Open Access
Review
Issue
Renew. Energy Environ. Sustain.
Volume 7, 2022
Article Number 9
Number of page(s) 16
DOI https://doi.org/10.1051/rees/2021056
Published online 06 January 2022

© S.A. Kaboli and R. Nazmabadi, Published by EDP Sciences, 2022

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

Energy is regarded as a significant indication of a nation's socio-economic and industrial growth [1]. Currently, renewable energy sources (RESs), such as wind, photovoltaic, bioenergy, coastal, and wave energy, have provided economic and environmental advantages to decreasing energy dependence and the adverse effects of classical energy sources such as oil, coal, and natural gas [2,3]. Wind energy, one of the most famous sources of energy that can provide the energy requirement of nations, plays a significant part in specifically producing electricity. It has tremendous promise as a sustainable energy source, and it is just as probable that power production from wind energy will grow significantly in the future. The installed wind power capacity was about 17.4 GW in 2000, but it rose to 651 GW by the end of 2019 [4]. From 2019 through 2024, the increased wind capacity will be about 355 GW, which implies that yearly new installations will be over 71 GW by 2024 [4]. Sustainable energy strategies and appropriate policy recommendations are essential elements of energy. Numerous strands are involved in contributing to sustainable energy policy. The bulk of the scholars have chosen to focus on the policies surrounding renewable energy. Numerous nations have promoted the use of renewable sources, using different incentive schemes. Furthermore, certain studies suggest that RESs should have a greater share in the production of energy needed by humans than conventional energy sources [5].

According to Kalair et al. [6], in the 21st century, energy engineers and researchers have shown a strong interest in green energy that minimizes the negative consequences of a substantial rise in fossil fuels consumption. Conventional fuels are projected to be the key energy source as they are consumed in conventional power plants to turn their chemical energy into heat used to produce electricity expected to release harmful pollution into the environment, primarily in greenhouse gasses (GHG) and contribute to climate change. This unsustainable reliance on conventional fuels to produce vast quantities of electric energy has led to a decline in their resources that could prevent future generations from producing enough energy to meet their needs [7]. Moreover, the unit price of energy rose dramatically because of the unsustainable increase in energy use.

According to Coherent Application Threads [8], wind energy is assumed to be one of the most important renewable energy sources. It has been used as a source of energy for more than 1500 years. There is kinetic energy in the wind. Wind turbines apply an energy conversion process to convert kinetic energy into mechanical power, which AC generators convert into electricity. It is worth mentioning that wind power relies on air density, wind speed, and the turbine's swept area. Moreover, the height of the turbine hub has a strong influence on the energy output of the wind turbine, as wind velocity increases at higher altitudes since the pressure gradient between the warm air and the cold air rises with height [9,10].

1.1 Aims and objectives

The proposed research has three main areas of study. The first is assessing the performance and efficacy of wind power generation by collecting, analyzing, and modeling engineering data. The proposal includes an environmental impact review, an economic and financial review, and a life cycle assessment (LCA) of all three primary application fields. The main goals for the assessments are:

  • To assess the energy recovery time for wind energy.

  • To determine the possibilities for mitigating global warming and reducing pollution in the form of toxic emissions.

  • To identify cost savings from implementing a clean energy system in Kuwait.

The main steps taken throughout the study are:

  • The most suitable sites for wind energy selected and evaluated.

  • RETScreen software was employed to estimate the efficacy of the selected wind turbine since RETScreen has built-in an extensive project database, and it is one of the most user-friendly software for designing power plants.

  • The analyses of the environmental, economic, and financial impact and a life cycle assessment (LCA) were completed to determine the energy recovery time for the designed wind farm that includes determining the decrease of global warming and pollution levels and the decreases in toxic emissions, and any cost savings from adopting a renewable energy system in Kuwait.

1.2 Status of the current technology

According to Reve [11], wind is an abundant natural resource that can be converted to electric energy using wind turbines. Environment-conscious jurisdictions, including the state of Kuwait, are actively exploring efficient options for raising the share of wind energy in their mixes of the three significant groups of energy resources. Wind energy is gaining popularity worldwide because it creates minimal pollution and superior operational, economic, and financial performance. Figure 1 presents the global annual additions of wind power capacity and the world totals between 2009 and 2020.

thumbnail Fig. 1

Accumulative global capacity of installed wind power between 2009 and 2020 [11].

1.3 The history of wind turbin

Increased investment in wind energy technologies during the 1970s accelerated wind turbine commercialization. As a result, the market evolved from primary domestic and agricultural uses to interconnected wind farm applications for electric utilities. In the 1980s, the technology spread to northern Europe, where wind resources are abundant. This created a small but steady market for wind energy. Nonetheless, wind energy development in other countries, including Kuwait, occurred much later, with significant achievements in the last two decades [12]. The earliest windmill in Kuwait was erected in the early 1900s on desert water, known as Ben Shlash Windmill, roughly 70 kilometers northwest of Kuwait City [13]. Since then, there has been no evidence of additional practical usage of wind energy in Kuwait, and interest in the future development of this technology has waned due to the late 1930s oil discovery. In 2018, renewable energy accounted for less than 1% (80 MW) of Kuwait's total electricity generation, but by 2035, renewables are expected to account for 16% of total capacity [13]. To achieve this goal, Kuwait has planned to grow both wind and solar installed capacity from 70 MW to 5 GW by 2027 [13].

