Assessing offshore wind plants for energy and green hydrogen production: A case study in the Mostaganem coastal region,Algeria

Abstract

This study assesses the feasibility and strategic implications of establishing a 500 MW offshore wind farm utilizing fixed wind turbine technology along Algeria’s western coastal economic zone, with a focus on the Mostaganem region. The study recommends integrating 33 Vestas V-235 wind turbines, each boasting a 15 MW capacity to harness offshore wind resources for electricity and green hydrogen production. Utilizing the Wind Atlas Analysis and Application Program (WAsP), offshore wind potential is assessed based on meteorological data from a land-based station, overcoming the lack of in situ sea level measurements. Results show that the proposed wind farm could yield 1361.63 GWh/year of electricity at a cost of 0.89 $/kWh. Additionally, it would produce 28,571 tons of hydrogen at a cost of 2.12 $/kg. This initiative is anticipated to significantly reduce CO₂ emissions, by approximately 595,450 tons/year, highlighting the effectiveness of offshore wind resources in promoting sustainable energy production and advancing green hydrogen as a viable energy carrier in this region.

Keywords

Wind resource assessment offshore wind energy green hydrogen cost analysis environmental impacts WAsP Algeria

Introduction

The wind resource assessment holds crucial importance in determining the feasibility of implementing wind energy systems within specific geographical regions. In this context, the Wind Atlas Analysis and Application Program (WAsP) remains one of the most widely applied model for assessing, analyzing and mapping wind potential. Regarding studies on wind resource assessment done in Algeria using WAsP program, it is important to cite some references in this fields, such as the first wind atlas of Algeria established by Hammouche (1991), based on data collected over a decade from 1980 at 37 meteorological stations. Furthermore, there was the integration of several economic analysis into the studies carried out using the WAsP in assessing the cost of wind energy production in different regions in Algeria as in the southwest region (Himri et al., 2020) and the North West part of the country (Boudia and Guerri, 2015). Additionally, environmental analysis has been incorporated as a new factor to consider in various studies, such as the one conducted in the Hassi R’mel region at the outset of the Algerian Sahara (Meziane et al., 2021). This study is notable as it marks the first instance of utilizing WAsP software to thoroughly assess the wind potential across a studied region, with a focus on analyzing and assessing the feasibility of wind-source hydrogen production.

In recent years, there has been a growing recognition of the importance of renewable energy sources in addressing environmental concerns and meeting global energy demands. The utilization of offshore wind energy has become a critical element in the transition towards sustainable energy solutions, offering substantial potential for both electricity generation and green hydrogen production. In this context, dos Reis et al. (2021) performed an economic analysis of offshore wind projects in the Brazilian sea, selecting suitable regions from an economic and energy point of view. Deveci et al. (2020) Evaluated offshore wind farms throughout the world, and developed a fuzzy sets based Multi-criteria decision-making (MCDM) approach to determine the most suitable coastal site in the Black Sea to establish a new offshore wind farm facility. Voormolen et al. (2016) provided insights into the cost evolution of offshore wind energy in Europe and scrutinized various operational and established offshore wind farms. Satir et al. (2018) and Dicorato et al. (2011) conducted feasibility analyses for offshore wind farms in the Aegean Sea, Turkey. The techno-economic analysis of offshore wind farm projects in Turkey’s coastal regions, focusing on the three most promising wind locations identified through a multi-criteria site selection method was done (Cali et al., 2018).

This study highlighted that offshore wind farms are economically viable only under specific techno-economic conditions, with radial electrical designs emerging as the optimal option from an economic standpoint.

