International Journal of Sustainable Engineering ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tsue20 Assessment of past and future potential of ocean wave power in the Gulf of Guinea Adeola M. Dahunsi & Bennet Atsu Kwame Foli To cite this article: Adeola M. Dahunsi & Bennet Atsu Kwame Foli (18 Oct 2023): Assessment of past and future potential of ocean wave power in the Gulf of Guinea, International Journal of Sustainable Engineering, DOI: 10.1080/19397038.2023.2269204 To link to this article: https://doi.org/10.1080/19397038.2023.2269204 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 18 Oct 2023. Submit your article to this journal Article views: 90 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tsue20 INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING https://doi.org/10.1080/19397038.2023.2269204 RESEARCH ARTICLE Assessment of past and future potential of ocean wave power in the Gulf of Guinea Adeola M. Dahunsia and Bennet Atsu Kwame Folib,c aUNESCO International Chair in Mathematical Physics and Applications (ICMPA), University of Abomey-Calavi, Cotonou, Benin Republic; bDepartment of Marine and Fisheries Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Ghana; cGlobal Monitoring for Environment and Security and Africa, College of Basic and Applied Sciences, University of Ghana, Legon, Ghana ABSTRACT ARTICLE HISTORY This study investigated the historical and future wave power potential in the Gulf of Guinea (GoG) with Received 8 May 2023 the aim of identifying high-density wave energy locations for potential exploitation. To estimate wave Accepted 4 October 2023 power density (WPD) for three time periods (past: 1979–2005, mid-century: 2026–2050, and end-century: KEYWORDS 2081–2100), we utilized significant wave height and mean wave period obtained from eight General Gulf of Guinea; wave power; Circulation Models. Using an ensemble of these WAVEWATCH III simulated datasets, we calculated WPD Renewable energy; climate and assessed overall and seasonal trends, projecting changes under Representative Concentration change; RCP scenarios Pathway (RCP) 4.5 and 8.5 scenarios. Results revealed higher potential WPD in the western GoG, particularly near the coast, with increased values offshore. Spatially, WPD change rates varied widely (−0.021 to 0.039 kW/m per year), suggesting both positive and negative trends, though generally low. Projections indicated a potential increase from 0.5 to 1.0 kW/m by the end of the century. The estimated potential power for harvesting exceeded 14,000 MW, with offshore regions showing better wave con- verter performance. This study concludes that GoG's wave energy is a promising renewable resource, offering a potential solution to future power needs and contributing to regional greenhouse gas emission mitigation. 1 Introduction The need for migration from fossil fuel dependent power gen- offer a more comprehensive view of their economic eration to more renewable means has become pressing as the viability. impacts of climate change continue to increase globally (Sawin One such alternative to LCOE is the Net Present Value et al. 2016). Relative to solar and wind, ocean waves have better (NPV) analysis, which takes into account both the initial predictability as a renewable energy resource in addition to higher investment costs and the future cash flows generated by energy density (Mwasilu and Jung 2019). The major cities in the a wave energy project. By factoring in the time value of Gulf of Guinea (GoG) such as Abidjan, Accra, Cotonou, Douala, money, NPV provides a more detailed assessment of the pro- Lagos and Lomé which are also where the major portion of the ject’s financial feasibility over its entire lifespan, incorporating power is consumed are all on the coast. The location of this major elements such as operational expenses, capital outlays, and load centres are close to the potential locations of the ocean wave revenue projections (Dalton, Alcorn, and Lewis 2010). The power generators, which is an added advantage in terms of Internal Rate of Return (IRR) is another metric that holds reduced cost for power distribution. significance in assessing wave energy projects (Simal et al. However, despite these aforementioned potentials, the 2017). Calculating the discount rate at which the net present exploitation of the ocean wave energy as a source of value of projected cash flows equals zero, IRR offers insight power is still under-utilised globally due to the high into the potential return on investment. It aids in comparing Levelized Cost of Energy (LCOE). The LCOE is the price wave energy projects with other investment opportunities and at which the generated electricity must be sold for power provides a gauge of profitability. In the West African context, generation to be considered profitable (Lehmann et al. Adesanya et al. (2020) found that though the initial investment 2017). This relatively high LCOE is expected to become is high, it is poised to bring about more economical electricity economical in the future as technologies for ocean wave generation and transmission, environmental enhancements, harvesting emerge. The LCOE has traditionally served as and a consistent energy supply that aligns with demand in a widely used metric to compare the cost-effectiveness of West Africa, contrasting favourably with the conventional different energy sources, including wave energy (Guillou, generation methods. Lavidas, and Chapalain 2020). However, due to the unique For a more immediate measure, the payback period pre- challenges and characteristics associated with wave energy sents an alternative approach by quantifying the time required projects, it’s important to consider alternative metrics that for the initial investment in a wave energy project to be CONTACT Adeola M. Dahunsi dahunsi_adeola@yahoo.com; dahunsi_adeola_michael@cipma.net UNESCO International Chair in Mathematical Physics and Applications (ICMPA), University of Abomey-Calavi, Cotonou 072 BP 50, Benin Republic Supplemental data for this article can be accessed online at https://doi.org/10.1080/19397038.2023.2269204 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 A. M. DAHUNSI AND B. A. K. FOLI recuperated through generated revenue. A shorter payback power. Their structural simplicity is another compelling facet period suggests a quicker return on investment and could be since they are designed with straightforward configurations, viewed favourably from a financial perspective. Considering facilitating their assembly and deployment. This simplicity the broader context, the Levelized Avoided Cost of Energy streamlines the installation process, potentially reducing both (LACE) accounts for the value generated by producing energy time and costs associated with setup (Cabral et al. 2020). from wave sources compared to not generating energy and Another crucial advantage of OWCs is their inherent reliability relying on an alternative source (Coe et al. 2022). It evaluates which is attributed to the strategic placement of all mechanical potential cost savings and benefits in relation to other options. components above the still water level (SWL). By avoiding In light of the uncertainties inherent to wave energy, sensitivity direct exposure to the challenging marine environment, these analysis becomes crucial. This approach assesses how varia- components are shielded from the corrosive effects and phy- tions in key parameters, such as equipment costs, operational sical stresses that prevail in such settings. This design consid- performance, and energy prices, impact the economic feasi- eration significantly enhances the longevity and dependability bility of the project. It provides a range of potential outcomes, of OWCs, minimising the need for frequent maintenance or offering a more comprehensive perspective. Furthermore, as repairs (Ciappi et al. 2022). wave energy projects can have significant environmental and A study by Yusov et al. (2021) gives details of other cost- social implications, metrics that encompass these factors are effectiveness WECs. In addition to OWCs, the field of WECs also valuable. Evaluating reductions in greenhouse gas emis- encompasses a diverse array of technologies that are actively sions, local job creation, and enhanced energy security con- being explored and developed. Each of these technologies tributes to the holistic assessment of a wave energy offers distinct approaches to harnessing the energy of ocean exploitation. waves, and they hold promise for contributing to renewable In evaluating the profitability of wave energy projects in energy generation. Point absorbers, for instance, are buoyant a developing region such as the GoG, it is important to adopt structures that rise and fall with the motion of waves. These a combination of these alternative metrics, aligning them with devices are tethered to the seabed and employ their vertical the project’s context and objectives. Singular metrics might not movement to generate mechanical power through hydraulic provide a comprehensive view, and the evolving landscape of systems, which is then transformed into electricity. This design technological advancements, policy shifts, and market benefits from its relative simplicity and adaptability to varying dynamics can significantly influence the economic viability of wave conditions. Attenuators present another innovative con- wave energy initiatives over time. For example, factors such as cept with elongated, floating structures oriented perpendicular capacity of the project which indicates the actual energy output to the direction of wave propagation (Yusov et al. 2021). They as a percentage of the maximum potential, and the reliability of consist of interconnected segments that flex and move in the technology in demanding ocean conditions can offer response to wave motion, creating relative movement between insights unique to wave energy assessment. Opportunity of segments that can be converted into mechanical power and job creation and attraction of related investments will be subsequently transformed into electrical energy. Overtopping other factors that are important to decide the feasibility of devices operate by utilising the potential energy inherent in the wave energy exploitation project in a region such as the waves as they wash over a barrier. This elevated water is GoG. In addition, the possibility of installing the wave power collected in a reservoir and subsequently released through generators alongside other generators like offshore wind farms turbines as it flows back down to its original level, generating is also expected to make the exploitation of ocean wave power electricity in the process. This design leverages the dynamic economical in the future (Osinowo 2019). Judging from the interaction between wave energy and gravity to produce previous information, one can conclude that the best metrics power. Terminators, on the other hand, capture wave energy for assessing cost-effectiveness of ocean wave energy in the by capitalising on the pressure difference between their front GoG will be a hybrid of different metrics which is beyond the and back sides. Positioned perpendicular to wave direction, scope of the present study. these devices convert the varying