Investigation of the potential for electrification of remote areas using parabolic solar collectors in southern Algeria

Abstract

The dish stirling technology holds great promise as a renewable energy solution for remote and off-grid electric regions, particularly in the southern areas of North Africa. In this research, we conducted simulations of a 100 kw Dish Stirling system to evaluate its feasibility in comparison to photovoltaic technology at five distinct locations in southern Algeria: Adrar (Bordj Badji Mokhtar), Illizi (Djanet), Tamanrasset (Ain Mertoutek), Tindouf, and Bechar. Our findings underscore the substantial potential of Dish Stirling Solar Power technology, with the Sahara region standing out as particularly promising. In this region, the Dish Stirling system consistently outperforms a 100 kw photovoltaic system across all selected locations. The Dish Stirling system achieves an average annual electricity generation of 256 mwh while simultaneously mitigating CO2 emissions by 177 tons annually. Among these locations, Djanet Illizi emerges as the most favorable, with the Dish Stirling system producing an impressive 288.43 mwh annually. This capacity is sufficient to meet the annual energy needs of 230 households, all while maintaining a competitive LCOE of 0.0378 USD/kwh. Comparative analysis with previous research illuminates the remarkable cost-effectiveness of Dish Stirling technology in southern Algeria, primarily due to its abundant direct normal irradiance levels. These findings underscore the immense potential of Dish Stirling systems as a clean and highly efficient energy solution, well-suited for demanding to address the energy needs of remote environments, such as those found in the southern border regions of Algeria.

Nomenclature

LCOE

Levelized Cost of Electricity

$\mathit{I}\mathit{n}$

Investments costs in project lifetime

${\mathit{M}}_{\mathit{n}}$

O&M costs in year t

${\mathit{F}}_{\mathit{n}}$

Fuel costs in project lifetime

${\mathit{E}}_{\mathit{n}}$

Electrical energy generated in project lifetime

$\mathit{O}\&\mathit{M}$

Operation and maintenance

$\mathit{S}\mathit{C}{\mathit{R}}_{\mathit{n}}$

Replacement costs in project lifetime

$\mathit{Cos}{\mathit{t}}_{\mathit{t}\mathit{o}\mathit{t}}$

The total cost of the system

${\mathit{\eta }}_{\mathit{c}\mathit{a}\mathit{r}\mathit{n}\mathit{o}\mathit{t}}$

Carnot efficiency

$\mathit{h}$

time in hours

$\mathit{t}$

Metric ton

$\mathit{K}\mathit{w}\mathit{h}$

Kilo-watt hour

$\mathit{M}\mathit{w}\mathit{h}$

Mega-watt hour

$\mathit{G}\mathit{w}\mathit{h}$

Giga-watt hour

${\mathit{E}}_{\mathit{c}\mathit{o}\mathit{r}}$

Corrected irradiance in [$W/m^2]$

${\mathit{E}}_{\mathit{D}\mathit{N}\mathit{I}}$

Direct normal irradiance in [$W/m^2]$

${\mathit{E}}_{\mathit{D}\mathit{N}\mathit{I}.\mathit{min}}$

Minimal direct normal irradiance for starting generation in [$W/m^2]$

a

Performance model constant in [$W_e/\left(W/m^2\right)]$

b

Performance model constant in [$W_e]$

${\mathit{f}}_{\mathit{t}\mathit{e}\mathit{m}\mathit{p}}$

Temperature correction factor

${\mathit{f}}_{\mathit{r}\mathit{e}\mathit{f}}$

Reflection correction factor

${\mathit{f}}_{\mathit{s}\mathit{h}\mathit{a}\mathit{d}\mathit{o}\mathit{w}\mathit{i}\mathit{n}\mathit{g}}$

Shadowing and blockage factor

${\mathit{f}}_{\mathit{i}\mathit{n}\mathit{t}\mathit{e}\mathit{r}\mathit{c}\mathit{e}\mathit{p}\mathit{t}}$

Intercept factor

${\mathit{\vartheta }}_{\mathit{n}\mathit{o}\mathit{r}\mathit{m}.\mathit{a}\mathit{m}\mathit{b}}$

Normalized temperature for performance model in[$°C$]

${\mathit{\vartheta }}_{\mathit{a}\mathit{m}\mathit{b}}$

Actual ambient temperature in[$°C$]

${\mathit{\vartheta }}_{\mathit{u}\mathit{p}.\mathit{s}\mathit{t}\mathit{i}\mathit{r}\mathit{l}\mathit{i}\mathit{n}\mathit{g}}$

Upper Stirling temperature in $[°C]$

${\mathit{c}}_{\mathit{c}\mathit{o}\mathit{o}\mathit{l}\mathit{e}\mathit{r}}$

Cooler constant in [$\circ C/\left(\frac{W}{m^2}\right)]$

${\mathit{P}}_{\mathit{g}\mathit{r}\mathit{i}\mathit{d}}$

Performance to/from the grid in $[W$]

${\mathit{P}}_{\mathit{g}\mathit{r}\mathit{o}\mathit{s}\mathit{s}}$

Gross performance (total power output of the Dish) in [ $W$]

${\mathit{P}}_{\mathit{p}\mathit{a}\mathit{r}\mathit{a}\mathit{s}\mathit{i}\mathit{t}\mathit{i}\mathit{c}}$

Total parasitics in [$W]$

DNI

Direct normal irradiance [w/m²]

DNIc

Corrected direct normal irradiance [$w/m^2]$

GHI

Global Horizontal irradiation $\left[w/m^2\right]$

T&D

Transmission and distribution

PV

Photovoltaic

CSP

Concentrating solar power

EPW

Energy plus weather

Tamb

Ambient temperature

PSDS

Prabolic solar dish stirling

NREL

National Renewable Energy Laboratory

USD

United States dollar [US $ ]

LFR

Fresnel Reflectors

PT

Parabolic Trough

DSS

Dish Stirling System

CRT

Central Receiver Tower

N/A

mixture CO2/Nitrogen gas

1. Introduction

The World Bank's most recent data from 2021 reveals that about 26 % of Algeria's population resides in rural areas [1], These rural areas can be quite isolated, with some communities situated in remote locations that are far removed from the cities. Moreover, a significant portion of the country's land surface, approximately 80 %, is covered by the Sahara Desert, which makes it challenging for people living in these areas to access essential services such as healthcare and education. In addition, inadequate infrastructure and transportation make it difficult for these communities to reach markets and other resources. As a result, rural populations in Algeria often experience significant obstacles in their daily lives. with no constant sustainable supply of electricity, due to the low population density, the challenging task of providing electricity using classical technologies like diesel generators, which are unsustainable with harmful impact on the environment and weather simply costly either in maintenance or installation, and the obstacles the government may face using conventional technologies either in extending electricity networks for large distances or relying on diesel generators. However, and presents unique challenges and new opportunities for investing in green renewable energy. big governmental Efforts are being made to improve the lives of those living in remote areas of Algeria, such as especially investing in solar renewable energy to bring electricity to more remote people. as well as building new roads and healthcare facilities. In another hand, Algeria's country situated in the north African continent are well positioned to be a leader in the solar and wind energy industries, and Algeria has an important role to play due to its abundant solar energy reserves despite abundant energy renewable resources. Algeria's current energy mix relies heavily on fossil fuels; Among the shortcomings and obstacles to be overcome to complete the integration of renewable energies; including government support for renewable energy projects, the establishment of renewable energy research and development centers, and the promotion of public renewable energy awareness and education [2].

