Title: A review of wave energy technology from a research and commercial perspective
Abstract: IET Renewable Power GenerationVolume 15, Issue 14 p. 3065-3090 REVIEWOpen Access A review of wave energy technology from a research and commercial perspective Bingyong Guo, Corresponding Author Bingyong Guo [email protected] orcid.org/0000-0003-3134-0043 School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China Centre for Ocean Energy Research, Maynooth University, Maynooth, Co. Kildare, Ireland Correspondence Bingyong Guo, School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China. Email: [email protected] for more papers by this authorJohn V. Ringwood, John V. Ringwood orcid.org/0000-0003-0395-7943 Centre for Ocean Energy Research, Maynooth University, Maynooth, Co. Kildare, IrelandSearch for more papers by this author Bingyong Guo, Corresponding Author Bingyong Guo [email protected] orcid.org/0000-0003-3134-0043 School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China Centre for Ocean Energy Research, Maynooth University, Maynooth, Co. Kildare, Ireland Correspondence Bingyong Guo, School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China. Email: [email protected] for more papers by this authorJohn V. Ringwood, John V. Ringwood orcid.org/0000-0003-0395-7943 Centre for Ocean Energy Research, Maynooth University, Maynooth, Co. Kildare, IrelandSearch for more papers by this author First published: 05 October 2021 https://doi.org/10.1049/rpg2.12302AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Although wave energy prototypes have been proposed for more than 100 years, they have still not reached full commercialisation. The reasons for this are varied, but include the diversity of device operating principles, the variety of onshore/nearshore/offshore deployment possibilities, the diversity of the wave climate at various potential wave energy sites, and the consequent lack of convergence in technology and consensus. This distributed effort has, in turn, lead to a slow rate of progression up the learning curve, with a significant number of wave energy company liquidations and technical setbacks dampening investor confidence. Although a number of reviews on wave energy technology are already in the published literature, such a dynamic environment merits an up-to-date analysis and this review examines the wave energy landscape from a technological, research and commercial perspective. 1 INTRODUCTION 'Carbon neutrality by 2050' is the world's most urgent mission, and António Guterres, the United Nations Secretary General, stressed on 11 December 2020 [1] that "By next month, countries representing more than 65 per cent of harmful greenhouse gasses and more than 70 per cent of the world economy will have committed to achieve net zero emissions by the middle of the century." To date, more than 110 countries have pledged to reach zero carbon emission by 2050. On the other hand, current energy demand mainly depends on fossil fuels, and is projected to rise by 1% per year until 2040 [2]. In the tension between global energy demand and carbon reduction promises, there exists a widening gap between rhetoric and action [3], and a significant transformation in the energy sector, with extra technical and non-technical efforts, is required to achieve carbon neutrality. Among various renewable energy resources, wave energy shows great potential in bridging the gap between the rhetoric of carbon reduction and the increasing energy demand, being a relatively untapped resource, with the global wave resource in the range 1–10 TW. However, the exact global estimate of extractable wave power is debatable [4]. The theoretical estimate of global wave power is about 32,000 TWh/year (with a mean power of 3.65 TW) [4]. In terms of the usable wave power resource, excluding areas with wave power level < 5 kW/m, the global estimate is around 3 TW [5], while the mean wave power experienced by global oceanic coastlines is about 2.11 TW [6]. The assessment method and data in [5] are used by the Ocean Energy Systems (OES) and the International Renewable Energy Agency, with an estimate of 29,500 TWh/year [7, 8], which exceeds global electricity consumption in 2018, around 22,315 TWh with two-thirds mix from fossil fuels [9]. Together with other renewable resources, wave energy can play an import role in satisfying both the requirements of carbon emission reduction, and energy supply increase. Thus, OES member countries plan to achieve over 300 GW of installed wave/tidal capacity, create 680,000 direct jobs and save 500 Mt of carbon emission by 2050 [7]. Compared with other renewable resources, especially solar and wind power, the advantages of wave power are multiple: (i) Wave power is characterised by a high-energy density, over 10 times that of wind and solar power [10]. (ii) Wave power has a high availability, up to 90%, while the availability of wind