1. Introduction
Renewable energy generation has gained importance all over the world with the increase in energy use and consumption internationally and the growing concern about climate change [
1,
2,
3,
4]. The decrease in the planet’s fossil fuel resources [
5] and accelerating climate change [
6] in the wake of their consumption has increased the demand for clean, renewable energy sources such as solar energy. The development of renewable energy resources has gradually become the axis of energy policy in a significant number of countries. Since the 1970s, due to the energy crisis of the period, solar energy has become a secondary source of electric power in those countries, with photovoltaic panels (PVPs) as the new power stations. To satisfy the huge energy demands involved, however, large areas of land are required to set up photovoltaic panels due to their low electrical energy output. Ways to overcome this include using the rooftops of houses and agricultural fields [
7,
8]. At the same time, using large areas of land for solar farms has an impact on agriculture and the land resources needed for food production. With food demand and energy demand both growing, agriculture and power generation are competing for limited land resources [
9,
10,
11]. This land challenge becomes particularly intense in densely populated regions, mountainous areas, and small inhabited islands [
12]. However, this does not have to be an ‘either-or’ competition for land, where the winner takes all, as the concept of agrivoltaics, or the joint development of the same area of land for both solar PV power stations as well as for conventional agriculture, ref. [
13] shows. These and other concepts and techniques with which solar and bioenergy resource and land suitability assessments are conducted have seen significant improvements in recent years [
11], suggesting agricultural and electricity generation uses are not incompatible goals in land development.
Tiwari et al. [
14] presented a review paper of various types of PV modules based on their applications and generation of solar cell. This review covers detailed information and a thermal model of PV and hybrid photovoltaic thermal (HPVT) systems where water and air are used as a working fluid. Throughout this study, it is found that PVT modules are very promising devices and there is a lot of scope in near further to improve their performances. Almodfer et al. [
15] performed analysis on a solar thermoelectric air-conditioning system in order to reduce the cost by analyzing the effects of environment and other parameters. El-Hadary et al. [
16] investigated tri-production of heat, power, and hydrogen generation by using a photovoltaic thermal collector. Bae et al. [
17] studied the feasibility analysis of a combined photovoltaic-thermal and ground source heat pump, which showed the valuable and significant application of this combined system. Zayed et al. [
18] demonstrated the dish/Stirling concentrated solar power system in which the assessments were done on the basis of design, optical and geometrical analyses, thermal performance assessment, and applications. Agostini et al. [
19] gave a good description of modeling the environmental and economic performances of an innovative agrivoltaic system which is built on a tensile structure in the Po Valley. It concluded the economic and environmental costs of agrovoltaic systems to reduce the impact on land occupation and the stabilization of crop production. Valle et al. [
20] performed an analysis by comparing fixed and dynamic agrivoltaic systems for two varieties of lettuce over three different seasons. Since regular tracking electricity production is large as compared to stationary PVPs, as a result, a high productivity per land area unit is achieved due to the use of trackers instead of stationary PV panels. Qoaider et al. [
21] studied the technical design and life cycle costs of a PV system. This PV generator can pump daily 111,000 m
3 of lake water to irrigate 1260 ha acreage plots and can produce electricity to give the adjacent village’s households. The electricity generation costs of a designed PV and diesel generator are compared to prove its competitiveness, with results showing that the genset electricity unit costs 39 c€ kW h
−1 while a unit of PV electricity costs only 13 c€ kW h
−1. Cossu et al. [
22] studied the climate conditions inside a greenhouse where 50% of the roof area is replaced by photovoltaic (PV) panels. They described the solar radiation distribution, change in temperature, and humidity. It was found that the availability of solar radiation inside the greenhouse is reduced by 64% while using the PV system, whereas the distribution of solar radiation is useful for crop and energy production. Gao et al. [
23] built computer simulation models of typical greenhouses with high-density and low-density PV layouts, where the high-density PVs under no-shading sun tracking generate 6.91% more energy than under conventional sun tracking. Li et al. [
24] addressed the economic and social performance of photovoltaic and agricultural greenhouses (PVGs) based on a case study, where PVGs showed could good economic performance, which implies PV agricultural companies should pay more attention to PV power generation for agricultural crop production.
Marrou et al. [
25] explained the requirement for both energy and food, but argued that PVPs and crops can share the same plots of land to reduce the competition for land by food and energy production. In such agrivoltaic systems, an upper layer of PVPs is used, which partially shades crop at ground level. Valle et al. [
20] proved the efficiency of such systems using stationary PVPs at half of their usual density. This offers the possibility to intercept the variable part of solar radiation as well as new ways to increase land productivity. Dinesh et al. [
12] established connections between the theoretical and experimental work on agrivoltaics and analyzed the potential crop yields and solar power output as a function of the incoming solar radiation. For fixed tilt agrivoltaic farms, the pitch is determined by the spacing requirements of a given crop harvesting method and the optimal tilt angle of the PV is determined with the objective of maximizing solar power output. Santra et al. [
26] designed and developed a PV-module which can generate electricity, where crops can be cultivated in the interspace area. Rainwater can be harvested from the top surface of the PV-module.
