Performance Analysis and Optimization of Central Receiver Solar Thermal Power Plants for Utility Scale Power Generation

sustainability

Article Performance Analysis and Optimization of Central Receiver Solar Thermal Power Plants for Utility Scale Power Generation

Praveen R. P.

Department of Electrical Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi Arabia; [email protected]; Tel.: +966-559851412

Received: 23 November 2019; Accepted: 20 December 2019; Published: 23 December 2019

Abstract: The paper puts forth the design, performance analysis, and optimization of a 100 MWe central receiver solar thermal power plant with thermal energy storage capability, which can be utilized effectively to meet the renewable energy targets of the Kingdom of Saudi Arabia (KSA). In this paper, three representative sites in KSA are selected for analysis as these sites experience an annual average direct normal irradiance (DNI) of more than 5.5 kWh/m2/day. The optimization approach presented in this work aims to arrive at the best possible design parameters that suit a particular location in accordance with its DNI profile. From the analysis, an annual energy of 559.61 GWh can be generated in Yanbu with eight hours of thermal energy storage, 18.19% plant efficiency, and a capacity factor of 61.1%. The central receiver plant in Abha would be able to offer an annual energy of 536.31 GWh with the highest plant efficiency of 18.97% and a capacity factor of 60.7%. The performance of the proposed design in the two locations of Yanbu and Abha fares better when compared to the operational plant data of central receiver plant in Crescent Dunes. Based on the findings, the proposed 100 MWe central receiver Solar thermal power plants can be effectively implemented in KSA to meet the energy demands of the region.

Keywords: central receiver plant; optimization; plant eﬃciency; capacity factor; annual energy; LCOE

1. Introduction The world’s total energy demand could increase by 50% or higher by 2030. To address tomorrow’s energy demands, secure, sustainable, and cost-eﬀective carbon-free energy production is imperative. The rise in the energy demand due to the growing population and economy cannot be met by resorting to conventional energy sources such as fossil fuels or nuclear power. As per the Paris agreement, most of the world’s countries are committed to reducing carbon emissions so as to limit the global average temperature rise to well below 2 ◦C. Renewables in the global energy mix stand at 26% in the year 2018 and they have to reach 50% by 2030 according to the Sustainable Development Scenario (SDS) of the International Energy Agency (IEA) [1]. The Kingdom of Saudi Arabia (KSA), as of 2018, has achieved only 147 MW of renewable energy (RE) capacity compared to the 2330.73 GW capacity of the rest of the world [2]. Therefore, it is prudent for the policy makers to accelerate the prospects of harnessing energy from the abundant RE resources in the region. For KSA, solar energy is the best option for harnessing RE as the country receives over 2000 kWh/m2/year in most locations [3]. The energy consumption of KSA is expected to increase three-fold by 2030, and there is an immediate requirement to harness electrical energy from the abundant renewable energy sources in the Kingdom. From statistics for the past 10 years, it is observed that the energy demand in the Kingdom is growing at a rate of eight percent with a substantial increase in demand from the housing, sector which accounts for 50 percent of the Kingdom’s total electricity

Sustainability 2020, 12, 127; doi:10.3390/su12010127 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 127 2 of 16 production. According to the Vision 2030 plan, KSA has announced plans to generate 9.5 GW of electricity from renewable sources by 2030, and the government aims to invest more than 109 billion USD in the solar energy sector. In continuance with the Vision 2030 plan, and to meet the energy challenges of tomorrow, King Abdullah City for Atomic and Renewable Energy (K.A.CARE) has set its target to install 54 GW of power from RE sources by 2032. Interestingly, the major share (25 GW) of this target is expected to come from concentrated solar power (CSP), and the rest, 16 GW, from photovoltaic (PV) systems that directly convert solar energy to electrical energy. Presently, the total installed solar energy capacity of Saudi Arabia is only 139 MW, of which 89 MW is generated from PV technology and 50 MW from CSP systems [2]. The research work will support the policy makers in identifying the potential sites across the Kingdom for the installation of central receiver (also called solar tower or power tower) plants, which is a type of CSP plant wherein an array of dual-axis tracking reflectors (heliostats) concentrates sunlight on a tower-mounted receiver that contains heat transfer fluid (HTF). The HTF can be heated to 500–1000 ◦C, which can be then used as a heat source for power generation. Stand-alone renewable energy plants have challenges to deliver stable power to the grid. These plants require backup support in terms of electrical or thermal energy storage to supply firm power. The electrical storage requires large capital outlay and maintenance. Although the capital cost for PV systems is lower than that of CSP systems, the former accounts for a higher cost of power generation when taking into account the aspects of energy storage and power stability. CSP with thermal energy storage offers lower energy generation costs compared to PV with electrical energy storage to provide stable dispatchable power at large-scale plant capacities. Solar thermal power plants are ideal for locations that offer high direct normal irradiance (DNI), preferably in the range of 2000 kWh/m2 to 2500 kWh/m2. The data from K.A.CARE indicate that most of the provinces in KSA offer high DNI and, hence, are suitable for the installation of CSP plants. There are four types of CSP technologies: Parabolic trough collector, central receiver, linear fresnel reflector, and parabolic dish systems. Among these technologies, parabolic trough collector and central receiver plants are most popular for utility scale power generation. Among the CSP technologies mentioned above, solar power tower or central receiver-based CSP technology is projected to be cheaper by 2020 [4]. They are gaining popularity when compared to parabolic trough systems due to their ability to operate at higher HTF temperature, thereby substantially bringing down the thermal energy storage (TES) costs. In addition, the land requirement for the solar tower-based CSP system is small when compared with other technologies. In this research work, a solar tower-based CSP system was designed and analysed so as to be installed in potential locations in the Kingdom of Saudi Arabia. Figure1 shows the working of a central receiver solar thermal power plant, which mainly includes three sub systems: Heliostat field, the central receiver, and power conversion system. Central receiver plants use point focus technology where a heliostat field consisting of a number of flat movable mirrors focuses the sunlight on to a receiver mounted on top of a tower. The HTF circulated in the central receiver absorbs the heat from the solar rays reflected by the heliostats. By using appropriate HTFs, a working temperature of even 1000 ◦C can be achieved, improving power cycle efficiency. A power conversion system employing the Rankine cycle then coverts the thermal energy absorbed by HTF into electrical energy. One of the main highlights of solar tower-based technology when compared to other CSP-based technologies is that by using appropriate HTF, temperature of even 1000 ◦C can be reached, which improves the power cycle efficiency. The main aim of this research work is to put forth an improved modeling and optimization approach that can be adopted for sizing the central receiver plants for any potential locations around the world. Most of the regions in the GCC (Gulf Cooperation Council) are considered ideal for the installation of CSP plants as their annual average direct normal irradiance (DNI) is greater than 5.5 kWh/m2/day. However, the potential of solar thermal energy in the region has not been tapped much when compared to that of PV plants, as is clear from the projected and ongoing project details in the region. Hence, this research work also explores the viability and potential of central receiver plants Sustainability 2020, 12, 127 3 of 16 in KSA, which can be a good indicator to the policy makers for ensuring a sustainable energy future of