pressure levels into mechan- A number of studies assessing the potential of wave energy ical power, which is then converted into electrical energy resources have been carried out in various parts of the world. through appropriate mechanisms. Submerged pressure differ- These include the Indian Ocean (Karunarathna et al. 2020), ential systems take advantage of the pressure discrepancies China (Liang et al. 2013), Persian Gulf (Kamranzad, Etemad- between the surface and subsurface due to wave activity. By Shahidi, and Chegini 2013), Malaysia (Mirzaei, Tangang, and effectively capturing and converting these pressure differences, Juneng 2014), Australia (Cuttler, Hansen, and Lowe 2020; these systems can generate mechanical energy that is then Hughes and Heap 2010). These studies observed that temporal converted into electricity, offering a unique approach to wave variations in wave power can result from even little change in energy extraction. Resonant devices align with the natural wind wave climate. Some of these studies have also explored frequencies of waves or oscillate at frequencies closely related the different wave energy converters (WEC) available and their to them. This resonance is harnessed to convert the motion of efficiency in varying locations. For example, Henriques et al. the device into usable electrical power through various con- (2019) assessed the oscillating-water-columns (OWCs) con- version mechanisms (Moretti et al. 2020). Babarit et al. (2012) verter type which ae considered one of the most efficient type proposed some power matrices through wave-to-wire (W2W) currently in use. This is owing to their advantage in modelling approaches for the selection of WECs by comparing a commendable energy conversion efficiency, enabling them different set-up at different sites. They did this by carrying out to effectively harness wave energy and transform it into usable a combination of computational and numerical modelling of INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 3 physical phenomena and W2W output for each device, of the century (Dahunsi et al. 2022). This projected change employing the equations of motion and hydrodynamic mod- warrants the need to assess the future wave power potential in elling methods. Subsequently, they used W2W models to the GoG to assess the magnitude of change likely to be experi- compute power matrices for individual devices, along with enced. This will aid the decision and policy on the feasibility of determining the average yearly power uptake across five dis- exploiting wave power as a source of electricity in the countries tinct representative wave locations along the European Coast. of the GoG. This will also be an addition to the steps taken This study provided the required background for the choice of globally to achieve carbon neutrality by 2050. Therefore, the power matrices needed in the current study. objective of this study is to examine future magnitudes of However, in the GoG, Osinowo et al (2018, 2019) ocean wave energy and the feasibility of its exploration for assessed ocean wave power potential based on 37-year power generation in the countries of the GoG. wave hindcast. Another study by Adesanya et al. (2020) explored the prospects of ocean wave as a renewable energy only a section of West Africa. Despite providing a much- 2 Study area needed baseline for comparison during intersecting period, The GoG, as defined in this study, covers the coastal waters these very few previous studies mostly cover a section of the from the Cape Palmas in Liberia down to the Cape Lopez in GoG. In another study assessing the feasibility of exploiting Gabon (Dahunsi et al. 2022). This covers several West and wave power in Ghana by Tulashie et al. (2022), the estimated Central African countries including the Bight of Benin in the total wave power of 7215 MW was found to be another northwest and Bight of Bonny in the southeast (Figure 1). The significant addition to renewable energy source in Ghana. GoG has the eastward flowing Guinea Current as the predo- However, all these previous studies in the GoG have been minant ocean current in the region (Foli et al. 2022). focussed on the past emphasising the need for future centred Biologically, the GoG is a very productive region resulting in assessment to give information about climate change- the upwelling region called the Guinea Current Large Marine induced variations in ocean wave power potential. It is Ecosystem (GCLME) (Abe and Brown 2020). also worthy of note that these studies are few in number In terms of wave climate, the GoG is known to have pre- compared to other regions of the world coupled with the fact dominantly swell waves with average conditions of approxi- that they are mostly done on national scales making them mately 1.36 m and 9.6 s for wave height and period respectively unrepresentative of the potential exploitable renewable (Almar et al. 2015). In extreme conditions, these values can be energy in the region. Therefore, the temporal coverage of up to 1.62 m and 10.86 s, respectively (Dahunsi et al. 2022). both past and future period will provide an update to the The type of microtidal condition in GoG has neap and spring previously available information while extending the knowl- tides ranges of 0.3 m and 1.8 m respectively (Alves et al. 2020). edge on what should be expected in terms of potential wave As seen in Figure 1, the GoG has a relatively narrow continen- energy around the GoG in the future. This is also needed to tal shelf with the wide areas seen around Cape Three Point take advantage of the regional coastal management strategy (Ghana) and the Niger Delta (Nigeria). previously proposed for the GoG region by Alves et al. (2020). This regional exploitation of ocean resource is expected to increase the feasibility and profitability of evol- 3 Data and methods ving renewable energy source such as ocean wave in a region with relatively uniform hydrodynamic and economic con- 3.1 Data source and acquisition ditions like the GoG. The data used for this study was the contribution of the Information on the hydrodynamics of a region may come Commonwealth Scientific and Industrial Research from various sources, which include observations relying on Organisation (CSIRO), Australia to the second phase of the visual aids by experts on Voluntary Observing Ships (VOSs) Coordinated Ocean Wave Climate Project (COWCLIP2.0) (Grigorieva, Gulev, and Gavrikov 2017; Swail et al. 2010; (Hemer and Trenham 2016). The modelling was done using Vettor and Guedes Soares 2019), in-situ buoy measure- a dynamical wave approach which was forced with 3-hourly ments, video camera and unmanned aerial vehicles 10 m above surface wind fields and monthly sea-ice fields from (UAVs) (Angnuureng et al. 2016, 2020; Arnaud et al. 8 General Circulation Models (GCMs) included in the 2021), satellite altimeters (Young, Zieger, and Babanin Coupled Model Intercomparison Project (CMIP5) (Taylor, 2011) as well as numerical wave models (Alves et al. 2014; Stouffer, and Meehl 2012). This dataset contains Booij, Holthuijsen, and Battjes 2001; Chen et al. 2018; WAVEWATCH III (WW3; Tolman 1991) wave model gener- Hasselmann et al. 1988). These various wave climate data ated data using the ST3 (BAJ) source-term physics on a 1° × 1° acquisition methods have their pros and cons. The most spatial resolution (Hemer et al. 2013). The details of the model important advantage models provide, is the high spatiotem- set-up including the number of GCM(s) used, atmospheric poral resolution at a relatively low cost. This gives numerical downscaling & wind-wave modelling method vis-à-vis the models an edge when analysing long-term metocean para- atmospheric corrections, calibration, source-term packages, meters, especially in areas with scanty observational data, spectral partition as well as the various contributing institu- like the GoG. tions (research centres) and nations are presented in Morim Wave power potential is dependent on the ocean wave et al. (2020). For consistency with other GCMs, minor changes climate of a region which is projected to experience significant were made to WW3 for some of the simulations for them to spatial as well as temporal changes in the GoG before the end properly simulate the 360 and 365-day year, respectively. 4 A. M. DAHUNSI AND B. A. K. FOLI Figure 1. Map of the GoG showing the bathymetry of the region, bordering countries and major cities (black labels), coastal and inland water bodies (blue lines). According to Bricheno et al. (2015), this is to account for climate by adopting technologies and climate change mitiga- difference in various GCMs such as GFDL-CM3 using a 365- tion and adaptation strategies whereas RCP8.5 scenario is the day calendar whereas HadGEM2-ES makes use of a 360-day higher emission scenario releasing more greenhouse gases into model year. Therefore, the wave model was set-up to run the environment by the end of the 21st century. uninterestingly including a warm-starts at the beginning of all years. This approach of modelling allows the use of the data for the starting conditions of one year as the ending 3.2 Wave power density computation and statistical conditions of the year before it. analysis Generally, the COWCLIP2.0 database contains 155 model Firstly, the GoG data spatial coverage for this study was results from various 10 modelling institutions with globally defined to range between longitudes 9° West −16° East and available information on wave climate (Morim et al. 2018). latitudes 3° South- 16° North. Since the various GCMs The simulations were validated against 26 years of satellite data included in the COWCLIP2.0 dataset used different parame- for significant wave heights. The downscaling methods and trisations to account for different ocean physics, a multi-model validation results are as presented by Morim et al. (2020). This ensemble of all 8 GCMs was preferred. This ensemble was dataset is very important as it provides the needed data for produced by aggregating data for the different wave climate future large-scale coastal studies into climate change impacts parameters after which an average was computed. This is risks and vulnerability assessments, especially for regions believed to give a better representation of the different model- where regional databases are not available for future projec- ling parametrisations. To assess the inter-seasonal variations in tions. Parameters produced in the COWCLIP2.0 include sig- the WPD, the ensemble data was group into two major seasons nificant wave height (Hs), mean wave period (Tm) and mean in the GoG. The dry season (November–March) and the rainy wave direction (Dm). season (April–October), sometimes referred to as winter and For the current study, the monthly average of significant summer, respectively. The resulting data were used in the wave height (hs_ave) and average wave period (tm_ave) were computation of the wave power density similar to the extracted. This extraction was done for 3 different time slices, approach previously employed by similar studies in the region one in the past (1979–2005) and two in the future (2026–2045 such as Osinowo et al. (2018) and Tulashie et al. (2022). This and 2081–2100). These time slices are