About 49% of the area of land can be used to cultivate crops when installing a solar PV system. Some selected crops suitable for agrivoltaics are mungbean, mothbean, clusterbean, isabgol, cumin, chickpea, aloe vera, sonamukhi, and sankhpuspi. Generally, all these crops are low height crops and they require less water, which makes them suitable for agrivoltaic systems (AVS). Weselek et al. [
27] discussed the microclimatic alterations and resulting impact of agri-photovoltaics (APV) on crop production. Their main findings were: (a) Crop cultivation underneath APV systems can lead to declining crop yields; (b) Combining energy and crop production, agrophotovoltaics can increase land productivity by up to 70%; (c) The impact on climate change must be considered; (d) The enhancement of APV in rural areas comes from its contributing to decentralized, off-grid electrification, the economic value of farming, and the improvement of agricultural productivity. Van Campen et al. [
28] described the developmental direction of the rural power co-construction shared operation system that agrivoltaics represents and the innovative industrial model that it introduces. They seek to develop a better understanding of the potential impact, and the limitations, of PVPs on sustainable agriculture and rural development (SARD), especially in terms of income-generating activities. One of their main findings was that it can provide a “package” of energy services to remote and rural areas, including in education, health care, agriculture, lighting, communication, and water supplies. Artru et al. [
29] observed the impact on sugar beet of the shade environment in terms of productivity, morphology, growth dynamic, and quality. They found that the shade treatment reduces the final root dry matter and sugar yield. In addition, it affects the sugar beet quality and sugar extractability, but to a lesser extent. Castellano et al. [
30], however, observed in their research with some broad-adaptive low-light-tolerant crops under partial shading conditions, that with improved cultivation measures, they can develop their full growth potential or even achieve more vigorous growth.
On the basis of these research results, it is the intention in this paper to determine and sort the impact of photovoltaic systems in terms of which factor has the greatest impact on the system, and what is the best combination of design parameters. The results of the analysis then are used as a basis for the design of a rural power symbiosis solar photovoltaic system. In addition to maximizing solar power generation, a symbiotic agricultural-electricity solar photovoltaic system should also seek to maximize the amount of crop insolation, achieving double benefits at one fell swoop and creating a win-win result for the combination of agriculture with green energy.
Since Taiwan’s leafy vegetables, heading vegetables, fruit trees, special crops, and other agricultural products all have low-light-tolerant high-quality varieties and all benefit from excellent cultivation techniques, they are suitable crops for farming-type agricultural-electricity co-construction and shared facility cultivation. In addition, the photovoltaic and solar energy industries are among Taiwan’s top industrial sectors, with high-tech and high-value output characteristics. If they can be combined for the compatible development of agriculture, and power generation, they could create an innovative new industrial model of co-construction and sharing of agricultural power, while they increase the ambition of farmers and the total value of agricultural output. At same time, from the point of view of agricultural benefits, PV green energy facilities with low shading ratios can reduce the direct irradiation of strong light on crops that cause high temperature injury, sunburn, plant canopy overheating, and moisture evapotranspiration. Crops harmed by extreme direct solar radiation can obtain a better growing environment, which should favor their production. Therefore, electricity generation and agriculture can both expect to profit from an appropriate compatible system that pays attention to the needs of both green energy and crop production. As the use of agricultural land with low productivity margins increases and the multiple utilization of farm land is promoted, agriculture can be expected to become more high-tech and have greater output value, which should benefit farming households. One point to consider in the systematic analysis of electrofarming symbiosis PV systems, are the many parameters that have an influence on PV power generation and sunhours on farm land. While the parameters influence each other, there is no reference determining which parameters have greater influence on the results. Therefore, it is necessary to research the coexistence optimization of solar energy conversion and plant cultivation when developing an electrofarming symbiosis PV system.