1.4 Performance assessment

Most wind turbines have a maximum power efficiency of 59.3%, known as Betz's limit. It means that at most, only 59.3% of the kinetic energy from wind can be utilized to spin the turbine and generate electricity. The difference in efficiency is due primarily to the nature of wind turbines, not the inefficiency of the generator [1417]. To achieve 100% efficiency, wind turbines should convert 100% of the wind; however, doing so would necessitate solid disc blades, which would prevent the rotor from turning because of their great weight, and no kinetic energy would be converted [18]. The maximum power efficiency must be considered when engineering requirements, turbine strength, and durability are decided. Other inefficiencies in turbine systems, such as the generator, bearings, and power transmission, reduce overall efficiency to 10–30%. Horizontal axis wind turbines are more efficient than vertical axis wind turbines. However, wind direction does not affect vertical axis turbines, so they save a lot of time and energy that would otherwise be wasted tracking wind direction [19]. As a result, when the wind direction changes rapidly in turbulent conditions, the vertical axis turbine generates more electricity despite its lower efficiency.

The major wind power plants in Kuwait are in the Shagaya area, with a total capacity of 10 MW and a lifetime of 20 years. The site was evaluated according to the guidelines established by Environmental Protection Law No. 42. The site is an open desert with no vegetation, inland water bodies, or coastal wetlands. The land use nearest the project is approximately 20 km away. In its assessment, the Geotechnical Inspection Company found no groundwater table within a depth of 30 m [20]. The area is also quiet, with no reports of earthquakes.

Soil samples analyzed at Kuwait University had no metal contamination. The study area has been designated a high-wind-energy desert. The area is vulnerable to high-quality sand encroachment [21].

The Kuwait Institute for Scientific Research conducted an economic and financial analysis to determine whether clean energy could contribute significantly to Kuwait's power and environmental protection needs over the next 20 to 40 years. According to the findings, renewable energy will have a cost-effectiveness index (defined by dividing the total costs by the outcome) of 11% of electricity generated in Kuwait by 2030. Because of the fuel cost savings from using renewable energy technologies, wind energy and other renewable sources of energy will have a netback value (defined as the value of volume for sale minus transport costs) of $2.35 billion [22].

The life cycle assessment has four stages: goal definition, scope analysis, inventory impact assessment, and result interpretation. Wind turbine environmental performance varies depending on the methods used to manufacture each part, the mode of transportation to the site, construction, operation, and maintenance, and the shape, size, and method for discharging waste residues [22]. Low wind speeds in Kuwait reduce the capacity factor (defined as the average power generated, divided by the rated peak power) of turbines, increasing their life cycle emissions. A turbine's lifecycle has been calculated to be 20 years.

1.5 The cost of installing wind energy

The initial costs of installing a wind turbine tend to be high, as with any renewable energy technology. This project's installation costs are based entirely on fixed costs, also known as ‘OPEX.’ The costs associated with installing wind turbines and purchasing towers constitute approximately 84% of the total fixed costs. This very high cost has become a deterrent for individuals to invest, as there is no possibility of price fluctuations once the wind turbines are in operation [23].

  • Wind energy project costs can be divided into four categories [23].

  • Turbine cost: includes the cost of the blades, the tower, and the transformer.

  • Civil works: the cost of infrastructure, planning, and foundation costs.

  • Grid-linking costs are incurred when purchasing and installing transformers and mini-stations and the costs of purchasing cables and connecting them to distribution lines.

  • Other costs, such as consulting fees, monitoring and evaluation fees, construction wages, and maintenance fees.

The cost of turbines includes nacelle components such as gear transformer and power converter gearbox, the rotor blades, and the tower cost. The estimated cost for wind turbine components is shown in Figure 2. As depicted, the generator, transformer, converter, and gearbox accounted for approximately 22%, and the remaining 78% was paid for other related items such as wiring, rotor hub, rotor shaft, rotor blades, and the tower. The disparity in component costs between countries comes from differences in the cost of turbines, location specifications, and other relevant expenses [23].

thumbnail Fig. 2

The estimated cost for wind turbine components [23].

1.6 Potentials of wind energy

Wind power is recognized because it depends entirely on the precision derived from wind power maps. As a result, significant effort is being made to update maps with current information on the world's wind resources. Much work is being done, and more tasks will be required to improve wind resource forecasts. Inadequate data on developing countries has been a significant impediment to exploring wind energy, particularly in countries at altitudes of more than 80 meters. Many parts of the world have strong winds, both on and off the coast, but they are dispersed unevenly and often in suitable locations. Progressive, comprehensive research has provided finer details on general wind energy for for-profit and non-profit industries, with sufficient data on potential wind energy locations. This makes it easier for project advocates and policymakers to understand how the resources can be used based on precise site measurements. Wind power capacity includes several variables, including meaningful assumptions and mean wind velocity. Some assumptions must be made about the size of the turbine, the mechanical strength of the turbine, the size of the rotor, the cost of research in various areas, and the availability of the unused land given the climatic conditions that the wind resource is near or otherwise required. Regardless of the unpredictability of the deciding factors, there are many advantages to onshore wind, and it can meet the need for electrical power for extended periods.