Sustainabilitythe GCC region. 2019, 11, x FOR PEER REVIEW 3 of 16

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2. LiteratureBy using Review appropriate HTFs, a working temperature of even 1000 °C can be achieved, improving powerThe cycle total efficiency. capacity ofA CSP-basedpower conversion commercially system operating employing projects the Rankine around thecycle world then ascoverts of 2018 the is thermal5.469 GW, energy and for absorbed the Middle by HTF East, itinto is onlyelectrical 156 MW energy. [2]. In One [4], of the th authorse main reviewedhighlights the of technologicalsolar tower- basedtrends technology and emerging when technologies compared into centralother CSP receiver-based systems technologies to operate is that at by temperatures using appropriate greater HTF, than temperature700 ◦C so as of to even achieve 1000 higher °C can e ffibeciency. reached The, which prospects improves of gas- the andpower liquid-based cycle efficiency. receiver systems thatThe can achievemain aim this of highthis research range of work operating is to temperaturesput forth an improved has also been modeling reviewed. and optimization The authors approachof [6] carried that out can a be comparative adopted for analysis sizing the between central the receiver different plants software for any tools potential used forlocations heliostat around field thedesign world. and Most analysis. of the Theregions paper in concludedthe GCC (Gulf that Cooperation SolTrace, Tonatiuh, Council) CRS4-2, are considered and MATLAB ideal for code the instyieldedallation similar of CSP results. plants Parametric as their annual analysis average of medium- direct to normal large-size irradiance central receiver (DNI) is plants greater is exploredthan 5.5 kWh/min [7]. The2/day. authors However, studied the potential the performance of solar thermal of the plantsenergy inin threethe region different has not locations, been tapped i.e., Seville, much whenDaggett, compared and Carnarvon. to that of PV The plants performance, as is clear of from the central the pro receiverjected and plants ongoing when project subjected details to directin the region.steam and Hence, molten this salt research configuration, work also with explores and without the viability thermal and energy potential storage of central and di rffeceivererent turbine plants incapacities, KSA, which was investigated.can be a good In indicator [8], a preliminary to the policy heliostatic makers field for layoutensuring in Southerna sustainable Tunisia energy was carriedfuture ofout the by GCC reducing region. the optical losses due to blocking and shading effects. A nonlinear mathematical model was developed for central receiver plants using thermal energy storage in [9], and numerical 2.optimization Literature Review techniques were used to solve the model. The research work presented in [10] proposed an optimizationThe total capacity technique of CSP for- centralbased commercially receiver systems operating where projects heliostats’ around location the andworld design as of of2018 solar is 5.469towers GW were, and considered for the Middle simultaneously East, it is only for 156 a fixedMW [2]. heliostat In [4], the size. authors The authors reviewed in [the11] technological developed a trendsmodel and for the emerging collector techno and receiverlogies insystem central ofreceiver a 1 MWe systems Dahan to solar operate tower at temperatures plant. The authors greater used than a 700STAR-90 °C so simulationas to achieve platform higher toefficiency. evaluate The the prospects dynamiccharacteristics of gas- and liquid of the-based plant. receiver The GEMASOLAR systems that cansolar achieve tower plantthis high behavior range inof selectoperating locations temperatures of China ha wass also evaluated been reviewed. in [12]. The The authors, authors basedof [6] carriedon their out analysis, a comparative recommended analysis thebetween installation the different of central software receiver tools plants used for in Chineseheliostat locationsfield design as andit is possibleanalysis. to The achieve paper annual concluded overall that e ffi SolTrace,ciency of Tonatiuh, 14%. The CRS4 common-2, and model MATLAB equations code used yielded for similarapproximating results. Parametric atmospheric analysis extinction of medium used in- to ray large tracing-size c toolsentral were receiver summarized plants is explored and compared in [7]. Thein [13 authors]. In [14 studied], the authors the performance evaluated theof the influence plants ofin operatingthree different strategy locations on the,system i.e., Seville, design Daggett of CSP, and Carnarvon. The performance of the central receiver plants when subjected to direct steam and molten salt configuration, with and without thermal energy storage and different turbine capacities, was investigated. In [8], a preliminary heliostatic field layout in Southern Tunisia was carried out by reducing the optical losses due to blocking and shading effects. A nonlinear mathematical model was developed for central receiver plants using thermal energy storage in [9], and numerical optimization Sustainability 2020, 12, 127 4 of 16 plants. The authors concluded that optimal control techniques when applied to thermal energy storage operation could improve the profitability of the plant. The potential of solar and wind energy-based distributed generation in Saudi Arabia was evaluated in [5]. The authors, based on detailed analysis, concluded that Saudi Arabia has large potential to tap renewable energy from its abundant solar and wind energy resources.

