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Renewable Energy 156 (2020) 1e13

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Renewable Energy

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Thermo-environomic assessment of an integrated greenhouse with anadjustable solar photovoltaic blind system

T. Alinejad a, M. Yaghoubi a, A. Vadiee b, *

a School of Mechanical Engineering, Shiraz University, Shiraz, Iranb School of Business, Society and Engineering, Department of Civil Engineering and Energy System, M€alardalen University, 721 23, V€asterås, Sweden

a r t i c l e i n f o

Article history:Received 30 January 2020Received in revised form8 April 2020Accepted 12 April 2020Available online 16 April 2020

Keywords:EnergyPhotovoltaicSolar blind systemSolar greenhouseThermo-environomic

* Corresponding author.E-mail addresses: taher.alinejad@shirazu.ac.ir (T.

ac.ir (M. Yaghoubi), amir.vadiee@mdh.se (A. Vadiee).

https://doi.org/10.1016/j.renene.2020.04.0700960-1481/© 2020 The Authors. Published by Elsevie

a b s t r a c t

Optimum energy consumption and renewable energy utilization reduce environmental impacts and arecost-effective. They are the key aspects of achieving sustainable energy management, such as in theagricultural industry. The contribution of the horticultural section in the global energy demand isapproximately 2%, and among its various sections, greenhouses are one of the main systems in modernagriculture that have a great share on energy consumption. In this study, a rose greenhouse is examinedand modeled in EnergyPlus as a greenhouse reference (GR). Validation of the developed greenhousemodel is carried out with a site experimental measurement. Using the GR as the basic model, 14 variousconfigurations of greenhouses have been assessed by considering a solar photovoltaic blind system(SPBS) in checkerboard arrays 1 m above the greenhouse roof. These modified greenhouses called solar-blind greenhouses (SBGs) have different shading rates and SPBS sizes. To perform a Thermo-environomicassessment, the effects of various parameters, including temperature, relative humidity, natural gasconsumption, electricity consumption, and carbon dioxide (CO2) emission reduction, are studied. Resultsindicate that covering 19.2% of the roof, with no significant change in the illumination level on the plantcanopy, will annually reduce natural gas consumption, electricity demand, and CO2 emission by 3.57%,45.5%, and 30.56 kg/m2, respectively. Moreover, with the SPBS, the annual electricity production isapproximated at 42.7 kWh/m2.© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license

(http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Energy is undoubtedly one of the main drivers of economicgrowth, and its sufficient supply is the key factor in the develop-ment of a country. In recent years, energy demand, particularlyelectricity, has been increased due to industrialization, moderni-zation, population, and welfare growth [1]. In the past decades,electric power was mainly supplied by fossil fuels, such as oil,natural gas, and coal. Likewise, in the twenty-first century, elec-tricity generation is still heavily dependent on fossil fuels. Thisposes environmental hazards, including global warming, green-house gas emission, and climate change. Sustainable energy man-agement is one of the newest research topics and has the utmostimportance for all sectors, such as in horticulture. The share of thehorticultural section in the global energy demand is 2% [2]. Hence,

Alinejad), yaghoubi@shirazu.

r Ltd. This is an open access article

sustainable horticulture is one of the current challenging issues inthe fields of industrial agriculture and energy consumption [3].

Among renewable energy sources, solar energy is one of themost promising energy sources owing to its high potential andavailability [1]. Among many solar energy systems, photovoltaic(PV) technology is increasingly competitive and it plays a crucialrole in producing clean and stable electrical energy [4]. Which canbe used for various applications, such as for greenhouses. As one ofthe crucial agricultural sectors with the highest amount of energyconsumption, greenhouses have been used for centuries to increaseproduction and control climate conditions [5,6].

Currently, electricity and fossil fuel (e.g., natural gas, fuel oil) arethe main energy sources used to control the indoor climate con-dition in commercial greenhouses and, as a result, contribute to theincrease in the final product cost due to continuous electricity andfossil fuel price increment [7e10]. Trypanagnostopoulos [11]declared that every greenhouse requires an annual energy of100e300 kWh/m2 for cooling, heating, lighting, and ventilation.Recently, a number of researchers have investigated the feasibilityof using PV and shading devices to supply or reduce greenhouse

under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

T. Alinejad et al. / Renewable Energy 156 (2020) 1e132

electricity consumption [4].There are plenty of shading methods, such as whitewash, plastic

nets, and thermal screens, utilized in the greenhouse industry.Moreover, using PV panels can adjust the illumination of the plantsand greenhouse climate conditions. Illumination level adjustmentin the greenhouse, which can be controlled by different shadingmethods, has a direct impact on the indoor temperature, relativehumidity (RH), illumination on the floor, plant growth, product’squality and quantity, water consumption, and evapotranspiration.For this purpose, numerous studies have been conducted. Oneprevious study showed that with the increase in the shading rate,greenhouse temperature decreases and RH increases [12e14].Other research presents that the increase in the shading rate causesa reduction in the illumination on the plant canopy (IPC) [14e22]and subsequently decreases electricity [12,16,19,22e26] and water[12,14,18,27] consumption. The effect of shading on plant growthand product quality is the determinant factor in choosing the typeof shading devices. Numerous studies indicated that the amount ofshading might increase or at least impact the product and plantgrowth depending on the types of plants; however, if the shadingrate exceeds the optimum value, the product and plant growth willdecrease [11,14,16,18,20,21,28].