henceforth referred to as method relies on the energy equation that relates the wave historical, mid-century and end-century respectively. The power per unit crest to Hs and Tm (Defne, Haas, and Fritz future simulations were done under two Representative 2009). The wave power density varies directly with the average Concentration Pathway (RCP) scenarios. The RCP4.5 is the wave period as well as the square of the significant wave height. lower emission scenario which captures efforts to stabilise the This means that higher wave power should be exploitable from INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 5 regions with high waves provided the variation of the average scenario, neglecting the inherent dissipative characteristics of wave period is not much. The average wave power density real ocean conditions. The formula also assumes uniformity (KW/m) was for each grid point over the GoG using equation across the wave field, assuming consistent wave parameters 1: across a vast expanse. Yet, in natural settings, wave condi- tions display substantial variance due to variables such as ρg2 2 2 wind patterns, coastal features, and underlying bathymetry. WPD ¼ H T ¼ 0:49H T (1) 64π s m s m A significant simplification emerges from considering a single mode of wave propagation characterised by where WPD represents the wave power density measured in a single frequency and phase speed. However, the real- kW per m of wave crest, ρ is the ocean water density given as world ocean contains a multitude of wave modes and fre- 1025 kg per m3, g is the acceleration due to gravity, Hs stands quencies, rendering this simplified perspective incomplete. for the significant wave height in m and Tm is the average wave Furthermore, the formula doesn’t factor in directional period given in s. This represents a simplified version of the spreading of wave energy. Real ocean waves come from existing wave energy flux, specifically the wave energy poten- diverse directions, leading to a dispersion of energy across tial per unit crest, which is the amount of energy received at various angles – an aspect that the formula overlooks. In a designated location. Its definition is rooted in the cumulative terms of context, the formula presumes a constant water representation of the wave energy spectrum. This version of depth. This assumption doesn’t encapsulate scenarios wave computation methodology is favoured due to the chal- where water depths fluctuate, a common occurrence in lenges inherent in obtaining the dispersion of wave energy diverse ocean environments. Moreover, the formula dis- across frequencies and directions especially in a data deficient misses wave interactions such as wave focusing, wave break- region such as the GoG where studies rely on global dataset. ing, and energy transfer between different wave modes. This complication arises as many wave datasets primarily These interactions can significantly impact wave behaviour encompass provide parameters like significant wave height but are absent from the formula’s scope. and wave period. Given these circumstances, streamlined for- The estimation of WPD is made with the assumption of mulations were embraced to estimate the accessible wave standard shapes of the wave energy spectrum which may lead power density. The adoption of these formulations hinged on to over- or under-estimation in ocean with two wave energy distinct assumptions based on whether the context pertained maxima with a combination of long-crested swell and short- to shallow waters, moderate water depths, or deep waters crested wind-sea waves, respectively. However, since the GoG (Wan et al. 2015). However, prior extensive assessments of is a swell dominated region and the deep ocean is considered in the available resource, such as the examination conducted for this study, it is assumed that these approximations will not the GoG in this analysis, predominantly relied on the assump- have significant effect. The relatively narrow continental shelf tion related to deep waters. As highlight in the review by seen in most places in the GoG (Figure 1) suggests relatively Guillou et al. (2020), in deep water where the product of the deep water. wave number (k) and water depth (d) is far great than 1, the In order to assess the pattern of the trends in the WPD of wave height and wave period are important parameters for the GoG for the past and future, a trend analysis was done. The wave energy computation. estimated trend was also subjected to Mann-Kendal test of Consequently, as expected for a simplified method such trend significance similar to the approaches employed in as this, there are assumptions associated with applying this Dahunsi et al. (2022). This allows to see the rate of increase method. The formula for calculating wave energy flux, as or decrease of the WPD over time and to see where the change previously provided, rests upon a series of assumptions and in rates can be adjudged to be statistically significant with simplifications that aid in arriving at a straightforward a 95% confidence limit. A positive trend suggests an increasing expression (Ciappi et al. 2022). These assumptions and rate while a negative trend corresponds to decreasing WPD simplifications play a pivotal role in shaping the applicability between the first and last years in the time slice being and accuracy of the formula within specific contexts. One of considered. such assumptions is that the formula assumes a scenario of Also, to quantify the changes expected in the future, the monochromatic waves, where all waves possess identical average for the past WPD was subtracted from the averages of frequencies and directions. This assumption simplifies cal- the different future time slices and RCP scenarios. This change culations but diverges from the reality of ocean waves, which estimation was done on a grid-by-grid basis i.e. the average of often comprise a mixture of waves with varying frequencies the WPD for each grid point for the past time slice was sub- and directions. Moreover, the derivation of the formula tracted from the future average WPD value for the same grid- relies on the framework of linear wave theory. This theore- point. A positive value means the WPD for the particular future tical foundation presupposes that wave amplitudes are mark- period is higher than that recorded in the past and vice versa. edly smaller compared to the wavelength. While this A one-wave analysis of variance (ANOVA) test was carried out approximation is suitable for gentle or small waves, it falters to check if the average WPD for the past is significantly different when considering larger, steeper waves. Additionally, the from those of the future. This was followed by the post-hoc formula operates under the assumption of a non- Tukey–Kramer tests to find where the differences occur. To also dissipative environment, where factors like wave breaking, confirm the strength of the changes in wave climate on WPD, bottom friction, and other energy loss mechanisms are over- correlation was done for the different wave components used looked. This limitation confines the formula to an idealised for the computation of the WPD i.e. Hs and Tm. 6 A. M. DAHUNSI AND B. A. K. FOLI Potential WEC power output estimation direction within which the wave energy machine extracts Applying a combination of numerical methods and power power from the waves. The other is the capacity factor (CF) matrices previously used in Babarit et al. (2012), Guillou and which is estimated as a ratio between the electric power (WPO) Chapalain (2018), Veigas and Iglesias (2014) and Rusu and generated by each WEC relative to the maximum rated power Onea (2016), the exploitable power in the GoG can be esti- (WRP) of each system. These are given by equations (3–5): mated for different WECs. This approach computes the wave energy absorption estimate for a specific device at a given WPOWPE ij¼ (3) location by multiplying the power matrix of the device with WPOmax the scatter diagrams representing wave statistics at that loca- tion in terms of Hs and Tm. Similar to the methods and values WPOCW ¼ (4) used by Rusu and Onea (2016), the performances of the three WPD WECs previously described in Guillou and Chapalain (2018) were assessed for the GoG region. The first WEC, Pelamis, WPOCF ¼ 100 (5) designed for water deeper than 50 m operates as a 750 kW WRP offshore floating machine. It comprises of interconnected semi-submerged cylinders through hinged joints, aligned with wave propagation. As waves traverse its length, the sec- 4 Results tions move, generating mechanical energy. This energy is then 4.1 Average wave climate in the GoG converted to electricity through power take-off systems within the joints. The second WEC, AquaBuoy, also designed for The wave climate of the GoG defined in terms of the water deeper than 50 m is rated at 250 kW and features average Hs and Tm for the past time slice (1979–2005) a buoy linked to an underwater cylinder housing an accelerator presented in Figure 2 show both spatial and temporal tube with a piston and hose pump. The buoy’s oscillations variations. Spatially, it can be observed that the wave compress the pump, channelling pressurised water to height shows a south-north and east -west increasing a Pelton turbine for electricity generation. The third device, pattern with the lowest waves experienced between the Wave Dragon, has a 5.9 MW rating and is a slack-moored, Niger Delta and Cameroon (5°E-10°E and (2.5°N-5°N)). floating system utilising overtopping designed for water deeper Higher waves are seen offshore which become lower as than 30 m. It employs two reflecting wings to guide waves they travel towards the coast. This clearly shows the towards a ramp, with overtopped water collected and directed evolution of the predominant swell waves which is gen- to Kaplan turbines for energy conversion. erated from the south-western part of the Atlantic Ocean The choice of WEC was informed by the public availability as they travel towards the GoG. This spatial variation can of information, as these technologies are already mature and in be seen for all periods (Figure 2a-c) though the magni- use in other parts of the world. In contrast, the assessment tude varies. Temporally, it can be observed that higher methods were adopted because the reference points in the waves are seen in the GoG during the rainy season previous studies where they have been applied also have an compared to the dry season. This is evidenced in the average wave climate similar to that of the GoG. The scatter average Hs of 1.25 m, 1.10 m and 1.36 m estimated for matrix plots showed a similar pattern both in terms of annual the annual overall, dry and rainy season respectively. total and seasonal changes, although the winter period is the A look at Figure 2d-f show a seemingly reverse in the most energetic in their case. To estimate power output (WPO) spatial variation compared to the Hs equivalence on the left from each WEC, the power matrices of the WEC are multi- side. For example, one can observe a north-south gradient plied elementwise by the scatter diagram representing the in Tm distribution. Generally, wave periods show increase bivariate distributions of wave climate. Equation (2) shows