 (EUR/t) | 130.99 |

UCe2025 | 0.0128 (EUR/kWh) | 0.0003 | 0.0161 (EUR/kWh) | 126.08 |

Years | 2024 | 2026 | 2038 | 2039 | 2040 | 2053 |
---|---|---|---|---|---|---|

Total production cost (EUR) | 1,120,666 | 1,452,053 | 1,822,206 | 1,271,534 | 1,375,870 | 2,631,363 |

Share of electricity in revenues | 1.00 | 0.91 | 0.87 | 1.00 | 1.00 | 0.85 |

Share of apples in revenues | 0.00 | 0.09 | 0.13 | 0.00 | 0.00 | 0.15 |

Production cost of electricity (EUR) | 1,120,666 | 1,317,682 | 1,584,243 | 1,271,534 | 1,375,870 | 2,242,002 |

Production cost of apples (EUR) | 0 | 134,371 | 237,962 | 0 | 0 | 389,361 |

Unit cost of electricity (EUR/kWh) | 0.015 | 0.018 | 0.024 | 0.020 | 0.021 | 0.040 |

Unit cost of apples (EUR/t) | 0 | 56 | 99 | 0 | 0 | 161 |

Unit cost of electricity in PV (without apples, EUR/kWh) | 0.013 | 0.013 | 0.017 | 0.018 | 0.018 | 0.029 |

Unit cost of apples (without PV, EUR/t) | 0 | 196 | 285 | 0 | 0 | 399 |

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## Share and Cite

**MDPI and ACS Style**

Chalgynbayeva, A.; Balogh, P.; Szőllősi, L.; Gabnai, Z.; Apáti, F.; Sipos, M.; Bai, A.
The Economic Potential of Agrivoltaic Systems in Apple Cultivation—A Hungarian Case Study. *Sustainability* **2024**, *16*, 2325.
https://doi.org/10.3390/su16062325

**AMA Style**

Chalgynbayeva A, Balogh P, Szőllősi L, Gabnai Z, Apáti F, Sipos M, Bai A.
The Economic Potential of Agrivoltaic Systems in Apple Cultivation—A Hungarian Case Study. *Sustainability*. 2024; 16(6):2325.
https://doi.org/10.3390/su16062325

**Chicago/Turabian Style**

Chalgynbayeva, Aidana, Péter Balogh, László Szőllősi, Zoltán Gabnai, Ferenc Apáti, Marianna Sipos, and Attila Bai.
2024. "The Economic Potential of Agrivoltaic Systems in Apple Cultivation—A Hungarian Case Study" *Sustainability* 16, no. 6: 2325.
https://doi.org/10.3390/su16062325