3. Selecting the Site and Assessing the Solar Resource After shortlisting potential locations in the Kingdom based on the solar irradiance data, an initial design for the proposed solar tower-based CSP system was developed adhering to the specific needs of the shortlisted sites. The initial design developed was analysed in commercial software, Solar Advisor Model (SAM [15]) from National Renewable Energy Laboratory (NREL), to assess the performance of the initial design. The next stage in the research was to further optimize the initial design of the solar tower-based CSP system, and detailed analysis of the optimized design was carried out in SAM so as to give the final recommendations of the research. Selecting an ideal site for the installation of a CSP plant is crucial for reaping maximum performance from the plant. CSP plants are economically viable to install at locations whose DNI is greater than 5.5 kWh/m2/day (1800 kWh/m2/year) [16]. The data from K.A.CARE indicate that the annual average daily global horizontal irradiance (GHI) measured for the 30 weather stations spread across the Kingdom ranged from about 5.7 kWh/m2 to 6.7 kWh/m2. Hence, it is clear that there is enormous potential for solar energy-based systems in the Kingdom. On analyzing the DNI profile from K.A.CARE, the western province of Saudi Arabia has tremendous potential for CSP system installation, followed by the southern and central provinces. In this research work, three representative sites located in different provinces of Saudi Arabia, i.e., Yanbu in the Western province, Abha in the southern province, and Dawadmi in the Central province, were selected for analysis as these sites experience an annual average DNI of over 5.5 kWh/m2/day. The potential of all these locations for the installation of commercial central receiver plants was assessed accurately after optimizing the performance of the proposed 100 MWe central receiver solar thermal power plant. The TMY (typical meteorological year) data that include hourly DNI, ambient temperature, wind speed, and atmospheric pressure were decisive parameters for accurately predicting the solar field thermal power of a CSP plant. Table1 shows the characteristics of the locations considered in this research work.

Table 1. Characteristics of the shortlisted locations analysed in this work.

Latitude and Annual DNI Average Elevation Location 2 Data Source Longitude (kWh/m /day) Temperature (◦C) (m) 24.1440 N, Yanbu (K.S.A) 6.86 28.7 7.9 ISD 38.0630 E 18.240 N, Abha (K.S.A) 6.52 19.0 2090.3 ISD 42.6570 E Dawadmi 24.450 N, 6.11 22.3 922.3 ISD (K.S.A) 44.1210 E

Figure2 shows a comparison between the monthly variation of average DNI for a typical year at the three locations analysed in this study. From Figure2, it is clear that the locations in Yanbu and Abha have a DNI greater than 5.5 kWh/m2/day all year round, which is considered ideal for installing CSP plants. The location in Dawadmi is also found to oﬀer a DNI level greater than 5.5 kWh/m2/day except for the month of December. For Yanbu, a maximum DNI of 7.47 kWh/m2/day is received for the month of June, while the minimum value of 6.23 kWh/m2/day is recorded during the month of November. The location in Abha oﬀers a peak value of 6.75 kWh/m2/day during June, whereas the lowest DNI Sustainability 2019, 11, x FOR PEER REVIEW 5 of 16

Figure 2 shows a comparison between the monthly variation of average DNI for a typical year at the three locations analysed in this study. From Figure 2, it is clear that the locations in Yanbu and Abha have a DNI greater than 5.5 kWh/m2/day all year round, which is considered ideal for installing CSP plants. The location in Dawadmi is also found to offer a DNI level greater than 5.5 kWh/m2/day Sustainabilityexcept for2020 the, 12month, 127 of December. For Yanbu, a maximum DNI of 7.47 kWh/m2/day is received5 of for 16 the month of June, while the minimum value of 6.23 kWh/m2/day is recorded during the month of November. The location in Abha offers a peak value of 6.75 kWh/m2/day during June, whereas the 2 oflowest 6.33 kWh DNI/ m of / 6.33day is kWh/m recorded2/day during is recorded the month during of February. the month Dawadmi of February. records Dawadmi a maximum records of a 2 2 6.82maximum kWh/m of/day 6.82 in kWh/m August2/day and in a lowAugust of 5.39 and kWh a low/m of/day 5.39 during kWh/m December.2/day during December.

FigureFigure 2. 2.Average Average direct direct normal normal irradiance irradiance (DNI) (DNI) for for selected selected sites. sites.