PV utilization for shading is a promising method assessedwidely in recent years, which is not only used to control the indoortemperature, RH, and other aforementioned parameters but alsosupplies the greenhouse electricity demand. Yano et al. [25] studiedthe energy performance of a greenhouse that is supplied by fixedPV modules located under and on the greenhouse roof. The resultsshowed that PVs on the roof of the greenhouse also produce moreelectricity than PVs under the roof; however, those on the roof aremore vulnerable to harsh weather conditions. Kadowaki et al. [19]mounted fixed PV panels in two checkerboard and straight-linearray formations under a greenhouse ceiling to prevent PVs frombeing exposed to harshweather conditions. In both cases, PV panelscovered 12.9% of the roof area of the greenhouse. The resultsshowed that PVs in the straight-line arrays cast continuousshadows on some special parts of Welsh onions. Therefore, thesePVs lead to a reduction in the weights of Welsh onion fresh and drymatters. Nevertheless, in the checkerboard array panels, theyobserved no significant difference between the fresh weight anddry matter weight of the Welsh onions with and without castingshadows because the checkerboard array cast shadows intermit-tently in the growth period. Similarly, Yano et al. [23] demonstratedthat the arrangement of PVs mounted under the roof of a green-house has a significant effect on the spatial distribution of solarradiation in the greenhouse. For this reason, straight-line and fixedcheckerboard array panels were examined, and the results showthat the checkerboard arrangements improved the spatial distri-bution of sunlight. In both cases, PV systems produced a consid-erable amount of electricity. Ure~na-S�anchez et al. [24] and Ezzaeriet al. [29] assessed the effect of shadows on the plant growth.Covering 9.8% of a greenhouse roof by the checkerboard PV arraycaused no change in the plant growth. Furthermore, PVs supplied apart of the electricity demand required for the greenhouse. Cossuet al. [26] covered 50% of a greenhouse roof in the straight-line PVarray. The results demonstrated that the PVs caused a 64%e82%reduction in the radiation on the area located under PVs andreduced the amount of tomato production, yet a good revenue wasachieved from the electricity generation. Tani et al. [17] surveyedlettuce growth under roof-mounted PVs. Their research representsthat the use of light diffuser films under PVs can improve lettucegrowth.

There are many types of semi-transparent PVs, such as thin-film

PVs, Si cells with covering glass panes, and organic PVs. Hassanienet al. [15] reported that using semi-transparent PVs on the roof willnot affect plant growth. In a similar vein, Trypanagnostopouloset al. [11] declared that using semi-transparent PVs is not cost-effective and instead opaque PV panels can be applied. Moreover,20% of greenhouse roof coverage with opaque PV panels can supplya significant part of the greenhouse energy demand or even thetotal energy demand for a low-energy greenhouse. PV trackingsystems also produced more electricity than fixed PVs. Marucci andCappuccini [30] studied roof-mounted dynamic PVs by shading0%e78% of the inside of a greenhouse and reported20e102 W m�2 PV power, respectively.

In conclusion, utilizing PV panels in a checkerboard array pro-vides intermittent shadows on plants but has no effect on the plantgrowth, and using PV trackers above the greenhouse generatesmore electricity. Furthermore, cooling the greenhouse by PVshading panels can reduce both the greenhouse temperature andenergy demand. The above review shows that opaque checker-board array PVs mounted above a greenhouse and appropriateshading are reasonable for greenhouse development.

The main aim of the present study is to illustrate that availablegreenhouses can be optimized to achieve a sustainable productionin terms of cost, quality of product, and environmental impact. Toaccomplish this aim, a Thermo-environomic assessment for acommercial greenhouse integrated with a solar photovoltaic blindsystem (SPBS) was performed. Based on the review no similarstudies reported to investigate and compare a commercial rosegreenhouse shaded by thermal screen with a rose greenhouseshaded by dynamic PVs in a checkerboard array thermo-environomic assessment. As well as, in this research PVs arecontrolled by special algorithm which hasn’t been observed inprevious studies. The new algorithm is described in the section 2.2.

In the next sections, a commercial rose greenhouse is modeledas the greenhouse reference (GR). Then, the SPBS is explained andadded to the model. Moreover, some parameters, such as micro-climate, fuel consumption, electricity demand, and environmentalissues, are discussed and presented. Finally, some cases are selectedas the most suitable integrated greenhouse with the SPBS.

2. Materials and methods

In this study, a commercial rose greenhouse in Shiraz, Iran, wasmodeled using the dynamic energy modeling tool EnergyPlus, withheating, cooling, and humidifier equipment and validated experi-mental data. Moreover, the SPBS, which is mounted above thegreenhouse and rotated on its longitudinal axis, was simulated andapplied to the modeled greenhouse. Thereafter, the energy balanceand parameters involved in the greenhouse energy interchangewere calculated. The monthly electricity production, reduction ofelectricity, natural gas demand, and CO2 emission were alsoassessed.

2.1. Greenhouse

The commercial rose greenhouse is located in Shiraz (Farsprovince, Iran, 29�31056.5"N, 52�31041.4"E). The angle between thetransverse wall and the north axis is 66� (Fig. 1a). Notably, theazimuth-angle variations have no considerable impact on thephotovoltaic annual energy production [31]. The annual averagetemperature, pressure, RH, and wind speed in Shiraz are 19.1 �C,849 mBar, 40.9%, and 1.9 ms�1, respectively [32]. Moreover, theannual average global horizontal and diffuse incident irradiationare 226.1 and 151.1 Wh m�2. The daily temperature, RH, irradiance,

Fig. 1. (a) Top view of the greenhouse (provided by Google Maps), (b) perspective and dimensions of the rose greenhouse.