The principle solar thermal electricity CSP, based on the concentrating the sun's energy onto a receiver to heat a working fluid, which in turn drives a turbine to generate electricity; They are various types of solar thermal power plants, such as parabolic troughs, power towers, and dish/engine systems. the use of thermal energy storage systems, which allow solar power plants to continue generating electricity even when the sun is not shining. high importance of research and development in the field of solar thermal electricity, particularly in the areas of heat transfer, materials science, and system integration. continued advancements in solar thermal electricity, will lead to increased efficiency and lower costs for solar thermal power plants, making them more competitive with other forms of energy generation [3].

Concentrated solar power generation is a promising technology that relies on the concentration of solar radiation to drive a heat engine and generate electricity. There are four main technologies currently utilized for concentrated solar thermal energy: parabolic trough systems, solar tower systems, Stirling solar dish systems, and linear Fresnel systems. Among these, solar dish systems offer high power density, efficiency, modularity, and versatility, and have the potential to provide an economically reasonable source of electricity in the near future. However, solar dish systems also have some complications such as the need for moving parts and a tracking system, which add to maintenance and total cost [4]. One of the challenges faced by solar concentrator technologies is finding a suitable and affordable reflective material. While various reflective materials are available in the market, low cost and durability are essential factors to consider. Generally, concentrators use either aluminum or silver reflective surfaces deposited on glass or plastic. Although silver/glass mirrors are the most durable, plain aluminum foil is the most commonly used due to its high reflectivity and ease of application using white glue or wheat paste [5]. CSP technologies comprise four different types, including Parabolic Troughs, Linear Fresnel, Parabolic Dish, and Power Tower [2]. Concentrating solar power (CSP) technology is a feasible investment opportunity, with the possibility of satisfying 7 % of worldwide electricity requirements by 2030 and 25 % by 2050, presuming a high-energy-saving, high-energy-efficiency scenario [6].

Among all four technologies, the PSDS (Fig. 1-a). has the highest energy conversion efficiency up to 32 % [[7], [8], [9]]of the incoming solar power is converted into electricity compared to 15 %–17 % of a typical solar panel. also The parabolic solar dish Stirling (PSDS) technology has been shown to have higher efficiency compared to the parabolic trough system, mainly due to the use of the Stirling engine [10], (Fig. 1-b), the component “eurodish” PSDS system are shown in Table 1. The PSDS is a highly efficient solar technology that uses concentrating solar irradiation at a focal point. Its concentration ratios are higher than those of other concentrating technologies. The PSDS system consists of three primary parts: the concentrator, the receiver, and the tracking system. The concentrator is designed in a parabolic shape and has a reflective material on its front face, while the receiver is responsible for converting the concentrated solar energy into desired forms of energy.

Fig. 1.

Design of the eurodish system [11].

Table 1.

Eurodish components [12].

Component	Description
Control cabinet	Houses electronics for system control, tracking, and engine control
Azimuth drive	Rotates the dish to track the sun's movement.
Azimuth rail	Track for the azimuth drive's horizontal movement
Elevation rail	Track for the elevation bearing's movement.
Ring truss	Supports the dish and other system components.
Turn table	Platform for the receiver and Stirling engine.
Elevation bearing	Supports the turn table and allows vertical movement.
Concentrator shell	Mirror focusing sunlight onto the receiver (16 sets).
Stirling engine	Converts solar heat into electricity.
Stirling engine support	Holds the Stirling engine securely in place.

Active research is currently being done on different designs of receivers. The solar tracking system tracks the position of the sun and improves the efficiency of the collector system by extracting more solar irradiation. There are two types of tracking systems available: single axis and dual axis tracking systems. Overall, PSDS has the potential to be an effective solution for renewable energy production [4].

The thermal-to-mechanical efficiency of the Stirling engine is approximately in excess of 40 % [13,14] The regenerator is a heat exchange that stores and releases thermal energy continuously. By adding a regenerator into a Stirling engine, the efficiency of the engine improves and the energy is conserved by maintaining heat within the system, which otherwise would leave the engine [15]. The highest theoretical efficiency of a PSDS system is 23.05 %, and an experimental study found 22.75 % overall efficiency at a levelized cost of energy of 0.2565 USD/kWh [4]. which further confirms the high efficiency of PSDS systems in converting solar to thermal energy. However, to enhance the net performance of PSDS, further research is needed to minimize losses in the Stirling engine and thermal receiver [16]. and also we need to develop hybrid innovative multi-generation systems to generate electricity and heat with reasonable cost and higher thermal efficiency [4].

Dish Stirling technology is highlighted as a small-scale power generation option, suitable for distributed energy systems, remote areas, and off-grid applications due to its compact size and modular design. It is known for providing relatively high efficiencies in small installations. On the other hand, Linear Fresnel Reflectors (LFR), Parabolic Trough (PT), and Central Receiver Tower (CRT) technologies are considered more appropriate for larger-scale power generation, often used in utility-scale CSP plants with higher power outputs. [17]; In their review, Mohamed E. Zayed et al. [18] review Concentrated Solar Power (CSP) Dish/Stirling systems (SDSS), focusing on requirements for design, optico-geometric parameters, and thermal performance analysis. Thermodynamic optimization and techno-economic factors to consider. They present experimental studies examining the status of independent SDSS in various countries, as well as operational power plants. SDSS is extensively discussed for its applications in solar power plants, hybridization, micro-cogeneration, water desalination, and solar cooking. The findings of this study show that SDSS plays a superior role in distributed energy systems, which have flexible production capacities ranging from 1.0 to 38.8 kW, overall efficiencies ranging from 13 % to 32 %, as well as production costs varying from USD 0.115 to USD 0.256 per kWh, according to concentrator size, design, and solar radiation levels. In overall, SDSS can provide promising environmental and technical benefits; however, more efforts are needed to encourage the viability of its commercial uses.

The paper by Mohamed E. Zayed et al. [19] presents an optimization of the structure as well as performance variables of a thermodynamically balanced dish/Stirling solar concentration system (SDSS) using multi-objective particle swarm optimization (MOPSO) [19].

The study created a theoretical framework that used opto-geometric and thermodynamic analyses of the SDSS, taking into account opto-geometric design criteria and the energy balance of the system's various components. Nine selection variables were considered simultaneously in order to maximize the SDSS's output power and overall efficiency. The results demonstrated that the proposed MOPSO approach can produce the desired results and enable technical viability for the design of SDSS based on the output power requirements for a specific application.

The study by Alhawsawi et al. [20] presents a thorough theoretical modeling and performance analysis of a single-effect evaporation desalination system. This system is powered by a hybrid Stirling solar system that employs a solar dish. The solar dish facilitates the simultaneous generation of electricity, heat, and freshwater in a combined process. Simulation results demonstrate that the energy performance of the Stirling solar hybrid system powered by a solar dish showed that its combined production of electricity, heat, and fresh water was efficient. Simulation results indicated that the daily electrical energy production by the system was 234.7, 194.6, 159.8, and 190.0 kWh/day for the months of June, September, January, and March, respectively. The daily fresh water production was 787.4, 657.3, 545.0, and 650.0 kg/day for the same months. Furthermore, the overall daily cogeneration efficiency of the hybrid system is increased to 62.60 %, 53.50 %, 47.40 %, and 52.96 % compared to only the average daily electrical efficiencies of 20.92 %, 17.82 %, 15.50 %, and 17.60 % achieved, respectively, when using a standalone Stirling solar power system.

