Simulation and experimental validation of a parabolic trough plant for solar thermal applications under the semi-arid climate conditions

Solar Energy 194 (2019) 969–985

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Solar Energy journal homepage: www.elsevier.com/locate/solener

Simulation and experimental validation of a parabolic trough plant for solar thermal applications under the semi-arid climate conditions

T

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Ammar Mouakya,b, Ahmed Alami Merrounic, , Nour Eddine Laadela,d, El Ghali Bennounaa a

Research Institute for Solar Energy and New Energies (IRESEN), Green Energy Park, Benguerir, Morocco TRE Research Team, Ecole Mohammadia d’Ingénieurs (EMI), Mohammed V Agdal University, 10080 Rabat, Morocco c Department of Physics, Faculty of Sciences, Mohammed First University, Oujda, Morocco d Ecole Nationale Supérieure d’Arts et Métiers ENSAM, Université Moulay Ismail (U.M.I), Marjane II, BP-4024 Meknès Ismailia, Morocco b

A R T I C LE I N FO

A B S T R A C T

Keywords: Solar heating Industrial processes Parabolic trough Soiling LCOH Morocco

In this paper, yield analysis of a 186 kWth parabolic trough collector (PTC) system for medium-temperature heat process applications, is conducted under a semi-arid climate (Benguerir, Morocco). For this purpose, a PTC model is simulated and validated using experimental data from a PTC test loop, installed at the Green Energy Park research facility. Following, the studied system is simulated using high-quality meteorological data measured at ground level for three years. The impact of soiling on the system’s heat production is studied, considering it as a time-dependent parameter. Finally, an economic analysis is conducted considering as indicators the Levelized cost of heat (LCOH) and the payback time. According to the results, the used model for PTC simulation is accurate, where the daily average deviation is around 4.8% under clear sky conditions. Furthermore, and based on the simulation results, integration of PTC in the Moroccan industrial processes can be very beneficial, where the proposed 186 kWth PTC plant can produce about 388 Tons of saturated steam at 5 bars, annually. Nevertheless, soiling is a limiting efficiency factor especially during the dry period of the year, where the solar field daily average thermal losses can reach 354 kWthh, which is equivalent to a drop of 27% in the production. Economic evaluation results showed that for a low-cost PTC (< 250 €/m2), an LCOH and payback period of respectively 0.03 €/kWthh and six years can be obtained, which makes the investment financially reliable.

1. Introduction In recent years, industrial activities have had a significant role in the economic growth of several countries. The development of these activities gives rise to an increase in energy consumption worldwide. According to the IEA (International Energy Agency) (Philibert, 2017), industrial sector is responsible for 32% of the global total final energy consumption; and heat represents around two thirds of this energy, with an annual average growth of 1.04% from 2015 until 2040 (International Energy Agency (IEA), 2018). Thermal energy use in industries generally revolves around process heating applications. Depending on the activity, heat is required at different temperature levels. Recent statistics report that low and medium temperature heat (below 400 °C), represent 52% of the total thermal energy consumption (International Energy Agency (IEA), 2016). The primary source of heat in industries is the combustion of fossil fuels (e.g., fuel oil, coal, and natural gas). Efforts to minimize their consumption aim to improve the

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efficiency of processes, as well as, to investigate alternative solutions for fossil fuels, providing clean and sustainable energy. In Morocco, energy consumption in the industrial sector is projected to increase by an annual average of 7% in the next years to match sector growth (Kousksou et al., 2015). As reported by IEA (International Energy Agency (IEA), 2014), the energy consumption of this sector represents 26% of the total final energy in the Kingdom. In terms of resources, the country’s energy mix is mostly dependent on imported fossil fuels, representing 96% of its needs. However, Morocco has abundant solar resources with approximatively 2600 kWh/m2/year. Thus, the use of solar energy as a primary source for heat generation could contribute significantly to reducing fossil fuel consumption and their associated drawbacks. Indeed, solar energy to generate heat for industrial applications (commonly known by SHIP: Solar Heat for Industrial Processes), is a promising field that shows a high potential to reduce fossil fuel consumption in industries. Over the world, more than 140 SHIP plants are installed with a total capacity of 280 MWth (AEE INTEC, 2018).

Corresponding author. E-mail address: [email protected] (A. Alami Merrouni).

https://doi.org/10.1016/j.solener.2019.11.040 Received 12 March 2019; Received in revised form 6 October 2019; Accepted 12 November 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.

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Nomenclature A cp CO2,eq CO2,tax Dev (QSF) Dint DNI F fc h i IIC KL ṁ O&MC P PBtime Pin Q r rs T W

Subscripts and superscripts

area, m2 specific heat capacity under constant pressure, J/kg K equivalent CO2 emissions, g CO2eq /kWthh carbon tax, €/T deviation of the daily solar field thermal output, % receiver tube inner diameter, mm direct normal irradiance, W/m2 focus state fuel oil specific cost, €/kWh enthalpy, kJ/kg discount rate, % initial investment cost, € longitudinal incident angle modifier heat transfer fluid flowrate, kg/s annual operation and maintenance cost, €/year steam production, kg payback time, years pinch point, K heat, kW mirrors’ reflectance, % row spacing, m temperature, °C aperture width, m

ABS clean dirty dry emissions htf HX loss in n N out piping PTC REC ref SF soiled ST t th w y

absorbed heat mirrors at clean state mirrors at dirty state dry period of the year gases emissions heat transfer fluid heat exchanger thermal losses inlet number of years nominal value outlet piping section parabolic trough collector receiver tube reflected solar field mirrors at soiled state steam time thermal water year