4.4. DesignDesign ofof thethe 100MW100MW CentralCentral ReceiverReceiver SolarSolar ThermalThermal Power Power Plant Plant TheThe e efficiencyﬃciency of of a a heliostat heliostat depends depends on on its its location location with with respect respect to to the the receiver. receiver. Unlike Unlike that that of of a a parabolicparabolic troughtroughcollector collector systemsystem wherewhere thethe cosinecosine eeffectﬀect dependsdepends onlyonly onon thethe hourhour ofof thethe day,day, forfor a a centralcentral receiver receiver system, system, it it depends depends on on the the location location and and the the position position of of the the sun sun as as well. well. TheThe cosine cosine angle angle can can be be determined determined in in line line with with [ 17[17]] and and is is given given as as s 1 + cos(2휃 ) √1 + cos 2θi,𝑖p,푝 (1) coscosθ(휃𝑖,푝)== (1) i,p 22

2 The total annual solar energy Es (Wh/m2 ) per mirror area is The total annual solar energy Es (Wh/m ) per mirror area is 8760 8760 퐸푠 = X∑ 퐷푁퐼𝑖 × cos (휃𝑖,푝) (2) Es = DNI cos θ (2) 𝑖=0 i × i,p i=0 where i refers to hour, and p the location of the point. wherePractically,i refers to hour,heliostats and p arethe arranged location ofin thea typical point. fashion with gaps between them for shadowing and Practically,blocking considerations heliostats are arrangedand maintenance in a typical requirements. fashion with gapsPacking between density them (PD) for is shadowing defined as and the blockingratio of the considerations mirror area andto land maintenance area and is requirements. different for various Packing locations density (PD)in the is heliostat defined asfield. the Thus ratio, offor the a c mirrorentral areareceiver to land system, area the and effect is di ffoferent PD also for various has to be locations considered in the to heliostatestimate field.the actual Thus, energy for a centralreflected. receiver system, the effect of PD also has to be considered to estimate the actual energy reflected. ActualActual annualannual reflectedreflected energyenergy perper unitunit landland area,area, atat aa pointpointp pin in the the base base field field considering considering the the eeffectffect of of PD, PD, is is given given by by 8760X Ea = (PD) DNI cos θ (3) i × i,p i=0 Sustainability 2020, 12, 127 6 of 16

At design conditions, the thermal power of the heat transfer ﬂuid (HTF) at which the plant generates the design electric power can be calculated as

P 106 = des Ethht f ,des × (4) ηE ηHE PB × Therefore, the thermal power that needs to be collected from the heliostat ﬁeld can be calculated by the following equation Eth = ht f ,des Ethdes (5) ηreceiver Substituting (4) in (5), P 106 = des Ethdes × (6) η ηHE ηE receiver × × PB One of the main characteristics of CSP plants is their ability to store thermal energy. Hence, these systems are capable of supplying ﬁrm power to the grid when solar radiation is weak and can generate power even after sunset, depending on their thermal energy storage (TES) capacity. The maximum energy that can be stored can be determined by the following equation:

E Htes thht f ,des × ETESmax = (7) ηSHE

The two main parameters that are used to assess the overall performance of the central receiver plant design is the capacity factor (CF) and solar-to-electric conversion efficiency. A utility level CSP plant design is said to be commercially viable only if both CF and solar-to-electric conversion efficiency of the plant is optimized with regard to appropriate TES capacity for the location. Capacity factor is defined as the ratio of actual output generated from the plant in a year to the maximum possible output from the plant during the period under ideal conditions and is given by the following expression: Gross Annual Electricity Generated CF = (8) P 106 8760 des × × The annual solar-to-electric conversion efficiency (ηse) is the ratio of the total annual electricity generated from the plant to the total incident solar energy received by heliostats.

Total Annual Electricity Generated η = (9) se Total incident solar energy received by the heliostats

Characteristics of the Proposed CSP Plant Design Table2 shows the design parameters of the proposed 100 MW central receiver solar thermal power plant. The various design parameters related to the heliostats, receiver, and the tower are listed in the table. For utility scale power generation, it is very important to operate the plant with optimum eﬃciency, TES capacity, capacity factor, and least levelized cost of energy (LCOE). Thus, for a central receiver solar thermal power plant, the main design parameters, such as solar multiple (SM) and number of hours of thermal energy storage, can only be arrived at after conducting detailed performance analysis of the proposed design. Sustainability 2020, 12, 127 7 of 16

Table 2. Design parameters of the central receiver plant.

Parameter Value

Name Plate Capacity 100 MWe Design Point DNI 950 W/m2 Heliostat width 12.2 m Heliostat height 12.2 m Receiver type Cylindrical Receiver Diameter 17.61 m Receiver Height 20.41 m HTF Type Hitec molten salt Storage Type Two Tank Tower Height 203 m

SolarPILOT software (NREL, Colorado, USA) is used to model the heliostat field layout corresponding to each value of SM in each of the locations. One of the main highlights of the software is that it can be integrated with the System Advisor Model (SAM) from NREL and can optimize the field layout for better performance. The software uses the analytical flux image Hermite series approximation to individual heliostat images rather than large groups or zones of heliostats and hence can characterize a wide variety of heliostat field layouts [18]. Table3 lists the number of heliostats for each value of SM in all the three locations. From the table, it is clear that as the SM value is increased, the number of heliostats in the solar field layout also increases. As an example, Figure3 shows the heliostat field layout of Yanbu corresponding to an SM of 2.4 comprising 8456 heliostats. The following section details the optimization procedure adopted in this research work so as to determine the optimal design parameters of the plant in each of the three locations. Furthermore, the performance analysis of this optimal configuration was carried out to determine the efficiency, capacity factor, and LCOE of the proposed plant configuration.

Table 3. Number of heliostats corresponding to solar multiple (SM) in each locations.