Fig. 2. Daily climate condition in Shiraz. (a) Global horizontal and diffuse irradiation, (b) mean air dry bulb temperature, (c) mean wind speed, (d) mean RH.

T. Alinejad et al. / Renewable Energy 156 (2020) 1e13 3

and wind speed in Shiraz are shown in Fig. 2.The greenhouse is a flat-arch type and has an area of 4081 m2

(77 m long, 53 m wide). It has seven spans with a gutter height of6 m, which is modeled in the OpenStudio SketchUp Plug-in, asshown in Figs. 1b and 3.

Due to the limitations of EnergyPlus, the surfaces are defined aspolygons. Therefore, as a good approximation, a four-sided roof wasutilized instead of the semilunar roof in EnergyPlus, as illustrated inFig. 3d. The greenhouse was clad with a 180 mm ultraviolet poly-ethylene film that transmits 87%e90% of solar radiation incident[33,34] and 63%e65% of normal-incident infrared radiation [34].

Rose flowers have been cultivated in 70% of the greenhouseunder study, and they required no supplementary lighting duringday and night. To grow flowers with best quality, the greenhouse

was kept in a special condition, as shown in Table 1. To measureillumination on the plants, five TES-1339 digital lux light meterswere mounted 2 m above the ground (Measuring Levels Ranging0.01 Lux to 999900 Luxwith the accuracy of ±3%). Temperature andrelative humidity were monitored at a height of 2m above theground level by utilizing thermometer and hygrometer in fivedifferent location of greenhouse in order to reach most accurategreenhouse mean interior microclimatic data. Thermometer &hygrometer gauge is able to quickly report measurements every10 s and has a wide measurement range of �10 �Ce50 �C, with anuncertainty of ±4%. Humidity sensor measures humidity from 10%to 99% with an uncertainty range about ±6%. In this study, uncer-tainty analysis is calculated according to Eq. (1) which proposed byRef. [35,36].

Fig. 3. (a) Inside of the greenhouse, (b) outside of the greenhouse, (c) real greenhouse roof, (d) greenhouse roof modeling in EnergyPlus.

Table 1Greenhouse climate condition.

Subject Magnitude

Temperature setpoint at day (�C) 21e22Temperature setpoint at night (�C) 16e18RH setpoint at day and night (%) 55e65IPCa at noon (January) (103 Lux) 30e35IPC at noon (February) (103 Lux) 33e38IPC at noon (March) (103 Lux) 37e42IPC at noon (April) (103 Lux) 40e45IPC at noon (May) (103 Lux) 43e48IPC at noon (June) (103 Lux) 47e52IPC at noon (July) (103 Lux) 50e55IPC at noon (August) (103 Lux) 47e52IPC at noon (September) (103 Lux) 43e48IPC at noon (October) (103 Lux) 40e45IPC at noon (November) (103 Lux) 37e42IPC at noon (December) (103 Lux) 33e38

a Illumination on the plant canopy.

Fig. 4. Schematic of the central heating system.

T. Alinejad et al. / Renewable Energy 156 (2020) 1e134

uðxÞ¼±100*

0BBBBBBBB@

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPn

i¼1

�x�Pn

i¼1xn

n

�2

n�1

vuutPn

i¼1xn

n

1CCCCCCCCA

Eq. 1

where u(x) is the standard uncertainty in %, n is number of mea-surements, xn is the nth value obtained through the measurement.

The meteorological data in Shiraz were obtained hourly fromEnergyPlus official website [37] and then normalized by Elementsoftware. To normalize the data, the measured data in Shiraz

Meteorological Station were used, which were measured andaveraged hourly for 20 years (1998e2018).

The main contribution of the total energy demand in a com-mercial greenhouse during the cold period of the year is heating. Inthis case study, a central heating system connected to a hot waterpipe network heating distribution system, shown in Fig. 4, wasconsidered to supply the greenhouse heating demand based on theproposed setpoint temperatures. Three boilers with natural gasdirect combustion and two pumps were used to provide theheating demand in the greenhouse, as presented in Table 2.

Table 2Heating system specification.

Facility Subject Magnitude Unit

Boilers 1 and 2 Model GND 304 e

Electric power 1100 WHeating capacity 212e600 MCal/hFuel type Natural Gas e

Efficiency 80 %

Boiler 3 Model PGN1B e

Electric power 450 WHeating capacity 200e415 MCal/hFuel type Natural Gas e

Efficiency 80 %

Pumps 1 and 2 Model of pumps Pemax CST550/4 e

Efficiency 77 %Flow 60 m3/hHead 15.7 mPower 4 kW

Table 3Fanepad system specifications.

Facility Subject Magnitude Unit

Fan Fan flow rate 12.2 m3/sFan motor speed 1400 RPMFan motor efficiency 0.9 e

Fan power 1119 WFan speed 437 RPMFan total pressure 57 PaFan voltage 380 VFan Size 140 x 140 cm � cm

Pad Area 154 m2

Depth 0.6 m

Pumps 1 and 2 Head 15 mPower 11 kW

Table 4Fog system specification.

Facility Subject Magnitude Unit

Pump Motor power 11 kWMotor pressure 150 barWater use 30 L/min

FogeMist Fog particle diameter 40e50 mm

T. Alinejad et al. / Renewable Energy 156 (2020) 1e13 5

Utilizing the cooling system to prevent the greenhouse tem-perature from exceeding the specified setpoint is inevitable. Byusing an adiabatic saturation process, the cooling pad lowers thedry bulb temperature close to the wet-bulb temperature [38]. Here,a fanepad system, which contains 18 fans, two pumps, and 154 m2

cellulose pad (Figs. 3 and 5, and Table 3), was used to provide thetemperature conditions mentioned in Table 1.