Greek symbols

Abbreviations

α γ δ Δ Δt ΔTN ηopt,N ηboiler ηclean ηSF ηSF,t ηshad θl θt λ ρ

CRF CSP CST DHI FPC GHI HTF LCOH LFC MHP PTC SHIP TraCS TMY

sun altitude angle, ° sun azimuth angle, ° mirror’s cleanliness difference period of time piping temperature drop, K nominal optical efficiency, % boiler efficiency, % cleanliness factor, % solar field efficiency, % solar field efficiency over a time period, % shading factor, % longitudinal incidence angle, ° transversal incidence angle, ° thermal conductivity, W/m K density, kg/m3

capital recovery factor concentrated solar power concentrated solar technologies diffuse horizontal irradiance flat plate collector global horizontal irradiance heat transfer fluid levelized cost of heat linear Fresnel collectors meteorological high precision parabolic trough collector solar heat for industrial processes tracking cleanliness system typical meteorological year

et al., 2018) or Ebsilon Professional (Soares and Oliveira, 2017) were therefore used for assessing the performances of systems involving PTC under variable conditions. Modeling indeed allows time and effortsaving while providing a significant amount of information, yet, it is essential to check the accuracy of the used models. Consequently, it is necessary to set up experimental facilities to permit the validation of the models’ results. However, few studies have been published in the literature regarding the development of outdoor test loops for smallsized PTC (Fernández-García et al., 2018). Regarding the use of PTC as a source of thermal energy for industrial processes, several studies have been carried out to investigate its potentiality. For instance, (Kalogirou, 2002) examined the viability of using PTCs for industrial heat generation in Cyprus. The proposed system was designed to deliver hot water at 85 °C at a flow rate of 2000 kg/h for the first three-quarters of each hour. (Silva et al., 2013) presented a cooperative simulation of a PTC plant coupled to an industrial process. (Coccia et al., 2015) reported the results obtained during the tests of a low-cost PTC, adapted for industrial medium temperature heat applications. (Silva et al., 2014) presented a thermo-

Studies conducted by the International Renewable Energy Agency (IRENA) (2015a), estimate that more than 800 million m2 of collector and mirror area would be installed by 2030 for SHIP applications around the world. Regarding the solar technology, flat plate collectors (FPC), linear Fresnel collectors (LFC) and parabolic trough collectors (PTC) are the most used technologies for SHIP applications (International Renewable Energy Agency (IRENA), 2015b; Kalogirou, 2003). PTC is the most mature concentrated solar technology (CST) (Hussain et al., 2017). 1-D steady-state modeling is the frequently used approach for the heat transfer analysis of PTCs since it allows obtaining results with sufficient precision relying on models with relatively low complexity level (Yılmaz and Mwesigye, 2018). However, solar energy is inherently intermittent and, in most of the cases, it is crucial to predict the yield of a PTC system accurately under fluctuating conditions, in which steady-state models are not able to give insightful results. Transient simulations are, therefore, critical to evaluate PTCs operation under dynamic conditions. Models or dynamic simulation tools, such as TRNSYS (El Ghazzani et al., 2017), Modelica (Desideri 970

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Soiling is, therefore, a crucial parameter to take into consideration during the yield analysis of a CST based plant. Besides, it is recommended to consider the temporal variation of the soiling to obtain realistic yield assessments. However, due to the lack of measured data, most of the studies in the literature are either neglecting the impact of soiling or dealing with it as a static factor, assuming in most of the cases constant cleanliness values (Biencinto et al., 2014; Soares and Oliveira, 2017), whereas few authors considered the soiling as a time-dependent parameter. Based on TraCS (Tracking Cleanliness Sensor) measurement at PSA, (Wolfertstetter et al., 2018) compared different cleaning strategies and their impact on the O&M cost for 50 MWe Concentrated Solar Power (CSP) plants if installed in Morocco and Spain. The same authors also showed that the assumed constant cleanliness values, generally considered for the yield analysis of CSP plants, can cause significant errors in the profit calculation. A thermal performance analysis of a PTC coupled to an industrial process in Chile was conducted by (Murray et al., 2017). Here again, the TraCS system has been used to assess the cleanliness measurements. Nevertheless, the authors only present the soiling data for one month without considering them in the conducted simulations and yield analysis. Generally, there is a lack of information and studies in the literature considering the time-dependency of soiling on the performances of CST systems, especially when they are coupled to industrial processes, which need to be deeply investigated mainly in regions with high interest in solar energy. Hence, this paper aims to demonstrate the technical and economic potential of a SHIP installation using an experimentally validated simulation model, considering as input highquality meteorological data and taking into consideration the timedependency of the soiling impact. This is the first study, to the best of the authors’ knowledge, adding the soiling impact as a dynamic factor in the yield analysis of SHIP integration. The main contributions can be summarized as follows:

economic design optimization of PTC plants for SHIP applications. The variables that have been considered in their study are the number of collectors in series, the number of collector loops, loops spacing, and storage volume. Recently, (Bellos et al., 2018) conducted a numerical investigation of a PTC plant integration to an industrial process with a heat demand of 100 kWth for temperature levels between 100 and 300 °C. Different combinations of solar field areas and storage tank volumes were studied under Greece climate. Even its considerable potential, the development of the SHIP field is still very limited, particularly in regions with high solar potential. Accurate yield analysis studies based on validated models and using as an input a high-quality data can contribute to proving the viability of SHIP projects in these areas. Soiling is among the parameters impacting the performances of systems based on CST technologies. It stands for dust, dirt and particle accumulation on the surfaces of solar concentrators, panels or receivers. It is a global issue highly affecting the optical properties of the reflecting mirrors and the receiver tubes, especially during the dry period of the year (Merrouni et al., 2015). This issue causes a significant drop in the efficiency and the heat production of concentrated solar plants, reaching up to 45% after a month without cleaning (Frein et al., 2018). The problem is compounded by the fact that; suitable locations for CST are mostly located in semi-arid and arid regions, suffering from high aerosols concentration and water resource scarcity (Xu et al., 2016). This is why several cleaning solutions with low or even no water use, including alternative cleaning systems (Bouaddi et al., 2018), antisoiling coatings (Aranzabe et al., 2018; Polizos et al., 2018) and wind barriers (Moghimi and Ahmadi, 2018) were suggested. On the other hand, measurement and analysis of the soiling rate are also fundamental to develop optimal cleaning scenarios for the CST plants. This will decrease operational costs and preserve water resources (Ba et al., 2017).

