Techno-economic optimization for implantation of parabolic trough power plant: Case study of Algeria Cite as: J. Renewable Sustainable Energy 12, 063704 (2020); https://doi.org/10.1063/5.0013699 Submitted: 14 May 2020 . Accepted: 27 November 2020 . Published Online: 18 December 2020 Khaoula Ikhlef, and Salah Larbi J. Renewable Sustainable Energy 12, 063704 (2020); https://doi.org/10.1063/5.0013699 © 2020 Author(s). 12, 063704 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse Techno-economic optimization for implantation of parabolic trough power plant: Case study of Algeria Cite as: J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Submitted: 14 May 2020 . Accepted: 27 November 2020 . Published Online: 18 December 2020 Khaoula Ikhlefa) and Salah Larbi AFFILIATIONS Ecole Nationale Polytechnique d’Alger (ENP), LGMD Laboratory, B.P. 182, El-Harrach, Algiers, Algeria a) Author to whom correspondence should be addressed: khaoula.ikhlef@g.enp.edu.dz. Fax: þ213 21 52 29 73 ABSTRACT This paper deals with Hassi R’mel’s solar power plant, a solar power plant one (SPPI) located in Algeria’s southern region. The current configuration of the SPPI is a 25 MW parabolic trough used with 130 MW connected cycle gas turbines. In this paper, a techno-economic analysis of the solar part of the SPPI is described. A multiobjective optimization process was carried out to find the most favorable conditions, the most suitable parameters, and the solar power plant’s optimum design configuration for better energy production in the future. We optimized the solar multiple, the fossil fill fraction of the backup system, and a full load of thermal storage system for the minimum levelized cost of electricity (LCOE) and the maximum capacity factor. The results showed that the lowest LCOE reached about 5.83 e/kWh for a solar multiple of 1.6. The optimum value of fossil fill fraction is 0.2, with a capacity factor of 60%, and the best optimization of the storage system is 4 h. The optimum power plant produced 118.26 GWh per year. It was concluded that the use of wet cooling was more economical than dry cooling and that among the heat transfer fluids, Therminol VP1 was the best. The validation of the theoretical results confirmed their agreement with the SPPI and solar energy generation systemVI power plants’ experimental data. These findings proved that concentrating solar power may be considered as the right solution in countries in the desert and equatorial regions, the Middle East and North Africa region, especially Algeria, where abundant solar energy is available. Published under license by AIP Publishing. https://doi.org/10.1063/5.0013699 NOMENCLATURE AEP CF DNI En FCR FFF FLH FOC LCOE LEC SM TCC TMY VOC Annual electricity production, (kWh) Capacity factor, (%) Direct normal irradiance, (W/m2, kWh/m2/year) Annual energy, (GWh) Fixed charge rate, (—) Fossil fill fraction, (—) Full load hours, (hours) Fixed annual operating cost, (e) Levelized cost of electricity, (e/kWh) Levelized energy cost, (e/kWh) Solar multiple, (—) Total capital cost, (e) Typical meteorological year, (s, h, or an) Variable operating cost, (e/kWh) Abbreviations ANN Artificial neural network J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing BS C1 C2 CC CSP DSG HCE HPH HSS HTF HTF1 HTF2 LSSVM MCRT MENA NREL PTC PV SAM Backup system Evaporative cooling Dry air cooling Combined cycle Concentrating solar power Direct steam generation Heat collecting element High pressure heater Hitec solar salt Heat transfer fluid Heat transfer fluid Therminol VP1 Heat transfer fluid Hitec solar salt Least squares support vector machine Monte Carlo raytracing method Middle East and North Africa National Renewable Energy Laboratory Parabolic trough collector Photovoltaic System advisor model 12, 063704-1 Journal of Renewable and Sustainable Energy SAPG SEGS SPPI SPT TES TRNSYS TVP1 VFPT Solar-assisted power generation Solar energy generation system Solar power plant one Standard parabolic trough Thermal energy storage Transient System Simulation Tool Therminol VP1 Variable focus parabolic trough I. INTRODUCTION The nonrenewable resources of fossil fuels cannot satisfy society’s rapidly increasing demand for energy forever in the future. Solar energy is green energy and presents significant advantages of renewability and low-cost. The photovoltaic technology (PV) and CSP technology are the most powerful and the most installed in the industry.1,2 CSP is one of the alternative renewable energy technologies for power generation. There are four well known commercial CSP systems: solar power tower, parabolic trough, parabolic dish Stirling, and linear Fresnel. Among these technologies, parabolic trough with organic or synthetic oil heat transfer fluid is the most attractive and widely used industrial technology.3 The development of CSP can be divided into three periods. The first period started at the end of the 1970s and extended until the 1980s. The development of CSP received significant support as a technology with the potential to deal with the dependence on fossil fuels for energy generation. This support was strengthened by the oil crisis that lasted from 1973 to 1979. During this period, scientists invested massive effort in developing various solar radiation methods for generating energy.4 Initially, in this period, the studies were focused on the solar power tower, parabolic trough, and dish Stirling, which appeared in the Solar One. It was based on solar power tower technology that performed well from 1982 to 1988.5 The primary purposed of the initiation of the Almeria Solar Platform in 1981 was to test various CSP technologies in use. It was initially focused on the solar power tower plant.6 In 1984, the Vanguard Dish-Stirling module recorded the highest efficiency of converting solar energy into electricity.7 During the 1980s, they created the parabolic trough technology-based 354 MW Solar Energy Generation System (SEGS). Most of these technologies are still operative.8 Several fundamental research studies during the last decade were aimed at optimization and simulation. Of these, the following were the most important. Spirkl et al.9 optimized a compact secondary reflector for the PTC power plant that included tubular absorbers. They noticed that the auxiliary reflector increased the concentration significantly, and it was optimal in a broad range of shapes. Golding and Becerra-Lopez10 presented a study on the design of an attractive technology. It consisted of a new hybrid system (renewable energy and natural gas). The system was a technology combining solid oxide fuel cells powered with renewable fuels, and it was developed and installed in Texas. This method had been optimized within a sustainability framework. The latter was converted into a multiobjective optimization study. They found that implementing an optimal array of power generation technologies reduced the environmental impact and proved economically affordable for developing countries. A year later, in 2009, Montes et al.11 demonstrated a technique for commercializing solar multiple without storage and hybridization. They used five PTC power plants, of which they adapted the multiple J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing ARTICLE scitation.org/journal/rse solar, and the optimal value found was 1.16. In 2012, Liu et al.12 explained the LSSVM approach based on optimizing the PTC power plant. This technique is operated to model the solar collector part. They extracted the complex associations between the solar flux, the solar collector performance and the flow rate, and the HTF’s inlet temperature. In conclusion, the new method presented the data that are essential for improving the PTC. In the same year, Tsai and Lin13 suggested a process for analyzing and optimizing the geometry of the concentrator of the power plant to ensure the uniformity of the irradiance distribution on the heat-pipe cover. They found that the variablefocus-parabolic trough (VFPT) improved the irradiance distribution uniformity by around 84% compared to that attained utilizing the standard-parabolic trough (SPT) reflector. In 2013, Abu-Hamdeh et al.14 developed a method and fabricated a prototype for solar adsorption refrigeration with specific conditions and characteristics. The system’s purpose was to achieve optimum parameters by experimental and analytical methods. They observed that the variable-focusparabolic trough (VFPT) improved the irradiance distribution uniformity by around 84% compared to that attained utilizing the standard-parabolic trough (SPT) reflector. One year later, in 2014, Pierucci et al.15 characterized the optimized PTC power plant. The ratio of concentration under specific boundary conditions and the optical efficiency were taken into consideration. They concluded that there were many configurations to maximize the performance and the geometrical concentration factor. Silva et al.16 developed a thermo-economic optimization methodology to promote the production of a PTC for industrial applications. The methodology combined simulation, configuration, and optimization stages to economize the cost of production. In 2015, several studies on the optimization of the PTC power plant were conducted. The most important ones are those of He and his collaborators.17 The authors developed an optical optimization model on the PTC power plant performance. It was based on the Monte Carlo raytracing process. The optimization results agreed closely with the citation data, demonstrating that the MCRT approach was reliable and achievable. This method has given outstanding results. Bandyopadhyay and Desai18 published their extensive economic and energy efficiency investigation for a PTC without a thermal storage system. They affirmed that the optimal turbine inlet pressure was ineffective for the purpose of design radiation. It increased with the power plant dimensions and several adjustments of the Rankine cycle. Also, Yang et al.19 carried out a mathematical examination of the mirror gap’s influence on wind load on parabolic trough collectors. The simulation returns validated the idea that the most considerable deformation of collectors’ weight and the reflecting mirrors were reduced by about 5.8% and 4.6%, respectively, compared with the fundamental model. Kasaeian et al.20 optimized and improved a useful solar collector model through PTC optimization, which aimed to maximize the nondimensional correlation of pressure drop with Reynolds and Richard numbers and Nusselt’s number employed as design restrictions. The results confirmed that the development of heat transfer was directly related to the concentration rate of nanoparticles. In contrast, it had an inverse relation with the operational temperature. Bello-Ochende et al.21 used thermodynamic optimization methods to examine the production of a PTC with perforated plate inserts. A multi-objective optimization was realized through the combined use of computational fluid dynamics, response surface methodology, 12, 063704-2 Journal of Renewable and Sustainable Energy design of experiments, and the Non-dominated Sorted Genetic Algorithm-II. In the thermodynamic optimization part, the entropy production minimization method was applied to achieve thermodynamically optimal configurations. One year later, in 2016, Chen et al.22 investigated a process to optimize the heat transfer processes in the PTC of DSG operations. The results showed that the generation of longitudinal vortices along the receiver’s mainstream by optimizing the solid receiver structure effectively decreased the circumferential temperature variance and increased reliability. Guo and Huai23 performed a multiparameter optimization of PTC by using a genetic algorithm. They observed that the PTC design parameters were interdependent and collaborative. Therefore, optimal solar receiver production could not be achieved by considering only a single parameter. In 2017, Sun et al.24 studied the double-axis parabolic trough collector numerically and submitted an optimized tracking approach to maximize the yearly collected solar radiation value. The results showed that production was measured over 0.6 comparing with the traditional single-axis and double-axis tracking methods. One year later, in 2018, Moghimi and Ahmadi25 focused their research on avoiding mirror soiling phenomena that affected the PTC performances by optimizing a substantial wind barrier around a PTC power plant to protect it from dust coating using the ANSYS-Fluent. The numerical simulation results showed that the optimal design of the wind barriers (in size and shape) reduced operating costs. Therefore, the price of LCOE electricity production is decreased, and the optimized windbreak design directed a considerable amount of dust particles (over 86%) out of the solar field. Moreover, it showed that the barrier wall effectively diverted the larger particles from the solar field. Kasaeian et al.26 optimized PTC systems of various sizes with three variables configurations employing the Monte Carlo process. The results registered that with the rim angle of 100 , the receiver diameter of 0.025 m, and the collector aperture width of 0.6 m, guaranteed an optical performance of 65%. In 2019, Ehyaei et al.27 examined a multiobjective optimization based on exergy, energy, and economic interpretation to examine PTC’s act and reach the most excellent exergy productivity and PTC’s minimum product cost. The optimum returns revealed that the efficiency of energy performance, exergy performance, and heat cost were 35.55%, 29.22%, and 0.0142 $/kWh, respectively. Moloodpoor et al.28 suggested an optimization strategy to the receiver tube’s discretized cross section to calculate the heat losses. The results were confirmed with experimental measures of Sandia National Laboratory reports.29 The observations and validation showed that the obtained analytical outputs were in close accord with the practical results. Khanmohammadi et al.30 performed a multidisciplinary optimization (exergy loss rate, output power, and average cost price) of PTCsupported power refrigeration. The results showed that the exergy examination indicated that the development of the system had potential. The PTC output power was reduced from 353.21 kW to 280.1 kW, and the average annual cost decreased from 8.215 $/h to 5.74 $/h. In 2020, May Tzuc and their collaborators31 proposed and presented an economic and environmental analysis for thermal solar technologies and