For the greenhouse under study, two similar humidifier systemsare responsible for adjusting the humidity and consequentlydecreasing the indoor temperature by evaporating the generatedfog with the specifications described in Table 4 and operatingconditions in Table 5. Because there is no predefined component inEnergyPlus to model the fog system, it is coded and developed bythe Energy Management System (EMS) module in EnergyPlus, asexplained in Section 2.3.

Thermal screens, as the conventional shading method in com-mercial greenhouses, are made from polyethylene, polyester, oraluminized polyester. In this study, the greenhouse was equippedwith an aluminized thermal screen to avoid heat loss during winternights and to adjust illumination on the plant canopy (IPC). Thecommercial rose greenhouse mentioned was modeled and used asthe GR.

2.2. SPBS

SPBSs are PV panels that are mounted above a greenhouse androtated on their longitudinal axis, as shown in Fig. 6. PV panels(SPBS) are in the shademode if the IPC exceeds themaximumvalue(MaxI), and they switch to the unshade mode if the IPC is less than

Fig. 5. Cellulose pad of the fanepad system.

the minimumvalue (MinI), as shown in Table 6. In the table, a is theangle between the horizon and the plate, which has a 24� azimuthangle and receives maximum radiation on average per month, andb is the perpendicular plate angle to a by the same azimuth angle(b ¼ aþ90).

By using SPBSs in the checkerboard arrays, the average IPC is notless than MinI and the shading surely has no adverse effect on therose plant growth. There are four PV rows on each span, and eachPV row comprises 20 PVs, covering 0.8% of the greenhouse roof. Inthis study, as summarized in Table 7, 14 various SPBS sizing con-figurations were studied and compared with the GR system, whichis called solar-blind greenhouses (SBGs). Each SBG is a GR withthermal screens eliminated and SPBSs replaced. That is, the illu-mination level in the SBGs was adjusted by the SPBSs, whereas inGR, thermal screens implemented a shading adjustment byconsidering the SPBS. To have homogenous shadows on the plantsand to simplify the control system, all PV panels were assumed torotate simultaneously. For instance, if the illumination on the plantsin SBG20 exceeds 45000 Lux in October, then all 400 PVs willswitch to the shademode. Fig. 7 shows SBG28 and its SPBS arrays inthe shade (a) and unshade modes (b).

The dynamic power production by the SPBS was calculated bythe PVWatts module in EnergyPlus. The utilized PV and inverter arerepresented in Table 8 and Table 9, respectively.

2.3. Energy balance

Many models have been used to investigate diverse green-houses’ energy balance with different types of cultivated plants.Evapotranspiration is one of the most important factors in green-house energy balance, which leads to changes in the amount of airenthalpy. Considering the greenhouse as a selective control volume,the control surface consists of different components, such asgreenhouse cover, construction envelope, soil, ventilation system,and cooling and heating systems. The energy exchanged from thecontrol surface of a greenhouse shown in Fig. 8 includes both latentand sensible heat. According to the study presented by Sabeh [41],the greenhouse energy balance can be written as Eq. (2):

Table 5Facility operation algorithm.

RH [%] Day or Night TG [�C] Central Heating System Fan-Pad System Fog System

RH � 55 Day TG < 21 On Off OffTG > 22 Off On Off

Night TG < 16 On Off OffTG > 18 Off On Off

RH < 55 Day TG < 21 On Off OnTG > 22 Off On On

Night TG < 16 On Off OnTG > 18 Off On On

Fig. 6. SPBS operation modes.

Table 6Angles between the shading modes and horizontal surface for various months.

Month a (Deg) В (Deg) MaxI (103 Lux) MinI (103 Lux)

January 132 222 30 35February 143 233 33 38March 155 245 37 42April 167 257 40 45May 180 270 43 48June 180 270 47 52July 180 270 50 55August 171 261 47 52September 157 247 43 48October 145 235 40 45November 138 228 37 42December 128 218 33 38

Table 7SBG type specification.

GreenhouseTypes

Fraction of Roof Covered bySPBS

Number of PVPanel

Total PVPanelsArea (m2)

SBG2 1.6% 40 66SBG4 3.2% 80 132SBG6 4.8% 120 198SBG8 6.4% 160 264SBG10 8.0% 200 330SBG12 9.6% 240 396SBG14 11.2% 280 462SBG16 12.8% 320 528SBG18 14.4% 360 594SBG20 16.0% 400 660SBG22 17.6% 440 726SBG24 19.2% 480 792SBG26 20.8% 520 858SBG28 22.4% 560 924

Fig. 7. SBG28 in (a) shade mode and (b) unshade mode (October 18 at 13:30). (Forinterpretation of the references to colour in this figure legend, the reader is referred tothe Web version of this article.)

T. Alinejad et al. / Renewable Energy 156 (2020) 1e136

QR þQF þ QComp þ QSoil þ QPlant þ QL þ QVent�Inf þ QHeat

þ QFan�Pad

¼ 0; Eq. 2

where QR is the heat transfer from the incident solar radiation(beamþ diffuse), which is calculated by EnergyPlus, meteorologicaldata, and Perez anisotropic sky model [42,43]. These incident solarradiations are composed of three parts: isotropic, circumsolardiffuse, and horizon brightening. Perez anisotropic sky model isrepresented by Eq. (3).