In their study Zayed et al. [20] present an innovative hybrid artificial intelligence model for estimating the dynamic performance of a solar dish/Stirling power plant. They create a hybrid prediction model that uses a revised version of the Random Vector Functional Link (RVFL) network to forecast the immediate and monthly energy output of a parabolic Stirling solar power plant. The Chimp Optimization Algorithm (CHOA), a new metaheuristic algorithm, has been coupled with the RVFL network to effectively identify the optimal RVFL parameter values. The effectiveness of the hybrid RVFL-CHOA model is assessed against four other artificial intelligence models: the original RVFL model, and three variations incorporating Particle Swarm Optimization (PSO), Spherical Search Optimization (SSO), and the Whale Optimization Algorithm (WOA). The findings indicate that the hybrid RVFL-CHOA model outperforms these alternatives in accurately predicting the hourly and monthly performance of a parabolic Stirling solar power plant.

The Dish Stirling system has been identified as a viable technological and economically viable option to tackle electricity challenges in the remote southern areas of Algeria. The system's LCOE ranges from 0.1155 to 0.2355 USD/kWh, relying on the location and system configuration. The Dish Stirling system is better suited to the Bechar and Tamanrasset locations, as well as a whole Algerian Sahara, than to the Algiers site. In accordance with the research, small-scale solar thermal power stations, such as the Dish Stirling system, are a suitable and cost-effective way to supply electricity to dispersed habitations in Algeria's vast Sahara. The annual solar radiation is a critical parameter in determining the viability of solar thermal power plants [10].

Previous studies in the field provide a comprehensive overview of the economic viability of solar dish/Stirling drive systems in different geographies, revealing notable disparities in LCOE values and capacity. The concept of LCOE values is key, representing the overall cost of producing each kilowatt-hour (kWh) of electricity over the lifetime of the system, including installation, maintenance, and operating expenses.

The following study summarizes the LCOE of solar dish/Stirling systems in different locations: Affandi et al. in Georgetown, Malaysia (2018) with LCOE of 0.83USD/kwh [21]; Jamshoro, Pakistan Lashari et al. (2021) with LCOE of 0.13USD/kwh [22]; Zayed et al. (2020) in Tianjin, China with LCOE of 0.2565USD/kwh [23]; Bataineh and Taamneh (2017) in Maan, Jordan with LCOE of 0.0.115USD/kwh [24] and Abbas et al. in Algiers, Algeria with LCOE of 0.235USD/kwh (2011) [25] Remarkably, the prevalence of a high power rating of 25 kW is shown in precedent studies except for Abbas et al. in Algiers with 9.7 kW rated power, these references, prompt the observation that solar dish/Stirling systems exhibit superior cost-effectiveness for smaller-scale applications. This underlines the need to seek and study systems with a capacity of less than 25 kW, which seem more adaptable to meet different power demands.

Objective of this study is to simulate a 100 kW PSPS system and evaluate its technical and economic performance using the GREENIUS software [26], across five off-grid sites located in the arid zones of southern Algeria. The aim is to assess the feasibility, potential, and comparison with the photovoltaic technology. The study will analyze the annual electricity production, CO2 mitigation, and discounted cost of electricity (LCOE) for the PSPS system in comparison to PV, while identifying the most productive and profitable locations for implementation. Additionally, the study will analyze the cost-effectiveness of PSPS technology in these regions, testing its potential in extreme off-grid environments.

2. Design and methodology

2.1. DNI and GHI map of Algeria

There are several sources of solar data available in any location on the world, including the NREL's Solar Prospecting Tool, which provides DNI, GHI, Photovoltaic power potential data, and several GIS platforms, such as ArcGIS, that can be used to access and analyze this data.

The Direct Normal Irradiation DNI in Algeria is one of the highest in the world, thanks to the country's location in the Sahara desert region [27]. According to data from the NREL, the annual average DNI in Algeria ranges from 2000 to 2600 kWh/m². This high DNI value makes Algeria an ideal location for the development of concentrated solar power CSP projects.

The amount of GHI in Algeria can vary depending on the location and time of year. Generally, the southern regions of Algeria receive more sunlight than the northern regions. Additionally, the amount of GHI can be affected by factors such as cloud cover and atmospheric conditions. Fig. 2.

Fig. 2.

Global horizontal irradiance map of Algeria [28].

The average annual DNI for Algeria is around 2200 kWh/m². However, this value can vary depending on the specific location within Algeria. the average annual DNI for the Tamanrasset region in southern Algeria is around 2600 kWh/m², while the Algiers region in the north has an average DNI of around 1800 kWh/m² Fig. 3.

Fig. 3.

Direct normal solar irradiation map of Algeria [28].

2.2. Corrected DNI

The Dish Stirling unit does not utilize 100 % of the incoming DNI and that's due to losses caused by two factors: shadowing factor and intercept factor that we take in consideration to” Correct” the DNI value.

The corrected DNI Eq. (1) can be used to calculate the effective amount of direct solar radiation available at a given location, taking into account the effects of shading and interception factors. This information is important in designing and optimizing solar energy systems, as well as in other applications such as weather forecasting and climate modeling [29]. utilizes, the equation for corrected DNI:

$$\text{DNIc}=\text{DNI}\cdot {\mathit{f}}_{\mathit{s}\mathit{h}\mathit{a}\mathit{d}\mathit{o}\mathit{w}\mathit{i}\mathit{n}\mathit{g}}\cdot {\mathit{f}}_{\mathit{i}\mathit{n}\mathit{t}\mathit{e}\mathit{r}\mathit{c}\mathit{e}\mathit{p}\mathit{t}}\cdot {\mathit{f}}_{\mathit{t}\mathit{e}\mathit{m}\mathit{p}}$$ (1)

$f_{shadowing}$: shadowing factor is the fraction of sunlight that is not blocked or shadowed by other dishes, 100 % means no shadowing, 0 % means total shadowing.

Typically, The Dish Stirling system is a large assemblage of dish units deployed on a field of finite dimensions. As a result, one dish may block (i.e., shadow) the sun, which would normally be covering the collector of another dish. Usually, enough land is provided so that the dishes can be spaced far enough apart to minimize shadowing. However,

Shadowing does occur:

The shadowing leads to an overall degradation of the power produced by the dish Stirling system.

${\mathit{f}}_{\mathit{i}\mathit{n}\mathit{t}\mathit{e}\mathit{r}\mathit{c}\mathit{e}\mathit{p}\mathit{t}}$: Intercept factor: The intercept factor is the fraction of the direct solar radiation reflected by the parabolic concentration that enters the aperture of the receiver, which is often between 90 and 99 %, by increasing the intercept factor it will increase the fraction of the solar radiation entering the receiver as well as it will affect the rate of energy production of the Dish Stirling system.