A traditional experimental planning method is to do one-factor-at-a-time test runs. That is, the experimenter changes only one variable at a time (the control variable), and fixes the other variables, and the obtained data are used to do regression analysis to get the best value of this manipulated variable [
31]. Then, the experimenter starts all over again to test the other variables of interest. The Taguchi method, with its orthogonal table design and variance analysis methods, can greatly reduce the number of experiments required, compared with traditional experimental planning methods, without compromising reliability, thereby saving costs, shortening the experimental period, and improving design flexibility [
32,
33]. However, previous research or practical applications of the Taguchi method have mostly focused on the optimization of the effect of a number of parameters on a single quality characteristic [
34]. The problem is the actual product quality characteristics are often not unique. If only a single quality characteristic is studied, the optimization of the manufacturing process or system parameters may be ignoring other important characteristics of the product, and therefore losing many opportunities to improve costs and overall quality characteristics [
35,
36]. Lin et al. [
37] explained that a disadvantage of the traditional method compared to the Taguchi method is that the research factors are not completely combined, so the experimental results are lacking.
To get around this problem, another commonly used experiment design is the full-factor experiment planning method. Although the full-factor experiment plan includes all possible test conditions, this can require a prohibitive number of experiments. When the number of factors and levels is quite large, only a slight increase in the number of factors or levels can cause the number of experiments to increase dramatically. Zhang et al. [
38] demonstrated the benefits of the Taguchi method in a soil erosion study, comparing the results from the full-factorial design method with the Taguchi orthogonal design method. The statistical results from the soil erosion experiments for all dependent variables under the different conditions as obtained by the Taguchi orthogonal design method were very close to those from the full-factorial design.
The Taguchi method is an experimental design commonly applied to evaluate the effect of parameters in order to optimize a single response. Although the Taguchi method is a powerful optimization tool, it is not suitable for the case of the simultaneous optimization of a number of different responses [
39]. In the multi-response optimization case, Grey Relational Analysis (GRA), coupled with the Taguchi method, can be used [
9]. GRA is a discrete sequence data analysis method that determines the relationship of one set of data to other sets. Hsu et al. [
9] explained the uses of Grey Correlation Analysis combined with the Taguchi experimental design method. First, the orthogonal table in the Taguchi experimental design method is used to screen the experimental factors to narrow their scope, and then the grey correlation theory is used to analyze the parameters of the rural electricity symbiosis solar system, in this case. Kazemian et al. [
40] performed a parametric analysis on the power outputs of a compound solar power module to examine the impact of operating conditions in different situations. Their study, based on a Taguchi–Grey relational investigation, indicated the optimum values for inputs like mass flow rate, solar radiation, coolant inlet temperature, ambient temperature, and wind speed and the corresponding output of these operating conditions. Senthilkumar et al. [
41] worked to improve the performance as an insulator of mineral oil blended with natural ester oils like sunflower oil, olive oil, palm oil, and rapeseed oil. The performance of the blends increased with the increase in mixing ratio up to a point, but after that point, it started decreasing. Here, the GRA method was used to select the optimal mixed liquid insulation point, showing it is a way to determine the optimal concentration of a mixed liquid insulator. Suji et al. [
42] presented the results of their multi-performance optimization of self-compacting concrete (SCC) mixtures containing fly ash and manufactured sand using Taguchi–Grey relational analysis. A Taguchi orthogonal array provides the basis for the design of the study and the results are analyzed using GRA. Additionally, Kuo et al. [
43] reported a processing parameter optimization of a flat-plate collector based on orthogonal arrays. Here, the data are the values for each quality characteristic that are produced by control factors in orthogonal arrays, and main effect analysis and analysis of variance (ANOVA) are conducted to determine the parameters which have significant effects on coefficient efficiency and heat dissipation. After that, the data are preprocessed by means of Grey relational generation and then through GRA and entropy measurement, the combination of best processing parameter levels is determined.
The limitation of the aforementioned studies [
12,
25,
26,
27,
28,
29,
30] related to agrophotovoltaic system is that the traditional method lacks research factors that are not completely combined, so the experimental results are lacking [
37]. From the above studies [
13,
39,
40,
41,
42,
43], it can be seen, in general, the GRA model deals with optimizing a multi-objective function, which is represented by a single relational grade. This allows the simultaneous evaluation of effects on different objectives and enables the determination of the optimal parameters for the all-purpose function. Each characteristic of the response is individually determined by the Taguchi method, and then all can be optimized together on the basis of the primacy of the multi-objective function by the Taguchi method and GRA [
44,
45]. For all qualities, the most effective respective parameters are obtained.
Agrivoltaic systems are a strategic and innovative approach to combining solar photovoltaic (PV)-based renewable energy generation with agricultural production [
46]. Therefore, in this study, the novelty is that we have proposed a configuration of a PV system combined with agricultural land to grow vegetables underneath the PV system. This study uses a Taguchi orthogonal array to design a set of experiments, which will be combined with GRA to achieve optimized PV power generation and sunhours on farm land to support the coexistence of solar-energy conversion and plant cultivation with high internal rate of return (IRR). This method can provide best optimal parameters based on location, upright column height, module tilt angle, and PV panel width in respective locations.