Several studies have concentrated on site selection for offshore wind farm installations in the Mediterranean Sea, Watson et al. (2002) developed an innovative wind resources assessment methodology, generating comprehensive, long-term, and spatially detailed estimates of wind conditions at coastal sites across a broad area. Other studies have explored the costs associated with offshore wind energy. Menendez et al. (2014) and Balog et al. (2016) reported other studies dealing with assessment of offshore wind power potential using numerical models in the Mediterranean Sea. Soukissian et al. (2016) evaluated the annual and seasonal offshore wind energy potential in the Mediterranean Sea, where the analysis was based on statistical examination of data obtained from the U.S. National Oceanic and Atmospheric Administration (NOAA), spanning the period from 1995 to 2014. Cottura et al. (2021) developed a new model designed with the capability to accurately simulate the behaviour of an offshore floating wind turbine and analyze its potential use in the Mediterranean Sea.

The different advantages of an offshore wind farm project in the Mediterranean Spanish Arc adopting a gravity-driven solution for the foundation of Wind Turbine Generators was presented (Abanades, 2019), the findings affirmed the technical and economic feasibility of the case study.

However, Algeria’s wind resources are predominantly, explored and harnessed on land, despite the nation’s extensive coastline stretching over 2000 km along the Mediterranean Sea, harbouring substantial wind energy potential. Currently, there exists no definitive strategy to spur the development and proliferation of offshore wind farms in Algeria. However, the first studies analyzing the potential of offshore wind in Algeria and assessing the feasibility of developing offshore wind farms were conducted recently (Meziane et al., 2023a, 2023b). It is within this context that the present study is carried out, dealing with evaluating the feasibility of offshore wind power and hydrogen production in the Mostaganem region, by utilizing data from the Mostaganem meteorological station and WAsP software, where the research assesses technical, economic, and environmental factors. Advocating for the development of offshore wind farms and hydrogen projects in Algeria, this study highlights the potential for clean and sustainable energy, marking the inaugural investigation of offshore wind energy and hydrogen production in the country.

The present paper is structured as follows: initially, a concise overview of the study area and its climatic attributes is provided. Subsequently, wind data collected at a height of 10 m undergo statistical analysis, including annual, monthly, and seasonal assessments through the use of wind histograms and wind roses. The resulting dataset serves as input for the WAsP software to predict wind characteristics over the sea in areas without direct measurements, leading to the establishment of wind maps covering the entire study area. Finally, an economic evaluation is conducted to assess the potential for energy and hydrogen production within a theoretical offshore wind farm located along the Mediterranean Sea in this region. The study underscores the substantial reduction in CO₂ emissions achievable through the utilization of wind energy, further reinforcing the environmental benefits of transitioning towards renewable sources.

Methodology

Study objective

The objective of this study is to assess the technical, economic, and environmental viability of establishing large-scale offshore wind power plants, with particular attention, to the potential for fixed foundations in the Mostaganem region, situated on the northwestern coast of Algeria. The first step involved utilizing provided data to evaluate, analyze, and map wind resources at a height of 10 m, using the WAsP Software. Subsequently, wind speed predictions were generated at sea level to assess offshore wind potential at the specified wind turbine height. Referring to the water depth map, the positioning of the wind turbines is done while adhering to the required depth. Following this, a comprehensive economic and environmental analysis was conducted. This assessment included estimating the potential hydrogen production derived from the wind farm’s energy output, along with its associated cost.

These estimations were vital components in assessing the overall cost-effectiveness and environmental impact of the proposed wind energy project. The process is, visually presented in Figure 1.

Figure 1.

Diagram sequencies.

Offshore wind technical potential in Algeria

Figure 2 illustrates the projected technical capacity for both fixed and floating offshore wind energy foundations in Algeria. The data on offshore wind development has been provided through an initiative led by the World Bank Group (WBG) and supported by the Energy Sector Management Assistance Program (ESMAP) (World Bank, 2020).

Figure 2.

Offshore technical wind potential for Algeria (World Bank, 2020).

Selected area and wind data

Based on the identified offshore wind technical potential outlined in Figure 2, the present study focuses on investigating the feasibility of deploying fixed offshore wind installations within the Mostaganem region, designated as a distinct coastal economic zone in western Algeria. This particular area encompasses Latitudes 35.77° to 36.26° North and longitudes ranging from 0.106° West to 0.405° East, as depicted in Figure 3.