Number of Heliostats Solar Multiple Yanbu Abha Dawadmi 1 3444 3465 3432 1.2 4098 4119 4084 1.4 4770 4785 4754 1.6 5465 5463 5446 1.8 6166 6175 6146 2 6903 6897 6881 2.2 7656 7658 7632 2.4 8456 8459 8429 2.6 9305 9293 9273 Sustainability 2020, 12, 127 8 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 8 of 16

FigureFigure 3. 3.Heliostat Heliostat ﬁeldfield layout layout for for Yanbu Yanbu generated generated from from SolarPILOT. SolarPILOT.

5.5. Results Results and and Discussion Discussion BecauseBecause the the DNI DNI characteristics characteristics vary vary distinctly distinctly with with respect respect to eachto each location, location, it is it not is not prudent prude tont fix to thefix SMthe correspondingSM corresponding to each to locationeach location and thereafter and thereafter optimize optimize the TES the capacity TES capacity to that to particular that particular value ofvalue SM. Thisof SM. kind This of optimizationkind of optimization approach approach will result will in reducedresult in plant reduced efficiency plant withefficiency high LCOE.with high LCOE.The optimization approach presented in this work aims to arrive at the best possible design parametersThe optimization suiting a particular approach location presented in accordance in this work with itsaims DNI to profile. arrive First,at the the best variation possible of LCOEdesign withparameters SM was suiting analysed a particularfor different location TES capacities in accordance in all the with locations its DNI investigated profile. First in, the study. variation This of analysisLCOE with was SM followed was analy by estimatingsed for different the effi TESciency capacities of the in plant all the with locations SM for investigated different TES in capacities. the study. BasedThis analysison these was two analyses,followed an by optimal estimating value the of efficiency SM was fixed, of the and plant subsequently, with SM for the differentoptimal TES TES capacitycapacities. of theBased plant on could these be two obtained analys basedes, an onoptimal the peak value effi ofciency SM wa value.s fixed, and subsequently, the optimalFigures TES4– capacity6 show theof the variation plant could of LCOE be obtained with SM based pertaining on the to pe diakfferent efficiency TES capacities value. in Yanbu, Abha,Figure and Dawadmi,s 4–6 show respectively. the variation From of LCOE the figures,with SM it pertaining is clear that to oncedifferent the SM TES value capacities increased, in Yanbu, the LCOEAbha,of and the Dawadmi plant started, respectively. to come down,From the depending figures, it on is theclear TES that capacity once the of SM the value plant. increased, This fact the is obviousLCOE of as the increasing plant start theed SM to increasescome down the, depending number of on heliostats the TES tapping capacity the of solarthe plant. energy, This thereby fact is generatingobvious as more increasing power the and SM subsequently increases the bringing number down of heliostats the LCOE. tapping However, the solar if the energy, plant does thereby not havegenerating TES capacity more power or has lessand TESsubsequently capacity, there bringing is no down point inthe increasing LCOE. However, the SM beyond if the plant a point does when not LCOEhave TES starts capacity to increase or has due less to underutilization TES capacity, there of the is solarno point field, in which increasing would the result SM in beyond capital a losses point andwhen uneconomic LCOE starts operation to increase of the due plant. to underutilization Figures4–6 also of depict the solar the factfield that, which it is notwould possible result to in fix capital the SMlosses solely and based uneconomic on variation operation of LCOE of the as plant. other plantFigure performances 4–6 also depict parameters, the fact suchthat it as is capacity not possible factor to andfix the plant SM e ffisolelyciency, based also on have variation to be accounted of LCOE as for other to reach plant the performance optimal design parameters parameters., such as However, capacity thisfactor analysis and p giveslant efficiency an initial, ideaalso regardinghave to be the accounted range of for SM to values reach that the canoptimal be investigated design parameters. further forHowever, optimization this analysis purposes. gives The an SM initial range idea that regarding can be considered the range of for SM investigation values that can in the be locationsinvestigated of Yanbufurther and for Abha optimization as per Figures purposes.4 and5 Thefalls betweenSM range 2.2 that and can 2.6, be whereas considered that for for Dawadmi investigation is between in the 2locations and 2.6. of Yanbu and Abha as per Figures 4 and 5 falls between 2.2 and 2.6, whereas that for Dawadmi is between 2 and 2.6. Sustainability 2020, 12, 127 9 of 16

SustainabilitySustainability 20192019,, 1111,, xx FORFOR PEERPEER REVIEWREVIEW 99 ofof 1616

LocationLocation :: YanbuYanbu 26 24

22 TES =0 20 TES = 2 18 TES = 4 TES = 6 16 TES = 6 TES = 8 14

TES = 10 LCOE LCOE (cents/kWh) LCOE LCOE (cents/kWh) 12 TES = 12 10 TES = 14 8 6 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 SolarSolar MultipleMultiple

FigureFigure 4.4. VariationVariation of s solarsolar m multipleultiple and levelized levelized cost cost of of energy energy (LCOE) (LCOE) with thermal thermal energy energy storage (TES)(TES) forfor thethe locationlocationlocation ininin Yanbu.YanbuYanbu..