1.7 Investment opportunity

Significant research has been conducted to bring the cost of wind turbines into an affordable range, and this is expected to encourage investors and decision-makers to consider this sector. Several analyses have produced quantitative results. Many studies have been conducted on the offshore wind regions to estimate the cost savings that can be achieved in the onshore regions. Most of these studies have focused on ways to reduce wind farms' initial and ongoing costs by improving the designs of designated wind farms [24].

Another factor influencing the fixed cost of a wind turbine is using an appropriate process to select a geographic location with a high mean wind velocity. Wind efficiency improvements can help to lower the LCOE of wind energy by increasing the mean ability impact. Increases in product prices, particularly for copper, cement, and steel, affect wind energy costs based on the inflation rates [24].

To reduce the cost of each component of a wind energy project, a great deal of attention is given to lowering the LCOE. Such efforts are required to improve the outputs generated by collecting wind energy. The following are the main stages of a process to reduce the overall cost of installing wind turbines in onshore and offshore systems [24]:

  • Project management and decision-making.

  • Foundations.

  • Wind blades.

  • Grid linking and wiring.

  • Installation.

2 Materials and methods

The techno-economic analysis in this study compromise different steps. Figure 3 illustrates the main steps of the framework. The first part of the model includes four categories. Site selection, mathematical modeling, choosing a proper wind turbine and collecting turbine specifications, and acquiring the information needed for financial analysis which includes, annual interest rate, GHG reduction credit rate, electricity export rate, installation costs, the cost for the land, operation and maintenance costs and electricity exporting to the grid expenses.

There are four main sections in the proposed model. The first part involves the mathematical modeling for evaluating the wind data and determining the technical performance of the wind farm in terms of production capacity and economic evaluation in terms of the simple payback period of the capital. Therefore, three sets of equations must be considered: the power equations, Weibull distribution, and related financial equations.

  • Wind turbine specifications: The wind turbine specification, including turbine geometry, tower height, and power curves, are required for the initial evaluation and assessments for selecting the appropriate model.

  • Site selection: The other important part of the model is site selection. Two main parameters are required to be considered in this step. The first parameter is the wind profile which includes the wind speed and direction and the second parameter is the distance of the selected site to an electrical grid.

  • Wind data: The mathematical models will be employed to calculate and evaluate the two-parameter Weibull distribution of wind direction at the specified site.

  • Cost data: The cost data is also required to develop the financial model. The cost data generally consist of two groups. The location dependant data includes parameters like electricity Tariff, rate of interest and inflation, and land cost. The second group includes data dependent on the wind turbine, i.e., cost of the wind turbine, transportation to the site location and installation, O&M expenses, and the turbine lifespan).

The required data about the wind speed and geological properties of the selected locations are found from Global Wind Atlas and RETScreen Software database. The initial evaluation of the Mean wind speed plot shown in Figure 4 reveals an outstanding potential of Kuwait for a wind farm and shows the selected sites for the location assessment.

thumbnail Fig. 3

Proposed model for techno-economic analysis of the wind farm.

thumbnail Fig. 4

Mean wind speed at 100 m elevation in Kuwait [25].

2.1 Assumptions

As demonstrated in the proposed model in Figure 3, the analysis requires some initial data and specifications in terms of technical specifications of the turbine, wind speed of the sites, and economics information. Table 1 shows the initial values and inputs of the model which has been used for the analysis.

Table 1

Initial and assumed parameter values.

2.2 Mathematical modelling

In order to correctly identify the wind speed profile, reliably assess the wind power potential, and analyze the economic feasibility of local regulation, it is essential to establish the distribution of the data that best matches the wind velocity of the site. Probability density functions are employed to estimate the wind potential in a given region [12]. Several distributions are employed to simulate wind speed; however, the most commonly used models include Weibull, Rayleigh, Lognormal, and Gamma distributions [34,35]. The Weibull distribution is undoubtedly one of the most efficient, reliable, and frequently used models for prospective wind evaluation at a specific site globally since it provides more versatility for fitting empirical data [36,37].