Figure 3.

Geographical situation of the study area.

Hourly wind data for the examined region was obtained from the National Office of Meteorology in Algeria at a height of 10 m, spanning from 2007 to 2021.

Water depth

Water depth is a crucial factor in the planning of offshore wind farm installations, as it significantly affects the choice of foundation technology.

In our case study, which focuses on fixed technology, an optimal installation depth should not exceed 50 m. Therefore, the white area illustrated in Figure 4 indicates water depths of up to 50 m, aligning with the suitability for the chosen fixed technology.

Figure 4.

Water depth and land elevation (Gebco, 2024).

Selected wind turbine

The Vestas V-236 offshore wind turbine is indeed one of the largest and most powerful wind turbines available, boasting a rotor diameter of 236 m and a power output of 15 Megawatts (MW). It operates efficiently within wind speeds ranging from 3 to 25 m/s and is renowned for its high-speed permanent magnet generator, advanced control systems, and modular design, all of which streamline transportation and installation processes.

The power and thrust curves for this wind turbine are illustrated in Figure 5, highlighting its operational characteristics and performance dynamics.

Figure 5.

Power and thrust coefficient curves.

WAsP model

The Wind Atlas Analysis and Application Program (WAsP) utilizes analytical models to predict wind energy resources for wind farm design. By integrating meteorological data with terrain characteristics, it maps wind potential, predicts energy output, and optimizes turbine layouts. WAsP utilizes a dual extrapolation approach, vertically extrapolating wind data to turbine hub height using a logarithmic law for changes in roughness (Troen et al., 1989). The equation for average wind speed as a function of height is as follows (Justus et al., 1978):

U (z) = \frac{U_{*}}{K_{*}} [\ln (\frac{Z}{Z_{0}}) - ψ (\frac{Z}{L})]

(1)

Ψ represents an empirical function, where L denotes the Monin-Obukhov length defining the height where pressure equals turbulence-produced shear (m). K_v stands for the Von Karman constant (approximately K_v ≈ 0.4), Z₀ represents ground roughness (m), and U* denotes the friction velocity related to the perturbation (m/s).

Furthermore, it conducts horizontal extrapolation utilizing an orographic model (Walmsley et al., 1982), which resolves turbulent boundary layer equations in polar coordinates. This model considers variations in ground roughness to calculate flow velocity potential based on the equation proposed by Troen and Petersen (Troen et al., 1989).

A generalized estimated solution for a point at radius R, where the potential reaches zero, is presented as follows:

χ_{j} = K_{n j} J_{n} (c_{j}^{n} \frac{r}{R}) \exp (\sin ϕ) \exp (- c_{j}^{n} \frac{z}{R})

(2)

J_n represents the Bessel function of order n, k_nj denotes arbitrary coefficients, r is the radius, $ϕ$ represents the azimuth, z stands for the height, and cⁿ_j denotes the jth roots of J_n.

Modelling

The expression for the probability density function f(v) is provided as follows (Mezidi et al., 2020):

f (V) = (\frac{k}{V}) . {(\frac{V}{C})}^{k - 1} \exp [- {(\frac{V}{C})}^{k}]

(3)

The determination of the two Weibull parameters is as follows (Meziane et al., 2021):

k = {(\frac{σ}{Vm})}^{- 1.086}

(4)

C = \frac{\bar{V}}{Γ (1 + \frac{1}{k})}

(5)

dos Reis et al. (2021) provides the different cost equations for each component of the offshore wind farm. The equation expressing the turbine cost is as follows:

C_{w t} = 1374 \times P_{w f}^{0.87}

(6)

Where P_wf is the installed power of the wind farm in (MW).