from south to north along the GoG. The spatial distribu- this mathematically: tion of Tm also displays an east-west variation, influenced 1 X X by coastal and bathymetric features. However, the eastern i¼nHs j¼nTm WPO ¼ x WPMijWPDij (2) regions such as the Cameroon section of the GoG, near the 100 i¼1 j¼1 coast, may experience shorter wave periods due to the where, WPDij denotes the percentage of wave energy asso- interaction of local winds and coastal morphology. ciated with the bin specified by column j and row i, while However, the interplay between the influence of swells WPMijsignifies the corresponding power from the power travelling from offshore and coastal morphology may matrix (given in Table S1-S3 in the supplementary section) determine the Tm in other parts especially in the south of the WEC for the same bin. around Gabon. Temporally, similar to Hs, the wave period Furthermore, in a bid to assess the efficiency of the different variation in the GoG is influenced by seasonal changes. WEC systems in the GoG, three other performance indicators During the rainy season when atmospheric conditions are were estimated. One is the normalised non-dimensional elec- more active, wave periods are longer due to the arrival of tric power (WPE) which is a ratio of the electrical power swells from distant storm systems. Conversely, in the dry produced at a point i,j (WPOijÞ to the maximum value season, shorter wave periods associated with locally gener- (WPOmaxÞ of the electrical power over the entire GoG in the ated wind waves are seen in the GoG. The average Tm duration covered for each WEC. Another one is the capture estimated in this study for the annual overall, dry and rainy width (CW) which is the width perpendicular to the wave season are 9.25, 9.06 and 9.38 respectively. INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 7 Figure 2. Spatial distribution of wave climate for the 1979–2005 time slice: (a) Hs overall (b) Hs dry season (c) Hs rainy season (d) Tm overall average (e) Tm dry season (f) Tm rainy season. In order to see the difference in terms of magnitude is highest by the end of century for the RCP 8.5 scenario. In between the past and future wave conditions, the wave climate contrast, the dry season is expected to experienced decrease in (Hs and Tm) for the past was subtracted from the future Hs ranging from 1.58 to 1.92 cm. The future scenarios also projections. For the projected wave climate and the expected showed that the rainy season will continue to have higher change in the GoG shown in Figure 3a, one can see that an waves compared to the dry season with an increase projected increase in Hs is to be expected in the annual average for all to range from 3.45 to 5.78 cm depending on the RCP scenario. RCP scenarios. This increase which ranges from 1.3 to 2.62 cm For the future wave period, a general increase is expected 8 A. M. DAHUNSI AND B. A. K. FOLI Figure 3. Average future wave climate and projected change for different RCP scenarios for (a) Hs (b) Tm (the overlapping by the dark orange colour represents the change shown by the axis on the right side). round the year for all RCP scenarios. This increases which are compared to the Nigeria-Cameroon section of the gulf. When highest during the rainy season range from 0.065 to 0.16 for one compares the dry season (Figure 4b) and rainy season the overall annual average, 0.044–0.096 s for the dry season (Figure 4c) distribution of wave power, it is observed that higher and 0.081–0.21 s for the rainy season. values are recorded in the rainy season. This is also similar to finding for wave height reported in previous section. 4.2 Average WPD for the past and future For the past time slice, the general overview of the average WPD values in the GoG ranges from 0.25 kW/m in the coastal The spatial distribution of the average WPD presented in part during the dry season to as high as 13.59 kW/m offshore Figure 4 is for the past time slice (1979–2005), which shows during the rainy season. Dry season records the lowest WPD very similar spatial trend to all other time slices and RCP while rainy season has the highest WPD, which corresponds to scenarios. The only difference observed are in the values from the low and high wave energy seasons respectively in the GoG. one part of the GoG to the other. It can be observed that the This same trend is recorded for all the various time slices and WPD has a north to south and east to west increasing pattern RCP scenarios though with varying magnitudes (Figures 5a-c). similar to that reported for Hs in previous section. Locations For the future time slices, though one can see a pattern of around Cote d’Ivoire-Ghana axis have relatively higher values higher values of WPD for the end-century RCP 8.5 scenario, INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 9 Figure 4. Spatial distribution of average WPD for the 1979–2005 time slice: (a) overall average (b) dry season (c) rainy season. 10 A. M. DAHUNSI AND B. A. K. FOLI Figure 5. Overall and seasonal averages of zonal WPD for different time slices and RCP scenarios: (a) past (b) mid-century (c) end-century. INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 11 Figure 6. Zonal variations in overall and seasonal averages of trend in WPD for different time slices and RCP scenarios: (a) past (b) mid-century (c) end-century. The black asterisks on the overall and rainy lines in (a) represent significant trend. 12 A. M. DAHUNSI AND B. A. K. FOLI Table 1. Summary of the average values of WPD for different time slices. Historical Mid-century Mid-century End-century End-century Time slices Seasons (kW/m) RCP 4.5 (kW/m) RCP 8.5 (kW/m) RCP 4.5 (kW/m) RCP 8.5 (kW/m) Average Overall 7.61 7.86 7.84 7.89 8.13 Dry season 5.64 5.50 5.46 5.46 5.53 Rainy season 8.99 9.52 9.52 9.58 9.96 Range Overall 0.35–11.65 0.36–11.95 0.37–11.62 0.37–11.78 0.40–12.03 Dry season 0.25–12.49 0.26–10.07 0.26–9.81 0.26–9.32 0.27–10.06 Rainy season 0.34–13.59 0.42–13.84 0.40–13.62 0.40–13.58 0.44–14.60 Table 2. Trends in mean overall and seasonal WPD for different time slices in kW/m per year. Mid-century Mid-century End-century End-century Time slices Seasons Historical RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5 Average Overall 0.017 −0.0020 0.0046 0.0055 0.001 Dry season 0.058 −0.0042 −0.0048 0.0025 −0.009 Rainy season −0.013 0.0049 0.016 0.016 0.028 Range Overall 0.00057–0.022 −0.0061–0.00098 −0.00013–0.0088 0.000035–0.012 0.00034–0.014 Dry season 0.0022–0.076 −0.0098–0.0017 -(0.00023–0.0081) −0.0026–0.019 -(0.00058–0.015) Rainy season -(0.00069–0.021) 0.00036–0.0087 0.00055–0.024 0.00080–0.027 0.0012–0.039 the pattern is not uniform. For example, the RCP 4.5 scenario the averaged overall trend is positive, meaning the WPD in the has relatively higher values than the RCP 8.5 scenario during GoG experienced increase between 1979 and 2005. This the mid-century which is not the same for the end-century. increase was higher in the dry season (Table 2). However, the RCP 4.5 suggests that wave energy will be stronger during the rainy season showed negative trend value during this period mid-century compared to the end-century whereas the reverse (Table 2). In Figure 6a, it can be seen that the values of the is the case for the RCP 8.5 scenarios. The overlaps seen in the overall averages of the trend (cyan line) are all above zero i.e. lines for the various RCP scenarios (Figures 5b-c) for the zonal positive trend values. Similar to the overall trend average, the averages suggest that the difference between the magnitudes is values plotted on the magenta line (dry season) are also all not large. above zero depicting positive trend for all places between 9°W A comparison of the WPD for both past and future time to 10°E in the GoG. However, for the rainy season, the values slices with the corresponding wave climate results presented in shown by the corresponding plot (blue line) show all values are section 4.1 emphasised the influence of wave height especially below the zero tick on the vertical axis interpreted as generally in determining the WPD of a region. For example, spatially, it negative trend in this season. It should be noted that the trends can be seen in Figure 5 that higher wave energy is also pro- being described are the rates of changes of WPD (KW/m jected for offshore (longitude of −9) which corresponds to the per year) rather than the trend of the lines themselves. In westernmost part of the GoG compared to the eastern section terms of magnitude, higher positive trends are seen in the (longitude of 10). The low wave power trough seen around western part of the GoG towards Cote d’Ivoire-Ghana axis longitude 8–9 corresponds to the Niger Delta-Cameroon axis compared to the eastern side from the overall and dry season which is known for the least wave energy as can be verified in trend values. In the rainy season, the negative trends are higher Figure 4. This same spatial distribution is seen for all the time in the western axis. slices and seasons. In addition, the temporal variations from A closer observation of the variations of the trend values in dry season to rainy season also agrees with higher WPD for the Figure 6a for all seasons showed that the transition from west rainy season with highest wave in the GoG. Likewise, the to east at 0° marks continuous decrease in the values (heading higher wave height projected for the future in previous section towards zero) for both positive and negative trends. This is is also evidenced in Figure 5b-c for all RCP scenarios. It can, more obvious in the lines for dry and rainy season plots therefore, be inferred that the spatiotemporal variation of the (magenta and blue lines). Since the magnitudes in Figures 4a- WPD in the GoG is strongly dependent on both wave height care meridional averages, the sharp decline seen in the east- and period. ernmost part coincides with the Niger Delta-Cameroon part of the GoG which have been previously shown to have low values 4.2 Trends in WPD for the past and future of WPD in Figure 5. It can also be seen that the magnitude of decrease reported in the rainy season is relatively lower (−4.9– The linear trend analysis done for each grid point in the GoG 17.4 × 10−3 kW/m per year) compared to the values 22.3–70.3 to check the rate of change of WPD within a particular time × 10−3 kW/m per year seen in the dry season (Figure 6a). The slice was averaged meridionally to give the zonal mean shown Mann–Kendall test conducted, shown by the black asterisk in Figure 6a-c. For the past time slice (Table 2), it is seen that where statistically significant trend exists, confirmed that INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 13 Figure 7. Averages projected changes in WPD by mid- and end-century for overall, dry season and rainy season. trend in dry season is statistically significant while the decreas- observed patterns and rate of change, whether it’s an increase ing trend reported for the rainy season cannot be said to be or decrease, within each time frame. Regarding future time significant at 95% confidence interval. spans, the analysis indicates a projected weakening of wave For the mid-century time slice, the highest positive trend is heights during the dry season, leading to reduced