From the findings of earlier studies, the current study uses a two-parameter Weibull probability density function to evaluate wind speed characteristics and estimate wind energy potential [38]. The probability density function describing the Weibull distribution may be represented by equation (1) [36].(1)where k (dimensionless) and c (m/s) indicate the shape and scale factor, respectively, k and c hold the complete information about the distribution. Therefore, the Weibull cumulative distribution function, derived by taking an integral of the Weibull probability density function, denoted by f (v), is given by equation (2) [39].(2)

The wind velocity at measurement height is related to the wind velocity at reference level by equation (3) [40].(3)where V is the wind velocity at the hub height h, V0 is the wind speed at ground level h0α is the surface roughness factor. Using equation (3) and the Weibull distribution, it is feasible to calculate the average velocity [41]:(4)

The gamma function may be approximated using equation (5) [41](5)

The wind power density, P (v), is independent of the Turbine features such as size and efficiency. It relies solely on the air density and the wind speed and is given by equation (6) [41].(6)

The basic wind power equation is utilized for calculating wind power, Pw, is provided by equation (7) [42].(7)

where ρ represents air density, A is the area of the wind turbine, and V is the wind velocity. However, a wind turbine cannot convert more than 59.3 percent of the wind's kinetic energy into mechanical energy, the Betz Limit. Equation determines the theoretical Betz limit power coefficient [43].(8)

In addition, wind turbines cannot function at this upper limit. The Cp value is unique to each turbine type and results from the wind speed the turbine is running. Hence, after multiplying the power coefficient into equation (7), the available power, Pavailable from the wind is provided by equation (9) [41].(9)

The produced energy over a year is found by equation (10) [44](10)

The capacity factor of a wind turbine or a wind farm is described as the actual energy production divided by the maximum potential energy output of a wind farm over time, given by equation (11) [45].(11)

A wind energy system is an investment that generates income. An economic study is performed to assess the profitability and feasibility of a wind energy project at a specific location. A recovery calculation balances income with expenses and calculates the time needed to recover an original investment. The simple recovery period (SPP) is provided by equation (12) [41,46].(12)

where I is the installed capital cost of the wind turbine plus the expenses of civil works, generally, the expenses of the civil work consist of the shipping and installation charges, which are generally around 20 to 30 percent of the wind turbine's overall price [47]. NAS is the net annual saving of the power plant. The annual net cost saving (NAS) is the least savings achieved after all the operational costs have been considered. If the recovery time of investments is less than the anticipated service life or operation of the implemented technological solution, then the investment is economically justifiable. According to equation (13), the Levelized Cost of the Electricity (LCOE) is used to estimate the installed cost of electricity generated by a wind energy conversion system according to equation (13) [47](13)where I is the installed capital cost of the wind turbine plus the expenses of civil works, and CRF and are the capital recovery factor and the present value of the yearly cost over the lifespan of the wind turbine. CRF and Com are provided by equations (14) and (15) [48].(14) (15)where Com denotes the operation and maintenance expenditures for the first year and is estimated 20–30 percent of the annual cost of the turbine, which is the price of the machine divided by its lifetime, e is the escalation rate of maintenance and operation, I is the interest rate, and n is the practical lifetime of the turbine in years [49]. All of these systems need investment, and in order to assess their financial advantages, a return on investment (ROI) study is necessary. The ROI is provided by equation (16) [45].(16)

where PVC is an estimate of the cost to execute a project (predicted cost), and PVB is an estimate of the profit from the project execution (predicted benefit) [48].

3 Results and discussion

The analysis was completed employing RETScreen software. The findings are summaries and reported in the following section.

3.1 Wind speeds analysis at the considered locations

Three locations across Kuwait have been chosen for the analysis. The primary parameter considered for the site selection was the wind speed throughout an entire year. Table 2 shows that the locations are estimated to provide different performance levels because of fluctuations in the weather. In Table 2, SD refers to standard deviation, defined a measure of the amount of variation or dispersion of a set of values.

The hourly wind speed for twelve months is determined using the System Advisor Model (SAM). Figures 5 and 6 show the daily maximum and minimum wind speeds averages and the mean for 2019–2020 and 2019–2020, respectively. The maximum and minimum wind speeds in 2019–2020 are 0.096 m/s and 9.216m/s, while the maximum wind speed in 2020–2021 increases to 11.3 m/s. The mean wind speed ranged from 0.7 to 6.74 m/s in 2019-2020 and from 0.7 to 8.4 m/s in 2020–2021.

Monthly averaged wind speeds are shown in Figure 7 for the two years along with their mean. The highest wind speeds were observed in August 2019 and July 2020 with 3.9464 and 3.9368 m/s, whereas the lowest was found in December for both years with values 2.0759 and 2.586 m/s. Overall, the monthly average wind speed over the two years stayed between 2.33 and 3.85m/s.

The seasonal average wind speeds for the two years and their mean are shown in Figure 8. According to this figure, the highest wind speed for both years occurred during summer, with values of 3.63 and 3.25 m/s. Also, the lowest wind speed for both years occurred during winter, with a value of 2.59 m/s.

Equations described in Section 2.2 are used to calculate the Weibull and Rayleigh probability density functions and cumulative density functions, which are compared with real data histograms in Figures 9 and 10 for 2019–2020 and 2020–2021.

Table 2

Selected locations wind speed comparison.

thumbnail Fig. 5

Daily maximum, minimum, and mean wind speed variations in Al-Ahmadi for 2019–2020.

thumbnail Fig. 6

Daily maximum, minimum, and mean wind speed variations for 2020–2021.

thumbnail Fig. 7

Monthly mean wind speed variations for 2019–2021.

thumbnail Fig. 8

Seasonal-mean wind speeds for the year (2019–2021).

thumbnail Fig. 9

Wind speed frequency distribution analysis for 2019–2020.

thumbnail Fig. 10

Wind speed frequency distribution analysis for 2020–2021.