Fig. 1. Layout of the experimental test loop. 971

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• Experimental validation • • •

2.1.1. Solar collector The studied solar collector is a PTM-24 (Fig. 2) supplied by Soltigua (Soltigua, 2018), which can deliver up to 31 kWth in the following reference conditions :

of a small-sized PTC simulation model, considering four days characterized by different solar irradiation conditions, Yield analysis of a SHIP plant, considering as input three years of meteorological on-site measured data carefully checked and quality controlled, Assessment of the soiling impact on the plant’s output, using in-situ soiling measurements data obtained from a TraCS sensor installed as the same location as the considered site for the studied plant, Investigation of the economic viability of the proposed plant, using the Levelized cost of heat and payback time as indexes and the assessment of the soiling impact on the plant’s economic savings.

• Reference day and time: 21st of June at solar noon. • DNI = 900 W/m . • Ambient temperature = 25 °C. • Wind speed = 2 m/s. • HTF inlet temperature = 160 °C. • Mirrors cleanliness factor (η ) = 1. 2

clean

The collector includes four modules, has a north–south orientation, 54.4 m2 aperture area, and 27.2 m length. The receiver tube is made from a 42.4 mm diameter stainless steel tube with a selective coating and is covered by a glass tube (non-evacuated).

The rest of the paper is structured as follows: in the second section, the details of a test loop installed at Green Energy Park research facility, which will be used for experimental validation of the PTC model, will be presented. Following, the PTC simulation model, as well as the validation approach, will be outlined. In the third section, the plant’s model will be described as well as the considered performance indicators for the thermodynamic and economic assessments. After that, the accuracy of the Direct Normal Irradiance (DNI), the soiling rate data and measurement sensors will be presented as well as the solar potential of the simulation location. In the fourth section, experimental validation results will be presented in addition to the results of a one-year yield analysis of the simulated system, the impact of soiling on the heat production losses as well as the results of the economic evaluation will be discussed. Finally, the main conclusions of the study will be summarized in the last section.

2.1.2. Heat transfer fluid circuit The used heat transfer fluid (HTF) is the mineral oil Delcoterm Solar E 15 (Delco srl, 2018). It is a paraffinic based oil that can be used in applications requiring temperatures up to 320 °C. A circulation pump is used to circulates the thermal oil in the circuit. An air cooler with a maximum cooling capacity of 35 kWth is used to maintain the heat transfer fluid (HTF) at the desired temperature. The test loop is equipped with two connection points to supply a given process by the produced heat. A feed-oil system including a 250 L tank, a filling-pump and a pressurized expansion vessel are used to fill the circuit with the HTF and allow thermal oil expansion from the HTF circuit.

2. Parabolic trough collector component validation 2.1.3. Measurement instruments and data acquisition system To monitor the performances of the PTC, several measurement instruments are installed (Fig. 3) and all data are recorded at an interval of 1 s and averaged each minute. DNI values are obtained via a high precision meteorological station (see Section 3.3). Two PT100 class B temperature sensors are placed at the inlet and the outlet of the collector. Besides, an additional PT100 temperature sensor allows the measurement of the ambient temperature. Flow measurement is performed using a Vortex type flowmeter (Foxboro 84 W) whose reading accuracy is ± 0.5%. Moreover, a wind speed sensor allows the measurement of the wind velocity and forces the collector to rotate automatically to its stowed position in case of high wind speeds.

2.1. Experimental setup The experimental results are obtained from a PTC test loop located at the Green Energy Park Test Platform in Benguerir, Morocco (latitude 32.2208 N, longitude 7.9286 W, altitude 449 m). The loop consists of a closed circuit in which the heat transfer fluid is recirculated and heated by a solar collector until reaching the desired temperature at its output. In addition to the collector, the test loop includes an HTF circuit, different measurement instruments, and a data acquisition system (Fig. 1). A detailed description of these components will be discussed in what follows.

Fig. 2. The studied parabolic trough collector loop installed at the Green Energy Park Test platform. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 972

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Fig. 3. Part of the measurement instruments of the test loop (a: Parabolic trough collector outlet temperature sensor, b: flowmeter, c: pump outlet pressure indicator, d: wind speed sensor).

2.2. Parabolic trough collector simulation model

Q ABS, REC = DNI ·APTC ·ηopt , N ·KL·ηclean ·ηshad ·F

The studied PTC is simulated using Ebsilon® Professional (Fig. 4), a widely used software for evaluating and optimizing systems driven by conventional and renewable energy sources (Petrakopoulou et al., 2016; Soares and Oliveira, 2017; Xu et al., 2017). Simulations were conducted on a 1-minute basis using the “Time series” calculation module considering a precision stopping criteria of 10−7. At each time step, the simulation process of the PTC can be described by the following steps: Meteorological data including DNI, ambient temperature, and wind speed are introduced in the “Sun” component. This element also allows the calculation of the longitudinal incidence angle (θl) on the PTC, based on local time and geographical coordinates using the following expression (Stine and Harrigan, 1985):

Solar collector’s dimensions, nominal optical efficiency, and incident angle modifiers values, were extracted from manufacturer datasheet (Soltigua, 2016). Cleanliness factor value is introduced based on measured data, whereas shading losses are calculated using the following equation (Soares and Oliveira, 2017):

cos (θl ) =

1−

cos 2

(α )·cos 2

(γ )

ηshad = 1 − min (1, max (0, 1 − rs·

cos (|θt |) )) WPTC

(2)

(3)

where rs is the row spacing, θt is the transversal incidence angle on the collector and WPTC is the aperture width of the collector. The useful heat produced by the solar collector (QPTC ) is expressed using Eq. (4): htf htf htf QPTC = Q ABS, REC − Qloss, REC = ṁ PTC ·(hPTC , out − hPTC , in )

(4)

htf is the heat transfer fluid’s mass flow rate circulating in the where ṁ PTC htf htf collector, hPTC , out and hPTC , in are respectively the heat transfer fluid enthalpy at the outlet and inlet of the collector. Qloss, REC represents the heat losses in the receiver tube, evaluated considering a linear heat loss coefficient following the manufacturer’s datasheet (Soltigua, 2016). Finally, and to take into consideration the thermal inertia of the collector, the indirect storage component is used. This component allows the representation of the receiver tube as a steel pipe in contact with the HTF and following, the calculation of the transient heat transfers occurring through the pipe and between the pipe and the HTF during non-steady-state conditions (Soares and Oliveira, 2017; Wagner