The equation for the cost of the jacket foundation (dos Reis et al., 2021), is formulated as follows:

C_{f o u n d} = (0.9181 \times W D^{2} - 31.433 \times WD + 747.4) \times P_{w f}

(7)

Where WD represents the water depth in meters.

The cost of engineering and administration is represented by the following equation (dos Reis et al., 2021):

C_{e n g_a d m} = 120 \times P_{w f}

(8)

The SCADA cost (C_scada), which depends on the number of wind turbines (N_wt), is calculated as follows (dos Reis et al., 2021):

C_{s c a d a} = 50.5 \times N_{w t}

(9)

The equations 10 and 11 defining the costs of collector system cables for acquisition (C_{array_aq}) and installation (C_{array_ins}) are as follows (dos Reis et al., 2021):

C_{a r r a y_a q} = (4.260 \times 10^{- 4} \times A + 2.324 \times 10^{- 1}) \times L_{c s}

(10)

C_{a r r a y_i n s} = (- 2.2684 \times 10^{- 3} \times N_{w t} + 3.8018 \times 10^{- 1}) \times L_{c s}

(11)

L_cs represents the total cable length of collector system in (m).

The acquisition and installation cost of offshore export cable are defined as follows (dos Reis et al., 2021):

C_{o f f_\exp_a q} = (3.3565 \times 10^{- 3} \times P_{w f} + 8.3872 \times 10^{- 2}) \times L_{eoff}

(12)

C_{o f f_\exp_i n s} = (- 6.3180 \times 10^{- 4} \times P_{w f} + 3.8125 \times 10^{- 1}) \times L_{eoff}

(13)

Where L_eoff is the offshore export cable length in (m).

The acquisition and installation costs of onshore export cable are determined by equations 14 and 15 (dos Reis et al., 2021).

C_{o n_\exp_a q} = 0.25 . (3.3565 \times 10^{- 3} \times P_{w f} + 8.3872 \times 10^{- 2}) L_{e o n}

(14)

C_{o n_\exp_i n s} = 0.530 \times L_{e o n}

(15)

Where L_eon is the onshore export cable length in (m).

The equation representing the cost of the offshore substation (C_{off_sub}), which depends on the installed capacity of the wind farm (P_wf), is as follows (dos Reis et al., 2021):

C_{o f f_s u b} = 2534 + 88.7 \times P_{w f}

(16)

The estimated mass of hydrogen M_H2 (in kg/year) is provided as follows (Cali et al., 2018):

M_{H 2} = \frac{η_{e l} \times A E P}{{L H V}_{H 2}}

(17)

The cost of the electrolyzer ($) is determined by the electrolyzer unit cost (C_{elec_u}), the hydrogen mass production (M_H2), the electrolyzer efficiency (η_el), and the theoretical specific energy required by the electrolyzer (K_{el_th}), as expressed in the following equation (Meziane et al., 2021):

C_{e l e c} = C_{e l e c_u} \times \frac{M_{H 2} \times K_{e l_t h}}{8760 \times η_{e l}}

(18)

In our case, it was assumed that the theoretical specific energy required by the electrolyzer (K_{el_th}) and the electrolyzer unit cost (C_{elec_u}) were 39.7 kWh/kg H₂ and 368 $/kWe, respectively (Meziane et al., 2021).

The cost of hydrogen (COH) is derived by combining various equations, as outlined below (Meziane et al., 2021).

C O H = \frac{C_{e l e c} + C_{w t}}{M_{H 2} \times T}

(19)

Results and discussion

Statistical analysis at 10 m height

Figure 6 illustrates the hourly variation of wind speeds throughout the year in the surveyed region, measured at a height of 10 m above the ground. This depiction highlights a pseudo-periodic pattern in the wind speeds, with an estimated average velocity of approximately 2.5 m/s. Notably, peak values are observed during January and March, reaching approximately 14 m/s.

Figure 6.

Hourly wind speed variations in Mostaganem at a height of 10 m.

Figure 7 presents the frequency histogram of wind speed with a fitted Weibull distribution at a height of 10 m above ground level.