their application industry for a sustainable combination of the PTC photothermal system with the base and medium enthalpy industrial projects for clean generation of heat. They J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing ARTICLE scitation.org/journal/rse succeeded in determining a methodology that encourages future research to compare solar thermal technologies and classify the most cost-effective option. Moreover, this method is confirmed to be a beneficial tool in making investment decisions in terms of process heat generation. Ali Abazaa and his collaborators32 established research aiming to develop a methodology for the cost-effectiveness of a 10 MW prototype concentrating solar tower with a thermal energy storage system through the LCOE calculation. During the same year, Shagdar et al.33,34 conducted a study for improving the technoeconomic and ecological indices of a small-scale electricity production system. The numerical analysis is performed using the IPSE pro simulation software based on the heat balance method for four different cases. They demonstrated that the Solar-Assisted Power Generation System (SAPG) study has significant practical importance in producing electricity with minimum pollutants and maximum efficiency. In a second study, the authors investigated solar energy integration with a 300 MW coal-fired thermal power plant by replacing the first highpressure heater (HPH # 1) with a parabolic collector-type solar field. They analyzed the performance of 300 MW solar energy production systems (SAPG) at different operating conditions in terms of technical, economic, and ecological indices. The authors concluded that the SAPG system’s integration reduces the consumption of coal by 8.82 tons per hour by increasing the output power by 20 MW. Through this literature review, we can note that most works were dedicated to optical and geometry optimization. The research focuses much more on the PTC design by seeking to maximize power production by adding auxiliaries or operating waste heat. In this study, a techno-economic analysis of a solar-assisted power generation system (SAPG) is described. A multiobjective optimization process was carried out to find the most favorable conditions, the most suitable parameters, and the optimum design configuration of the solar power plant part for better energy production in the future. II. ALGERIA CASE STUDY BACKGROUND Algeria is the biggest country in Africa, with a land area of 2 381 741 km2 and a population of 43.73  109 (May 2020). It is situated in the northwest part of the continent between longitudes 8 /12 and latitudes 19 /38 north. Algeria possesses notable variations in topographic, climatic, and socio-economic conditions.35 The estimated sunshine duration period per year is more than 3000 h, and more than 5 kWh energy can be generated per day.36 Figure 1 illustrates the Algerian solar radiation potential map where we notice that the daily solar radiation varies between 4.6 and 6.6 kWh/m2. The idea of the CSP plants Algeria case study is motivated by this remarkable solar energy potential. The country’s population growth has been over 250% during the past 50 years,37 and so energy demand increases with population growth. A national energy development program was discussed and adopted by the Algerian government. This program runs from 2011 to 2030 and integrates a large part of renewable energy and sustainable development. The state intended 70% of the total power projects for CSP plants. In this context, Ghezloun et al.38 presented a comprehensive study on Algerian energy procedure in the setting of sustainable advancement. Their study stated that the energy demand would explode by 2020. Much research to study CSP plants’ performance, principally PTC systems, has been conducted in Algeria. Indeed, Behar et al.40 12, 063704-3 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse FIG. 1. Algerian solar radiation potential map.39 introduced a combined power plant with a single pressure, and it utilized a process for transforming solar energy into electricity. The outcomes of the statistical research showed that the power plant generated 134 MW. It worked at 57.5% efficiency at night, and the net solar electricity generation was impressive during the day. One year later, in 2012, Zaaraoui et al.41 conducted a techno-economic comparison of a specific model of 30 MW SEGS VI using TRNSYS software. The results proved a correspondence between the estimated model J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing and the measured data. In addition, economic research was conducted to find the best region, based on the cost of electricity generation, for installing the power plant, and the Bechar (southern region of Algeria) site was selected as the most suitable one. Derbal et al.42 performed a numerical model analysis using TRNSYS software to compute the annual power production of a parabolic trough of standard parameters using Algerian meteorological data. The simulation showed that the system initially produced 35 MW. After that, the production 12, 063704-4 Journal of Renewable and Sustainable Energy decreased, and the joined electric cycle power stayed constant. In 2013, Abbas et al.43 replicated a techno-economic study of the PTC technology at four specific regions in Algeria to examine these systems’ economic utility. It was apparent that PTC, which is intended to work under intense solar radiation, represented Algeria’s excellent opportunity, specifically in the Sahara. Boukelia and Mecibah’s44 review of studies evaluated the CSP potential of Algeria furnished information on the first installations of the PTC. In 2018, Achour et al.45 investigated the production of an Integrated Solar Combined Cycle power plant using the southern Algerian meteorological data. The investigation showed that performance reached 14.4% through sunny days. About the optimization of PTC systems in Algeria’s case study, there are only a few studies, according to the literature review. Messai et al.46 analyzed and optimized the production from the PTCs installed in various regions of Algeria. The results showed that Algeria is one of the best locations in the world for generating solar power. Also, the results could help identify the locations for such projects. Trad and Ait Ali47 performed a pre-feasibility study by determining the optimum design of a projected PTC power plant operating under the Algerian climate for different funding scenarios. The authors investigated the possibility of PTC technology if it is beneficial to the Algerian economy under the country’s actual policy of domestic energy pricing. They concluded among all the analyzed designs, the most advantageous one in terms of benefit to cost ratio is a design with a power output 100 MWe with eight full load hours thermal energy storage. In 2015, Boukelia et al.48 made an optimization study for parabolic trough installation for 50 MWe energy output. They applied two various HTF with a combined backup and thermal storage system. The results showed that the molten salt HTF used the least quantity of water annually, about 800.482 m3, but it occupied more area. The authors49 presented a comparative study based on energy, exergy, environment, and