LocationLocation :: AbhaAbha 26

24 22 22 TES = 0 20 TES = 2 18 TES = 4

16 TES = 6 TES = 8 14 TES = 8

LCOE LCOE (cents/kWh) TES =10 LCOE LCOE (cents/kWh) TES =10 12 TES =12 10 TES =14 8

6 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 SolarSolar MultipleMultiple

Figure 5. VariationVariation of s solarsolar m multipleultiple andand LCOELCOE withwith TESTES forforr thethe locationlocation inin Abha.AbhaAbha.. Sustainability 2020, 12, 127 10 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 10 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 10 of 16

32 Location : Dawadmi 32 Location : Dawadmi 30 30 28 28 TES = 0 26 TES = 0 26 TES = 2 24 TES = 2 24 TES = 4 22 TES = 4 22 TES = 6 20 TES = 6 20 TES = 8 18 TES = 8

LCOE LCOE (cents/kWh) 18 TES = 10 LCOE LCOE (cents/kWh) 16 TES = 10 16 TES = 12 TES = 12 14 14 TES =14 TES =14 12 12 10 10 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 1 1.2 1.4 1.6 Solar 1.8Multiple 2 2.2 2.4 2.6 Solar Multiple

Figure 6. Variation of solar multiple and LCOE with TES for the location in Dawadmi. Figure 6. VariationVariation of s solarolar m multipleultiple and LCOE with TES for the location in Dawadmi.Dawadmi. Figures 7–9 show the variation of efficiency with SM for different TES capacities in Yanbu, Abha, FiguresFigures7 7––99 show show thethe variationvariation ofof eefficiencyfficiency with with SM SM for for different different TES TES capacities capacities in in Yanbu, Yanbu, Abha Abha,, and Dawadmi, respectively. From the figures, it is clear that the efficiency value peaks for a particular and Dawadmi Dawadmi,, respectively. From the figures,figures, it is clear that the efficiency efficiency value peaks for a particular particular value of SM for each TES value, and thereafter, it decreases. But other parameters such as LCOE and value of SM for each TES value,value, and thereafter,thereafter, itit decreases. But other parameters such as LCOE and CF also have to be considered simultaneously to decide the optimal SM value. Thus, based on the CF also havehave to bebe consideredconsidered simultaneouslysimultaneously to decide the optimal SM value.value. Thus, based on the tradeoff between the two performance parameters such as efficiency and CF of the plant, as well as tradeotradeoffff betweenbetween thethe twotwo performance performance parameters parameters such such as as effi efficiencyciency and and CF CF of theof the plant, plant, as wellas well as the as the economical parameter LCOE, it was found that the ideal value of SM for all the three locations is economicalthe economical parameter parameter LCOE, LCOE it was, it wa founds found that that the idealthe ideal value value of SM of forSM allfor the all threethe three locations locations is 2.4. is 2.4. 2.4.

20 Location : Yanbu 20 Location : Yanbu 19 19 18 TES = 0 18 TES = 0 TES =2 17 TES =2 17 TES = 4 TES = 4 16 TES = 6 16 TES = 6 TES = 8

Efficiency Efficiency (%) 15 TES = 8

Efficiency Efficiency (%) 15 TES = 10 TES = 10 14 TES = 12 14 TES = 12 TES = 14 13 TES = 14 13 12 12 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 1 1.2 1.4 1.6 Solar 1.8Multiple 2 2.2 2.4 2.6 Solar Multiple

FigureFigure 7.7. Variation ofof plantplant eeﬃfficiencyciency withwith solarsolar multiplemultiple withwith fullfull loadload hourshours ofof TESTES forfor thethe locationlocation Figure 7. Variation of plant efficiency with solar multiple with full load hours of TES for the location inin Yanbu.Yanbu. in Yanbu. Sustainability 2020, 12, 127 11 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 11 of 16

Location : Abha 20

19 TES = 0 18 TES = 2 17 TES = 4

16 TES = 6 TES = 8

Efficiency Efficiency (%) 15

Efficiency Efficiency (%) 15 TES = 10 14 TES = 12 13 TES = 14

12 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 Solar Multiple

Figure 8.8. Variation of plantplant eﬃfficiencyciency withwith solarsolar multiplemultiple withwith fullfull loadload hourshours of TES for the location inin Abha.Abha.

Location : Dawadmi 20

TES = 0 18 TES = 2 17 TES = 4

16 TES = 6

TES = 8 Efficiency Efficiency (%) Efficiency Efficiency (%) 15 TES = 10 14 TES = 12 TES = 14 13

12 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 Solar Multiple

Figure 9.9. Variation of plantplant eﬃfficiencyciency withwith solarsolar multiplemultiple withwith fullfull loadload hourshours ofof TES for thethe location inin Dawadmi.Dawadmi.

The next step of the the optimization optimization procedure is to findfind the the optimal optimal number of hours of TES correspondingcorresponding to an SMSM of 2.42.4 inin eacheach ofof thethe locations.locations. FiguresFigures 10–12 depictdepict thethe variationvariation of plantplant eefficiencyfficiency andand LCOE for didifferentfferent TES values at SM = 2.42.4 at at Yanbu, Yanbu, Abha Abha,, and and Dawadmi Dawadmi,, respectively. respectively. From the figures, figures, it is clear that that the the efficiency efficiency of of the the proposed proposed plant plant in in all all the the locations locations peaks peaks when when 8 8h hof of thermal thermal energy energy storage storage is is adopted. adopted. For For Yanbu, Yanbu, plant plant efficiency efficiency of of 18.19% can be achieved with an LCOE of 10.75 centscents/kWh./kWh. The highest value of plant efficiency efficiency can be obtained from the location inin Abha,Abha, whichwhich can oofferffer anan eefficiencyfficiency ofof 18.97%18.97% withwith anan LCOELCOE ofof 10.8110.81 centscents/kWh./kWh. The location in Dawadmi, which has relatively weak DNI characteristics when compared to the other two locations, has a higher LCOE value of 12.25 cents/kWh and the lowest plant efficiency of 18.01%. Sustainability 2020, 12, 127 12 of 16

Dawadmi, which has relatively weak DNI characteristics when compared to the other two locations, has a higher LCOE value of 12.25 cents/kWh and the lowest plant eﬃciency of 18.01%. Sustainability 20192019,, 1111,, xx FORFOR PEERPEER REVIEWREVIEW 1212 of 1616