WPD. seen in the rainy season for both RCP 4.5 and 8.5 though RCP Conversely, the anticipated strengthening of wave conditions 8.5 shows values which are an order of magnitude lower than during rainy seasons corresponds to an increase in WPD for the values reported for the past time slice. In other words, most both mid- and end-century periods (Figures 6b-c). trend values in the mid-century are in the order of 10−4 compare to the 10−3 seen in the past. Additionally, this is in contrast to what was seen for 1979–2005, where dry season 4.3 Projected future change in WPD trends are higher. Also, the end century rainy season values are As shown in Figure 7, the average projected change in overall generally positive as can be seen in Figure 6c and Table 2. As WPD between the past and the future ranges between 0.2 and for the dry season, the reverse is the case with majority grid 0.5 kW/m with the largest change seen in the RCP 8.5 climate points showing negative trend values resulting in an overall change scenario. This same pattern is seen in the rainy season negative mean trend. The seemingly west to east decrease in though with higher magnitudes ranging from 0.5 to 1.0 kW/m. the trend as well as the previously reported lowest trend value During the dry season, the WPD is projected to reduce from region in the Niger delta-Cameroon axis was also captured by the past to the future in all future RCP scenarios. the future projections. The obvious absence of the asterisk The ANOVA equality of means test conducted and signifying the locations where statistically significant trend depicted in the boxplots in Figure 8 with tags ‘_a’ for overall was found by the Mann–Kendal test suggest that the trend in average, ‘_s’ for rainy and ‘_w’ for dry seasons, respectively. the future time slice cannot be adjudged to be significant at Figure 8 showed that the historical time slice has a WPD that 95% confidence level. However, these values are higher for the can be judged to be statistically significant from the future RCP end century time slice compared to the mid-century though projections in most cases. This is easily explainable from the less than the rates seen in the past. This suggests a reduction in absence of overlap between the past and the future especially the rates of change in WPD by mid-century which will pick up during the rainy season. Though the projection based on RCP towards the end of the 21st century again. 8.5 for the end-century time slice seems to be different from The trend analysis, similar to the spatiotemporal variations the other future scenarios, the others can be seen to have of the average WPD, demonstrates a comparable distribution overlaps in most cases. pattern to the wave climate. It is important to note that the applied linear regression method takes into account distinct Performance assessment of different WECs in the GoG periods: 1979–2005, 2026–2045, and 2081–2100. This In order to see the expected electricity output distribution, the approach ensures an independent assessment of trends for wave climate occurrences are presented as a binned joint dis- the historical, mid-century, and end-century eras respectively. tribution of Hs and Tm divided into cells of 0.5 m x 0.5 As a result, the outcomes remain representative of the s respectively in the case of Wave Dragon. However, the 14 A. M. DAHUNSI AND B. A. K. FOLI Table 3. Projected wave power output (MW) from 3 WECs for different RCP The annual and seasonal total power output projected for the scenarios. 3 WECs summarised in Table 3 showed that the magnitude varies Mid 4.5 Mid 8.5 End 4.5 End 8.5 widely based on deployed WECs. Wave Dragon showed highest Overall 105751.6 105459.3 105510.4 107557.8 expected output up to 178 GW during the rainy season for all the Wave Dragon Dry 71447.12 70922.83 70960.97 71065.18 locations in the GoG in the last 20 years of the 21st century. This is Rainy 166459.7 166975.4 170111 178122.6 an average of 14.2 MW per time for every grid cell if divided by Overall 1377.47 1373.8 1352.78 1419.2 the number of points (625) included in the study. Aqua Buoy Dry 897.77 891.13 891.18 902.24 In order to see the performance of the WECs from one point Rainy 2707.65 2741.13 2760.51 2980.53 to the other in the GoG, the power output was estimated for Overall 3999.6 3988.95 3928.33 4113.92 every grid point for the different WECs. This output was used to Pelamis Dry 2631.8 2612.35 2616.9 2640.44 estimate the normalised non-dimensional electric power from Rainy 7763.75 7860.48 7902.07 8473.26 one location to another to identify hotspots of power genera- tion. This was used to show region where the WEC is perform- ing better in generating power relative to other locations. The binning of the other two WECs were structured into 0.5 m × results showed higher values close to 1 offshore suggesting the 0.5 s to conform with the power matrix. The colour shading of best locations for most profitable exploitation of wave energy. each cell represents the total power output from that bin. For The capture widths for the different WECs showed that the a basin-wide and temporally representative overview, every Wave Dragon has values which are two orders of magnitude point where data exists in the GoG was taken into account higher than both of Aqua buoy and Pelamis (Figures 10a-c). for all the years. An overview of the contribution from differ- This is not surprising as similar result is shown by the power ent bins was assessed using the Wave Dragon (Figure 9a-c) and output which is a result of the power matrix and power ratings Aqua buoy (Figure 9d-f) systems for the past time slice. The of each system. The Wave Dragon also showed higher values overall result obtained for the period 1979–2005 showed that for the capacity factor (Figures 10d-f) with values ranging about 70% of the wave power generation in the GoG comes between 2 and 8 compared toothers 1–4. from wave heights of 1 m and 9 s. This corresponds to an average of 3363 and 57.9 MW per year if the total shown in Figure 9a,d are divided by 27 for Wave Dragon and Aqua 5. Discussion buoy, respectively. This same distribution is seen for the dry The wave conditions in the Gulf of Guinea, as outlined in the season with the attributed percentage of 80% though less results section, corroborate previous reports on the region’s power was generated during this period with an average of maritime environment. During the dry season, the sea experi- 2935.4 and 40.2 MW per year. The rainy season showed ences relatively calm conditions, a phenomenon attributed to a slight difference in percentage of contribution with a more the strengthening of trade winds from the northeast. This even distribution of the power output from different bins. seasonal pattern aligns with established climatic patterns in Figure 8. Boxplots showing differences in averages WPD for all time slices and seasons (black=past time slice, blue=future RCP 4.5 and red=future RCP 8.5). INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 15 Figure 9. Bivariate distributions of the total wave power output with the corresponding Hs and Tm the period 1979–2005 for wave Dragon (a) overall (b) dry (c) rainy and Aqua buoy (d) overall (e) dry (f) rainy. the area. Conversely, the rainy season is marked by the pre- (Almar et al. 2015; Bird 2008). It is noteworthy that the average valence of the Intertropical Convergence Zone (ITCZ), which wave height of 1.36 metres and wave period of 9.6 seconds, as can trigger increased storm activity and, consequently, higher reported by Almar et al. (2015), falls within the annual range of wave heights. This connection between the ITCZ and wave wave heights (0.34–1.60 m) and wave periods (5.86–10.23 s) height variations has been documented in previous research identified in this study. This consistency in findings 16 A. M. DAHUNSI AND B. A. K. FOLI Figure 10. Capture widths and capacity factors of the 3 WEC systems (a&d) Aqua buoy (b&e) Pelamis (c&f) wave Dragon. INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 17 underscores the reliability and alignment of the results with The analysis conducted in this study consistently reveals existing knowledge of wave conditions in the Gulf of Guinea. a trend of increasing Hs in the GoG across all RCP scenarios. The relatively low wave heights observed in the Niger This observed trend aligns with broader expectations of cli- Delta-Cameroon region of the Gulf of Guinea can be mate change impacts, where warmer ocean temperatures and attributed to a combination of factors, including the influ- altered atmospheric conditions are anticipated to result in ence of geographical features and wind patterns. These changes in wind patterns and ocean dynamics, consequently factors work together to create a subdued wave climate in influencing the generation and propagation of ocean waves this particular area. Firstly, the presence of offshore islands (De Leo, Besio, and Mentaschi 2021; Hemer et al. 2013). like Sao Tome and Principe plays a role in sheltering the The projected increase in Hs by the end of the century, region from the full force of incoming waves. These islands especially under the RCP 8.5 scenario with higher greenhouse act as barriers, causing waves to break and lose energy as gas emissions, is in line with findings from previous global stu- they approach the coastline. This dampening effect contri- dies. This scenario indicates the most significant increase in wave butes to the overall reduction in wave height. Secondly, the heights, reflecting the potential intensification of extreme weather wide continental shelf in this section of the GoG also plays events and storms associated with climate change. Notably, the a significant role. A wide continental shelf allows waves to higher increase in wave heights during the rainy season further propagate over a larger area, which can result in energy underscores the influence of enhanced atmospheric activity, such dissipation. As waves travel across this extensive shelf, they as storms, on wave conditions in the future. Additionally, the tend to lose some of their energy, leading to lower wave observation of a projected increase in Tm suggests that not only heights along the coast. will wave heights increase, but the waves will also exhibit longer Additionally, the orientation of the coastline relative to the intervals between crests (Dahunsi et al. 2022). This implies prevailing wave and wind directions is a crucial factor. Coastal a potential shift in the distribution of wave energy within the regions that align more closely with the prevailing winds, such GoG. These findings collectively highlight the dynamic and inter- as the northwesternmost part of the GoG, are more exposed to connected nature of climate change impacts on ocean waves, wind-driven wave generation. This exposure can lead to higher emphasising the need for continued research and monitoring in wave heights in these areas compared to regions with a more understanding and adapting to these changes. sheltered orientation (Dahunsi et al. 2022). In other words, the The magnitude of the average WPD in the GoG follows subdued wave heights observed in the Niger Delta-Cameroon a distribution pattern consistent with findings from previous section of the GoG are