${\mathit{f}}_{\mathit{t}\mathit{e}\mathit{m}\mathit{p}}$: Temperature correction factor∶ The temperature correction factor is a factor linked to the cooling system that contributes to the performance model eventually leading to the ambient temperature as it directly effects the cooling system, the lower the ambient temperature the higher the temperature correction factor.

2.3. Remote chosen locations

We use the weather data given by Fig. 2 for five remote locations:

• 1.Adrar (Bordj Badji Mokhtar) is a city located in the Adrar Province in the south-central part of Algeria. It is situated in the Sahara Desert.
• 2.Illizi (Djanet) is a commune in the Illizi Province, which is located in the southeast of Algeria near the border with Libya. It is known for its breathtaking landscapes, including the Tassili n'Ajjer mountain range and the sand dunes of the Sahara Desert.
• 3.Tamanrasset (Ain Mertoutek) is a city located in the Tamanrasset Province in southern Algeria. It is the largest city in the Hoggar Mountains and is often referred to as the “Gateway to the Sahara".
• 4.Tindouf is a province located in the western part of Algeria, bordering Western Sahara and Mauritania. It is largely desert and is known for its harsh climate and difficult living conditions.
• 5.Bechar is a city located in the Bechar Province in western Algeria. It is situated in the Saoura Valley, a region known for its agricultural productivity and scenic beauty. Bechar is also known for its unique architecture and rich cultural heritage.

These five locations are the best candidates for implementing a CSP technology due to their geographical location advantage which allows high values of DNI. Note that even though the DNI is an important criterion for a solar plant, there is another parameter that can impact the performance of our particular system, the Dish Stirling system, and that is the ambient temperature and we're going to discuss that further in this paper.

2.3.1. Weather files

In order to run a solar system performance simulation, weather files are important data sources that contain climate information, such as temperature, humidity, and solar radiation, for specific locations around the world. The following are some of the most common weather file extensions and may vary depending on the software;

• •CLM: “climate file format” and is used by the EnergyPlus software program to simulate building energy performance; CLM files contain hourly weather data. for a specific location, including temperature, humidity, wind speed, and solar radiation.
• •DDY: “design day data file” and is used to specify weather data for a building design day. DDY files typically contain data for the hottest and coldest days of the year, and are used to estimate the energy requirements for heating and cooling a building.
• •TMY: “typical meteorological year” and is a standard format used for weather data files in the United States. TMY files contain a year's worth of hourly weather data for a specific location, and are used to represent typical weather conditions for energy modeling and building design.
• •EPW: “energyplus weather” and is a standard format used for weather data files in EnergyPlus software [30], a building energy simulation program. EPW files contain hourly; weather data for a specific location, including temperature, humidity, wind speed, and solar radiation, and are used for building energy analysis and optimization.

The weather data for the different locations were provided by (Climate.OneBuilding.Org) which is a website that contains weather data for over than 13,000 different places in earth globe [31].

Fig. 4 present the DNI values the five location in southern Algeria; In Adrar, the yearly average DNI value is 254.21 W/m². The maximum value recorded in Adrar is 878.0 W/m², indicating a high potential for solar energy generation in this area. In Illizi, the yearly average DNI value is slightly higher at 273.84 W/m². The maximum value recorded in Illizi is 879.0 W/m², which is the highest value among all the zones listed. Tamanrasset has the highest yearly average DNI value among the listed zones at 291.11W/m². However, the maximum value recorded in Tamanrasset is 860.0 W/m2, which is lower than the maximum values recorded in Adrar and Illizi; In Tindouf, the yearly average DNI value is 259.26 W/m2, and the maximum value recorded is 870.0 W/m². In Bechar, the yearly average DNI value is the lowest among the listed zones at 246.41 W/m2. The maximum value recorded in Bechar is 869.0 W/m².

Fig. 4.

Maximum DNI and yearly average of 5 selected locations.

In Fig. 5 displaying temperature data for four zones in Southern Algeria - Adrar, Illizi, Tamanrasset, and Tindouf; yearly average, maximum, and minimum. The yearly average temperatures for each city are all relatively warm, with Adrar having the highest average temperature at 26.75 °C and Tamanrasset having the lowest at 23.55 °C. The maximum temperatures for each city are quite high, with Adrar having the highest at 47.7 °C and Tamanrasset having the lowest at 38.3 °C. This indicates that these cities can experience very hot weather conditions. On the other hand, the minimum temperatures for each city are relatively low, with Illizi and Tindouf experiencing sub-zero temperatures at 0.0 °C and 2.0 °C respectively. This suggests that these cities can also experience very cold weather conditions.

Fig. 5.

Maximum GHI and yearly average of 5 selected locations.

2.4. Load profile sizing

It's worth noting that the energy consumption of household appliances can vary significantly depending on the model and usage habits. However, these assumptions provide a useful estimate for modeling the household's energy consumption. To further analyze the energy usage patterns, it would be helpful to consider other factors such as the number of occupants in the household, their daily routines, and the climate conditions in the region. Additionally, exploring ways to reduce energy consumption through energy-efficient appliances and behavioral changes can result in significant cost savings and reduce the household's carbon footprint. Renewable energy solutions such as solar panels can also be considered to supplement or replace the use of grid electricity.

The graph below depicts the daily energy consumption of a typical household in a village located in the southern region of the country. The household consumes 2.973 kWh of electricity daily during winter and 4.383 kWh during summer, which lasts for four months (May, June, July, and August). Therefore, the annual electricity consumption is estimated to be 1239.48 kWh. The energy consumption profile was modeled using Homer software, based on the following assumptions about the household appliances:

• ∗7 lamps
• ∗1 refrigerator
• ∗1 television.

2.5. Greenius Dish Stirling model equations

• •Gross electrical performance [32].

$$p_{gross}=\{\begin{array}{c}a\cdot E_{cor}+b\text{if}{E}_{DNI}\ge E_{DNI.\mathit{min}}\\ 0,\text{else}\end{array}$$ (2)

• •Corrected irradiance

$$E_{corr}=E_{DNI}\cdot f_{ref}\cdot f_{temp}$$ (3)

• •Reflection correction factor

$$f_{ref}=f_{shadowing}\cdot f_{intercept}$$ (4)

• •Temperature correction factor

$$\begin{gathered} f_{temp}=\{\begin{array}{c}\frac{{\vartheta }_{norm.amb}+273.15^{\circ }C}{{\vartheta }_{amb}+f_{ref}\cdot E_{DNI}\cdot c_{cooler}+273.15^{\circ }C}\text{Reciprocal} \\ \text{model}\\ \frac{{\vartheta }_{up.stirling}-\left({\vartheta }_{amb}+f_{ref}\cdot E_{DNI}\cdot c_{cooler}\right)}{{\vartheta }_{up.stirling}-{\vartheta }_{norm.amb}}\text{Carnot} \\ \text{model}\end{array} \end{gathered}$$ (5)

• •Parasitics

$$p_{parasitic}=\{\begin{array}{c}{P}_{oper}\text{for}{E}_{DNI}\ge E_{DNI.\mathit{min}}\\ P_{sleep}\text{for}{E}_{DNI}<E_{DNI.\mathit{min}}\end{array}$$ (6)

• •Power to/from the grid

$$P_{grid}=P_{gross}-P_{parasitic}$$ (7)

Fig. 7. Illustrate the flowchart of the algorithm for the Greenius Dish Stirling model equations, with a detailed explanation of each step.