economy during the same year. Different configurations of PTC power plants are analyzed using Therminol VP1 oil and molten salt as working fluids, with and without integrating thermal energy storage or/and backup fuel system. Their results showed that the configurations using molten salt are better from the environment and economic viewpoint. Those of Therminol VP1 oil with thermal energy storage and fossil fuel backup systems presented important values of exergy efficiency (21.77%), capacity factor (38.20%), and annual energy generation (114 GWh). One year later, the authors50 conducted a techno-economic optimization study of the PTC power plant based on Artificial Neuron Network (ANN) models. They claimed that this technique could be easily used to design and optimize complex solar thermal power plants. Their results showed that the LM algorithm, a variant of ANN algorithms, as the best tool to predict the annual power generation and Levelized cost of electricity of the presented PTC power plant. In 2017, Mihoub and his collaborators51 examined the process for deciding the optimum configuration of CSP power plants in Algeria. They considered diverse parameters and scenarios. The financial results were encouraging and helped the Algerian government to formulate policies for installing CSP technologies. The present investigation involved optimizing a 155 MW solar equipment (solar-assisted power generation system) named SPPI power plant located in Hassi R’mel (southern region of Algeria). We evaluated the annual energy output (electricity), the LCOE, and the capacity factor for diverse solar multiple values, fossil fill fraction of J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing ARTICLE scitation.org/journal/rse backup system, and full load hours of the thermal storage system for several models using SAM software.52 This study aims to develop a new methodology for the CSP power plant with the most effective parameters and optimum configurations with the maximum annual power output at least the cost for the consumer. On the one hand, this methodology will help the investor decision support in the field of solar power plants (spend less and gain more). On the other hand, it will make it easier for future researchers to collect (the best HTF, the best thermal storage system, better SM, hybridization, etc.). III. METHODOLOGY The power plant prototype is an SPPI solar-gas hybrid power plant. It is located in Hassi R’mel (southern region of Algeria) and was installed on July 14, 2011.53 It combines two elements; the solar field and the combined cycle.54 The distribution of the solar energy potential of Algeria is divided into three regions:55 • • • North (coastal regions with 4% area); Middle (high plateaus, the surface represents 10%); South (Sahara has the most considerable part with an area of 84%). The power plant’s site location was chosen because it lies in a dish-like depression in the ground (this location does not require structural work, the technical complexity of which is likely to influence the cost of construction).56 In addition, the site is located directly on the gas field to benefit from the gas supply with a high level of DNI. It is at 700 m of altitude. The wind speed there is less than 5 m/s. The average daily sunshine hours are 9.5, the average annual humidity is less than 39%, Tmax 45  C in August, and the precipitation is approximately 100 mm/year. There is an international airport in its vicinity. (The importance of an international airport near an industrial zone is dictated by economic considerations where air transport has a relevant economic and strategic role for developing the country.) Table I summarizes the technical and economic data of the SPPI. Figure 2 illustrates the schematic of the SPPI power plant with the SAPG system. The contour in black dotted lines delimits the SAPG system. In red, the solar heat exchanger connects the combined cycle thermal power plant with the SAPG system. Based on fossil Combined Cycle (CC) fired with natural gas steam turbines additionally fed with solar-generated steam during the daytime. At night, the power plant operated like conventional CC. This section describes the method used to define the optimum parameters and configurations and propose a methodology for deciding the above aspects of future CSP plants for maximum annual power production with the minimum LCOE. This method is indispensable for optimizing the solar field’s dimension, the fossil fill fraction of the backup system, and the thermal storage system’s full load hours, depending on the local climatic conditions. SAM software was used to estimate solar power plants’ energy generations at their optimum technical and economic utility. Meteorological data, such as DNI, ambient temperature, wind speed, sun angle at Hassi R’mel site, were obtained using the METEONORM7 software database, and hourly time cases were obtained from TMY3 in the usual format. SAM and other software 12, 063704-5 Journal of Renewable and Sustainable Energy TABLE I. Technical design and parameters of the SPPI power plant.56–59 Background and location Technology Status Country Region Coordinates DNI Ambient temperature Climate Wind speed Parabolic trough Operational Algeria Laghouat Hassi R’mel city 03 160 0500 East, 776 m altitude 950 W/m2 (maximum value in summer) 22.6  C Saharan and arid 3.6 m/s Solar field Area of the power plant Collectors number Number of loops Collector type Heat transfer fluid type Water consumption 183 860 m2 224 56 LS-3 Therminol VP1 2500–3000 m3/day Power block Installation type Electrical capacity Turbine specification Output type Cooling method description Cooling method Administration and financing Date of operation Owner Cost of the investment Power Purchase Agreement/ tariff period Project type LCOE Solar-gas hybrid power plant 150 MW ! 130 MW combined gas cycle 25 MW Solar field 2 Simens SGT-800 gas turbines Steam Rankine Aero condensers Dry cooling July 2011 SPPI: Abener 66%, Neal 20%, and a consortium or banking pool (External Bank of Algeria, Popular Credit of Algeria, and National Bank of Algeria) 14% 315.8  106 of euros 25 years Commercial 0.1 e/kWh (without state subvention) such as TRNSYS, WINDELSOL, and DELSOL are widely used. Their powerful character was used in previous studies on CSP power plant technologies to furnish precise results. The description of the basic design parameters of this research is: Full Load Hours (FLH): the estimate of hours of thermal storage potential at the disposal of the control section’s design thermal input level.61 Fossil Fill Fraction (FFF): the ratio of fossil fuel used by the generator can be reached by the backup system (hybrid system).61 J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing ARTICLE scitation.org/journal/rse Levelized cost of electricity (LCOE): described as the unit cost of electricity over the lifetime of current production. The LCOE used by SAM is given by:61 sum of costs over lifetime sum of electrical energy produced over lifetime FCR  TCC þ FOC þ VOC: ¼ AEP LCOE ¼ A. Influence of essential parameters and optimization case studies To have an optimal and most advantageous power plant, we introduced various configurations and essential