3.2 Wind turbines technical analysis

The power curve and energy curve for the selected wind turbine are illustrated in Figure 11. The selected wind turbine is Suzlon model S.88/2,100–100 m. It was decided that 50 turbines would be installed and have 105 MW of power generation capacity. The power curve shows that the power generated from each turbine at the turbine hub altitude previously assumed 100 m is around 850 kW at an average wind speed of 6 m/s. However, the power capacity of each turbine is around 2.1 MW at 15m/s windspeed. The cost of energy generation is $0.053/kWh, and the plant would generate about 105 MWh. The wind turbine farm is estimated to occupy the same area (325,000 m2), increasing fixed cost.

Technical details of the Wind Turbine are shown in Table 3. The parameters are determined using the RETScreen database.

The technical analysis of the wind farm with 50 turbines and the specifications mentioned above resulted in the outcomes reported in Table 4.

thumbnail Fig. 11

Power and Energy curve for the selected turbine.

Table 3

Technical specifications of the turbines.

Table 4

Technical analysis results of the wind farm.

3.3 Wind turbines financial analysis

Several parameters need to be considered for the financial analysis of the designed wind farm. The main cost elements are determined and shown in Table 5.

Table 5

Financial analysis results of the wind farm.

3.4 Environmental benefits

In terms of environmental benefits, it was estimated that the designed wind turbine farm would reduce CO2 emissions by approximately 188,260 tonnes per year because electricity was produced from the environment. After 20 years of operation, this represents savings of approximately $8,778,410 that would have been spent on constructing carbon capture systems in conventional power plants.

According to the RETScreen database, each produced MWh of electricity in Kuwait results in 0.7872425 tonnes of CO2. This rate was used to investigate the designed plant's environmental advantages, assuming that the transmission and distribution losses between the power plant and the end-user are 10%. As a result, Kuwait's greenhouse gas emissions will be 0.8747 per MWh of electricity produced. Carbon capture systems have recently been used to prevent CO2 from being released into the atmosphere and accumulating. It is assumed that preventing each tonne of CO2 from being released into the atmosphere costs $95 and that the project will last 20 years. The revenue from decreasing GHG is $8 million. In Table 6, the base case refers to the gas turbine power plant with gasoline, and the proposed case refers to the mentioned wind turbine farm.

According to Figure 12, the reduction of GHG emission is equivalent to 42,786.4 Acres of forest absorbing carbon or 80,890,213 liters of gasoline not consumed.

Table 6

GHG emission reduction.

thumbnail Fig. 12

CHG reduction comparison to the base case.

3.5 Economic viability analysis

Common financial indexes, including NPV and IRR, have been used to evaluate the project's viability. Based on the project's annual cash flow and initial investment price, the project's simple recovery was calculated at 9.1 years. Details of the feasibility study are reported in Table 7 in terms of simple recovery and equity recovery.

The cumulative cash flow diagram illustrated in Figure 13 shows a simple recovery of 9.2 years for the proposed project.

Table 7

Financial Viability analysis of the wind farm.

thumbnail Fig. 13

Cumulative cash flow.

3.6 Risk and sensitivity analysis

The risk analysis was implemented based on Pre tax-IRR- assets of the project. Figures 14 and 15 show the results of the impact risk analysis and distribution analysis.

The sensitivity analysis was implemented using RETScreen software with a range of 25% and for the project's IRR-Pre Tax asset and NPV. Results are shown in Figures 1618.

The summary of the risk and sensitivity analysis is shown in Table 8.

thumbnail Fig. 14

Impact- pre-tax IRR-assets analysis.

thumbnail Fig. 15

Distribution- pre-tax IRR-assets analysis.

thumbnail Fig. 16

Sensitivity analysis of electricity export rate for (a) pre-tax IRR asset analysis (b) NPV analysis.

thumbnail Fig. 17

Sensitivity analysis of O&M costs for (a) pre-tax IRR asset analysis (b) NPV analysis.

thumbnail Fig. 18

Sensitivity analysis of GHG reduction credit rate for (a) pre-tax IRR asset analysis (b) NPV analysis.

Table 8.

Risk analysis summary of the wind farm.

4 Conclusions

Power engineers and academics have shown a strong interest in green energy, reducing the negative consequences of significant increases in energy consumption world wide. In general, conventional fuels are estimated to be the primary energy source as they are consumed in conventional power plants to transform their chemical energy into heat used to generate electricity. This is expected to release toxic materials, such as greenhouse gases (GHG), into the atmosphere, promoting climate change. Moreover, the unsustainable dependence on conventional fuels to generate large amounts of electrical energy has decreased their resources. This could diminish the capacity of future generations to produce enough energy to meet essential needs.

Moreover, unit energy prices have risen significantly because of the unsustainable increase in energy demand. Wind energy is often considered to be one of the most important sources of renewable energy. The kinetic energy that exists in the atmosphere can be used to produce electricity using wind turbines. The operating theory depends on the use of wind turbines to implement the energy conversion mechanism as the wind's kinetic energy is transformed into mechanical energy. The created mechanical power can then be converted to electricity by AC generators. It is worth noting that wind power depends on the air density, velocity, and the turbine's swept area.