and solar is generally in the range 20–30% [11]. (iii) Wave energy technology has little impact on the environment [12, 13]. (iv) Wave energy output can also be integrated with existing wind or solar power plants as a complementary resource for smoothing power output and reducing variability [14-19]. (v) Wave power is more predictable [20, 21], giving more flexibility for regional or national power management, and planning. Despite the enormous potential of wave power, currently active wave capacity is as small as 2.31 MW [8, 22], and these operating wave energy projects are focused on research and demonstration. Currently, wave energy technology is at its 'infant' age, and there is no fully commercial scale wave energy converter (WEC) farm in operation, even though hundreds of WECs have been developed [23]. Crucially, there still exist several technical and non-technical challenges: (i) Technically, it is difficult to generate electricity from low-frequency (0.1 Hz, i.e., low velocity) oscillating motion and large force (1 MN). This requires extremely reliable structures and power take-off (PTO) systems and, consequently, high capital expenditure (CapEx). (ii) WECs operate in an offshore environment, with high installation, operation and maintenance costs. Thus, the operating expenditure (OpEx) is relatively large. (iii) The wave power resource varies on both a wave-by-wave, hour-by-hour, and site-by-site manner, in terms of wave frequency, height, direction, spectrum and power level, resulting in disparate WEC concepts without any convergence, diluting the efforts of research and development (R&D) and commercialisation. (iv) Extreme sea conditions occur from time to time, and the possibility of structural failure and device loss is relatively high. This adds extra risk for the finance sector to invest in WEC technology. (v) Currently, WEC technology is characterised by low maturity, high uncertainty and risk, and requires significant initial capital, which further discourages private investors. That is, diminishing private and public investments has been playing the most important recent role in advancing WEC technology by stimulating R&D activities. In general, current WEC technologies or devices have not yet demonstrated their capability to harness enough wave energy at a low enough cost at commercial scale. Based on simplistic estimates of the levelised cost of energy (LCoE), some early stage WEC concepts, for example, the M4 device [24, 25], have showed their possibility to achieve a low LCoE for some specific installation sites. Further, geometric optimisation can improve WEC's hydrodynamic performance, in terms of power capture in moderate waves and survivability in extreme waves. On the other hand, sophisticated control approaches can significantly improve power capture, while marginally increasing the CapEx and, hence, dramatically reduce the LCoE [26]. However, WEC hydrodynamics and control are inherently and non-linearly coupled [27, 28], and a co-design approach is needed. Current R&D activities mainly focus on wave resource assessment, WEC concept developing, hydrodynamic modelling, PTO innovation and control design. As shown in Figure 1(a), the topics in the inner ring are well studied, and plenty of reviews have summarised the state-of-the-art of wave resource assessment [16, 29-31], WEC technology [11, 32-38], modelling [38-47], PTO [11, 36, 48-50], and control [51-55]. The R&D topics in the middle and outer rings in Figure 1(a) are not fully understood yet. There are a few surveys summarising WEC survivability [56], performance [57], economic characteristics [58, 59], mooring [60] and shape optimisation [61, 62]. However, only a few studies aim to investigate critical development factors, as shown in Figure 1(c) and (d), for successful commercialisation of WEC technology at each phase [63-66]. FIGURE 1Open in figure viewerPowerPoint Possible pathway from WEC R&D activities to commercial deployment, with (a) R&D foci, (b) industry-academia-government collaboration, (b) commercialisation phases, and (d) critical development factors In contrast to the aforementioned reviews, this review aims to discuss potential pathway of WEC commercialisation, from lab to market, by (i) summarising R&D activities in wave resource assessment, PTO innovation, WEC modelling and control, (ii) reviewing ongoing pre-commercial WEC demonstration projects, (iii) identifying potential market opportunities, including the utility market for electricity and niche markets related to ocean applications, and (iv) discussing industry-academia-government (IAG) collaboration to improve some critical development factors for bridging the valley of death (VoD) between R&D and commercialisation activities. WEC technology commercialisation relies not only on technical readiness level (TRL), but also on technical performance level (TPL), which attempts to measure the