IT ¼ IbRb þ Idð1� F1Þ�1þ cosb

2

�þ IdF1

abþ IdF2sinb

þ Irg

�1� cosb

2

�; Eq. 3

where IT represents the total solar radiation incident on a tiltedsurface, Ib is the beam radiation, Rb is the ratio of beam radiation onthe tilted surface to that on the horizontal surface, Id is the diffuseradiation, F1 and F2 are the brightness coefficients, b is the anglebetween the plane of the surface and the horizontal surface, and Irgis the reflected radiation from the surroundings on the surface.

In Fig. 8, QF is the conduction and convection through the wall.EnergyPlus uses the adaptive convection algorithm [44] to calculatethe convection heat transfer. Thermal response factors and con-duction transfer functions were utilized [45e47] to calculate theconduction heat transfer through the cover. QComp is the heattransfer between the components in the greenhouse, includingfans, thermal screen’s motors, and other components. QSoil is theheat transfer between the soil and air in the greenhouse. In thisresearch, the Kiva model was used to calculate the temperature ofthe earth and heat exchanged between the earth and the

Table 8PV specifications utilized in SPBS [39].

Case Subject Magnitude

Electrical parameters at Standard Test Conditions Model YGE 60 Cell 40 mm YL260P-29bPower output Pmax (W) 260Power output tolerances (W) 0.5Module efficiency (%) 15.9Voltage at Pmax (V) 30.9Current at Pmax (A) 8.41Open-circuit voltage (V) 38.9Short-circuit current (A) 8.98Dimensions (m � m � m) 1.64 � 0.99 � 0.04

Electrical parameters at Nominal Operating Cell Temperature Power output Pmax (W) 260Voltage at Pmax (V) 28.1Current at Pmax (A) 6.70Open-circuit voltage (V) 35.9Short-circuit current (A) 7.27

Table 9Inverter specifications utilized in the SPBS [40].

Case Subject Magnitude

Model SUNNY TRIPOWER 20000 TLMaximum efficiency 98.4%

Input DC Maximum DC power (at cosf ¼ 1)/DC rated power 20440 W/20440 WMaximum input voltage 1000 VMPP voltage range/rated input voltage 320 Ve800 V/600 VMin input voltage/start input voltage 150 V/188 VMaximum input current input A/input B 33 A/33 ATHD �3%

Output AC Rated 20000 WMaximum AC apparent power 20000 VAAC voltage range 180e280 VMaximum output current/Rated output current 29A/29A

Fig. 8. Greenhouse energy balance components.

T. Alinejad et al. / Renewable Energy 156 (2020) 1e13 7

greenhouse. Of note, 70% of the greenhouse ground was cultivatedwith rose flowers. QPlant represents the evapotranspiration heattransfer that is equal to the amount of the enthalpy (DH) needed toevaporate the amount of water (resulting from the evapotranspi-ration) at 20 �C into the greenhouse. DH is calculated by Eq. (4).

DH¼Dmv,hfg; Eq. 4

where hfg [kJ/kg] is the water enthalpy of vaporization at 20 �C andDmv (kg) is the mass of water entered in the greenhouse from theevapotranspiration. The actual evapotranspiration (ETC) is providedin Eq. (5).

ETC ¼KC,ET0; Eq. 5

where KC is the dimensionless average crop coefficient over thegrowth period [48]. ET0 (mm day�1) (Eq. (6)) is the referenceevapotranspiration provided by the FAO PenmaneMonteithmethod [49].

ET0 ¼0:408DðRn � GÞ þ g 900

Tþ273u2ðes � eaÞDþ gð1þ 0:34u2Þ

; Eq. 6

where Rn is the net radiation at the crop surface (MJ m�2 day�1), Gis the soil heat flux density (MJm�2 day�1), T is themean of daily airtemperature at 2 m height (�C), u2 is the airflow speed at 2m height(m s�1), es-ea is the saturation vapor pressure deficit (kPa), D is theslope vapor pressure curve (kPa �C�1), and g is the psychrometricconstant (kPa �C�1).

The airflow speed in the greenhouse is negligible, and Eq. (6) canbe simplified as Eq. (7).

ET0 ¼0:408DðRn � GÞ

Dþ g: Eq. 7

QFog is the amount of heat absorbed by fog droplets to evaporate,which is equal to the amount of enthalpy (DH) needed to evaporatethe amount of water (sprayed by the fog system) at 20 �C into thegreenhouse (Eq. (4)). Here, Dmv [kg] is the mass of water entered inthe greenhouse and sprayed by the fog system when RH dropsbelow 55%.

Q(Vent-Inf) shows that the heat exchanged between the outdoorand indoor air consists of ventilation and infiltration. The amount of

T. Alinejad et al. / Renewable Energy 156 (2020) 1e138

infiltration that is proposed by L�opez et al. [50] for a greenhousewith polyethylene coverage is estimated by Eq. (8). Table 10 pro-vides a segregated ventilation ratio for each month that is suppliedby fans. There was no demand for ventilation in hot months, owingto the air exchange via the fanepad evaporative cooler system.

FInf ¼0:8808ðV0Þ � 0:2868; Eq. 8

where FInf is the infiltration air change per hour (ACH) and V0 is thewind speed (m s�1). Likewise, QHeat is the load that is supplied by acentral heating system and Q(Fan-Pad) represents the supplied loadby the fanepad evaporative cooler.

2.4. Environment

The amount of CO2 produced by the natural gas combustion is1.8862 kg/m3 as reported by the US Environmental ProtectionAgency [51]. Similarly, Iran’s Ministry of Energy reported 660.65 gCO2 emission per kWh electricity generation [52]. In this study, theamount of CO2 emission reduces by utilizing SPBSs, and it iscalculated according to Eq. (9).