20 18 19 17 19 18.1918.19 18.1818.18 18.1718.17 18.1618.1617 17.7717.77 18 16 17 15 15.61 16 15.61 14

15 13

LCOE LCOE 14 12

11.9611.96 Plant Efficiency Plant Efficiency (%) Plant Efficiency Plant Efficiency (%) 13 11 13 11.4111.4111 11.1911.19 10.9710.97 12 10.7710.77 10.7510.75 10 11 9 10 8 0 2 4 6 8 10 12 14 TES (Hrs) Plant Efficiency LCOE Location : Yanbu Plant Efficiency LCOE Figure 10. Plant e efficiencyﬃfficiencyciency and and LCOE LCOE variation variation with with full full load load hours hours ofof TES TES atat SMSM == 2.42.4 in Yanbu Yanbu...

20 18 18.9718.97 18.9618.96 18.9518.95 18.7318.73 19 18.7318.73 17 18 16 16.7616.76 17 15 16 14

15 13

LCOE LCOE

14 12 Plant Efficiency Plant Efficiency (%) Plant Efficiency Plant Efficiency (%) 13 11.711.7 11.4811.48 11 11.2511.25 11.0311.03 12 10.7310.73 10.8110.81 10 11 9 10 8 0 2 4 6 8 10 12 14 TES (Hrs) Location : Abha Plant Efficiency LCOELCOE FigureFigureigure 11.11. Plant e efficiencyﬃfficiencyciency and and LCOELCOE variationvariation withwith fullfull loadload hourshours ofof TESTES atat SMSM == 2.4 in Abha Abha... Sustainability 2020, 12, 127 13 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 13 of 16

20 18 19 17 18.01 17.99 17.98 17.91 17.97 18 16

17 16.38 15 16 14 15 13

13.01 LCOE 12.88 12.76 14 12.5 12 12.07 12.25 Plant Efficiency Plant Efficiency (%) 13 11 12 10 11 9 10 8 0 2 4 6 8 10 12 14 TES (Hrs) Location : Dawadmi Plant Efficiency (%) LCOE

Figure 12. Plant e fficiencyﬃciency and LCOE variation with full load hours of TES at SM = 2.4 in Dawadmi Dawadmi..

Figure 13 shows the performance comparison of the optimized centralcentral receiverreceiver solar thermal plant configurationconfiguration in each of the three locations. From the figure, figure, it is evident that Yanbu and Abha are most suitable for the installation of c centralentral r receivereceiver CSP plants as they can offer offer CF of 61.1 and 60.7,60.7, respectively respectively,, with superior plant plant efficiency efficiency and and low low LCOE. LCOE. This This high high value value of of CF CF obtained obtained as as a aresult result of of the the optimization optimization approach approach adopted adopted in in the the study study is is a a good good indicator indicator that that a a plant can be operated at its optimum output power level. Table Table 44 summarizessummarizes thethe resultsresults obtainedobtained forfor thethe finalfinal optimized plant configuration configuration in all the three locations investigated in this research work. From the table,table, it it is is clear clear that that plant plant efficiencies efficiencies ofof 18% 18% and and above above can can be achieved be achieved in all in the all three the three locations. locations. The TheLCOE LCOE,, which which depends depends on th one amount the amount of annual of annual energy energy that thatcan canbe generated be generated from from the plant the plant,, is the is theleast least in Yanbu, in Yanbu, 10.75 10.75 cents/kWh cents/kWh,, while while the thetotal total amount amount of energy of energy generated generated from from the thelocation location is the is thehighest, highest, 539.61 539.61 GWh. GWh. The CF The of CF the of proposed the proposed plant plantin Yanbu in Yanbu is also is the also highest the highest with a withvalue a of value 61.1%. of 61.1%.The location The location in Abha in, which Abha, can which generate can generate 536.31 536.31GWh annually GWh annually,, offers an off ersLCOE an LCOEand CF and of CF10.81 of 10.81cents/kWh cents /andkWh 60.7% and 60.7%,, respectively. respectively. The annual The annual energy energy generated generated from Dawadmi from Dawadmi is the isleast, the 470.5 least, 470.5GWh, GWh, which which can be can attributed be attributed to the to theweak weak DNI DNI prof profileile of ofthe the region region,, and and the the LCOE LCOE and and CF CF values are respectively 12.25 centscents/kWh/kWh andand 53.3%.53.3%.

Table4. Annual energy yield and comparison of performance parameters for the optimized concentrated solar power (CSP) plant conﬁguration for the three locations.

Parameter Yanbu Abha Dawadmi Annual Energy Generated (GWh) 539.61 536.31 470.5 Solar Multiple 2.4 2.4 2.4 Full load hours of TES 8 8 8 Capacity Factor (%) 61.1 60.7 53.3 LCOE (cents/kWh) 10.75 10.81 12.25 Plant Eﬃciency (%) 18.19 18.97 18 Sustainability 2020, 12, 127 14 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 14 of 16

600 75 580 70 560 540 65 520 60 500 55 480

460 Capacity Factor(%)

Annual(GWH) Energy 50 440 45 420 400 40 Yanbu Abha Dawadmi Location Annual Energy Capacity factor

Figure 13. PerformancePerformance comparison of optimized p plantlant configurations conﬁgurations in three locations.locations.