potential economic performance of a wave energy device/project, and some external development factors, for example, investment environment, market data and national incentives. As the LCoE of WEC technology is still too high to compete with other renewable energy technologies, revenue and capital support from public sectors remains crucial [8]. Thus, public or government-related sectors play an important role in bringing together researchers and investors through support programs, market incentives, and regional policy and legislation, to form a solid IAG collaboration, as shown in Figure 1(b). The reminder of the paper is organised as follows: Section 2 summarises the basic foundations of ocean waves and wave resource assessment, while Section 3 investigates various WEC concepts, classification, and modelling methods. Section 4 summarises the development of PTO systems and control strategies, with Section 5 examining possible development trajectories of a WEC prototype or project. Section 6 discusses historical and commercial efforts devoted to wave energy technology. Section 7 summaries potential market opportunists for WEC technology, while Section 8 identifies some key factors and incentives for commercialising WEC technology. Finally, some concluding remarks and future perspectives are drawn in Section 9. 2 QUANTIFYING THE WAVE ENERGY RESOURCE In ocean observation, the wave height H and period T can be directly measured. A simple illustration of wave propagating from deep water to shallow water is given in Figure 2. In Figure 2, h and λ are the water depth and wavelength, respectively. The shallow and deep waters are defined by h ≤ λ 20 and h ≥ λ 2 , respectively. As water depth decreases, the shallow water effect reshapes the wave profile, which may result in non-linear waves, wave breaking and energy loss [10, 67]. However, WECs normally operate in moderate sea states with H ≪ λ . Thus, linear wave theory is normally valid and is applied in this section, with an overview of regular and irregular waves introduced with specific foci on quantifying the wave resource, its variability, and predictability. FIGURE 2Open in figure viewerPowerPoint Wave from deep water to shallow water 2.1 Regular waves and wave power Swell waves, characterised by narrow bandwidth and relatively low frequency, are of primary interest for wave energy harvesting, and can be approximated by regular waves. In addition, calculations involving regular waves are relatively simple and can be used as a starting point for WEC R&D activities, particularly at low TRLs. For a regular (monochromatic) wave, the free surface elevation η, in Figure 2, can be written as η ( x , t ) = H 2 cos ( ω t − k x + φ ) , (1)where ω = 2 π T , k = 2 π λ and φ are the wave frequency, wave number and initial wave phase, respectively. The wavelength can be determined by λ = g T 2 2 π tanh ( 2 π h λ ) , (2)where g is the gravity constant. Alternatively, Equation (2) is generally rewritten as the dispersion relation, given as ω 2 = g k tanh ( k h ) . The group velocity of wave propagation in Figure 2 is expressed as c g = λ 2 T 1 + 2 k h sinh ( 2 k h ) . (3) The potential and kinetic wave energy per unit horizontal area are E p = E k = ρ g 2 η 2 ( x , t ) ¯ . (4)For regular waves, η 2 ( x , t ) ¯ = H 2 8 holds. Thus, total wave energy per horizontal area is given as E = E p + E k = ρ g H 2 8 . (5)Hence, the wave power per unit width of the wave front, J r , also called wave-energy transport [68] or wave power level (used hereafter), is given as J r = c g E = ρ g H 2 8 g T 4 π tanh ( k h ) 1 + 2 k h sinh ( 2 k h ) , (6)Based on the deep-water assumption, the wave power level can be further simplified as J r ≈ ρ g 2 32 π H 2 T . 2.2 Irregular waves and wave power In general, real waves are random and irregular, and can be approximated by the superposition of a group of sinusoidal waves as η ( x , t ) = ∑ i = 1 N H i 2 cos ( ω i t − k i x + φ i ) , (7)where H i , ω i , k i and φ i are the wave height, frequency, wave number and initial phase of the i th sinusoidal wave of N components, respectively. Based on linear wave theory, Equations (2)–(4) still hold. Thus, total wave energy power per horizontal area for irregular waves can be written as E = ρ g η 2 ( x , t ) ¯ = ρ g ∫ 0 ∞ S ( ω ) d ω , (8)where S ( ω ) is the wave energy spectrum. Based on time-domain ocean observations, the wave spectrum can be estimated via FFT or spectrum estimation methods. A variety of wave spectral models have been discussed in [67, 69], based on their application scenarios, pros and cons, of which the notable ones are the Pierson–Moskowitz (PM) [70] and joint North Sea wave project spectral models [71]. Here, the PM spectrum, generally used to describe fully developed wind waves, is taken as an example, given as S ( ω ) = 5 H s 2 16 ω p 4 ω 5 exp − 5 ω p 4 4 ω 4 , (9)where H s , ω p = 2 π T p and T p are the significant