CO2Reduction¼RðFÞ þ RðEÞ þ RðPÞ; Eq. 9

where R(F) represents the reduction in CO2 emission owing to thedecrease in the natural gas demand in winter and cold days. Uti-lizing SPBSs causes a reduction in the greenhouse electricity de-mand by shading and electricity generation. Hence, R(E) is theamount of CO2 reduction in the greenhouse electricity demand, andR(P) is the reduction in CO2 emission owing to the SPBS electricitygeneration, which is sent to the grid. The electricity sold to the gridcaused a reduction in the power plant electricity generation.

3. Results and discussion

3.1. Model validation

Here, the modeled GR was validated using the real measureddata from the case studied greenhouse. For this purpose, thefollowing parameters were measured and compared with thesimulated results: indoor temperature, RH, infiltration ratio,maximum IPC, electricity demand, natural gas demand, and PVelectricity generation.

Fig. 9a demonstrates the monthly average indoor temperatureof the GR and the measured data. According to Fig. 9a, GR and itsheating, as well as the cooling systems, could perfectly control theindoor temperature. There is a little deviation (less than 3%) insummer and winter, owing to the difference between the systemresponse to the indoor temperature fluctuations in the real andmodeled cooling and heating systems.

RH is another factor in plant growth. Fig. 9b shows that GRmodeling very well predicted the greenhouse RH. The RH was seton 55%, but the results showed the growth of RH from March toOctober. This is due to the working of the fanepad evaporative

Table 10Ventilation ratio in the reference greenhouse.

Month Ventilation (ACH) Month Ventilation [ACH]

January 1.2 July 0February 1.2 August 0March 0 September 0.7April 0 October 2.3May 0 November 2.3June 0 December 2.3

cooler and the supplied wet air.When it comes to energy management, infiltration is one of the

most energy-consuming components. According to a previousstudy, the infiltration rate of a greenhouse that uses polyethylenefor covering could be 0.5e1.0 ACH [34]. As illustrated in Fig. 9c, theGR infiltration rate is always between 0.5 and 1 ACH except inNovember and December, which is due to the minimum windspeed in these months in Shiraz. The infiltration results wereclearly held in the acceptable range.

The monthly averaged maximum illumination on the plantcanopy is shown in Fig. 9d. There is a good agreement between themeasured and modeled data. The difference between the modeledand measured data is always less than 8%.

Fig. 9e shows the monthly electricity supplied by the grid. Themodeled greenhouse perfectly estimated the electricity demandthroughout the year except in June. There has been some mainte-nance out of the greenhouse scope in themonth of June. The reasonfor reporting the maintenance of the electricity demand andgreenhouse electricity demand together is that the measuredelectricity has been read from a single electricity meter and it wasnot possible to measure the greenhouse electricity separately. Theamount of maintenance of electricity demand is approximately3 MWh. Hence, the GR electricity demand is reliable.

The collected data from the gas meter in Fig. 9f indicate thatthere is a perfect agreement between the measured and GR pre-dicted data. There has been no gas demand from May to August.

The annual performance of the PV system in Shiraz was calcu-lated and compared with that reported by Ref. [53], and a very goodagreement (less than 2% difference) was achieved. Having a verifiedmodel of the greenhouse and PV system, it is reliable to simulatevarious SBG systems shown in.

Table 7 and compare their performance with a GR.

3.2. Temperature and RH

The essential factors relating to the microclimatic effects of agreenhouse are temperature, RH, and irradiation [54], which affectthe plant growth. There was no significant difference (always lessthan 0.1%) between the GR and SBG temperature and RH. Notably,SBG greenhouses, similar to the GR, are controlled by heating,cooling, and fogging systems. Therefore, the modeled greenhousesystems can perfectly control the greenhouse microclimate. In fact,temperature and RH are initially controlled and set to desiredamount then energy performance of system are assessed. Hence,the proper response of the heating and cooling systems does notessentially account for the amount of energy savings or waste.Therefore, fuel consumption and electricity demand need to beexamined separately to clarify the energy consumption pattern.

3.3. IPC

The monthly mean, maximum, and annual mean IPC for the GRand SBG greenhouses were determined for all cases, as shown in.

Table 7. The results showed that with the increase in the shadingeffect by SPBSs, the amount of IPC is decreased both monthly andannually, which is consistent with previous studies [14e22]. Thisamount of reduction results from the increase in the castingshadow by adding PV panels, as shown in Fig. 10 and Fig. 11.However, the IPC changing pattern remains the same. Theexceeding maximum IPC threshold can adversely affect the roseflowers. All SBGs could create the above condition throughout theyear except from June to September. However, SBG24eSBG28 canbe nominated as appropriate choices (Fig. 10a), in which the SBG28has the most similar condition to GR. The whole thermal screen isremoved in the SBGs and then replaced with SPBS. As SPBS will not

Fig. 9. Comparison of the monthly mean measured values and simulated data. (a) Indoor temperature, (b) RH, (c) infiltration rate, (d) maximum illumination on the plant canopy,(e) electricity demand, (f) fuel consumption for the heating purpose.

T. Alinejad et al. / Renewable Energy 156 (2020) 1e13 9

cover the whole greenhouse roof, the IPC for each SBG will behigher than the GR condition.

The considerable reduction of the IPCmay also cause a reductionin the plant growth [11,14,16,18,20,21,28]. Fig. 11 demonstrates thatSBG18eSBG24 have less than 2% difference in annual average IPC incomparison with GR. As well as, on monthly bases, SBG18eSBG24are the most acceptable configurations due to the IPC reductionaspect as shown in Fig. 10b.