Fig. 7.

Flowchart of the algorithm for the Greenius Dish Stirling model equations.

2.6. Dish stirling system

The Eurodish system utilized in this research is the CNRS-promes Dish Stirling system, established in 2004 as the final of three nationwide reference units developed under the “Envirodish” initiative. It has demonstrated outstanding performance, reaching a 10.85 kW peak electric power output and an efficiency of approximately 22.5 %, as stated in Refs. [15,33], the system consists of 10 dish Stirling units each with a nominal capacity of 10 kW which makes the whole system 100 kW, the system will be tested in different sites to determine the electricity output and thus the number of houses the system can power assuming each house has the same load profile that was previously introduced.

This is an important metric for determining the amount of solar energy that the concentrator can collect, The focal length of the concentrator is 4.5 m, which is the distance from the center of the dish to the point where the solar radiation is focused. This distance is important for determining the size and position of the receiver that captures the focused energy, we use the rectified DNI equation E_corr Eq. (3) to calculate the effective amount of direct solar radiation available at a given location while accounting for shading and interception factor. the average concentration factor of the concentrator is 2500, which means that it can concentrate the solar radiation by a factor of 2500 times. This high concentration factor allows the concentrator to achieve high temperatures ${\mathit{\vartheta }}_{\mathit{u}\mathit{p}.\mathit{s}\mathit{t}\mathit{i}\mathit{r}\mathit{l}\mathit{i}\mathit{n}\mathit{g}}$, and therefore high thermal efficiencies ${\mathit{f}}_{\mathit{t}\mathit{e}\mathit{m}\mathit{p}}$ shown in Eq. (5), and The reflectivity of the concentrator is 94, which means that it can reflect 94 % of the incident solar radiation onto the receiver this is shown in Eq. (4). This high reflectivity is important for maximizing the amount of energy collected by the concentrator. In General, these characteristics highlight the potential of Stirling dish solar concentrators for generating renewable energy. They are particularly well-suited for applications that require high temperatures and can be used in combination with a Stirling engine to generate electricity.

To estimate the amount of power delivered to the grid, represented by ${\mathit{P}}_{\mathit{g}\mathit{r}\mathit{i}\mathit{d}}$, we require the calculation of the gross electrical performance ${\mathit{P}}_{\mathit{g}\mathit{r}\mathit{o}\mathit{s}\mathit{s}}$ which is the theoretical maximum electricity produced and the determination of parasitic losses ${\mathit{P}}_{\mathit{p}\mathit{a}\mathit{r}\mathit{a}\mathit{s}\mathit{i}\mathit{t}\mathit{i}\mathit{c}}$ which energy losses due to factors like reflection and inefficiencies as denoted in Eq. (7). To calculate ${\mathit{P}}_{\mathit{g}\mathit{r}\mathit{o}\mathit{s}\mathit{s}}$ and ${\mathit{P}}_{\mathit{p}\mathit{a}\mathit{r}\mathit{a}\mathit{s}\mathit{i}\mathit{t}\mathit{i}\mathit{c}}$ separately, we need to refer to Eq. (2) and Eq. (6) respectively.

2.7. Dish stirling system simulation data

To simulate the Dish Stirling system we need various parameters relating to its performance characteristics; Performance model constant “a" was set to 19 m2, shadowing factor to 0.954 as shown in Eq. (2), and intercept factor to 0.970 as shown in Eq. (4), since Greenius uses dated values related to a previous Eurodish installment the values had to be changed to be consistent with the latest CNRS-promes Dish Stirling system performance [33].

Fig. 8. Shows the flowchart of the processing steps involved in the Greenius software computation, showing the input data, the intermediate calculations, and the output results.

Fig. 8.

Flowchart of processing Greenius Software Computation.

The flowchart in Fig. 9 illustrates the total cost distribution in the Greenius software calculation, showing the cost of each component and the overall system cost:

Fig. 9.

total Cost Distribution in the Greenius Software Calculation Flowchart.

3. Results and discussion

We'll be presenting, discussing, and comparing the results of the simulation concerning the Dish Stirling system and the PV system, the main focus of the simulation is to determine the power output of each system at the exact same weather conditions since the same weather files were used to run both systems simulations.

3.1. Dish stirling system performance analysis

3.1.1. Dish stirling system annual power output

The Dish Stirling system simulation shows in Fig. 10 promising results, the system averages about 256 Mwh of annual generated electricity across the 5 chosen locations, the system managed to achieve the highest amount of annual generated electricity in Tamanrasset: 288.43 Mwh, coming next is Illizi with 265.61 Mwh, 3rd is Tindouf with 248.37 Mwh, 4th is Adrar with 245.42 MWh and relatively the lowest is Bechar with 232.48 Mwh.

Fig. 10.

Dish Stirling system annual generated electricity in 5 different locations.

3.1.2. Gross power and grid-connected power

In order to understand the difference between gross energy and grid-connected power, and knowing this study applies to all the locations, we took Illizi district, Fig. 11 represents the chart of the monthly gross power output (E gross) and grid-connected power (E grid) in Illizi location, gross power output represents the total amount of power generated by the Dish Stirling system without taking in account parasitics while the grid-connected power is the net power received by the consumer and it includes parasitics which represent transmission and distribution losses and tracking system losses.

Fig. 11.

Monthly gross power (E gross) and grid-connected power (E grid) in Illizi.

The graph shows the difference between the two values, where we can see that the (E grid) values are lower than (E gross) values and that's due to T&D losses plus sleeping parasitics referring to electricity consumed during none operating hours (DNI < starting DNI) to power the tracking system which together represents on average 6.7 % in power losses.

3.1.3. Dish stirling system achieved efficiency

The specified Dish Stirling system efficiency is 22.5 % in ideal operating conditions which represent ambient temperature and DNI, to evaluate the system performance it is important to analyze the system efficiency in the studied locations to get an idea of whether the system can achieve said efficiency which translates into maximized power outputs. Taking Illizi as an extreme Est-southern district, Fig. 12 represent the average monthly efficiency of the Dish Stirling system with a peak average monthly efficiency of around 20.2 % in January and a minimum of 18.8 % in August.

Fig. 12.

Dish Stirling system monthly efficiency in Illizi.

In order to understand the differences between January and August and what contributes to present such results, a comparison was made between the two main parameters in Illizi that dictates the system performance which are the ambient temperature and DNI Fig. 13.

Fig. 13.

DNI and Tamb daily average values in January and August in Illizi.

We can see that not only the DNI that impacts the system performance but the temperature too, even though the average daily DNI in August is consistent and constantly higher than the average daily DNI in January that does not translate into a higher efficiency as we previously highlighted and that is due to the low average daily temperatures in January which are significantly lower than their counterparts in August.

Note that the average DNI is sub 400's and that's because the values are averaged over 24 h of the day which includes night (low light) where DNI is 0, the same goes for the temperature.

Taking Bechar which is Ouest-southern district; where the Dish Stirling system preformed relatively the least out of the other locations we can notice the same trend, Fig. 14 represent the average monthly efficiency of the Dish Stirling system in Bechar with a peak average monthly efficiency of 19.5 % in March and a minimum of 18.5 % in October.

Fig. 14.

Dish Stirling system monthly efficiency in Bechar.