parameters as input in the SPPI. They are based on the size of the solar field, the heat transfer fluid type (Therminol VP1, and Hitec Solar Salt), the condenser type (wet cooling: evaporative, and dry cooling: air-cooled), the fossil fill fraction of the backup system, and FLH of the thermal storage system. Four optimization models have been considered (M1, M2, M3, and M4). The cases of optimization that have been analyzed correspond to: • Case (1) Optimization 1 (M1): solar field alone (without thermal storage and hybrid systems). Figure 3 illustrates the schematic representation of the system related to the M1 model. This first model is adopted to optimize the SM for a solar installation without hybridization and a storage system. The system is composed of a solar field (parabolic trough collector) that generates thermal energy to the power block (power cycle) through an HTF heat transfer fluid. The power block contains the turbine (Rankine cycle) and an alternator for electricity production. • Case (2) Optimization 2 (M2): combination of the backup system, hybridization (without thermal storage system). Figure 4 shows the schematic representation of the system related to the M2 model. In this second model, the same previous solar installation was hybridized with a gas power plant (backup system), which provides additional heat during periods when solar energy is insufficient to drive the power block unit to its nominal capacity (stability of electricity production). The operation of the hybridization is very simple. The BS is composed of a boiler (combustion chamber and gas fuel). The boiler is used to heat the HTF when it cannot reach the operating temperature during the night and in the periods where the solar radiation is insufficient (cloudy days). The M2 was presented to optimize the FFF. • Case (3) Optimization 3 (M3): with the addition of a storage system (without hybridization). Figure 5 depicts the schematic representation of the system related to the M3 model. For this configuration, a thermal storage system was integrated into the first installation to optimize the TES’s value. The system stores heat from the solar field in a tank, and this heat can drive the power pack turbine during periods of low or no sunshine. TES is very beneficial. It allows the installation to store the excess of the energy produced (days/nights) or (summer/winter) and separate solar energy collection from the power block unit’s operation. • Case (4) Optimization 4 (M4): with solar field, backup system, and thermal storage system. 12, 063704-6 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse FIG. 2. Schematic of SPPI power plant with SAPG system.60 Figure 6 gives the schematic representation of the system for the M4 model. This latest installation is composed of a solar field, a thermal storage system, and a backup system (hybridization). Due to the intermittence of solar energy, the integration of BS and TES systems allows the installation to benefit from constant, continuous, and reliable energy production. B. Financial parameters The baseline scenario describes the required economic provisions for investment under standard conditions with no state’s financial support (base case). We simulated the power plant with the financing and economic parameters suggested for the basic scenario used for all calculation models. Table II illustrates the financial parameters used, and Table III gives the economic ones (system costs). IV. VALIDATION To validate the results derived from this study, the results from the operation of the first model M1 have been compared with the SEGS VI power plant. This power plant was built in 1988 by Luz International Limited in the Mojave Desert64,65 Southern California. The obtained results using the M2 model have been compared with the SPPI power plant, as shown in Fig. 7. Table IV illustrates the results of these comparisons. The table shows a verifiable correspondence between the analysis results and the measured data, mainly in the period of sunshine production. Figure 7 represents the solar field’s power output and the power generated by the auxiliary system (hybrid system) for both experimental and theoretical results. The BS works for both systems when there is no sunshine and maintains stable production. Notice that there is an FIG. 3. Schematic representation of the system for the M1 model. J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing 12, 063704-7 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse FIG. 4. Schematic representation of the system for the M2 model. FIG. 5. Schematic representation of the system for the M3 model. FIG. 6. Schematic representation of the system for the M4 model. J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing 12, 063704-8 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse TABLE II. Financial parameters.62,63 Financial data Analysis period Loan term Loan rate Inflation rate Real discount rate Nominal discount rate Value Unit 30 20 8 4.6 4 8.78 Year Year %/Year %/Year May 2018 %/Year May 2018 %/Year excellent agreement between these results, which validated our models and methodology. V. RESULTS AND DISCUSSION In the following 4 case studies, CF, LCOE have been examined, and the annual energy output from the SPPI power plant for all the models has been optimized by varying SM, FFF of BS, and FLH of TES. The numerical analysis was based on the power plant that has assumed existence for 30 years, as estimated in several other research studies.67,68 The factors used in financial predictions include inflation assumes at an annual rate of 4.6%. • Case (1) Optimization 1 (M1): solar field alone (without thermal storage and hybrid systems). The first model was created to restrict the solar field’s optimal size, the most suitable heat transfer fluid, and the most competent condenser. The heat transfer fluids generally used in solar power plants are Therminol VP1, Hitec solar salt, Caloria HT 43, and Hitec XL. In a previous study,49,69 Therminol VP1, and Hitec solar salt were more efficient and effective than other HTFs. In this case, two configurations were considered: the heat transfer fluid type used is HTF1 (Therminol VP1) or HTF2 (Hitec solar salt), and the condenser type is C1 (evaporative cooling) or C2 (dry air-cooling). The collector and receiver selected in this study are Luz LS-3 and Schott PTR70, respectively. The collector consumes 0.5 l/m3 of water per wash, and they need 63 washings per year. Table V presents the characteristics and proprieties of the approved fluids. Figures 8 and 9 depict the SM’s influence on En and LCOE for the solar power plant, which operates only with the solar field, without storage and BS, and for several technologies and under several scenarios. Figure 10 describes the DNI and the average annual power output of the M1 model. Figure 8 illustrates that LCOE decreases when the SM increases until an optimal value. This value is reached when the net electricity FIG. 7. Comparison between measured and calculated hourly power outputs for SPPI plant. TABLE IV. Correlation between experimental data and calculated theoretical results. Indicator name Power plant capacity (MW) Collector type Solar field area (m2) DNI (kWh/m2/year) Annual