In summary, comparing SBGs with the GR, the IPC resultsshowed that SBG2eSBG22 exceeded the maximum illuminationlimits, SBG26eSBG28 reduced illumination more than the mini-mum bound, SBG24 provide a more suitable IPC compared withother SBGs in total. Thus, 480 PV panels can be used above thegreenhouse (19.2% roof area) and observe no significant change inthe IPC compared with the GR.

3.4. Fuel consumption

There is no significant difference between the various types ofSBGs in terms of natural gas consumption, as illustrated in Fig. 12.The GR annually consume 140000 m3 natural gas, whereas, onaverage, the SBGs annually consume 5000 m3 less (1.23 m3/m2,3.57% of the GR gas demand). That’s why maximum gas demand

occur at nigh which there is no IPC but GR has the thermal screenwhich causes a slight reduction of natural gas consumption at nightin comparison with SBGs. During the day, there is maximum 7%difference between annual mean IPC of GR and SBGs (Fig. 11) thatresult a minor reduction in SBGs natural gas consumption. In total,it’s evident that SBGs gas consumption be nearly the same as GR.

3.5. Electricity demand

Several parameters of SBGs are assessed in this section: theenergy demand by the SBGs and GR without considering the PVelectricity generation, the electricity energy generation through theSPBS for all studied SBG configurations, the amount of electricitysupplied by the grid in which PVs were unable to supply, and theexcess electricity sold to the grid.

Fig. 13a demonstrates the annual electricity demand irre-spective of the SPBS electricity generation. The electricity demandfor SBG2eSBG24 is more than that of the GR due to the highercorresponding cooling demand in the SBGs compared with the GR.However, the electricity consumption of SBG26 and SBG28 isalmost equal to that of the GR (they consume approximately 0.2%less). The higher cooling demand and electricity demand in theSBGs compared with the GR is mainly due to the illumination level

Fig. 10. Monthly variation of (a) maximum IPC, (b) mean value of IPC for all studied cases.

Fig. 11. Annual mean IPC for all studied cases.

Fig. 12. Comparison of the fuel consumption for the least and most covered green-house areas by the SPBS.

T. Alinejad et al. / Renewable Energy 156 (2020) 1e1310

(IPC) in each case. The excess radiation that entered into thegreenhouse with the SPBS (in comparison with the GR, Fig. 11)made the greenhousewarmer, and thus, more energy is required bythe fanepad systems to keep the SBGswithin the proposed setpointindoor temperature range.

The generated electricity by the SPBSs is shown in Fig. 13b andTable 11. As expected, the amount of electricity generated by theSBG greenhouses is increased almost linearly. A similar observationhas been reported in other papers [24,26]. However, non-linear

behavior is because of SPBSs different operation time. In thisstudy, SBG28 (22.4% roof coverage) annually produces electricity upto 47.1 kWh/m2. Therefore, SBG28 electricity generation is 1.29times greater than the GR electricity demand.

Fig. 13c represents the annual grid electricity supply byconsidering the PV electricity production. In all SBG cases, theSPBSs will cover the electricity demand of the greenhouse, whereasthe remaining demandwill be covered by the grid due to the lack ofa storage system (battery) to reduce the investment cost. However,the SPBSs in all SBG cases generatemore electricity than needed forsome periods, which can be sold to the grid. The results show thatthe annual electricity supplied by the grid did not significantlychange (less than 5%) between SBG10 and SBG28.

The self-sufficiency ratio (SSR) is a key performance indicator(KPI) for PV systems integrated in buildings that represents howmuch of the electricity consumption is covered by a PV systemduring a year [55]. For SBG28, the value of SSR is equal to 46%,which means that the electricity production from the SPBS systemcovers 46% of the SBG28 system electricity consumption. Fig. 13dshows that the SSR grows in SBGs by adding SPBS but remainsrelatively unchanged (increase less than 3%) from SBG12 to SBG28.This is due to the greenhouses annual electricity demand whichremained relatively constant from SBG12 to SBG28 (decreased lessthan 1%, Fig. 13a) and the electricity supplied by the grid whichmarginally changed (less than 7%) through SBG12 to SBG28 asshown in Fig. 13c.

The self-consumption ratio (SCR) is a further KPI for building-integrated PV systems, which shows how much of the electricityproduction is self-consumed at the same time [55]. It is an impor-tant indicator because the electricity bought from the grid and theelectricity sold to the grid (power produced by the PV system that isnot auto-consumed) have different economic values. A lower SCR ismore profitable in countries where the electricity sold price ishigher than the purchase price (such as Iran). A higher SCR is moresuitable for countries where the selling price is less than the pur-chase price. As indicated in Fig. 13d, the SCR decreased byincreasing the SPBSs. SBG28 has the lowest SCR among all SBGs.Moreover, SBG26, SBG24, and SBG22 have a close SCR value toSBG28 (difference is less than 5%). The annual surplus electricity,which is sent to the grid, is provided in Fig. 13e.

Fig. 13c shows that 46.1% of the total SBG28 electricity demandcould be supplied by utilizing only 31.9% of the SPBS electricitygeneration. As shown in Fig. 13e, SBG24eSBG28 exported 65.5%,66.8%, and 68.1% of their electricity generation to the grid (28, 30,and 32.1 kWh/m2/year, respectively).