(1)

where α is the sun altitude angle and γ is the sun azimuth angle. The “line focusing solar collector” component is used to simulate the performances of the PTC. The heat absorbed by the receiver tube (Q ABS, REC ) is calculated as the product of the DNI , the net aperture area of the collector ( APTC ), the nominal optical efficiency of the collector (ηopt , N ), the longitudinal incident angle modifier (KL ), the cleanliness factor of the mirrors (ηclean ), the shading losses factor (ηshad ), and the focus state of the collector F (F = 1 in focusing state and 0 in defocusing state) :

Fig. 4. Parabolic trough collector loop simulation. 973

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following equation:

and Wittmann, 2014). The main simulation model′s parameters are summarized in Table 1.

ST ST w QST = QSF − Qloss, piping = ṁ SF ·(hHX , out − hHX , in )

where is the water/steam mass flow rate, is the steam enw thalpy at the heat exchanger outlet, hHX , in is the water enthalpy at the heat exchanger inlet, Qloss, piping represents the piping heat losses, specified as a temperature drop, ΔTN (see Table 2). In off-design conditions, this value is calculated assuming the following relation (STEAG Energy services GmbH, 2014) :

2.3. Validation approach As stated in Section 2.1, a comparison was carried out between the PTC simulation results and the experimental data measured by the test loop in four days, characterized by different irradiation conditions (clear sky, little cloud cover, average cloud cover and high cloud cover). Simulations were conducted on a 1 min-by-1-minute basis and assuming similar operating conditions to those adopted in the experimental facility; these concerns mainly:

htf

2

⎛ ṁ SF , N ⎞ Qloss, piping = ⎜ htf ⎟ ∗ ΔTN ⎝ ṁ SF ⎠ where

• The focusing process during the start-up phase or after a passing cloud, • The DNI threshold values for operating the solar collector.

htf ṁ SF ,N

(7)

is the nominal HTF flow rate circulating in the solar field.

3.2. Performance indicators Plant’s performances are assessed using thermodynamic and economic indicators. Solar field thermal output, recovered heat by the process and steam production over a given time period Δt (year, month, etc.), denoted respectively by QSF , t , QST , t and PST , t are evaluated using the following equations :

Hence, at each time step, experimental input conditions, including meteorological data (DNI, ambient temperature and wind speed), mirrors cleanliness, HTF inlet temperature and HTF flow rate are introduced in the simulation model; the predicted heat gain within the receiver tube of the PTC is then compared to the measured heat gain. Fig. 5 represent DNI and HTF mass flow variations for the considered days. For the 21st of September, DNI is in a range of 604 to 767 W/m2 between 09:00 and 16:00 with the quasi-absence of fluctuations, leading to the stable operation of the PTC as implied by the nearly constant HTF mass flow rate values. During the 26th of May, higher DNI values are achieved (780 to 926 W/m2) during the same period, and more instabilities are observed during the day; yet, no significant impact on the PTC operation is noticed. In contrast to the preceding days, higher DNI fluctuations are encountered for the 18th of May (0 to 864 W/m2 between 09:30 and 16:00) resulting in temporary shutdowns of the system at four different periods during the day. Finally, on a cloudy day with high DNI volatility as the 17th of May, the PTC can suffer from extended shutdown periods reducing its thermal output drastically.

QSF , t =

∑ QSF ·Δt

(8)

t

QST , t =

∑ QST ·Δt

(9)

t

PST , t =

∑

ST ṁ SF ·Δt

(10)

t

Similarly, solar field efficiency during a specific time period Δt is calculated as the ratio of the solar field thermal output to the available solar irradiation at the solar field aperture (Valenzuela et al., 2014):

ηSF , t =

QSF , t ∑t DNI ·ASF ·cos (θl )·Δt

(11)

On the other hand, the Levelized Cost of Heat (LCOH ) and the payback time (PBtime ), are used as indicators for the economic evaluation of the plant and are calculated using the following equations (Duffie and Beckman, 2013; Gabbrielli et al., 2014; Short et al., 1995):

3. Parabolic trough collector plant simulation 3.1. Simulation description

LCOH = The scheme of the studied plant is represented in Fig. 6. The plant is supposed to be located in Benguerir and includes six PTM-24 collectors arranged in two loops, with a North-South orientation. The solar field is used to heat the HTF, from 160 °C to 185 °C. The generated heat by the solar field allows then the evaporation of liquid water at 100 °C to produce saturated steam at 5 bars (151.83 °C), feeding the steam supply line of a given industrial to reduce its fossil fuel consumption (fuel oil). In addition to the plant’s components, two controllers were used in the simulation model. Controller_1 aims to maintain a fixed temperature at the outlet of the solar field by varying the HTF flow rate between the minimal and maximal threshold values. In contrast, Controller _2 aims to adapt the water flow rate to the available heat at the inlet of the heat exchanger. The salient parameters of the simulation model are given in Table 2. Simulations were conducted on an hourly basis. During the calculations, kinetic and potential energies were neglected, as well as pressure losses in the heat exchanger. The thermal output of the entire solar field QSF , can be calculated as htf ) and the HTF a function of the HTF flow rate in the solar field (ṁ SF enthalpies at the outlet and the inlet of the solar field, respectively htf htf hSF , out and hSF , in : htf htf htf QSF = ṁ SF ·(hSF , out − hSF , in )

(6) ST hHX , out

ST ṁ SF

PBtime =

IIC × CRF + O&Mc QSF , y ln( Q

IIC × i

SF , y / ηboiler × fc

(12)

+ 1) (13)

ln(1 + i)

where IIC is the initial investment cost, CRF the capital recovery factor, O&Mc the annual operation and maintenance cost, QSF , y the yearly Table 1 Main parabolic trough collector simulation model’s parameters. Parameters Collector geometry