Moreover, the height of the turbine hub dramatically affects the energy performance of the wind turbine since the wind speed rises at a comparatively higher altitude. The wind is an abundant natural resource that can be transformed into electric energy by wind turbines. Environmentally responsible jurisdictions, including the State of Kuwait, are aggressively pursuing successful options for enhancing the profile of wind energy in their renewable and overall energy mixes. Wind energy is gaining popularity worldwide because it creates minimal pollution and superior operational, economic, and financial performance.

The proposed research mainly consists of three critical areas of study, including assessing wind power output and efficiency through collecting, analyzing, and modeling engineering data. Moreover, the plan would include an Environmental Impact Analysis, an Economic and Financial Review, and a Life Cycle Assessment (LCA) of all three primary application areas. This will be done to calculate the energy recovery time for wind energy, evaluate the mitigation of global warming and pollution levels, and decrease toxic emissions and any cost savings resulting from introducing a renewable energy system in Kuwait.

  • The results supported the following estimates:

  • The energy price would have cost about $0.053 per kWh, while the entire plant would have produced about 105 MWh. The proposed plant is estimated to occupy the same area (325,000 m2), increasing the fixed cost.

  • The proposed wind turbine farm will have the effect of avoiding the emission of CO2 by approximately 1,848,086 tons per year as the produced energy was obtained from the atmosphere. This resulted in savings of approximately $8,778,410 after 20 years of service that would be spent on the installation of carbon capture systems in conventional power plants.

  • The recovery time of the plant is estimated to be 9.2 years.

Recommendations for designers

  • As you consider optimizing the geographic locations of wind turbine farms, do not use expensive locations. This has a strong influence on the recovery time of the farm and the economic and environmental advantages of the locations you choose.

  • Wind turbine farms are characterized by high aerodynamic performance and short recovery time. This is worth considering in any study of the feasibility of building a renewable energy plant in Kuwait.

  • The types of wind turbines and the manufacturer's reputation are estimated to be the most critical parameters to be considered during the selection process.

  • It is worth monitoring the performance of implemented wind turbines to ensure a stable and efficient energy-generation process.