In summary, according to the annual greenhouse electricity

Fig. 13. (a) Greenhouse annual electricity demand without considering the SPBS electricity generation. (b) SPBS annual electricity generation. (c) Annual electricity supplied by thegrid, considering the electricity provided by SPBS. (d) SBG SSR and SCR. (e) Annual surplus electricity sent to the grid. (f) SBG annual CO2 reduction.

Table 11Annual electricity generation by the SPBSs.

Greenhouse Electricity Generation(kWh/m2)

Total Electricity Demand without Consideringthe SPBS (kWh/m2)

Greenhouse Electricity Generation(kWh/m2)

Total Electricity Demand without Consideringthe SPBS (kWh/m2)

SBG2 3.9 37.1 SBG16 29.8 36.8SBG4 7.8 37.0 SBG18 32.8 36.8SBG6 11.6 37.0 SBG20 36.4 36.7SBG8 15.5 37.0 SBG22 39.9 36.7SBG10 19.0 36.9 SBG24 42.7 36.7SBG12 22.8 36.9 SBG26 44.9 36.6SBG14 26.1 36.8 SBG28 47.1 36.5

T. Alinejad et al. / Renewable Energy 156 (2020) 1e13 11

demand, SBG28, SBG26, and SBG24 could be nominated as the besttrade-off alternatives due to their corresponding electricity de-mand, SCR, and SSR values.

3.6. Environment

The amount of CO2 reductionwas assessed in Section 2.4. Fig.13fshows the amount of CO2 reduction by SBGs. These types ofgreenhouses annually decrease CO2 emission up to 136.8 tons or

33.5 kg/m2 (SBG28). In general, SBG28, SBG26, and SBG24 are moreenvironment-friendly alternatives compared with all other cases.

The SBG performance in different aspects is compared andsummarized in Table 12. As shown, the best trade-off case that has agood performance in all studied aspects is SBG24. Hence, accordingto the results, the GR can be replaced with SBG24 with high prof-itability and a considerable CO2 mitigation effect. Table 13 showsthe details of SBG24.

Table 12Comparison of the SBG performance rate.

Subject Better Performance

Temperature All SBGs are the sameRH All SBGs are the sameIllumination SBG24Gas demand All SBGs are the sameElectricity SBG (24e28)Environmental SBG (18, 20, 24e28)

Table 13Detail of the SBG24 performance.

Subject Magnitude Dimension

o Fraction of roof covered by the SPBS 19.2 %o Number of PV panels 480 -o Annual gas demand reduction 5098 (3.57) m3 (%)o Annual electricity demand reduction

(without considering PV electricityproduction)

273 (<0.2) kWh (%)

o Annual electricity demand reduction(considering PV electricity production)

67933 (45.5) kWh (%)

o Annual electricity generation 174204 (42.69) kWh (kWh/m2)o Annual electricity sent to grid 114099 (27.96) kWh (kWh/m2)o SCR 34.5 %o SSR 45.6 %o Annual CO2 emission reduction 124.7 (30.56) Ton CO2

(kg/m2)

T. Alinejad et al. / Renewable Energy 156 (2020) 1e1312

4. Conclusion

In this study, a real commercial rose greenhouse was modeledand validated as a GR. Adopting a newapproach, the thermal screenwas replaced with SPBSs in checkerboard arrays, representing newgreenhouses (SBGs). For the analysis, 14 cases (Table 7) of com-mercial greenhouses with SPBS in 14 different shading rates wereinvestigated on different aspects, such as total annual energy de-mand and CO2 mitigation.

1. The obtained results showed no significant difference betweenthe SBGs and GR temperature and RH.

2. For the illumination level, some SBGs can perfectly provideillumination required by the plants. SBG2eSBG22 exceeded themaximum illumination limits, SBG26eSBG28 reduced illumi-nation more than the minimum bound, and SBG24 controlledthe shading well.

3. Regarding fuel consumption, the same variation was observedfor all SBG types, which means that all types of SBGs annuallyreduce 3.57% of the natural gas demand (almost 5000 m3/yearby average).

4. For the electricity demand, SBGs could supply a fraction of theirelectricity while the maximum SSR was achieved in SBG28 with46% and the minimum SCR was reached with 31.9%. In general,SBGs can annually produce electricity up to 47.1 and 32.1 kWh/m2 to the grid.

5. Eventually, regarding the environmental evaluation, SBGsannually reduce CO2 between 4.8 and 33.5 kg/m2.

In summary, the above-mentioned analysis highlighted thatSBG24 configuration performed well in all studied parameters andcan be selected as themost desirable greenhouse in terms of energyand environment performance for a similar system. SBG24 annuallyreduced 3.57% of the total natural gas consumption, 45.5% of theelectricity demand, and 30.56 kg/m2 CO2 emission in comparison

with the current existing greenhouse (GR), and the SPBS in SBG24generated 42.69 kWh/m2 of the greenhouse floor.

Declaration of competing interest

The authors declare that they have no known competingfinancial interests or personal relationships that could haveappeared to influence the work reported in this paper.

CRediT authorship contribution statement

T. Alinejad: Software, Validation, Formal analysis, Investigation,Writing - original draft. M. Yaghoubi: Project administration, Su-pervision, Writing - review& editing. A. Vadiee: Conceptualization,Methodology, Resources, Writing - review & editing, Supervision,Funding acquisition.

Acknowledgements

The authors would like to thank Mr. Seddighi, the owner of thecommercial rose greenhouse, for lending this greenhouse to theresearchers and collaborating in collecting and measuring theexperimental data. The author would like to acknowledge all sup-ports recieved from Future Energy Center-MDH (FEC).

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