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26 m • Length: Aperture width: 2.37 m

Indirect storage

•

HTF

•

Water

•

(5)

The available heat for steam production (QST ) is obtained from the

Values

Focal length: 0.8 m Receiver tube inner diameter Dint: 42.4 mm Thickness of the steel pipe: 3 mm Steel density: 8000 kg/m3 Steel thermal conductivity: 50 W/m K Steel heat capacity: 0.5 kJ/kg K cp (T) = 1.811 + 0.0037 T–8.88·10−15 T2 + 1.38·10−17 T3–10−20 T4 (kJ/kg K) ρ(T) = 840.2–0.68 T–2.27·10−12 T2 + 1.78·10−15 T3– 1.69·10−20 T5 (kg/m3) λ(T) = 0.136 −7·10−5 T–4.58·10−16 T2 + 1.19·10−18 T3–8.47·10−22 T4 + 8.27·10−25 T5 (W/m K) Calculated from REFPROP 9.11 (Lemmon et al., 2013)

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Fig. 5. Direct normal irradiance (a) and heat transfer fluid’s mass flow rate (b) variations for the considered days.

energy yield of the solar field, i the discount rate, ηboiler the boiler efficiency and fc is the fuel oil specific cost. The CRF is defined by Eq. (14):

CRF =

i. (1 + i)n (1 + i)n − 1

long-term and large spatial coverage irradiation data. However, the accuracy of the datasets needs to be checked. Indeed, plenty of satellite services and portals provide irradiation time series with different resolutions, which affects the precision of the simulation outputs (Merrouni et al., 2017c). Additionally, even with datasets with high accuracy (1 km2/pixel) such as HelioClim4, the DNI values have a relatively high BIAS of 7.9% for the hourly data (Cebecauer et al., 2011). In the same direction, some authors use Typical Meteorological Year (TMY) files from commercial software, which are, in some cases, interpolated data and with low accuracy. In this study, the irradiation data used for simulations are highquality data, measured in-situ using a Meteorological High Precision (MHP) station installed at Green Energy Park since 2015 (Fig. 7). This station, in addition to the regular weather parameters, measures the three components of solar irradiation separately and simultaneously. The Kipp&Zonen CHP1 pyrheliometer is used to measure the direct component. This sensor is a first-class ISO standard, and it provides DNI measurements with a maximum uncertainty of 2% for hourly data. For the GHI (Global Horizontal Irradiance) and DHI (Diffuse Horizontal Irradiance), the CMP21 pyranometers are used. The CMP21 is an ISO 9060 secondary standard sensor measuring the global irradiation with an uncertainty of ± 1 W/m2. The three sensors are mounted in a Solys2 sun tracker. This tracker is equipped with a sun sensor, and the whole system has a tracking accuracy of less than 0.02°. Another problem that appears when using in-situ irradiation

(14)

where n represents the number of years. 3.3. Meteorological data measurement To have a clear idea about the energy production and other technical performances of a CST plant before its operation, researchers and engineers usually call for simulation software (Merrouni et al., 2017b, 2017a, 2016). Besides the accuracy of the software used in the simulation, meteorological data are critical parameters influencing the simulation results; thus, the feasibility of CST-based projects implementation. In fact, the ideal way to have an accurate simulation result is the use of long-term datasets measured at ground level using secondary standards radiometers. Nevertheless, meteorological stations are costly; they require a lot of maintenance and to cover a large area, the one needs to implement a high number of stations for representative measurements. For this reason, in areas where the ground DNI measurements are scarce (like the case of Morocco and Middle East North Africa (MENA) region), researchers usually use irradiation data extracted from satellite images. Satellites have the advantage of providing 975

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Fig. 6. Plant’s simulation model.

measurements is crucial. Generally, a two years measurement campaign at ground level is required for the pre-feasibility study of CSP plants implementation (Gueymard, 2014). In this paper, three years (starting from 2015) of high-quality data measured at ground level, using the MHP station, were used to conduct the simulations. These data were carefully checked, and quality controlled using the method described in (Wolfertstetter et al., 2014). Besides, the sensors are cleaned every day (except for the weekends), and they are well-calibrated.

Table 2 Main plant’s model parameters. Parameters

Values

Solar field aperture ( ASF ) Row spacing (rs ) HTF mass flow rate (ṁ htf )

324 m2 6m 1–5 kg/s

Water/steam mass flow (ṁ ST ) Piping temperature drop(ΔTN ) Heat exchanger pinch-point(PinHX )

0–0.5 kg/s 2 K (at nominal HTF flow rate = 3 kg/s) 5K

3.4. Solar potential for the simulation location measurements is the annual and inter-annual variability (Kariuki and Sato, 2018). Indeed, the amount of solar irradiation received at ground level changes from one year to the other, and the use of long-term

Green Energy Park research facility (our field of study) is located in Benguerir, Morocco. Benguerir is an area characterized by a semi-arid climate. The daily average temperature values are around 19.53 °C

Fig. 7. The High Precision Meteo-station (a). Tracking cleanliness (TraCS) system (b). 976

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which may have a daily variation between 0.2 °C and 45.8 °C. The average relative humidity records are about 58.42%, and they vary from 0% to 100%. Benguerir is also characterized by the lack of precipitations (the three years rainfall average is 77.11 mm). Wind speed at the field of study can be considered as light /moderate, with an annual average of 2.87 m/s. The main meteorological parameter for our simulation is the DNI. Green Energy Park receives an annual DNI irradiation of 2239 kWh/m2; thus, it can be considered as one of the representative locations, from a technical and economic point of view, to evaluate the performance of a CST system. Fig. 8 presents the DNI measurements (at the site of Benguerir over three years) plotted over a running day number and hour of the day. This configuration is the most relevant one for solar plants operators and project managers because it shows the yearly variation of the DNI during the day; consequently, it can help to generate optimal operation strategies of the field. From Fig. 8, it can be seen that Benguerir is a well-irradiated site and that a CST-based plant can operate with high efficiency (with DNI values ≥ 700 W/m2) for at least 6–7 h during winter and fall and 12–13 h during summertime. Another way to evaluate the irradiation received in a selected site is the calculation of the distribution functions. In this paper, the hourly distribution function for the measured DNI values was calculated, see Fig. 9. From this plot, it can be observed that the occurrence of DNI values above 450 W/m2 is the most dominant with 62.3% (a total number of 2611), in comparison to the other ranges. The selected DNI of 450 W/m2 does not refer to any specific topic. We are considering this value as a representative threshold for an efficient starting operation point of our test loop. In fact, in the literature, there is no fixed DNI threshold for an efficient production starting point. However, according to (Desideri and Campana, 2014), a minimum DNI threshold of 150 W/m2 can ensure an acceptable efficiency of the heat transferred from the solar radiation to the HTF. Additionally, it is important to mention that DNI values less than 50 W/ m2, were not considered in Fig. 9 since the plant will not operate under such conditions.