Upon inspecting the difference of the main parameters between the two months, we find the same results as we previously have shown, even though the average daily DNI in October is slightly higher than the counterpart in March throughout the days of the month that doesn't translate into a better efficiency since the average daily temperatures in March are significantly lower Fig. 15 in fact, we notice the same trend in every location which make absolute sense since essentially the Stirling engine relies on temperature gradient to produce work which concludes that the ambient temperature is as impactful as the DNI since ambient temperature directly effect the cooling system.

Fig. 15.

DNI and Tamb daily average values in March and July in Bechar.

3.1.4. The effect of the ambient temperature and corrected DNI on the dish stirling system power output

So far we established that the main parameters that directly impact the Dish Stirling system performance are the DNI and ambient temperature, to highlight the effect of these two parameters we graphed the following 3 variables together: DNI, Tamb, and E grid.

In Fig. 16; Fig. 17; Fig. 18; Fig. 19, Fig. 20 we can clearly see that the DNI curve roughly follows the same curvature as the E grid curve, if DNI increases E grid increases but it's not always the case, if we take a closer look at Fig. 16 in January and February DNI increased but E grid decreased and that's due to the ambient temperature increasing which causes the E grid to decrease even though DNI increased.

Fig. 16.

DNI, Tamb, and E grid monthly average values in Adrar.

Fig. 17.

DNI, Tamb, and E grid monthly average values in Illizi.

Fig. 18.

DNI, Tamb, and E grid monthly average values in Tamanrasset.

Fig. 19.

DNI, Tamb, and E grid monthly average values in Tindouf.

Fig. 20.

DNI, Tamb, and E grid monthly average values in Bechar.

The previous results are not limited to Adrar only, but the same effect is present in all studied locations, in Illizi we can detect the same effect in January/February and May/June where the increase of DNI results in a decrease in E grid due to increased ambient temperature not only that but we find the exact opposite of the previous notion about the proportional relation between DNI and E grid where we find in September and October that a decrease in DNI resulted in an increase in E grid and that's due to the decrease of the ambient temperature which further suggests the importance of low ambient temperatures for a better performing system, where significant variations in the ambient temperatures can sometimes overweight the increase in DNI.

We can summarize all previously acquired results by returning to the main principle of which the Stirling engine function as shown in Eq. (8), which is the utilization of temperature gradient which by necessity causes an increase in entropy that allows the system to extract useful work, the bigger the temperature gradient the more work can be extracted by the system since Stirling engine maximum possible efficiency is equal to Carnot efficiency there for the best possible location for a Dish Stirling system to operate is a location with a high DNI and low ambient temperature.

$${\mathit{\eta }}_{\mathit{C}\mathit{a}\mathit{r}\mathit{n}\mathit{o}\mathit{t}}={\mathit{\eta }}_{\mathit{s}\mathit{t}\mathit{i}\mathit{r}\mathit{l}\mathit{i}\mathit{n}\mathit{g}}=\frac{\mathit{U}\mathit{s}\mathit{e}\mathit{f}\mathit{u}\mathit{l}\mathit{M}\mathit{e}\mathit{c}\mathit{h}\mathit{a}\mathit{n}\mathit{i}\mathit{c}\mathit{a}\mathit{l}\mathit{W}\mathit{o}\mathit{r}\mathit{k}}{\mathit{H}\mathit{e}\mathit{a}\mathit{t}\mathit{A}\mathit{d}\mathit{d}\mathit{e}\mathit{d}\mathit{T}\mathit{o}\mathit{T}\mathit{h}\mathit{e}\mathit{S}\mathit{y}\mathit{s}\mathit{t}\mathit{e}\mathit{m}}$$ (8)

Note that: Useful Mechanical Work = Heat Added To The System - Heat Extracted From The System.

3.1.5. Dish stirling system load hours obtained and capacity factor

The Dish Stirling system capacity factor represents the ratio between the generated power by the system over the maximum theoretical power the system can generate over a specific period, Greenuis software already provides full load hours obtained by the system at the course of a year, therefore, we can simply calculate the capacity factor by taking the ratio of full load hours obtained by the system over how many hours in a year since the relation between time and power is linear.

Since the Dish Stirling system performs (as it shown in Fig. 21) is the best in Tamanrasset where it produces the highest amounts of electrical power, the capacity factor is 32.90 % next is Illizi with 30.32 % capacity factor, 3rd is Tindouf with 28.35 %, 4th is Adrar witch 28.02 % and relatively the lowest is Bechar with 26.54 %.

Fig. 21.

Dish Stirling system capacity factor in 5 different locations.

Note that the capacity factor for solar energy systems can never exceeds 50 % or even approach it and that's due to the fact that the sun is not present 24 h a year therefore the ability of the system to produce electricity is constrained by the system exposure to the sun where the system can get enough DNI to operate consistently plus the weather conditions like clouds or dust in upper atmosphere that can block the sun's radiations.

3.2. Comparison between the dish stirling system and the PV system performance

3.2.1. Dish stirling system and the PV system annual power output

The simulation results (Fig. 22.) shows a noticeable dominance across all locations of the Dish Stirling system over the PV system with fixed option regarding tracking system with the highest value coming up in Tamanrasset with 51.68 % increased power output following up is Illizi with 46 %, Adrar 42.74 %, Tindouf 39.25 %, and Bechar coming up last with 36.71 % increased power output.

Fig. 22.

Dish Stirling system annual generated electricity in 5 different locations.

Though that is not the case regarding the other two tracking options of the PV system as we can see a considerable increase in the PV system power output of about an average of 20.46 % concerning the 1 axis tracking option and 34.26 % concerning the 2 axis tracking option.

Taking into consideration the 2 axis tracking option for the PV system being the best possible performance of the PV system greatly impacts preliminary results that showed explicit superiority of the Dish Stirling system over the PV system as the average 43.28 % increase in power output favoring the Dish Stirling system drops down to 6.70 % average increase, the increase is shown in all studied locations which shows the superiority of the Dish Stirling system.

Note that the PV results also take in consideration T&D losses and other losses related to the PV system like AC wiring and DC losses which together represent 6.7 % without taking into consideration losses due to the tracking system in 1 axis and 2 axis tracking options which can result in additional losses.

3.2.2. Dish stirling system and the PV system powered households

Fig. 23 represents the number of households that can be powered by Dish Stirling and PV systems in five different locations. The calculation of the number of households powered by these systems is based on their annual power output and the average annual power needs of a household. To determine the number of households that can be powered by each system, the annual power output of each system is divided by the annual power need of an individual household.

Fig. 23.

Comparison between Stirling Dish and PV systems powered houses.

According to the load profile explained in Fig. 6., we considered a typical household in a village in the great south area (which where the 5 locations are) have an annual consumption of 1239.48 kWh.

Fig. 6.

Daily load profile of a typical village household in the great south.

Based on the results presented in Fig. 23., the Dish Stirling system managed to surpass all the different PV system configurations in powering households in all the 5 locations, and that's due to the difference in the power output between the Dish Stirling system and the PV system, as shown in figure subsection 3.3.1.