the result of a complex interplay of model dataset-based studies, particularly regarding the average geographical features, continental shelf characteristics, and and extreme wave climates, with a focus on Hs including Almar prevailing wind patterns. These factors collectively contribute et al. (2015 and Osinowo et al. (2018). This observed pattern to the unique wave climate experienced in this part of the Gulf. indicates a trend of increasing WPD from north to south and The relative north-south decreasing pattern in Tm observed from east to west within the GoG, mirroring the spatial distribu- in the GoG can be attributed to the influence of distant swell tion reported for Hs in the preceding section. The north-south systems originating from both the North Atlantic and the and east-west increase in WPD can be attributed to the complex South Atlantic oceans. These swell systems carry waves with interplay of various factors, including swell systems, local wind varying periods as they propagate across the open ocean regimes, and bathymetry. This distribution pattern aligns with the (Forristall et al. 2013). This influence is particularly pro- regional wave climate overview presented by Dahunsi et al. (2022), nounced during the dry season when the North Atlantic which is not surprising given the direct proportionality between swell system dominates the wave climate. The northwestern- WPD and Hs, as evident in the equation used for WPD computa- most part of the GoG is more exposed to the effects of the tion. A correlation test conducted to assess the strength of this North Atlantic swell, which pushes waves with longer periods relationship yielded a high correlation coefficient of 0.98, indicat- closer to the easternmost coast of the Gulf. This leads to ing a strong association between WPD and Hs. Consequently, a noticeable north-south gradient in wave periods, with longer changes in wave height are expected to correspond to similar wave periods in the north and shorter periods in the south. changes in WPD, a relationship supported by a similar study The east-west variation in wave period within the GoG along the southeastern coast of the United States conducted by is primarily shaped by the presence of coastal features Defne et al. (2009). Furthermore, the observation of consistent such as headlands, bays, and inlets. These coastal features overlaps in WPD values for different scenarios, as previously can cause wave refraction, diffraction, and reflection, reported for the wave climate of the GoG by Dahunsi et al. resulting in variations in wave period along the coast. (2022), prompted the conduct of statistical tests to assess signifi- However, it’s important to note that the relationship cant differences in this previous study. Ultimately, their findings between coastal features and wave period is not always concluded that these differences were not statistically significant, straightforward, as the influence of other factors, like the further reinforcing the interconnected nature of wave height and South Atlantic swell, can sometimes overshadow the WPD within the GoG and the robustness of the observed patterns. impacts of coastal morphology. Therefore, the complex Furthermore, the transition from lower WPD values during interplay of distant swell systems, prevailing wind pat- the dry season to higher values in the rainy season mirrors the terns, and coastal features contributes to the spatial and seasonal variability observed in wave energy, akin to the pat- temporal variations in wave period observed in the GoG. terns in the wave climate. During the rainy season, more These factors collectively shape the unique wave climate vigorous weather conditions prevail, characterised by elevated of the region. wind speeds and extended fetch (the distance over which the 18 A. M. DAHUNSI AND B. A. K. FOLI Figure 11. Spatial distribution of the normalised non-dimensional power output from wave Dragon. INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 19 wind influences wave formation). These conditions result in estimate, let’s assume a parallel orientation of wave energy con- greater energy transfer to the waves, leading to the observed verters along half of the coastlines of the countries surrounding increase in WPD. This alignment underscores the strong con- the Gulf of Guinea. This would result in an average of 1500 nection between wave energy and atmospheric conditions, kilometres of potential sites for energy harvesting. Based on this highlighting the pivotal role of seasonal weather patterns in average coverage, the estimated potential for wave power genera- influencing wave power dynamics. It is worth noting that this tion during the low-energy dry season is approximately 8190 consistent trend of seasonal variations in WPD persists across MW, calculated using the minimum WPD value of 5.46 kW/m various time slices and RCP scenarios, albeit with varying obtained for the dry season (as presented in Table 1). However, magnitudes. This consistency underscores the robustness of with the utilisation of the appropriate type of wave energy con- the findings and demonstrates the predictability of wave power verter, this potential could soar to as high as 14,940 MW, taking dynamics under different climate scenarios. into account the projected WPD value of 9.96 kW/m for the rainy The negative trend in WPD identified for the mid-century season by the end of the century. period aligns with findings reported by other studies in different However, in order to instal WECs for effective exploitation regions. Ribeiro et al. (2020), for instance, observed a similar of wave energy in the GoG, several key performance indicators reduction in wave power in their assessment of wave energy (KPIs) that play pivotal roles in the feasibility and potential converters in the North Atlantic. Likewise, Karunarathna et al. benefits of the project should be considered. These KPIs are (2020) documented a 12–20% decrease in wave power along the needed to determine the post-installation economic metrics coasts of Sri Lanka by mid-century, attributing it to changes in the such as LCOE, NPV, IRR etc previously highlighted in the tropical monsoon system driven by global climate change. In the introductory section. Judging from the previously presented GoG, Almar et al. (2015) associated alterations in the region’s wave characteristics of the GoG including the dry and rainy wave climate, particularly the dominance of swell waves, with the seasons variabilities and the emphasis on relatively highly impacts of climate change on the extratropical Southern Annular available WPD offshore, important KPIs include power output Mode (SAM). This mode influences storm conditions in the which helps to determine the total energy output generated by southern Atlantic extra-tropical storm track, which contributes the WEC over the course of a year taking into account the to the swell waves experienced in the GoG. Furthermore, global diverse wave conditions experienced during both dry and studies by Lobeto et al. (2021) and Meucci et al. (2020) have also rainy seasons. Another critical KPI is Energy Conversion reported a reduction in wave power during the mid-century, Efficiency, which quantifies how effectively the WEC har- followed by an increasing trend towards the end of the century nesses the kinetic energy of waves and converts it into usable for the tropical Atlantic Ocean, where the GoG is located. Given electricity. This efficiency is a fundamental indicator of the the high correlation between Hs and WPD, it is reasonable to WEC’s technological prowess and its ability to make the most expect similar impacts of climate change on both parameters in of the available wave resources. The Capacity Factor further the GoG. refines the assessment by considering the proportion of actual The estimated values for the Gulf of Guinea (GoG) region energy output to the maximum energy generation potential during the past time slice align with findings from similar studies under ideal conditions. Reflecting both fluctuations in wave in the area. For instance, Tulashie et al (2022) reported WPD energy and operational downtime for maintenance or other values ranging from 7.5 to 10.5 kW/m for the Ghanaian coast, reasons, the Capacity Factor speaks to the practicality of WEC which closely resembles the values exceeding 7.5 kW/m observed deployment. In tandem with these indicators, understanding along the western coast of the GoG. As one moves eastward the Power Capture Width becomes essential. This parameter towards the Niger Delta, Osinowo (2019) affirmed the presence delineates the spectrum of wave frequencies that the WEC can of very low WPD, generally less than 4 kW/m, in this part of the efficiently capture. Given the variation in wave characteristics GoG. These studies also corroborated the seasonal variability in between seasons, this adaptability becomes crucial in ensuring wave conditions and WPD, with higher Hs during the rainy consistent energy production. season leading to increased WPD magnitude. According to Wei The results for the three WECs considered in this study Zheng and Pan (2014), wave energy is considered available when indicate that the Wave Dragon stands out with higher power the WPD exceeds 2 kW/m and rich when it surpasses 20 kW/m. output and a larger capture width, which is advantageous for Based on this criterion, it can be confidently stated that the GoG a vast region like the GoG. These findings align with similar boasts abundantly available wave energy resources throughout cases reported for other regions, reinforcing the effectiveness the year, even during the low-energy dry season. of the Wave Dragon design. For instance, studies conducted The spatial distribution of wave power potential in the Gulf of on European islands, as assessed by Rusu and Onea (2016), Guinea, as previously illustrated in Figures 4a-c, emphasises the and on the island of Fuerteventura, as investigated by Veigas need for an offshore-focused approach in harnessing this abun- and Iglesias (2014), have yielded comparable results. These dant renewable energy resource. Such a strategy would enhance findings demonstrate the suitability of the Wave Dragon the economic feasibility of exploitation and mitigate the impacts WEC for harnessing wave energy efficiently in various geogra- of seasonal variations in wave power within the region. phical contexts, making it a promising choice for the Gulf of Additionally, it would expand the available sites for the installa- Guinea region. The consistency of these results across different tion of wave energy converters. The Gulf of Guinea encompasses regions highlights the robust performance and adaptability of a vast area, approximately 2,350,000 square kilometres, including the Wave Dragon WEC, further emphasising its potential as small islands, offshore oil rigs, and marine protected areas, as a viable solution for renewable energy generation in coastal documented by Osinowo et al. (2018). To provide a conservative areas with varying wave conditions. 