r (t ) =

DNIref (t ) DNI (t )

(15)

where r (t ) is the mirror’s reflectance, DNIref (t ) is the Direct Normal Irradiance reflected by TraCS mirror and DNI (t ) is the Direct Normal Irradiance. For a better presentation of the soiling impact on solar mirrors, the Cleanliness index is the parameter that will be used in this study. The Cleanliness index is a metric calculated by dividing the reflectivity values of the mirror on dirty state by its reflectivity on the clean state (see Eq. (16)).

δ (t ) =

rdirty (t ) rclean (t )

(16)

where δ (t ) is the mirror’s cleanliness, rdirty (t ) is the mirror’s reflectance at the dirty state and rclean (t ) is the mirrors′ reflectance at the clean state. The cleanliness is a unitless parameter. A cleanliness value equals 1, means that the mirror is totally clean and there is no soiling affecting the mirror’s reflectance. Whereas, a cleanliness value equals to 0 implies a maximum soiling effect and a total drop of the mirror’s reflectance. The results of the soiling measurements campaign and their impact on the heat production will be discussed in detail on the results section. 4. Results and discussion 4.1. Parabolic trough collector model validation Comparative results between the measured and the heat gain values within the receiver tube for the studied days are illustrated in Fig. 10. As can be seen, experimental and simulated results are in a good agreement where the same trends are followed by the simulated and experimental curves. For the sake of precision, the deviations between heat gain values (simulated and measured) were calculated considering a time step of one minute. Results showed that the average deviation for the considered days is around 1.21 kWth which is equivalent to an average deviation of 8.1%. The daily average deviation decreases significantly (4.75–4.85%) under clear sky conditions (21/09 and 26/05), while it increases to 9.7–16% during days characterized by very fast DNI variations with extended shutdown periods (17/05 and 18/05), leading to a limited operation time under stable conditions.

3.5. Soiling measurement In this paper, soiling was measured for the dry period of the year using TraCS (see Fig. 7). TraCS is a fully automatic sensor that measures the specular reflectivity of a CST mirror in real-time and using the whole solar spectrum (Wolfertstetter et al., 2014). To measure the mirror’s reflectivity, TraCS uses the ratio of the DNI reflected values from the CST mirror over the DNI values, using Eq. (15):

4.2. Yield analysis of the plant Fig. 11 illustrates the solar field thermal output, the available heat

Fig. 8. Hourly direct normal irradiance values over running day number (X-axis) and hour of the day (Y-axis). The direct normal irradiance values are presented on the color bar (W/m2). 977

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Fig. 9. Frequency distribution of the hourly direct normal irradiance measurements.

for generating steam and solar field efficiencies variations during the different months; tabulated values of these indicators are listed in Table 3. Figure results report that the solar field thermal output and the recovered heat by the process vary respectively in a range of 8220–41,680 kWthh and 6510 to 37,560 kWthh. In contrast, solar field

monthly efficiencies values are varying in a range from 33 to 57.18%. It can be noticed that during winter months, the limited operation time of the plant (6–7 h according to Fig. 8) in addition to the longitudinal incidence angle modifier effect are negatively impacting the solar field efficiency. Thus, reducing drastically the thermal output of the solar

Fig. 10. Comparative results between the measured and the predicted heat gain values within the receiver tube (a: 21/09/2018, b: 26/05/2016, c: 18/05/2016, d: 17/05/2016). 978

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Fig. 11. Monthly variation of the solar field thermal output, the available heat for steam production and the solar field efficiency.

whereas the minimum value is recorded in December (10.06 T).

Table 3 Monthly and annual solar field thermal output, available heat for steam production and solar field efficiency. Months

January February March April May June July August September October November December Annual

QSF : Solar field thermal output (kWthh)

QST : Available heat for steam production (kWthh)

ηSF : Solar field efficiency (%)

11074.53 14513.82 25883.90 34482.66 33822.26 41680.74 33532.64 26464.20 28486.63 15720.44 14259.80 8218.98 288140.65

9124.41 12126.08 21991.09 29739.13 30280.78 37564.41 29713.36 23492.41 24864.00 13382.70 11945.80 6507.12 250731.34

36.43 43.80 53.27 56.30 54.78 57.18 57.10 54.10 54.20 46.30 41.44 33.00 48.99

4.3. Soiling impact on the field production It is true that Morocco possesses a high potential and that the integration of “solar heat” in the industrial sector can be very beneficial. Nevertheless, the country has a harsh climate, with the presence of dust, that can affect the durability and the optical performances of the mirrors and the receiver tubes, especially during the dry period of the year. In this part, the impact of soiling on the heat production of the simulated plant during the dry period of the year will be assessed. For this reason, the cleanliness values measured by the TraCS system from April the 28th until August 17th, 2018 will be presented and discussed. After that, the simulation results for the considered system with and without taking into consideration soiling/cleanliness as an input variable will be presented and compared. 4.3.1. Soiling campaign results The TraCS system has been used to measure the cleanliness drop. TraCS measures the mirror’s specular reflectance in a one-minute step. For a better presentation of the measured data, the daily cleanliness values were calculated (see Fig. 13). Besides, the TraCS mirror was cleaned every two weeks to avoid the saturation phenomenon. It is important to mention that an early cleaning event has been done (on purpose) on May the 18th, to mitigate a bird drop on the surface of the mirror to avoid affecting the measurements.