3.2.3. Dish stirling system overall costs

The latest version of Eurodish has significantly reduced costs due to improvements in its overall design, particularly the concentrator design. The prototype version of Eurodish incurs an annual cost of 16,626.75 USD/kW. The projected costs for producing 500 to 5000 units per year are 2226.85 USD/kW and 1484.53 USD/kW, respectively [34]. with annual O&M costs of 17.86 USD/kW, and annually replacement costs representing 2 % of the total investment cost respectively.

equation (9) show The total 100 kW capacity Dish Stirling system cost in US dollars assuming 25 years system lifetime turns out to be:

$${\text{Cost}}_{\text{tot}}=\text{In}\times \left(1+\text{SRCn}\right)+\text{Mn}$$ (9)

$$\mathbf{C}\mathbf{os}{\mathbf{t}}_{\mathbf{tot}}=2,232.47\times 100\times \left(1+0.02\times 25\right)+17.86\times 100\times 25$$

$$\mathbf{Cos}{\mathbf{t}}_{\mathbf{tot}}=272,361.94\text{USD}$$

Note that the cost assumption excludes land cost since the project is assumed to be governmental and the transportation and instillation fees are not included in the equation which will be explained further in this paper.

3.2.4. PV system overall costs

The SunPower X22-360 is considered one of the best PV panels on the market with 22.2 % nominal efficiency and belonging to SunPower X series panels.

Concerning the SPR X22-360 market cost range between 2710 USD/kW and 4060 USD/kW with a 3390 USD/kW market place average (the cost includes inverters) [18], the O&M costs for one axis tracking PV system which include inverter and module replacements are 14.14 USD/kW annually. equation (9) show the total cost for 100 kW one axis tracking PV system assuming the lowest market price possible in US dollars and assuming 25 years system lifetime turns out to be:

$$\mathbf{Cos}{\mathbf{t}}_{\mathbf{tot}}=\mathbf{I}\mathbf{n}+\mathbf{M}\mathbf{n}$$ (10)

$$\mathbf{C}\mathbf{os}{\mathbf{t}}_{\mathbf{tot}}=2710\times 100+14.14\times 100\times 25=306,350.00\text{USD}$$

3.2.5. The LCOE of the dish stirling and PV systems

The LCOE is a parameter that represents the average net present cost of generated electricity by a power plant over its lifetime, LCOE is the ratio of the sum of all discounted costs of the electricity generation project over the discounted sum of energy delivered over the lifetime of a said project.

In order to use LCOE as a parameter to compare the Dish Stirling system and the PV system two assumptions must be established, one is the intention of the previous studied system is not profitable but simply a better solution to an existing problem and two is the LCOE parameter used to evaluate the Stirling Dish and PV systems from a simplified financial standpoint where Cost = Return, in other words as shown in Eq (11), no profit and no losses thus the simplified LCOE equation by getting rid of the discount rate is as follows:

$$\text{LCOE}=\frac{\text{Sum}\text{of}\text{costs}\text{over}\text{lifetime}}{\text{Sum}\text{of}\text{electrical}\text{energy}\text{produced}\text{over}\text{lifetime}}=\frac{I_n+M_n+F_n+SR{C}_n}{E_n}$$ (11)

The total power output $E_n$ during the two systems lifetime which in this case is set to 25 years is basically the annual power output multiplied by 25.

In Fig. 24 we can observe the lowest LCOE value obtained in Tamanrasset equals to 0.0378 USD/kWh for the Dish Stirling system and 0.0528 USD/kWh for the PV one axis tracking system due to the fact that Tamanrasset is the most suited place to implement a solar energy technology, as we saw previously that both systems perform the best in Tamanrasset location where in Bechar location the LCOE is relatively high due to the relatively low power output for both systems in said location where the LCOE in Bechar is equal to 0.0469 USD/kWh for the Dish Stirling system and 0.0613 USD/kWh for the PV 1 axis tracking system.

Fig. 24.

Comparison between Stirling Dish and PV 1 axis systems LCOE.

Note that the fuel cost was not calculated in the total cost for both systems and that's simply because the systems do not consume fuel to generate electricity as the conventional methods we further explain this in the following section.

Concerning the LCOE calculations, the transport and installation costs were not included in the calculation as there is no information regarding this since the costs vary tremendously and there is no general average for the various PV system configurations but according to [ [35]19] dual-axis tracking systems have the highest energy yield and capacity factor, followed by single-axis tracking systems and fixed systems. However, the installation cost and land requirement for dual-axis tracking systems are also the highest among the three types, but The LCOE for all three systems decreases with an increase in capacity, with fixed systems having the lowest LCOE at higher; and that's why the LCOE comparison was made between the Dish Stirling system and the PV one-axis tracking configuration system as it resembles a more realistic scenario.

From an economic standpoint, the studied PV system specific cost is higher than the Dish Stirling system according to Refs. [18,36] which is based on an assumption concerning cost projection for the Eurodish unit for 2 different production rate at which the 500 units per year was selected, the O&M costs are higher for the Stirling Dish unit compared to the 3 PV system configurations but even with the high O&M and replacement costs the overall Stirling Dish system costs assuming 25 years of service comes out cheaper than the PV system combined with the high power output of the Dish Stirling system we get a low LCOE for the Dish Stirling system in all locations compared to the PV system with its 3 configurations.

3.3. Dish stirling system avoided carbon dioxide emissions

Renewable energy has an important part to play in cutting back carbon dioxide emissions in the energy sector, to show the potential impact of the studied Dish Stirling system as a renewable energy technology on the environment, avoided carbon dioxide emission has been investigated as a parameter which represents the amount of CO2 gas mitigated as a result of using a clean energy source, the following emission factor was used to estimate the greenhouse gas emissions avoided [37]:

7.07 × 10⁻⁴ metric tons CO₂/kWh Note that metric tons is commonly used in the United States and it is equal to 1000 kg.

The following Table 2 shows the annual mitigated CO2 emissions due to the implementation of the Dish Stirling system in different locations which can be interpreted as the bigger the power output of the system in a particular location the bigger the amount of CO2 mitigated since the relation between the power output and the avoided CO2 emission is directly proportional.

Table 2.

CO2 avoided emissions tonne/year in 5 different locations.

State	Adrar	Illizi	Tamanrasset	Tindouf	Bechar
CO2 avoided emissions	173.5 t	187.8 t	204 t	175.6 t	164.4 t

To put it in perspective, burning 1 L of gasoline will release 0.00234 metric tons of CO2 gas also burning 1 L of diesel will release 0.00268 metric tons of CO2 gas [38,39].

Taking Tamanrasset as the location with the highest value of avoided CO2 emissions equal to 204 metric tons annually it would be equivalent to burning 87179.5 L of gasoline a year or burning 76119.4 L of diesel a year (Fig. 25).

Fig. 25.

Volume of burned diesel mitigated in studied locations.

As of 04-March-2023 the diesel price in Algeria is 0,21 USD per liter [40], therefore the Dish Stirling system mitigates 16,316.74 USD in fuel costs each year in Tamanrasset (Fig. 26.), in other meaning, for a diesel generator to produce as much electricity as the Dish Stirling

Fig. 26.

Mitigated fuel costs in studied locations.

System in Tamanrasset location, it would roughly require 16,316.74 USD worth of diesel fuel annually.

3.4. A comparative analysis between the current study and previous research

In this dedicated sub section, we are poised to engage in an in-depth comparive analysis of our current study, carefully aligning it with several prominent previous research endeavors. This body of significant research encompasses the notable contributions made by.