energy production (GWh/year) Capacity factor (%) LCOE (e/kWh) SEGS VI64,65 30 LS-2 188 000 2891 89.4 34 15 M1 SPPI M2 25 25 25 LS-3 LS-3 LS-3 180 000 183 860 180 000 2800 2828 2800 70.45 120.54 96.37 35.7 8.14 60.1 0.1066 48.9 6.07 produced is more significant than the life cycle cost, which is an essential economic parameter to which an operation is compared. This parameter includes different costs over the course cycle life of the system: installation costs, operating costs, maintenance costs, financing costs. Exceeding this value, the LCOE increases due to the notable maintenance costs and the substantial investment in the solar field power plant. For the annual energy output vs SM, Fig. 9 shows that with the increase in SM, the annual energy grows due to the collector’s massive area (solar field). Notice that the wet cooling condenser is TABLE III. Economic parameters (system costs). Systems costs Net capital costs ($) Backup system costs ($) Storage system costs ($) Operation and maintenance costs (for all models) J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing M1 M2 M3 M4 59 589 472 0 0 82 339 472 22 750 000 0 77 005 202 0 17 415 730 117 170 932 22 750 000 17 415 730 66 4 ($/kW-year) ($/MWh) Fixed cost by capacity Variable cost by generation 12, 063704-9 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse TABLE V. Heat transfer fluids characteristic also used as storage fluids.63 Name Hitec Solar Salt Therminol VP1 Type Min optimal operating temp  C Max optimal operating temp  C Freeze point  C Nitrate salt The mixture of biphenyl 238 12 593 400 238 12 (Crystallization point) more suitable than the dry one, and the best HTF is Therminol VP1. Thus, HTF1-C1 gives better energy production at a low cost of LCOE. Figure 10 describes the DNI and the average annual power output of the M1 model. The figure shows that the produced electrical power is essentially between 6 and 19 h, which corresponds to the sunshine period. The maximum power output is practically constant (25 MWe) and corresponds to 800 W/m2 of DNI. This power is reached between 10 h and 15 h. Notice that the curve representing the electric power output evolution vs time looks practically the same as that of DNI evolution. In the remaining models, all simulations were accomplished with the outputs obtained from the optimization of the M1 model. • Case (2) Optimization 2 (M2): combination of the backup system, hybridization (without thermal storage system). In the second model, we use the same parameters and configurations used in the first model for the optimal results obtained. This model includes a backup system (hybrid system) for various values of FFF in the range of 0.1–0.8. Figure 11 displays the influence of BS for several values of FFF. This figure shows that the evolution of the capacity factor, CF, and the annual energy produced, En, is practically linear apart from the optimal value 0.2 of FFF, and these curves evolution have the same shape. Beyond this value, the LCOE rises slightly and stabilizes on reaching FIG. 8. Influence of SM on LCOE using the M1 model. J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing FIG. 9. Influence of SM on En using the M1 model. the value of the sale of the nonrenewable kWh (electricity generated by a conventional power plant). The combination of the BS leads to an increase in annual energy by 11.2% approximately. Figure 12 depicts the average annual power output vs time of the day using the M2 model. The solar power delivered by the second FIG. 10. Average annual power output and DNI vs time using the M1 model. 12, 063704-10 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse TABLE VI. Storage system parameters. Storage fluids Tank THERMINOL VP1 Heater capacity 25 MWe Heater efficiency 25 0.98 HITEC SOLAR SALT Parallel pairs 1 height 12 m FIG. 11. Influence of FFF on CF, En, and LCOE using the M2 model. model is the same as by the first model except that the backup system guarantees that the power remains stable at 25 MW. When there is no solar radiation, the BS starts operating to ensure the delivery of energy. • Case (3) Optimization 3 (M3): with the addition of a storage system (without hybridization). Using the same parameters and configurations as in the first model, this model offers a sensitivity examination of FLH on LCOE, En, and CF. Table VI presents the storage parameters of the simulation. Figures 13–15 depict respectively LCOE, En, and CF evolution vs FLH for two types of HTF. Figure 16 presents the average annual power output vs time for the M3 model. Figure 13 represents the influence of FLH on LCOE; we can note that the LCOE decreases with the FLH increase until an optimal value FIG. 12. Average annual power output using the M2 model. J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing FIG. 13. Influence of FLH on LCOE using the M3 model. FIG. 14. Influence of FLH on En using the M3 model. 12, 063704-11 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse TABLE VII. Optimal results obtained using the M4 model. SMopt ¼ 1.6 FFFopt ¼ 0.3 Input HTF1-C1 FLH ¼ 4h of FLH. The optimal storage period FLH is obtained for a time duration of 4 h. Beyond this value, LCOE increases. About the HTF influence on LCOE, we can underline that LCOE is slightly affected by the HTF type even if Therminol VP1 appears as the optimal storage fluid. Figures 14 and 15 represent the FLH factor’s influence on the annual energy production, En, and CF capacity factor. Notice that despite the increase in FLH, the En and the CF present practically constant values beyond FLH ¼ 4h, which indicates that the solar field’s surface is not sufficient to collect, deliver, and store energy. From Fig. 16, it is seen that the power generation continues after the disappearance of the global solar irradiance (null DNI). The storage system produces this generation of energy. The optimum parameters are presented in Table VIII. • FIG. 15. Influence of FLH on CF using the M3 model. Case (4) Optimization 4 (M4): with solar field, backup system, and thermal storage system. The last model is adapted to appreciate TES and BS’s interest on energy production, CF, and LCOE. The optimum results of models 1, 2, and 3 were used as input parameters for the M4 model simulation. The optimal results of all the previous models were used as inputs for the last model simulation, M4. Table VII presents the simulation’s input data, and Table VIII shows the results found using the M4, and Fig. 17 shows the average annual power output from M4. The results show that the annual energy delivered and the capacity factor are more useful for this model than other models. Therefore, the LCOE has decreased. The power generated is the same as that of M3, except that the backup system stabilizes the output at 25 MW. The optimal results registered for all the models are summarized in Table VIII. VI. CONCLUSION FIG. 16. Average annual power output using the M3 model. In this study, a techno-economic evaluation of the PTC power plant located in Algeria’s southern region was made to establish the optimum design, parameters, configuration, performance, operation, and, especially, identify the least expensive system. This study is based on the technical and economic parameters of the SPPI power plant. The SAM software was used to establish the optimum configurations TABLE VIII. Optimal results registered for all the models. MODELS SMopt HTFopt Condenser type FFFopt FLHopt (h) CF (%) Annual energy (GWh/y) LCOE (e/kWh) M1 M2 M3 M4 1.6 TVP1 Evaporative cooling … … 36.1 71.15 8.06 1.6 TVP1 Evaporative cooling 0.2 … 47.3 92.3 5.97 1.6 TVP1 Evaporative cooling … 4 41.8 82.46 8.16 1.6 TVP1 Evaporative cooling 0.2 4 60 118.26 5.83 J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing 12, 063704-12 Journal of Renewable and Sustainable Energy ARTICLE scitation.org/journal/rse 5 FIG. 17. Average annual power output using the M4 model. and parameters (SM, TES, and BS) of the power plant’s several models. The results proved the following: 1. The condenser type evaporative cooling is the most efficient for PTC power plants in the environment of southern Algeria. 