20 A. M. DAHUNSI AND B. A. K. FOLI As previously discussed, and corroborated by both the bivari- Gulf of Guinea. The assessment has clearly indicated that offshore ate distribution of wave power in the GoG and the normalised locations are the most promising for wave energy converter power output depicted in Figure 11, it becomes evident that the installations, as wave power potential diminishes significantly offshore regions characterised by wave heights around 1 m and closer to the coastline, making nearshore exploitation economic- periods of 9 s represent the most reliable and efficient locations for ally unviable. Additionally, given the ecological significance of the the installation of any of the selected WECs. This spatial distribu- Guinea Current Large Marine Ecosystem in the region, it becomes tion of optimal wave energy exploitation remains consistent for all imperative to minimise the environmental impact of energy three WECs considered in this study, despite variations in wave installations in coastal upwelling areas. output magnitude. Seasonal variations also exhibit a similar pat- Furthermore, among the wave energy converter systems con- tern, with increased efficiency closer to the coast during the rainy sidered, the Wave Dragon has demonstrated superior perfor- season. However, most locations demonstrate an efficiency rate mance across all key indicators, making it a compelling exceeding 50%, with the exception of the Cameroon axis, known candidate for deployment in the Gulf of Guinea to maximise the for its lower wave energy levels throughout the year. The efficiency utilisation of the region’s abundant wave energy resources. This values observed in this study fall within the range of 0.86 to 0.96, research paves the way for informed decision-making and sustain- a range previously reported by Rusu and Onea (2016). These able energy development in the Gulf of Guinea, contributing to findings emphasise the reliability and consistent performance of both energy security and environmental conservation efforts. the selected WECs in effectively harnessing wave energy across Furthermore, this study has laid a crucial foundation for various geographical areas within the GoG, making them promis- exploring additional potential benefits beyond electricity gen- ing options for renewable energy generation in this region. eration, particularly in the realm of coastal erosion mitigation. In addition to the significant benefits of power generation and Coastal erosion represents one of the most pressing challenges other socio-economic advantages that can be derived from the faced by many countries situated along the GoG. The capacity installation of WEC systems in the GoG, there is also the potential of WECs to mitigate coastal erosion, as previously investigated to address the pressing issue of coastal erosion faced by all the by Veigas and Iglesias (2014), offers a multifaceted approach countries within the GoG region. This additional benefit has been that can deliver significant economic advantages to GoG highlighted in the work of Veigas and Iglesias (2014), who con- nations. Traditional methods of coastal protection, such as ducted modelling studies to assess the impact of WECs on the seawalls and groynes, often come at the cost of altering natural nearshore wave climate. Their findings revealed that the presence beach landscapes, thereby diminishing their appeal as tourist of WECs led to a reduction in wave height in the regions where destinations. In contrast, leveraging WECs to dampen wave these systems were installed. This reduction in wave height sub- energy while preserving the integrity of beaches holds sub- sequently resulted in a decrease in the energy reaching the near- stantial promise. This approach not only contributes to elec- shore areas, leading to lower rates of coastal erosion. This tricity generation but also safeguards the income-generating discovery underscores the potential for WEC installations not potential of these coastal regions. Importantly, it offers only to provide clean and sustainable energy but also to contribute a sustainable solution that remains effective in the face of positively to the mitigation of coastal erosion, offering a holistic anticipated increases in wave intensity driven by climate solution to some of the environmental challenges faced by the change. To delve deeper into these prospects, future studies GoG countries. are expected to employ dynamic modelling approaches, encompassing wave simulation and behaviour analysis under 6. Conclusion various WEC setup scenarios. By adopting such comprehen- sive methodologies, we can further unlock the synergistic In conclusion, this comprehensive study has provided valuable potential of wave energy utilisation for both sustainable energy insights into the past and potential future wave power condi- generation and coastal protection in the Gulf of Guinea. tions within the GoG. The spatial distribution of average WPD has revealed consistent trends in WPD patterns across various time frames and climate scenarios. The study has shed light on Disclosure statement the seasonal variations and the intricate relationship between WPD, Hs, and atmospheric conditions, highlighting the com- No potential conflict of interest was reported by the author(s). plex interplay of oceanic and atmospheric factors that influ- ence wave energy potential in the region. These findings offer critical information for understanding the feasibility of harnes- Funding sing wave energy in the Gulf of Guinea and for formulating This research received no external funding. sustainable energy strategies tailored to the region’s unique characteristics. The projections presented here will serve as fundamental data for future initiatives involving the installa- Notes on contributors tion of wave energy converters, helping to identify the most economically and environmentally viable locations for these Adeola Michael Dahunsi is a physical oceanographer with a special energy harvesting systems. interest in coastal hydrodynamics especially its evolution under climate Notably, this study underscores that ocean wave energy holds change. His research interest covers the study of both living and non- living resources in the coastal and marine environment. This interest is significant promise as a source of electric power generation to inspired by the desire to contribute to the data-driven sustainable man- meet the increasing energy demands of the countries within the agement of these resources in the Gulf of Guinea (GoG) region. INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 21 Therefore, he continues to work on research increasing data availability in Shore-Based Video and Unmanned Aerial Vehicles (Drones): the data-scarce region of the GoG. Complementary Tools for Beach Studies.” Remote Sensing 12 (3): Bennet Atsu Kwame Foli, an accomplished Oceanographer and Earth 394. https://doi.org/10.3390/rs12030394. Observation Analyst, boasts over a decade of professional expertise in Arnaud, K. K., F. Bonou, Z. Sohou, D. B. Angnuureng, and R. Almar. Marine and Fisheries Sciences, with a focus on remote sensing and 2021. “Evaluation of Hydromorphological Conditions of Grand Popo geospatial data analysis. Holding advanced degrees, including a Ph.D. in Beach Using Two Unique Video Cameras.” Interpretation 9 (4): SH57– Marine Science, Bennet excels in ocean wave analysis, modelling, and SH66. https://doi.org/10.1190/INT-2021-0023.1. early warning system development. His dedication to the field is evident Babarit, A., J. Hals, M. J. Muliawan, A. Kurniawan, T. Moan, and in his contribution to international projects and mentorship programs, J. Krokstad. 2012. “Numerical Benchmarking Study of a Selection of highlighting his strong publication record and commitment to training Wave Energy Converters.” Renewable Energy 41:44–63. https://doi. and capacity development. During his tenure as an Oceanographer at the org/10.1016/j.renene.2011.10.002. University of Ghana, Bennet played a vital role in enhancing coastal Bird, E. C. F. 2008. Coastal Geomorphology: An Introduction. New York, vulnerability and ocean state early-warning services. He actively engages USA: John Wiley & Sons. stakeholders, showcasing strong communication skills and a proactive Booij, N., L. Holthuijsen, and J. Battjes. 2001. “Ocean to Near-Shore Wave approach to problem-solving. Bennet's expertise was pivotal in securing Modelling with SWAN.” Coastal Dynamics’ 1:335–344. https://doi.org/ funding for the GMES & Africa phase-2 project, highlighting his grant 10.1061/40566(260)34 . proposal writing and project execution abilities. Bennet's technical skills Bricheno, L., H. Cannaby, T. Howard, K. McInnes, M. Palmer, include GIS and satellite data application, MATLAB programming, and A. K. M. Saiful Islam, and A. Haque. 2015. “Extreme Sea Level the use of Unmanned Aerial Vehicles (UAVs) for environmental mon- Projections.” Environmental Science Processes & Impacts 17 (7): itoring. Beyond his professional pursuits, he finds joy in photography and 1311–1322. https://doi.org/10.1039/c4em00683f. DIY electronics. Overall, Bennet stands as a dedicated and innovative Cabral, T., D. Clemente, P. Rosa-Santos, F. Taveira-Pinto, T. Morais, professional in the field of Oceanography and Earth observation, con- F. Belga, and H. Cestaro. 2020. “Performance Assessment of tributing significantly to environmental research and sustainability. a Hybrid Wave Energy Converter Integrated into a Harbor Breakwater.” Energies 13 (1): 236. https://doi.org/10.3390/en13010236. Chen, T., Q. Zhang, Y. Wu, C. Ji, J. Yang, and G. Liu. 2018. “Development Data availability statement of a Wave-Current Model Through Coupling of FVCOM and SWAN.” Ocean Engineering 164:443–454. https://doi.org/10.1016/j.oceaneng. The Coordinated Ocean Wave Climate Project phase 2.0 (COWCLIP2.0) 2018.06.062. wave climate database used in this study is publicly available at https:// Ciappi, L., I. Simonetti, A. Bianchini, L. Cappietti, and G. Manfrida. 2022. cowclip.org/data-access/ (Accessed: 20 July 2022). “Application of Integrated Wave-To-Wire Modelling for the Preliminary Design of Oscillating Water Column Systems for Installations in Moderate Wave Climates.” Renewable Energy Author contributions 194:232–248. https://doi.org/10.1016/j.renene.2022.05.015. Coe, R. G., G. Lavidas, G. Bacelli, P. H. Kobos, and V. S. Neary. 2022. A.M.D.: conceptualisation; data curation; methodology; validation; visua- “Minimizing Cost in a 100% Renewable Electricity Grid: A Case Study lisation; writing – original draft; writing – review and editing. B.A.K.: of Wave Energy in California.” International Conference on Offshore methodology; validation; writing – reviewing and editing. Mechanics and Arctic Engineering Hamburg, Germany 85932: V008T09A073. Cuttler, M. V. W., J. E. Hansen, and R. J. Lowe. 2020. “Seasonal and References Interannual Variability of the Wave Climate at a Wave Energy Hotspot off the Southwestern Coast of Australia.” Renewable Energy Abe, J., and B. E. Brown. 2020. “Towards a Guinea Current Large 146:2337–2350. https://doi.org/10.1016/j.renene.2019.08.058. Marine Ecosystem Commission.” Environmental Development Dahunsi, A. M., F. F. Bonou, O. A. Dada, and E. Baloïtcha. 2022. “Spatio- 36:100590. https://doi.org/10.1016/j.envdev.2020.100590. Temporal Trend of Past and Future Extreme Wave Climates in the Adesanya, A., S. Misra, R. Maskeliunas, and R. Damasevicius. 