Affandi et al. (2018) [21], Lashari et al. (2021) [22], Zayed et al. (2020) [23], Bataineh and Taamneh (2017) [24], Abbas et al. (2011) [25] (Fig. 27. & Table 3). Through this comprehensive examination, we endeavor to discern the intricate intricacies and nuances inherent in our investigation by setting them against the backdrop of these foundational studies.

Fig. 27.

Comparative analysis of LCOE of current study with respective cited studies: [[21], [22], [23], [24], [25]].

Table 3.

Comparison of CSP-tech performance in different locations.

Ref	City, country	DNI (kWh/m2/y)	Annual energy (MWh)	Sys net efficiency (%)	LCOE (USD/kWh)	Rated power (kW)	CSP-tech	Simulation software	Working fluid
Current study	Bechar, Algeria	2730	23.248	26.54	0.0469	10	PSDS	Greenius	Helium
	Tindouf, Algeria	2717	24.837	28.9	0.0439
	Tamanrasset, Algeria	2708	28.843	32.9	0.0378
	Adrar, Algeria	2717	24.542	28.02	0.0444
	Illizi, Algeria	2757	256	30.32	0.041
Lashari et al. (2021)	Jamshoro, Pakistan	1719.15	38.6	23.39	0.13	25	PSDS	SAM	Helium
Zayed et al. (2020)	Tianjin, China	1198.2	28.748	19.55	0.2565	25	PSDS	MATLAB	Helium
Affandi et al. (2018)	George town, Malaysia	1246	8.385	N/A	0.83	25	PSDS	MATLAB	N/A
Bataineh and Taamneh (2017)	Maan, Jordan	2700	17.53	21	0.115	9.7	PSDS	SAM	H2
Abbas et al. (2011)	Algiers, Algeria	1500	27	24	0.235	25	STP	SAM	H2

Our primary objective in conducting this comparative evaluation lies in acquiring a comprehensive understanding of the LCOE associated with the PSDS. This endeavor entails considering the multifaceted aspects of climatic conditions, notably the sum of the annual accumulation of DNI and the working fluid inside of the solar Stirling engine. By juxtaposing our results with those of the aforementioned studies, we aspire to elucidate the variances and similarities that emerge when contextualizing our findings within the broader landscape of dish solar Stirling energy technology research.

In our study, we discovered that the LCOE for the five mentioned locations in southern Algeria ranged from USD0.0378 to USD0.0469 per kWh. These LCOEs are significantly lower than those reported in previous studies conducted at different sites, as documented by Affandi et al. (2018), Lashari et al. (2021), Zayed et al. (2020), Bataineh and Taamneh (2017), and Abbas et al. (2011). This variance can be attributed to the exceptionally high levels of DNI in southern Algeria, which range from 2708 to 2757 kWh/m²/year.

Noteworthy comparisons emerge from the examination of “Lashari et al.” in Jamshoro, Pakistan, as well as “Bataineh and Taamneh (2017)" in Maan, Jordan, with their respective LCOE values of 0.13 and 0.115 USD/kWh respectively. These values are more economical for certain cities in northern Algeria (such as Algiers cities with a value of 0.235 USD/kWh), but not the case of the sites in southern Algeria; This is due to the fact that the DNI of certain cities located in the north of Algeria is relatively low (e.g. Algiers 1500 kWh/m2/y.)

One of the most striking similarities is the relatively low LCOE values for the sites in southern Algeria. This is likely due to the high levels of DNI in this region. The DNI is a measure of the solar energy that is available at a particular location, and it is a key factor in determining the efficiency of solar power systems.

Another similarity is the higher LCOE values for the study in George Town, Malaysia. This is also likely due to the working fluid used in the system. The study by Affandi et al. (2018) used a CO2/Nitrogen gas mixture, while the study by Zayed et al. (2020) used helium gas. Helium is a more efficient working fluid than CO2/Nitrogen, and this is reflected in the lower LCOE value for the study by Zayed et al. (2020).

4. Conclusion

The Dish Stirling system is a highly promising technology for producing electricity from solar energy.

By using the Greenuis DLR code; We have conducted extensive tests of the CNRS euro-dish Stirling system at 05 various remote locations in southern Algeria, to evaluate its performance across a range of meteorological conditions. And we have concluded that this technology serves as an outstanding example of the potential of solar power, the simulation results showed a clear advantage in power output favoring the Dish Stirling system in all studied locations compared to the PV system. In Tamanrasset, both systems performed the best among the five studied locations. Among the three configurations tested, the PV 2-axis tracking configuration performed the best with a 34 % increase in power output going from fixed to 2-axis tracking. However, even with this increase, the Dish Stirling system outperformed the PV 2-axis tracking configuration in all studied locations.

The study used a 100 kW Dish Stirling system consisting of ten Eurodish units, each with a nominal capacity of 10 kW. The performance of the system was simulated using weather data from five different provinces (Adrar, Illizi, Bechar, Tamanrasset, and Tindouf). The same weather data was used to simulate a similar capacity PV system with three different configurations, running 280 SPR-x22-360 panels with 20 SBS5.0-US-10 [240V] inverters for comparison between the two technologies.

It's important to note that there is a more powerful Dish Stirling system that can achieve a higher efficiency of up to 31 %, which could yield significantly better results regarding power output, as the Dish Stirling technology continues to evolve in the future. Further research is needed from an economic standpoint to det financial viability of the Dish Stirling system as a better contender to the PV systems. Two Scenarios are considered:

• •Scenario 1 assumes that the specific transport and installation cost of the Dish Stirling system is similar or lower than that of the PV 1 axis tracking system configuration. This would make the Dish Stirling system a more viable option, as the LCOE of the Dish system would be lower compared to the PV system.
• •Scenario 2 assumes that the specific transport and installation cost of the Dish Stirling system is high enough, that the increased power output of the Dish Stirling system compared to the PV 1 axis tracking configuration becomes negligible. This would make the PV system a more viable option.

The simulations showed that the power output and efficiency achieved by the Dish Stirling system depended on the ambient temperature and the normal direct irradiance (DNI). However, there are some limitations to the use of solar parabolic dish collectors. They are most effective in areas with high levels of direct sunlight, and their efficiency can be reduced by factors such as cloud cover, shading, and dust accumulation on the mirrors. They are also relatively expensive to install and maintain, and their use may be limited by their size and the space required for installation.

Our study research has revealed that the LCOE of PSDS is notably lower in North African desert compared to other sites locations. This discrepancy can be attributed to the exceptionally high levels of direct normal irradiation DNI in these regions.

To enhance the PSDS in the future, the primary emphasis should be on lowering the LCOE through the utilization of thermal storage employing PCM materials like molten salt. This approach proves cost-effective compared to electrical storage methods like lithium batteries and boasts a longer lifespan.

Data availability statement

The data associated with this study will be made available upon request. Interested parties may contact Mhamed DERNOUNI at der.mhamed@gmail.com to request access to the data.

CRediT authorship contribution statement

Mhamed Dernouni: Writing – review & editing, Writing – original draft, Methodology, Investigation. Bachir Bouchekima: Supervision. Djilani Necib: Conceptualization. Abdelkarim Arab: Software, Resources. Fethi Ben Kheridla: Software, Resources.

Declaration of competing interest

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