Figure 7.

Annual wind speed frequency with fitted Weibull distribution at 10 m height.

The region of Mostaganem covers a range of wind speeds which reaches 16 m/s. Additionally, it maintains an average velocity of approximately 2.5 m/s with an annual mean wind power density of 31 W/m².

The wind rose plays a crucial role in determining the optimal orientation for the installation of wind turbines. As depicted in Figure 8, the predominant wind direction in Mostaganem originates from the West, representing 14% of occurrences. These results underscore the significance of utilizing wind patterns strategically to optimize the siting and effectiveness of wind energy infrastructure in this region.

Figure 8.

Annual wind rose at a height of 10 m.

The Weibull histograms displayed in Figure 9 for each season at 10 m illustrate that the maximum for wind speeds up to 10 m/s for the summer and autumn, 12 and 16 m/s occur during spring and winter, respectively. The most frequent wind speeds are between 1 and 3 m/s for the different seasons, with frequencies of 12% during winter, 16% in spring, and 18%, 20% during summer and autumn respectively.

Figure 9.

Seasonal wind speed frequency with fitted Weibull distribution at 10 m.

The observed Weibull parameters for each season are displayed in Figure 9. Notably, the low shape parameter value is occurred during winter with 1.05 (indicating the unstable wind speeds), followed by the summer season with a shape factor of 1.43. However, the high value is observed during spring and autumn with 1.48.

In turn, the high scale factor value of 3.2 m/s indicates that spring is a windy and productive season. The maximum wind power density is observed in winter with 45 W/m², while the minimum observation of 15 W/m² is obtained in autumn.

As depicted in Figure 10, the predominant direction is west during autumn and winter, with an occurrence frequency of 11.9%. In spring, winds predominantly originate from the southwest, with a frequency of 17.9%, while during summer, the prevailing winds stem from the northwest, with a frequency of 14.2%. These observations are crucial for determining the optimal positioning and alignment of wind turbines to capitalize on prevailing winds and enhance energy production efficiency.

Figure 10.

Seasonal wind roses at 10 m for Mostaganem.

Roughness map

The variation in terrain roughness length, ranging from 0 to 0.6 m, is a significant factor to consider in environmental conditions analysis, and is depicted in Figure 11.

Figure 11.

Roughness length map of Mostaganem region.

This roughness length map visually represents areas where this variation is observable. Regions marked by values of 0 m indicate smooth or flat surface areas, while those reaching 0.6 m indicate more roughness that is pronounced. These variations in roughness can influence wind speed, direction, and the effectiveness of wind turbines, particularly in contexts such as wind farm development. It is crucial to consider these roughness variations for accurate planning and optimal utilization of wind resources in the concerned region.

Wind speed mapping at 10 m height

As demonstrated in Figure 12, the wind speeds in Mostaganem at a height of 10 m exhibit a moderate range, fluctuating between 0 and 8 m/s. Notably, it is observed that offshore wind speeds exceed those measured onshore, surpassing 4 m/s.

Figure 12.

Wind speed variation at a height of 10 m.

Figure 13 illustrates the wind speeds in Mostaganem at a height of 150 m where the maximum wind speed reaches 7.8 m/s at hub height of 150 m.

Figure 13.

Wind speed and power density variation at hub height of 150 m.

Wind plant production and costs

Upon analysis of the energy generated by a 500 MW wind farm, as it is shown in Figure 14, it becomes evident that the output reaches 1361.64 (GWh/year) with a cost of 0.89 ($/kWh).

Figure 14.

Offshore wind turbines layout.

Regarding offshore energy costs, Mostaganem shows a cost of 0.89 $/kWh, which is notably higher compared to the hypothetical wind farms in Hong Kong’s Western Water and Mirs Bay, with costs of 0.3500 and 0.4125 $/kWh, respectively (He et al., 2020) (Figure 15).

Figure 15.