field and therefore the available heat for steam production. On the opposite, peak values for the different indicators are obtained during spring and summer months, particularly in June, due to the higher solar irradiations, extended operation time and lower impact of the longitudinal incidence angle modifier around noon time in comparison to the other months. These results are directly influencing the steam production (saturated steam at 5 bars) as shown in Fig. 12. Hence, maximum steam production quantity is reached in June (58.08 T),

Fig. 12. Monthly steam production. 979

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Fig. 13. Daily cleanliness values measured using Tracking cleanliness (TraCS) system.

words, the average daily cleanliness values are different. For instance, a cleanliness drop of 2.15% was measured between the 6th and the 7th of May, while between the 14th and the 15th of July the cleanliness drop was of only 0.6%. Besides, some natural cleaning events have been detected. In the literature, rain and humidity are generally the most reported

As can be seen from Fig. 13, soiling is an issue in Morocco, where the cleanliness can reach 79%, which is critical for plants’ operators. Another observation is that except for the jumps due to rain and dew accumulation on the mirror′s surface - May the 8th and August the 5th for instance- the daily cleanliness drops between two cleaning events generally follow a linear fitting. Nevertheless, the slops, or in other

Fig. 14. One-minute values of the humidity and temperature measured at the Green Energy Park on (a) the 31st of May and (b) the 5th of August. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 980

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deviations present thermal energy losses due to soiling.

parameters, acting as cleaning agents on solar power plants (Micheli and Muller, 2017). Still, these parameters play a double role, and they can contribute to the mirror’s cleanliness drop, by increasing the adhesion mechanisms especially in desert locations like Morocco (Ilse et al., 2018). In our case, and as mentioned above, some cleaning events were detected. For instance, in May the 8th rainfall caused a cleanliness jump of around 6%. Also, on May the 31st and August the 5th, dew accumulation on the mirror’s surface was the reason for the increase in the cleanliness values. Indeed, dew accumulation on the mirror’s surface occurs at the moment when the humidity reach high values, and the temperature values are low (Figgis et al., 2018). As can be seen from Fig. 14, in the early morning the humidity reaches high values (around 84% on the 31st of May and 70% on the 5th of August) while, the measured temperature values are low (12.4 °C on the 31st of May and 20 °C on the 5th of August). This drives to the dew formation and the accumulation of small water droplets on the surface, representing, therefore, a cleaning event.

ΔQSF = QSF , clean − QSF , soiled

Dev (QSF ) =

QSF , clean − QSF , soiled QSF , clean

(17)

(18)

where QSF , clean and QSF , soiled are respectively the solar field thermal output at the clean and the soiled states. As can be observed from Fig. 16, the impact of soiling is considerable. Indeed, the daily average difference of the thermal output is around 105 kWthh, and it can reach 354 kWthh, which is equivalent to a drop of 26% in the solar field production. This energy drop can increase dramatically if long term exposition without cleaning is assumed. With the considered cleaning scenario and according to Table 4, the cumulative thermal output losses are close to, or above 1200 kWthh for the majority of the exposition periods (a period is defined as the time duration between two consecutive cleaning events). The average thermal output drop of the exposition periods is 1301 kWthh, and the maximum drop is measured in the period between the 15th and the 29th of June, with a heat production loss of 2249 kWthh. This is equivalent to an average and a maximum deviation of 10.23 and 14.81% respectively. To conclude this part and taking into consideration the results above, it can be remarked that soiling is a major limiting efficiency factor for CST-based plants; since it has a direct impact on the solar field heat production. Unfortunately, this parameter is not thoroughly considered in the yield analysis of CST-based systems.

4.3.2. Soiling impact on the solar field′s output To evaluate the soiling effect on the solar field’s thermal output (QSF ) , a simulation run was conducted, taking into consideration the cleanliness values measured in the soiling measurement campaign, discussed in the previous subsection. During this campaign, the mirror was cleaned every two weeks. Consequently, the simulation of the soiling impact was conducted considering this hypothesis. The results of the soiled solar field simulation were compared afterward to the ones on the clean state. Fig. 15 presents the daily DNI sums and the daily thermal output (QSF ) from the simulated solar field with and without considering soiling. The simulations were conducted for the period from April the 28th until August 17th, 2018. As can be seen, the heat production values of the clean solar field are higher than the ones from the soiled field. We need to mention that the DNI is the most important parameter that influences the heat production, and with low DNI values (such as the one in May the 4th), the one may not see the difference in the production between the clean and the soiled scenario. For this reason and to better evaluates and visualize the soiling’s impact on the production, the two metrics: difference (ΔQSF ) and deviation (Dev (QSF ) ) between the daily solar field thermal output (QSF ) on the “clean” and the “soiled” status were calculated. A clean status of the solar field means a value of cleanliness equal 1, fixed in the simulation. The difference and deviation results are plotted in Fig. 16. These

4.4. Economic evaluation In addition to the thermodynamic analysis, a preliminary economic evaluation was conducted to assess the cost-effectiveness of the proposed system, and to evaluate the financial losses originating from soiling. As mentioned in section 3, LCOH and PBtime were considered as key indicators for the economic evaluation. The calculation of the LCOH and PBtime was conducted considering Eqs. (12)–(14). The main parameters used to calculate these indicators are summarized in Table 5, whereas economic evaluation’s salient results are listed in Table 6. Fig. 17 depicts the variation of the LCOH as a function of the specific cost of the solar field. As can be seen from the figure, the LCOH values are obviously decreasing as the solar field-specific cost is decreasing. Additionally, for solar field specific costs lower than 250 €/m2, the

Fig. 15. Daily thermal output of the solar field with and without taking into consideration soiling (cleanliness index). 981

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Fig. 16. Thermal energy losses due to soiling.