Presently,Table 4. Annual there areenergy only yieldtwo central and comparison receiver solar of p thermalerformance power parameters plants that for havethe optimized a capacity of moreconcentrated than 100 MWe solar operational power (CSP) inplant the configuration world. The Ivanpahfor the three Solar locations Tower. plant in California, United States, with a capacity of 377Parameter MW is the largest and hasYanbu no thermal Abha energyDawadmi storage capability. The design parameters of the 110 MWe Crescent Dunes Central Receiver plant in Nevada, United States, Annual Energy Generated (GWh) 539.61 536.31 470.5 which has 10 h of TES capacity, is used for comparing the performance parameters of the proposed Solar Multiple 2.4 2.4 2.4 design arrived in this paper. Table5 shows the performance comparison of the proposed plant design Full load hours of TES 8 8 8 with the performance parameters of the Crescent Dunes Central Receiver plant. Capacity Factor (%) 61.1 60.7 53.3 LCOE (cents/kWh) 10.75 10.81 12.25 Table 5. Performance comparison of the proposed design with parameters of Crescent Dunes Central Plant Efficiency (%) 18.19 18.97 18 Receiver plant.

PresentlyParameter, there are only two c Crescententral receiver Dunes [ 19solar,20] thermal Yanbu power plants Abha that ha Dawadmive a capacity of more than 100Plant MWe Capacity operational in the world. 110 MWe The Ivanpah 100 Solar MWe Tower 100 plant MWe in California, 100 MWe United States, withTES a capacity capacity (Hours) of 377 MW is the largest 10 and has no thermal 8 energy 8 storage capability. 8 The Expected Annual Energy design parameters of the 110 MWe Crescent500 Dunes Central Receiver 539.61 plant 536.31 in Nevada, 470.5United States, which hasGenerated 10 h of TES (GWh) capacity, is used for comparing the performance parameters of the proposed Capacity Factor (%) 51.89 61.1 60.7 53.3 design arrivedPlant Einﬃ thisciency paper. (%) Table 5 shows 16.86the performance comparison 18.19 of 18.97 the proposed 18plant design with the performance parameters of the Crescent Dunes Central Receiver plant.

FromTable 5. Table Performance5, it is clear comparison that both of the proposed plant e ffi designciency with and parameters capacity factorof Crescent of the Dunes proposed Central utility levelReceiver central receiver plant. plant design in all the three shortlisted sites are superior than those in the Crescent Dunes plant. Similar is the case for the expected annual energy that can be generated from the two locations in YanbuParameter and Abha. Thus, the optimizationCrescent approachDunes [19 presented,20] Yanbu in this workAbha can beDawadmi effectively used to reach thePlant best Capacity possible design configuration110 of theMWe central receiver100 MWe solar thermal100 MWe plant 100 suiting MWe a potential site.TES capacity (Hours) 10 8 8 8 Expected Annual Energy Generated (GWh) 500 539.61 536.31 470.5 6. ConclusionsCapacity Factor (%) 51.89 61.1 60.7 53.3 Plant Efficiency (%) 16.86 18.19 18.97 18 The design, performance analysis, and optimization of a 100 MWe central receiver solar thermal powerFrom plant Table with 5, thermalit is clear energy that both storage the plant meant efficiency for utility and scale capacity applications factor of werethe proposed carried out utility for threelevel c locationsentral receiver in Saudi plant Arabia. design The in results all the show three that shortlisted by properly sites optimizing are superior the than design those of centralin the Crescent Dunes plant. Similar is the case for the expected annual energy that can be generated from the two locations in Yanbu and Abha. Thus, the optimization approach presented in this work can be Sustainability 2020, 12, 127 15 of 16 receiver plants, it is possible to obtain a plant configuration that can offer superior plant efficiency and a capacity factor with the lowest value of LCOE. From the analysis of the optimized design, an annual energy of 559.61 GWh can be generated in Yanbu with eight hours of thermal energy storage, plant efficiency of 18.19%, and a capacity factor of 61.1%. The central receiver plant in Abha can offer an annual energy of 536.31 GWh with the highest plant efficiency of 18.97% and a capacity factor of 60.7%. The performance of the proposed design in the two locations of Yanbu and Abha fares better when compared to the operational plant data of the central receiver plant in Crescent Dunes. Based on the findings of the study, the proposed 100 MWe central receiver solar thermal power plants with thermal energy storage were found to be ideally suited for specific locations in Saudi Arabia.

Funding: This research was funded by [Deanship of Scientific Research, Majmaah University] grant number [38/119]. Acknowledgments: The authors would like to thank the Deanship of Scientific Research, Majmaah University (Contract No. 38/119), Majmaah, 11952, Saudi Arabia for supporting this research project. Conflicts of Interest: The author declares no conflict of interest.

Nomenclature

th θi,p Angle of incidence at point p for i hour (degree) 2 Es Total Annual Solar Energy (Wh/m ) PD Packing Density at point p of heliostat ﬁeld Pdes Design Electrical Capacity of the plant (MW)

ηEPB Power Block Eﬃciency ηreceiver Receiver Eﬃciency

ETESmax Maximum thermal energy storage capacity (Wh) Direct normal irradiance at the location for ith hour DNI i (W/m2) Actual Annual reflected Energy per unit land area Ea (Wh/m2) Thermal power of HTF required for design electric E thht f ,des power (W) Design point thermal power to be collected from field E thdes (W) ηHE Heat exchanger efficiency

ηSHE Storage heat exchanger eﬃciency Htes Number of hours of storage

Abbreviations

PTC Parabolic Trough Collector HTF Heat Transfer Fluid CSP Concentrated solar power ISD Integrated Surface Database DNI Direct normal irradiance SAM Solar Advisor model CF Capacity Factor

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