Wind power plant production and costs.

The variation in offshore energy costs between Mostaganem and the wind farms in Hong Kong’s Western Water and Mirs Bay can be attributed to the unique characteristics inherent in each location. Factors such as distinct geographical conditions, differing levels of infrastructure development, varying economic landscapes, regulatory environments, and differences in scale and design collectively contribute to the divergent costs associated with offshore wind energy production in these regions.

Moreover, the absence of offshore in-situ measurements may potentially lead to an underestimation of results. Utilizing data derived from measurements in the city of Mostaganem could contribute to this possibility.

In the context of hydrogen production, this amount equates to 28,571 tons with a cost of 2.12 $/kg. This statistical representation underscores the substantial contribution of this wind farm to both electricity and hydrogen production within this coastal region.

The hydrogen production cost in Mostaganem, set at 2.12 $/kg, is notably lower than the LCOH values reported by Correa et al. (2022), which indicated a range between 3.9 and 6.7 $/kg. This suggests that Mostaganem’s hydrogen cost stands comparatively lower, potentially influenced by distinct efficiencies or specific factors, including a variety of production sources, generation technologies, and electrolyzer characteristics.

Carbon dioxide (CO₂) emissions saving

As depicted in Figure 16, the wind farm’s energy production results in emissions of 16,339.7 tons of CO₂. In contrast, producing the same energy using natural gas as the primary source in Algeria would emit 612,738 tons of CO₂. Consequently, utilizing wind energy leads to a reduction of 596,398.3 tons of CO₂ emissions. This underscores the significant role of wind energy in mitigating large volumes of CO₂ emissions into the atmosphere. The generation of energy from wind resources significantly contributes to the mitigation of substantial volumes of CO₂ emissions into the atmosphere.

Figure 16.

Carbone dioxide emissions.

Conclusion

This paper examines the offshore wind resource potential in Mostaganem, Algeria, utilizing the WAsP Model to assess wind farm performance. Statistical analysis indicates moderate wind potential, with an annual mean wind speed of 2.53 m/s and prevailing westward winds 14% of the time.

A hypothetical 500 MW offshore wind farm could generate 1361.64 GWh/year, with energy costs estimated at 0.89 $/kWh and hydrogen production at 28,571 tons/year, priced at 2.12 $/kg. Implementation of such a wind farm could avoid 595,450 tons of CO₂ emissions annually, demonstrating the feasibility of offshore wind farms.

This technical analysis offers insights into wind potential, energy production, and associated costs. Additionally, it highlights opportunities for clean energy generation and hydrogen production, aligning with global initiatives for sustainability.

Prospective directions include technology refinement, policy implementation, industry growth, environmental impact assessments, system integration, collaborative efforts, and community engagement. These initiatives aim to advance renewable energy and foster economic growth, sustainability, and technological progress in the region.

A potential future perspective involves offshore in-situ measurements to enhance accuracy and mitigate potential underestimation due to reliance solely on onshore data. This additional data collection could significantly improve assessments and provide a comprehensive understanding of the region’s offshore wind potential.

Moreover, the environmental and economic benefits highlighted in these findings denote a significant progression towards renewable energy and sustainable hydrogen production, in accordance with global initiatives for environmental conservation and clean energy solutions. This opens undeniable prospects for the production of green ammonia and its derivatives.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Assessing offshore wind plants for energy and green hydrogen production: A case study in the Mostaganem coastal region,Algeria

Abstract

Keywords

Introduction

Methodology

Study objective

Offshore wind technical potential in Algeria

Selected area and wind data

Water depth

Selected wind turbine

WAsP model

Modelling

Results and discussion

Statistical analysis at 10 m height

Roughness map

Wind speed mapping at 10 m height

Wind plant production and costs

Carbon dioxide (CO2) emissions saving

Conclusion

Footnotes

Declaration of conflicting interests

Funding

References

Carbon dioxide (CO₂) emissions saving