Table 4 Average cleanliness, cumulative heat production losses due to soiling and average deviations from the clean status for each exposition period. Periods

Average cleanliness

Total ΔQSF (kWthh)

Average Dev (QSF) (%)

Per1 Per2 Per3 Per4 Per5 Per6 Per7 Per8 Per9 Total

0.902 0.957 0.949 0.943 0.913 0.921 0.918 0.900 0.959 0.929

1497.74 362.47 1189.29 1266.64 2249.78 1628.70 1596.27 1574.07 349.08 11714.09

14.81 6.87 7.16 7.81 11.84 10.86 11.49 14.63 6.62 10.23

Table 6 Main results of the economic analysis. Solar field specific cost (€/m2)

100 150 200 250 300 350 400 450

Values

Solar field specific cost

100–450 €/m2 (Karellas and Braimakis, 2016; Li et al., 2012) 30 €/m2 (Tola et al., 2017) 5623 € (Braimakis and Karellas, 2017; Peters et al., 2002) 0.03–0.05 €/kWthh (market prices) 327 g CO2eq /kWthh (ADEME, 2014)

Piping cost Heat exchangers cost Fuel oil specific cost (fc) Fuel oil emissions (CO2, emissions ) Boiler efficiency (ηboiler) Operation and maintenance cost (O&Mc ) Yearly energy yield (QSF , y ) Project lifetime (n) Discount rate (i)

0.015 0.020 0.026 0.031 0.036 0.041 0.046 0.051

Payback time (years) fc = 0,03 €/kWthh

fc = 0,05 €/kWthh

4.37 5.63 6.85 7.99 9.07 10.11 11.09 12.02

2.71 3.56 4.38 5.16 5.91 6.64 7.34 8.02

makes the investment profitable. As discussed in Section 4.3, soiling can be a limiting efficiency factor for CST systems, leading to significant thermal output drop, and thus, financial losses. This is particularly true during the dry period of the year, even when considering a two-weeks cleaning scenario. Fig. 19 illustrates the economic savings for the plant owner during the dry period of the year, as a function of the fuel oil price for three scenarios:

Table 5 Parameters of the economic evaluation. Parameters

LCOH (€/kWthh)

• A base scenario, S0, corresponding to clean mirrors (cleanliness = 1), • The scenario S1, where the cleanliness factor was considered according to the soiling campaign results presented in Section 4.3, • In the scenario S2, soiling impact was considered as in S1. Moreover,

85% (Karellas and Braimakis, 2016) 2% of the solar field and heat exchanger total costs (Sharma et al., 2018) 288,141 kWthh (calculated) 25 years (Bellos et al., 2018) 5% (Allouhi et al., 2017)

it was assumed that the consumed fuel was subject to a carbon tax. A carbon price of 60 €/TCO2eq, consistent with achieving the temperature goal of the Paris Agreement (World Bank and Ecofys, 2018), was considered.

LCOH of the plant is not exceeding the minimum specific cost of fuel oil. These results indicate that using PTC for industrial heat generation, allows obtaining competitive LCOH values, especially when the cost of fuel oil is high. The payback time of the plant was calculated as a function of the solar field and fuel oil specific costs. The results of the calculations are presented in Fig. 18. As can be seen, the payback time of the solar field is evidently increasing with higher solar field specific cost and decreasing with higher fuel oil cost. For a low-cost solar field (specific cost < 250 €/m2) and considering an intermediate fuel oil price (0.04 €/kWthh), a payback time of about six years can be achieved, which

The calculation of the economic savings (E) for the different scenarios was, therefore conducted using the following relations:

forS 0 ⎧QSF , dry, clean × fc forS1 E = QSF , dry, soiled × fc ⎨ ⎪QSF , dry, soiled × fc − CO2, tax × CO2, eq forS 2 ⎩

(19)

where QSF , dry, clean and QSF , dry, soiled represent the thermal energy produced by the solar field during the dry period of the year by respectively 982

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Fig. 17. Levelized cost of heat as a function of the solar field specific cost.

Fig. 18. Payback time as a function of the solar field′s and fuel oil′s specific costs.

Fig. 19. Economic savings for the different scenarios as a function of the fuel oil specific cost.

5. Conclusions

neglecting and considering the impact of soiling, CO2, tax is the supposed value of the carbon tax (€/TCO2eq) and CO2, eq represents the CO2 equivalent emissions derived from the additional fuel consumed to compensate the energy losses due to the impact of soiling in comparison to the clean state. As shown in Fig. 19, the economic savings achieved using solar energy are evidently lessening when considering the impact of soiling. The effect is noticeably higher if the carbon taxes are considered. Thus, the relative difference from S0 vary between 9.23% for S1 and can reach up to 16.33% for S2. Accurate measurement of the soiling rate and developing optimized cleaning strategies, are therefore, fundamentals to ensure the economic viability of CST-based systems under semi-arid climate conditions.

The objective of this work was to accurately assess the potential of solar heat generation for medium temperature industrial applications, especially, for regions with semi-arid/arid climate and abundant solar resources, such as Morocco. Thus, a 186 kWth parabolic trough solar plant was simulated using real input data and considering the soiling impact as a dynamic parameter. The obtained results were then evaluated, considering technical and economic aspects. The main conclusions of this work are summarized as follows:

• The used model for PTC simulation is accurate and allows the si• 983

mulation of the solar collectors’ performances with a daily average deviation of 4.8% under clear sky conditions, Yield analysis results report an annual solar field production of

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•

• •

288,140 kWthh. The highest monthly thermal outputs are recorded during June, April, and May with respectively 41,680 kWthh, 34,482 kWthh, and 33,822 kWthh. Soiling has a significant impact on the plant’s production, where the average daily thermal losses are around 105 kWthh. This drop could be much higher if the cleaning was not conducted. For instance, in the period between the 15th and the 29th of June, the heat production loss of the system is 2250 kWthh which is equivalent to 11.84% energy drop in the production. These results can also impact the economic savings achieved using the solar field, The obtained LCOH for a PTC plant-specific cost varying between 100 and 450 €/m2 is ranging from 0.015 to 0.051 €/kWthh, which is very promising considering the fuel oil market prices, The payback time is depending on the fuel oil price, and it is not exceeding six years for a low-cost PTC plant (about 250 €/m2), which makes the investment financially reliable.

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