2. Therminol VP1 fluid can be considered the most profitable HTF and the most desirable storage fluid because it has been formulated to withstand the most demanding conditions of the vapor phase or liquid phase systems. 3. The incorporation of the backup system in the power plant raises the annual energy output by 11.2% as compared with the solar field alone. It can provide energy in a block to meet more critical part-load requirements and various applications, such as generating heat or cooling. 4. The LCOE is occasionally deficient, insufficient, and inadequate to yield the optimum results. Therefore, it becomes essential to employ additional factors, such as the power plant’s capacity factor or the annual energy output. 5. The study leads to the conclusion that the M4, based on the SPPI power plant with an SM of 1.6, a backup system of 0.2, and 4 h of storage, is the optimum power plant with great potential in Algeria. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding author upon reasonable request. REFERENCES 1 Technology roadmap, Solar Thermal Electricity (International Energy Agency (IEA), 2014). 2 Technology roadmap, Solar Photovoltaic Energy (International Energy Agency (IEA), 2014). 3 I. Purohit, P. Purohit, and S. Shekhar, “Evaluating the potential of concentrating solar power generation in Northwestern India,” Energy Policy 62, 157–175 (2013). 4 J. Duffie and W. Beckman, Solar Engineering of Thermal Processes, 2nd ed. (John Wiley and Sons, 1991). J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing L. Poole, “Concentrating solar power: Energy from mirrors,” Technical Report No. DOE/GO-102001-1147 (National Renewable Energy Laboratory, Golden CO, 2001). 6 See http://www.psa.es for “PSA, Plateforme Solaire d’Almeria, 2012.” 7 B. Washom and I. Vanguard, Solar Parabolic Dish-Stirling Engine Module (Advanco Corporation, El Segundo, CA, 1984). 8 See www.nrel.gov/csp/solarpaces/ for “National Renewable Energy Laboratory, Concentrating Solar Power Projects.” 9 W. Spirkl, H. Ries, J. Muschaweck, and A. Timinger, “Optimized compact secondary reflectors for parabolic troughs with tubular absorbers,” Sol. Energy 61(3), 153–158 (1997). 10 P. Golding and H. R. Becerra-Loepez, “Multi-objective optimization for capacity expansion of regional power-generation systems: Case study of far west Texas,” Energy Convers. Manage. 49, 1433–1445 (2008). 11 M. J. Montes, A. Abanades, J. M. Martinez-Val, and M. Valdes, “Solar multiple optimization for a solar-only thermal power plant, using oil as heat transfer fluid in the parabolic trough collectors,” Sol. Energy 83, 2165–2176 (2009). 12 Q. Liu, M. Yang, J. Lei, H. Jin, Z. Gao, and Y. Wang, “Modeling and optimizing parabolic trough solar collector systems using the least squares support vector machine method,” Sol. Energy 86, 1973–1980 (2012). 13 C. Y. Tsai and P. D. Lin, “Optimized variable-focus-parabolic-trough reflector for solar thermal concentrator system,” Sol. Energy 86, 1164–1172 (2012). 14 N. H. Abu-Hamdeh, K. A. Alnefaie, and K. H. Almitani, “Design and performance characteristics of solar adsorption refrigeration system using parabolic trough collector: Experimental and statistical optimization technique,” Energy Convers. Manage. 74, 162–170 (2013). 15 G. Pierucci, D. Fontani, P. Sansoni, and M. De Lucia, “Shape optimization for parabolic troughs working in non-ideal conditions,” Energy Procedia 57, 2231–2240 (2014). 16 R. Silva, M. Berenguel, M. Perez, and A. Fernandez-Garcia, “Thermo-economic design optimization of parabolic trough solar plants for industrial process heat applications with memetic algorithms,” Appl. Energy 113, 603–614 (2014). 17 Y. L. He, Z. D. Cheng, B. C. Du, K. Wang, and Q. Liang, “Geometric optimization on optical performance of parabolic trough solar collector systems using particle swarm optimization algorithm,” Appl. Energy 148, 282–293 (2015). 18 S. Bandyopadhyay and N. B. Desai, “Optimization of concentrating solar thermal power plant based on parabolic trough collector,” J. Cleaner Prod. 89, 262–271 (2015). 19 M. C. Yang, W. Fu, Y. Z. Zhu, and L. Yang, “The wind-structure interaction analysis and optimization of parabolic trough collector,” Energy Procedia 69, 77–83 (2015). 20 A. B. Kasaeian, P. Mohammad Zadeh, T. Sokhansefat, F. Kowsary, and A. Akbarzadeh, “Hybrid optimization algorithm for thermal analysis in a solar parabolic trough collector based on nanofluid,” Energy 82, 857–864 (2015). 21 T. Bello-Ochende, A. Mwesigye, and J. P. Meyer, “Multi-objective and thermodynamic optimisation of a parabolic trough receiver with perforated plate inserts,” Appl. Therm. Eng. 77, 42–56 (2015). 22 Q. Chen, Y. Liu, K. Hua, and J. H. Hao, “Flow field optimization for the solar parabolic trough receivers in direct steam generation systems by the variational principle,” Int. J. Heat Mass Transfer 102, 1073–1081 (2016). 23 J. Guo and X. Huai, “Multi-parameter optimization design of parabolic trough solar receiver,” Appl. Therm. Eng. 98, 73–79 (2016). 24 J. Sun, R. Wang, H. Hong, and Q. Liu, “An optimized tracking strategy for small-scale double-axis parabolic trough collector,” Appl. Therm. Eng. 112, 1408–1420 (2017). 25 M. A. Moghimi and G. Ahmadi, “Wind barriers optimization for minimizing collector mirror soiling in a parabolic trough collector plant,” Appl. Energy 225, 413–423 (2018). 26 A. Kasaeian, H. Hoseinzadeh, and M. B. Shafii, “Geometric optimization of parabolic trough solar collector based on the local concentration ratio using the Monte Carlo method,” Energy Convers. Manage. 175, 278–287 (2018). 27 M. A. Ehyaei, A. Ahmadi, M. E. Haj Assad, and T. Salameh, “Optimization of parabolic through collector (PTC) with multi-objective swarm optimization (MOPSO) and energy, exergy and economic analyses,” J. Cleaner Prod. 234, 285–296 (2019). 28 M. Moloodpoor, A. Mortazavi, and N. Ozbalta, “Thermal analysis of parabolic trough collectors via a swarm intelligence optimizer,” Sol. Energy 181, 264–275 (2019). 12, 063704-13 Journal of Renewable and Sustainable Energy 29 V. E. Dudley, G. J. Kolb, A. R. Mahoney, T. R. Mancini, C. W. Matthews, M. Sloan, and D. Kearny, “Test results: SEGS LS-2 solar collector,” Technical Report No. SAND94-1884 (Sandia National Laboratories, 1994). 30 S. Khanmohammadi, O. Kizilkan, and W. A. Faraedoon, “Tri-objective optimization of a hybrid solar-assisted power refrigeration system working with supercritical carbon dioxide,” Renewable Energy 156, 1348–1360 (2019). 