2020. Gulf of Guinea Driven by Climate Change.” Journal of Marine Science “Prospects of Ocean-Based Renewable Energy for West Africa’s and Engineering 10 (11): 1581. https://doi.org/10.3390/jmse10111581. Sustainable Energy Future.” Smart & Sustainable Built Environment Dalton, G. J., R. Alcorn, and T. Lewis. 2010. “Case Study Feasibility 10 (1): 37–50. https://doi.org/10.1108/SASBE-05-2019-0066. Analysis of the Pelamis Wave Energy Convertor in Ireland, Portugal Almar, R., E. Kestenare, J. Reyns, J. Jouanno, E. J. Anthony, R. Laibi, and North America.” Renewable Energy 35 (2): 443–455. https://doi. M. Hemer, Y. Du Penhoat, and R. Ranasinghe. 2015. “Response of the org/10.1016/j.renene.2009.07.003. Bight of Benin (Gulf of Guinea, West Africa) Coastline to Defne, Z., K. A. Haas, and H. M. Fritz. 2009. “Wave Power Potential Anthropogenic and Natural Forcing, Part1: Wave Climate Variability Along the Atlantic Coast of the Southeastern USA.” Renewable Energy and Impacts on the Longshore Sediment Transport.” Continental Shelf 34 (10): 2197–2205. https://doi.org/10.1016/j.renene.2009.02.019. Research 110:48–59. https://doi.org/10.1016/j.csr.2015.09.020. Alves, B., D. B. Angnuureng, P. Morand, and R. Almar. 2020. De Leo, F., G. Besio, and L. Mentaschi. 2021. “Trends and Variability of “A Review on Coastal Erosion and Flooding Risks and Best Ocean Waves Under RCP8.5 Emission Scenario in the Mediterranean Management Practices in West Africa: What Has Been Done and Sea.” Ocean Dynamics 71 (1): 97–117. https://doi.org/10.1007/s10236- Should Be Done.” Journal of Coastal Conservation 24 (3): 38. 020-01419-8. https://doi.org/10.1007/s11852-020-00755-7. Foli, B. A. K., K. Appeaning Addo, J. K. Ansong, and G. Wiafe. 2022. Alves, J.-H. G. M., A. Chawla, H. L. Tolman, D. Schwab, G. Lang, and “Ocean State Projections: A Review of the West African Marine G. Mann. 2014. “The Operational Implementation of a Great Lakes Environment.” Journal of Coastal Conservation 26 (6): 1–14. https:// Wave Forecasting System at NOAA/NCEP.” Weather and Forecasting doi.org/10.1007/s11852-022-00908-w. 29 (6): 1473–1497. https://doi.org/10.1175/WAF-D-12-00049.1. Forristall, G. Z., K. Ewans, M. Olagnon, and M. Prevosto. 2013. “The West Angnuureng, D. B., R. Almar, K. Appeaning Addo, B. Castelle, Africa Swell Project (WASP).” Proceedings of the International N. Senechal, S. W. Laryea, and G. Wiafe. 2016. “Video Conference on Offshore Mechanics and Arctic Engineering - OMAE 2 Oberservation of Waves and Shoreline Change on the Microtidal B (June). https://doi.org/10.1115/OMAE2013-11264. James Town Beach in Ghana.” Journal of Coastal Research 75 (sp1): Grigorieva, V. G., S. K. Gulev, and A. V. Gavrikov. 2017. “Global 1022–1026. https://doi.org/10.2112/SI75-205.1. Historical Archive of Wind Waves Based on Voluntary Observing Angnuureng, D. B., P.-N. Jayson-Quashigah, R. Almar, T. C. Stieglitz, Ship Data.” Oceanology 57 (2): 229–231. https://doi.org/10.1134/ E. J. Anthony, D. W. Aheto, and K. A. Addo. 2020. “Application of S0001437017020060. 22 A. M. DAHUNSI AND B. A. K. FOLI Guillou, N., and G. Chapalain. 2018. “Annual and Seasonal Power Generation 13 (3): 363–375. https://doi.org/10.1049/iet-rpg. Variabilities in the Performances of Wave Energy Converters.” 2018.5456. Energy 165:812–823. https://doi.org/10.1016/j.energy.2018.10.001. Osinowo, A. A. 2019. “Combined Exploitation of Onshore Wind-Wave Guillou, N., G. Lavidas, and G. Chapalain. 2020. “Wave Energy Resource Energy in the Niger Delta Coasts Based on a 37-Year Hindcast Assessment for Exploitation—A Review.” Journal of Marine Science Information.” Energy Management Research Journal 2 (1): 19–38. and Engineering 8 (9): 705. https://doi.org/10.3390/jmse8090705. Osinowo, A. A., I. A. Balogun, and E. O. Eresanya. 2018. “Assessment of Hasselmann, S., K. Hasselmann, E. Bauer, P. Janssen, G. J. Komen, Wave Energy Resource in the Mid-Atlantic Based on 37-Year L. Bertolli, P. Lionello, A. Guillaume, V. J. Cardone, and Numerical Hindcast Data.” Modeling Earth Systems and Environment J. A. Greenwood. 1988. “The WAM Model—A Third Generation 4 (3): 935–959. https://doi.org/10.1007/s40808-018-0484-3. Ocean Wave Prediction Model.” The WAM Model—A Third Osinowo, A. A., E. C. Okogbue, E. O. Eresanya, and O. S. Akande. 2018. Generation Ocean Wave Prediction Model Journal of physical oceano- “Extreme Significant Wave Height Climate in the Gulf of Guinea.” graphy 18 (12): 1775–1810. https://doi.org/10.1175/1520-0485(1988) African Journal of Marine Science 40 (4): 407–421. https://doi.org/10. 018<1775:TWMTGO>2.0.CO;2. 2989/1814232X.2018.1542343. Hemer, M. A., Y. Fan, N. Mori, A. Semedo, and X. L. Wang. 2013. Ribeiro, A. S., M. deCastro, L. Rusu, M. Bernardino, J. M. Dias, and “Projected Changes in Wave Climate from a Multi-Model M. Gomez-Gesteira. 2020. “Evaluating the Future Efficiency of Wave Ensemble.” Nature Climate Change 3 (5): 471–476. https://doi.org/10. Energy Converters Along the NW Coast of the Iberian Peninsula.” 1038/nclimate1791. Energies 13 (14): 3563. https://doi.org/10.3390/en13143563. Hemer, M. A., and C. E. Trenham. 2016. “Evaluation of a CMIP5 Derived Rusu, E., and F. Onea. 2016. “Estimation of the Wave Energy Dynamical Global Wind Wave Climate Model Ensemble.” Ocean Conversion Efficiency in the Atlantic Ocean Close to the Modelling 103:190–203. https://doi.org/10.1016/j.ocemod.2015.10.009. European Islands.” Renewable Energy 85:687–703. https://doi.org/ Henriques, J. C. C., J. C. C. Portillo, W. Sheng, L. M. C. Gato, and 10.1016/j.renene.2015.07.042. A. F. D. O. Falcão. 2019. “Dynamics and Control of Air Turbines in Sawin, J. L., F. Sverrisson, K. Seyboth, R. Adib, H. E. Murdock, C. Lins, Oscillating-Water-Column Wave Energy Converters: Analyses and Case I. Edwards, M. Hullin, L. H. Nguyen, and S. S. Prillianto. 2016. Study.” Renewable and Sustainable Energy Reviews 112:571–589. https:// Renewables 2017 Global Status Report. doi.org/10.1016/j.rser.2019.05.010. Simal, P. D., S. T. Ortega, B. Bas, N. Elginoz, R. G. Garcia, F. Del Hughes, M. G., and A. D. Heap. 2010. “National-Scale Wave Energy Jesus, E. Giannakis, A. Giannouli, P. Koundouri, and Resource Assessment for Australia.” Renewable Energy 35 (8): A. Moussoulides. 2017. “Socio-Economic Assessment of 1783–1791. https://doi.org/10.1016/j.renene.2009.11.001. a Selected Multi-Use Offshore Site in the Atlantic.” The Ocean Kamranzad, B., A. Etemad-Shahidi, and V. Chegini. 2013. of Tomorrow: Investment Assessment of Multi-Use Offshore “Assessment of Wave Energy Variation in the Persian Gulf.” Platforms: Methodology and Applications 1 (56) :69–84. https:// Ocean Engineering 70:72–80. https://doi.org/10.1016/j.oceaneng. doi.org/10.1007/978-3-319-55772-4_5 2013.05.027. Swail, V., R. Jensen, B. Lee, J. Turton, J. Thomas, S. Gulev, Karunarathna, H., P. Maduwantha, B. Kamranzad, H. Rathnasooriya, and M. Yelland, P. Etala, D. Meldrum, and W. Birkemeier. 2010. K. De Silva. 2020. “Impacts of Global Climate Change on the Future “Wave Measurements, Needs and Developments for the Next Ocean Wave Power Potential: A Case Study from the Indian Ocean.” Decade.” Proceedings of The OceanObs 9 (2): 10. https://doi.org/ Energies 13 (11): 3028. https://doi.org/10.3390/en13113028. 10.5270/OceanObs09.cwp.87 . Lehmann, M., F. Karimpour, C. A. Goudey, P. T. Jacobson, and M.- Taylor, K. E., R. J. Stouffer, and G. A. Meehl. 2012. “An Overview of CMIP5 and the Experiment Design.” Bulletin of the American R. Alam. 2017. “Ocean Wave Energy in the United States: Current Meteorological Society 93 (4): 485–498. https://doi.org/10.1175/ Status and Future Perspectives.” Renewable and Sustainable Energy BAMS-D-11-00094.1. Reviews 74:1300–1313. https://doi.org/10.1016/j.rser.2016.11.101. Tolman, H. L. 1991. “A Third-Generation Model for Wind Waves on Liang, B., F. Fan, Z. Yin, H. Shi, and D. Lee. 2013. “Numerical Modelling Slowly Varying, Unsteady, and Inhomogeneous Depths and Currents.” of the Nearshore Wave Energy Resources of Shandong Peninsula, Journal of Physical Oceanography 21 (6): 782–797. https://doi.org/10. China.” Renewable Energy 57:330–338. https://doi.org/10.1016/j. 1175/1520-0485(1991)021<0782:ATGMFW>2.0.CO;2. renene.2013.01.052. Tulashie, S. K., R. Odai, A. M. Dahunsi, S. Atisey, and J. Amenakpor. Lobeto, H., M. Menendez, and I. J. Losada. 2021. “Future Behavior of 2022. “Feasibility Study of Wave Power in Ghana.” International Wind Wave Extremes Due to Climate Change.” Scientific Reports Journal of Sustainable Engineering 15 (1): 299–311. https://doi.org/10. 11 (1): 1–12. https://doi.org/10.1038/s41598-021-86524-4. 1080/19397038.2022.2145384. Meucci, A., I. R. Young, M. Hemer, E. Kirezci, and R. Ranasinghe. 2020. Veigas, M., and G. Iglesias. 2014. “Potentials of a Hybrid Offshore Farm “Projected 21st Century Changes in Extreme Wind-Wave Events.” for the Island of Fuerteventura.” Energy Conversion and Management Science Advances 6 (24): 1–10. https://doi.org/10.1126/sciadv.aaz7295. 86:300–308. https://doi.org/10.1016/j.enconman.2014.05.032. Mirzaei, A., F. Tangang, and L. Juneng. 2014. “Wave Energy Potential Vettor, R., and C. Guedes Soares. 2019. “Comparison of VOS and Along the East Coast of Peninsular Malaysia.” Energy 68:722–734. ERA-Interim Wave Data.” Proceedings of the International https://doi.org/10.1016/j.energy.2014.02.005. Conference on Offshore Mechanics and Arctic Engineering - OMAE, Moretti, G., M. S. Herran, D. Forehand, M. Alves, H. Jeffrey, R. Vertechy, and New York, NY: USA 3, V003T02A045. https://doi.org/10.1115/ M. Fontana. 2020. “Advances in the Development of Dielectric Elastomer OMAE2019-95287 Generators for Wave Energy Conversion.” Renewable and Sustainable Wan, Y., J. Zhang, J. Meng, and J. Wang. 2015. “A Wave Energy Resource Energy Reviews 117:109430. https://doi.org/10.1016/j.rser.2019.109430. Assessment in the China’s Seas Based on Multi-Satellite Merged Radar Morim, J., M. Hemer, N. Cartwright, D. Strauss, and F. Andutta. 2018. Altimeter Data.” Acta Oceanologica Sinica 34 (3): 115–124. https://doi. “On the Concordance of 21st Century Wind-Wave Climate org/10.1007/s13131-015-0627-6. Projections.” Global and Planetary Change 167:160–171. https://doi. Wei Zheng, C., and J. Pan. 2014. “Assessment of the Global Ocean Wind org/10.1016/j.gloplacha.2018.05.005. Energy Resource.” Renewable and Sustainable Energy Reviews Morim, J., C. Trenham, M. Hemer, X. L. Wang, N. Mori, M. Casas-Prat, 33:382–391. https://doi.org/10.1016/j.rser.2014.01.065. A. Semedo, T. Shimura, B. Timmermans, and P. Camus. 2020. Young, I. R., S. Zieger, and A. V. Babanin. 2011. “Global Trends in Wind “A Global Ensemble of Ocean Wave Climate Projections from Speed and Wave Height.” Science 332 (6028): 451–455. https://doi.org/ CMIP5-Driven Models.” Scientific Data 7 (1): 1–10. https://doi.org/ 10.1126/science.1197219. 10.1038/s41597-020-0446-2. Yusov, M., J. Thwaites, A. Kurniawan, J. Orszaghova, and H. Wolgamot. 2021. Mwasilu, F., and J. W. Jung. 2019. “Potential for Power Generation from “New Cost-Effectiveness Metric for Wave Energy Converters-Extensive Ocean Wave Renewable Energy Source: A Comprehensive Review on Database and Comparison.” Proceedings of the 14th European Wave and State-Of-The-Art Technology and Future Prospects.” IET Renewable Tidal Energy Conference (EWTEC) Plymouth, UK.