References

  1. R. Alizadeh, P.D. Lund, L. Soltanisehat, Outlook on biofuels in future studies: a systematic literature review, Renew. Sustain. Energy Rev. 134, 110326 (2020) [Google Scholar]
  2. R. Alizadeh, L. Soltanisehat, Stay competitive in 2035: a scenario-based method to foresight in the design and manufacturing industry, Foresight 22, 309–330 (2020) [Google Scholar]
  3. L. Soltanisehat, R. Alizadeh, N. Mehregan, Research and development investment and productivity growth in firms with different levels of technology, Iranian Econ. Rev. 23, 795–818 (2019) [Google Scholar]
  4. NTUA, Renewables 2019: global status report, renewable energy policy network for the 21st century (2019) ISBN 978-3-9818911-7-1 [Google Scholar]
  5. N. Mouraviev, Renewable energy in Kazakhstan: challenges to policy and governance, Energy Policy 149, 112051 (2021) [Google Scholar]
  6. N. Abas, A. Kalair, N. Khan, Review of fossil fuels and future energy technologies, Futures 69, 31–49 (2015) [Google Scholar]
  7. R. Gnatowska, A. Was, Wind energy in poland − economic analysis of wind farm, E3S Web Conferen. 14 (2017) [Google Scholar]
  8. B. University, Coherent application threads (2020). Available: http://people.bu.edu/dew11/files/main.pdf [Google Scholar]
  9. R. Susan-Resiga, G.D. Ciocan, I. Anton, F. Avellan, Analysis of the swirling flow downstream a francis turbine runner, J. Fluids Eng. Trans. ASME 128, 177–189 (2006) [Google Scholar]
  10. C. Hansen, A. Butterfield, Aerodynamics of horizontal-axis wind turbines, Annu. Rev. Fluid Mech. (1993) [Google Scholar]
  11. Reve, Wind energy expanded 19% in 2019, with around 60 GW of new capacity (2020). Available: https://www.evwind.es/2020/07/05/wind-energy-expanded-19-in-2019-with-around-60-gw-of-new-capacity/75563 [Google Scholar]
  12. A.E. Suzer, V.E. Atasoy, S. Ekici, Developing a holistic simulation approach for parametric techno-economic analysis of wind energy, Energy Policy 149, 112105 (2021) [Google Scholar]
  13. W. Al-Nassar, S. Alhajraf, A. Al-Enizi, L. Al-Awadhi, Potential wind power generation in the State of Kuwait, Renew. Energy 30, 2149–2161 (2005) [Google Scholar]
  14. J. Xu, Z. Han, X. Yan, W. Song, Design optimization of a multi-megawatt wind turbine blade with the NPU-MWA airfoil family, Energies 12 (2019) [Google Scholar]
  15. P. Bachant, M. Wosnik, Effects of reynolds number on the energy conversion and near-wake dynamics of a high solidity vertical-axis cross-flow turbine, Energies 9, 73 (2016) [Google Scholar]
  16. A.Z. Baig, T.A. Cheema, Z. Aslam, Y.M. Khan, H. Sajid Dar, S.B. Khaliq, A new methodology for aerodynamic design and analysis of a small scale blended wing body, J. Aeronaut. Aerospace Eng. 07, 1–6 (2018) [Google Scholar]
  17. A. John Rosenberg, A. John, B. Aaron John Rosenberg, A. Sharma, M. Professor Partha Sarkar Eugene Takle Hui Hu Baskar Ganapathysubramanian, A Computational Analysis of Wind Turbine and Wind Farm Aerodynamics with a Focus on Dual Rotor Wind Turbines Recommended Citation “A Computational Analysis of Wind Turbine and Wind Farm Aerodynamics with a Focus on Dual Rotor Wind Turbines” (201, Iowa State University (2016) [Google Scholar]
  18. M.J. Pasqualetti, R. Righter, P. Gipe, History of wind energy, in Encyclopedia of Energy (Elsevier, 2004), pp. 419–433 [Google Scholar]
  19. F. Alqurashi, M.H. Mohamed, Aerodynamic forces affecting the h-rotor darrieus wind turbine, Model. Simul. Eng. 2020, 1–15 (2020) [Google Scholar]
  20. F.M. Al-Ruwaih, G. Shafiullah, Geochemical processes and assessment of water quality for irrigation of Al-shagaya field-C, Kuwait, Int. j. Environ. Agric. Biotech. 2, 164–180 (2016) [Google Scholar]
  21. I. Conference, S. Science, Energy and Society in Transition: 2nd International Conference on Energy Research and Social Science. 28- 31 May 2019. Arizona State University. Tempe, USA, (2019) [Google Scholar]
  22. R. Bhandari, B. Kumar, F. Mayer, Life cycle greenhouse gas emission from wind farms in reference to turbine sizes and capacity factors, J. Clean. Prod. 277, 123385 (2020) [Google Scholar]
  23. A.S.M.M. Hasan, M.T. Hoq, A. Kabir, T. Sakib, The Cost Factor Analysis of Wind Power Plants-An Economic Perspective (2018) [Google Scholar]
  24. M. Junginger, A. Faaij, W.C. Turkenburg, Cost Reduction Prospects for Offshore Wind Farms, Wind Engineering 28, 97–118 (2004) [Google Scholar]
  25. GlobalWindAtlas, Global Wind Atlas, (2021). https://globalwindatlas.info/area/Kuwait [Google Scholar]
  26. E. Commission, European-Commission geographical information system (2021) [Google Scholar]
  27. K. Yessian, P. DeLaquil, B. Merven, M. Gargiulo, G. Goldstein, Economic analysis of clean energy options for Kuwait, Int. J. Energy Sect. Manag. 7, 29–45 (2013) [Google Scholar]
  28. H.H. Pourasl, V.M. Khojastehnezhad, Techno-economic analysis of wind energy potential in Kazakhstan, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 235, 1563–1576 (2021) [Google Scholar]
  29. W. Du, Y. Zhao, Y. He, Y. Liu, Design, analysis and test of a model turbine blade for a wave basin test of floating wind turbines, Renewable Energy 97, 414–421 (2016) [Google Scholar]
  30. H. Polinder, D. Bang, R.P.J.O.M. Van Rooij, A.S. McDonald, M.A. Mueller, 10 MW wind turbine direct-drive generator design with pitch or active speed stall control, Proceedings of IEEE International Electric Machines and Drives Conference, IEMDC 2007 2, 1390–1395 (2007) [Google Scholar]
  31. Opensooq, Farms for sale in Kuwait, 2021. https://kw.opensooq.com/en/realestate-for-sale/land-for-sale [Google Scholar]
  32. M.M. Riaz, B.H. Khan, Techno-economic analysis and planning for the development of large scale offshore wind farm in India, Int. J. Renew. Energy Dev. 10, 257–268 (2021) [Google Scholar]
  33. G.C. Institute, Global Costs of Carbon Capture and Storage, Global CCS Institute. (2017), [Online]. Available: https://www.globalccsinstitute.com/archive/hub/publications/201688/global-ccs-cost-updatev4.pdf [Google Scholar]
  34. H.E. Akyuz, H. Gamgam, Statistical Analysis of Wind Speed Data with Weibull, Lognormal and Gamma Distributions, Cumhuriyet Science Journal 68–76 (2017) [Google Scholar]
  35. S. Ali, S.-M. Lee, C.-M. Jang, Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data, Energies 10 (2017) [Google Scholar]
  36. K.A. Adeyeye, N. Ijumba, J.S. Colton, A Techno-Economic Model for Wind Energy Costs Analysis for Low Wind Speed Areas, Processes 9, 1463 (2021) [Google Scholar]
  37. M. Gul, N. Tai, W. Huang, M.H. Nadeem, M. Yu, Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power, Sustainability 11 (2019) [Google Scholar]
  38. C. Bak, Aerodynamic design of wind turbine rotors. Woodhead Publishing Limited, (2010) [Google Scholar]
  39. M.S. Adaramola, S.S. Paul, S.O. Oyedepo, Assessment of electricity generation and energy cost of wind energy conversion systems in north-central Nigeria, Energy Convers. Manag. 52, 3363–3368 (2011) [Google Scholar]
  40. A.N. Celik, A Techno-Economic Analysis of Wind Energy in Southern Turkey, Int. J. Green. Energy. 4, 233–247 (2007) [Google Scholar]
  41. A.L. Manwell, J.F. McGowan, J.G. Rogers, Wind Energy Explained Theory, Design and Application, 2nd ed. West Sussex, UK: John Wiley & Sons Ltd (2009) [Google Scholar]
  42. S. Rahimi, M. Meratizaman, S. Monadizadeh, M. Amidpour, Techno-economic analysis of wind turbine–PEM (polymer electrolyte membrane) fuel cell hybrid system in standalone area, Energy 67, 381–396 (2014) [Google Scholar]
  43. A. Tazay, Techno-Economic Feasibility Analysis of a Hybrid Renewable Energy Supply Options for University Buildings in Saudi Arabia, Open Engineering 11, 39–55 (2020) [Google Scholar]
  44. O. Charrouf, A. Betka, M. Becherif, A. Tabanjat, Techno-economic Analysis of Wind Turbines in Algeria, Int. J. Emerg. Electr. Power. Syst. 19 (2018) [Google Scholar]
  45. J.D. Rhodes, A geographically resolved method to estimate levelized power plant costs with environmental externalities, Energy Policy 102, 491–499 (2017) [Google Scholar]
  46. P. Bortolotti, C.L. Bottasso, A. Croce, Combined preliminary–detailed design of wind turbines, Wind Energy Science 1, 71–88 (2016) [Google Scholar]
  47. R. Bakhshi, P.A. Sandborn, A Return on Investment Model for the Implementation of New Technologies on Wind Turbines, IEEE Transactions on Sustainable Energy 9, 284–292 (2018) [Google Scholar]
  48. A. Botchkarev, Estimating the Accuracy of the Return on Investment (ROI) Performance Evaluations, Interdisciplinary Journal of Information, Knowledge, and Management, 10, 217–233 (2015) [Google Scholar]
  49. P.M. Heuser, D.S. Ryberg, T. Grube, M. Robinius, D. Stolten, Techno-economic analysis of a potential energy trading link between Patagonia and Japan based on CO2 free hydrogen, Int. J. Hydrog. Energy. 44, 12733–12747 (2019) [Google Scholar]