31 O. May Tzuc, A. Bassam, L. J. Ricalde, O. A. Jaramillo, M. Flota-Banuelos, and M. A. Escalante Soberanis, “Environmental-economic optimization for implementation of parabolic collectors in the industrial process heat generation: Case study of Mexico,” J. Cleaner Prod. 242, 118538 (2020). 32 M. Ali Abazaa, W. M. El-Maghlanya, M. Hassaba, and F. Abulfotuh, “10 MW concentrated solar power (CSP) plant operated by 100% solar energy: Sizing and techno-economic optimization,” Alexandria Eng. J. 59, 39–47 (2020). 33 E. Shagdar, Y. Shuai, B. Guene Lougou, E. Ganbold, O. P. chinonso, and H. Tan, “Analysis of heat flow diagram of small-scale power generation system: Innovative approaches for improving techno-economic and ecological indices,” Sci. China Technol. Sci. 63, 2256 (2020). 34 E. Shagdar, B. Guene Lougou, Y. Shuai, J. Anees, C. Damdinsuren, and H. Tan, “Performance analysis and techno-economic evaluation of 300 MW solar assisted power generation system in the whole operation conditions,” Appl. Energy 264, 114744 (2020). 35 R. Boudries and R. Dizene, “Potentialities of hydrogen production in Algeria,” Int. J. Hydrogen Energy 33(17), 4476–4487 (2008). 36 Strategie et Veille, Recueil trimestriel de l’hebdomadaire N02/2018/Sonelgaz/ DGDS/DS. 37 See www.ons.dz for “National Office of Statics (Algeria), 2019.” 38 A. Ghezloun, S. Chergui, and N. Oucher, “Algerian energy strategy in the context of sustainable development (legal framework),” Energy Procedia 6, 319–324 (2011). 39 See https://globalsolaratlas.info/map for “Global Solar Atlas.” 40 O. Behar, A. Kellaf, K. Mohamedi, and M. Belhamel, “Instantaneous performance of the first integrated solar combined cycle system in Algeria,” Energy Procedia 6, 185–193 (2011). 41 A. Zaaraoui, M. L. Yousfi, and N. Said, “Technical and economical performance of parabolic trough collector power plant under Algerian climate,” Procedia Eng. 33, 78–91 (2012). 42 H. Derbal, S. Bouaichaoui, N. E. Gharbi, M. Belhamel, and A. Benzaoui, “Modeling and numerical simulation of an integrated solar combined cycle system in Algeria,” Procedia Eng. 33, 199–208 (2012). 43 M. Abbas, Z. Belgroun, H. Aburidah, and N. K. Merzouk, “Assessment of a solar parabolic trough power plant for electricity generation under Mediterranean and arid climate conditions in Algeria,” Energy Procedia 42, 93–102 (2013). 44 T. E. Boukelia and M. S. Mecibah, “Parabolic trough solar thermal power plant: Potential, and projects development in Algeria,” Renewable Sustainable Energy Rev. 21, 288–297 (2013). 45 L. Achour, M. Bouharkat, and O. Behar, “Performance assessment of an integrated solar combined cycle in the southern of Algeria,” Energy Rep. 4, 207–217 (2018). 46 A. Messai, Y. Benkedda, S. Bouaichaoui, and M. Benzerga, “Feasibility study of parabolic trough solar power plant under Algerian climate,” Energy Procedia 42, 73–82 (2013). 47 A. Trad and M. A. Ait Ali, “Determination of the optimum design through different funding scenarios for future parabolic trough solar power plant in Algeria,” Energy Convers. Manage. 91, 267–279 (2015). 48 T. E. Boukelia, M. S. Mecibah, B. N. Kumar, and K. S. Reddy, “Optimization, selection, and feasibility study of solar parabolic trough power plants for Algerian conditions,” Energy Convers. Manage. 101, 450–459 (2015). J. Renewable Sustainable Energy 12, 063704 (2020); doi: 10.1063/5.0013699 Published under license by AIP Publishing ARTICLE scitation.org/journal/rse 49 T. E. Boukelia, M. S. Mecibah, B. N. Kumar, and K. S. Reddy, “Investigation of solar parabolic trough power plants with and without integrated TES (thermal energy storage) and FBS (fuel backup system) using thermic oil and solar salt,” Energy 88, 292–303 (2015). 50 T. E. Boukelia, O. Arslan, and M. S. Mecibah, “ANN-based optimization of a parabolic trough solar thermal power plant,” Appl. Therm. Eng. 107, 1210–1218 (2016). 51 S. Mihoub, A. Chermiti, and H. Beltagy, “Methodology of determining the optimum performances of future concentrating solar thermal power plants in Algeria,” Energy 122, 801–810 (2017). 52 See https://sam.nrel.gov/ for “System Advisor Model (SAM) version 2018.11.11.” 53 See http://www.energy.gov.dz/francais/index.php?page¼projet-centrale-hybridesolaire-gaz-de-150-mw for “Ministre de l’Energie et des Mines, Projet Centrale Hybride Solaire Gaz de 150 MW.” 54 N. E. Gharbi, “La centrale hybride de Hassi R’mel,” Bulletin des Energies Renouvelables 21, 17–18 (2011), https://www.cder.dz/vlib/bulletin/pdf/ bulletin_021_11.pdf. 55 B. Bairi, Renewable Energies in Algeria Egalities & Perspectives (AlgerianItalian Cooperation on Renewable Energies, Milan, 2007). 56 A. Benidir, “Calcul energetique de l’installation hybride thermique pour la production d’electricite,” Ph.D. thesis (Benmachiche Abdelmoumen. H, Universite de Biskra, 2013). 57 See https://fr.wikipedia.org/wiki/Centrale_%C3%A9lectrique_mixte_de_Hassi_ R%27Mel for “Centrale electrique mixte de Hassi R’Mel, 2011.” 58 F. Khaldi, “Energy and exergy analysis of the first hybrid solar-gas power plant in Algeria,” in Proceedings of ECOS, Cost, Optimization, Simulation, and Environmental Impact of Energy Systems, Perugia, Italy, June 26–29 (2012). 59 E. Bellini, see https://www.pv-magazine.fr/2019/08/16/en-algerie-lappel-doffresde-50-mw-pour-les-projets-hybrides-se-termine-avec-loffre-la-plus-basse-de-010ekwh/ for “En Algerie, l’appel d’offres de 50 MW pour les projets hybrides se termine avec l’offre la plus basse de 0,10e/kWh, 2019.” 60 K. Harald, Solar Thermal Power Generation, Potential in Algeria & Hassi R’Mel Project (Renewable Energy Department, Lahmeyer International GmbH, Solar Energy, Weltmesse f€ ur Erneuerbare, Energien, Berlin, 2008). 61 M. J. Wagner and P. Gilman, “Technical manual for the SAM physical trough model,” Technical Report No. NREL 5500-51825 (2011). 62 Projected costs of generating electricity. International Energy Agency (IEA)/ Nuclear Energy Agency (NEA) report. https://doi.org/10.1787/9789264084315en, 2010 (2010). 63 OECD Development Center, African Economic Outlook, Structural Transformation and Natural Resources (African Economic Outlook, 2013). 64 LUZ International Limited, Solar Electric Generating System IX Technical Description (LUZ International Limited, 1990). 65 W. Meineeke, Solar Energy Conversion and Photo Energy Systems: Parabolic Trough Collectors-Vol. IV (EOLSS, 2010). 66 A. Trad, Z. Belgrounand, and A. M. Djebiret, “Effect of wind speed on the efficiency of the integrated solar combined cycle power plants SPPI in Algeria,” Rev. Energies Renouvelables 16(4), 691–708 (2013). 67 H. Nezammahalleh, F. Farhadi, and M. Tanhaemami, “Conceptual design and techno-economic assessment of integrated solar combined cycle system with DSG technology,” Sol. Energy 84, 1696–1705 (2010). 68 N. Caldes, M. Varela, M. Santamarıa, and R. Saez, “Economic impact of solar thermal electricity deployment in Spain,” Energy Policy 37, 1628–1636 (2009). 69 K. Ikhlef and S. Larbi, “Analyse technique de l’apport solaire de la centrale thermique hybride solaire-gaz de Hassi R’Mel (SPPI),” Rev. Energies Renouvelables 21(1), 27–36 (2018), https://www.cder.dz/download/Art21-1_4.pdf. 12, 063704-14