Cite this article as: Seyed Amir Kaboli, Reyhaneh Nazmabadi, Techno-economic analysis and modelling of the feasibility of wind energy in Kuwait, Renew. Energy Environ. Sustain. 7, 9 (2022)

All Tables

Table 1

Initial and assumed parameter values.

Table 2

Selected locations wind speed comparison.

Table 3

Technical specifications of the turbines.

Table 4

Technical analysis results of the wind farm.

Table 5

Financial analysis results of the wind farm.

Table 6

GHG emission reduction.

Table 7

Financial Viability analysis of the wind farm.

Table 8.

Risk analysis summary of the wind farm.

All Figures

thumbnail Fig. 1

Accumulative global capacity of installed wind power between 2009 and 2020 [11].

In the text
thumbnail Fig. 2

The estimated cost for wind turbine components [23].

In the text
thumbnail Fig. 3

Proposed model for techno-economic analysis of the wind farm.

In the text
thumbnail Fig. 4

Mean wind speed at 100 m elevation in Kuwait [25].

In the text
thumbnail Fig. 5

Daily maximum, minimum, and mean wind speed variations in Al-Ahmadi for 2019–2020.

In the text
thumbnail Fig. 6

Daily maximum, minimum, and mean wind speed variations for 2020–2021.

In the text
thumbnail Fig. 7

Monthly mean wind speed variations for 2019–2021.

In the text
thumbnail Fig. 8

Seasonal-mean wind speeds for the year (2019–2021).

In the text
thumbnail Fig. 9

Wind speed frequency distribution analysis for 2019–2020.

In the text
thumbnail Fig. 10

Wind speed frequency distribution analysis for 2020–2021.

In the text
thumbnail Fig. 11

Power and Energy curve for the selected turbine.

In the text
thumbnail Fig. 12

CHG reduction comparison to the base case.

In the text
thumbnail Fig. 13

Cumulative cash flow.

In the text
thumbnail Fig. 14

Impact- pre-tax IRR-assets analysis.

In the text
thumbnail Fig. 15

Distribution- pre-tax IRR-assets analysis.

In the text
thumbnail Fig. 16

Sensitivity analysis of electricity export rate for (a) pre-tax IRR asset analysis (b) NPV analysis.

In the text
thumbnail Fig. 17

Sensitivity analysis of O&M costs for (a) pre-tax IRR asset analysis (b) NPV analysis.

In the text
thumbnail Fig. 18

Sensitivity analysis of GHG reduction credit rate for (a) pre-tax IRR asset analysis (b) NPV analysis.

In the text

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