wave height, peak frequency and peak period, respectively. For a given wave spectrum, the significant wave height and energy period, T e , can be estimated from spectral moments as H s = 4 m 0 , (10) T e = m − 1 m 0 , (11)where m i is the i th moment of the spectrum, given as m i = ∫ 0 ∞ ω i S ( ω ) d ω . (12)Thus, the wave power level for irregular waves can be written as J = ρ g 2 64 π T e H s 2 . (13) 2.3 The global wave power resource and spatial variability To estimate the wave resource over a large area, numerical wave models are generally used, of which the notable ones are the wave model, wavewatch 3, simulating waves nearshore, MIKE21-SW and TOMAWAC models, with their limitations and application scenarios discussed in [30, 31]. To achieve accurate wave resource assessment, observed wave data, at a set of discrete spatial points, are used to calibrate the models. Although the exact global estimate of extractable wave power is debatable, depending on assessment method, wave model, and temporal and spatial resolution, a small set of studies conclude that the applicable wave power in the world is about 3 TW by excluding areas of J < 5 kW/m [5, 7, 8]. Considering the area with 30 nautical miles to the coastline, extractable wave power decreases to 2.11 TW [6], and decreases further to 1.85 TW (approximating 16,000 TWh/year), when wave direction and coastline alignment are considered [4]. The wave power resource is evenly distributed between the Southern and Northern Hemispheres, as shown in Figure 3(a), but is concentrated within 30–60 degrees of latitude. Thus, latitude is one main factor affecting the spatial variability of the wave power resource. One typical example is the wave power resource along the Chilean coast, as shown in Figure 3(b), with the wave power level increasing from 20 to 100 kW/m, as the latitude increases from 15 ∘ S to 55 ∘ S. It also shows that water depth has some influence on the wave power level. As waves propagate to the coastline, the shallow water effect causes energy loss. Consequently, spatial variability has a significant influence on WEC performance [72, 74, 75]. As shown in Figure 3(c), the capacity factor of 3 WECs increases, as the latitude and wave power level increase. When wave power level is low, WECs should be scaled down accordingly to improve their performance [72, 75]. FIGURE 3Open in figure viewerPowerPoint (a) Global wave resource distribution [6], (b) spatial variability along the Chilean coast, data from [72], (c) influence of wave power level variation and water depth on WEC capacity factor, data from [72], (d) monthly variability in the Northern and Southern Hemispheres, data from [6], and (e) seasonal and annual variability at the Atlantic Marine Energy Test Site, data from [73] 2.4 Temporal variability and predictability of wave power Wave power is characterised by significant temporal variability, ranging from seconds to decades. Such high temporal variability is one reason for the diversity of WEC concepts, and points to a required focus on WEC optimisation, PTO, control, survivability, power prediction, and management. Temporal variability can be classified into short-, medium- and long-term variations. Short-term variation is characterised by irregularity in height, period and direction, varying from seconds to minutes. As the WEC control problem is typically non-causal, short-term prediction of wave elevation or excitation force is required, and prediction requirements for real-time control are investigated in [76]. Several prediction methods are discussed in [76-83], including the AR, ARMA, NARX and Bayesian learning methods. In addition, short-term variation results in a highly varying instantaneous wave power and a high peak-to-average power ratio, and extra design effort is required to smooth WEC harvested power, for example, PTO systems with accumulators/flywheels, to smooth high-frequency power variation. Medium-term variation is represented by a change in wave spectrum or sea states, on an hourly or/and daily basis. Such variation may challenge the power management system of WEC farms, and accurate wave prediction over 1–72 h is required for power planning [16], and WEC installation and maintenance [83]. Compared with other renewable resources, wave power has an advantage in predictability [20, 21, 84], and the significant wave height can be accurately predicted in advance by a couple of days [16, 83, 85]. In addition, forming WEC arrays, or integrating WECs with wind turbines, can smooth power output to overcome medium-term variation [14]. Long-term variation concerns intra-annual and inter-annual variability of the wave power resource [4, 86-88]. Intra-annual variability includes monthly and seasonal variability, while the inter-annual variability refers to wave power variation over decades. In general, wave power is high in winter and low in summer, as shown in Figure 3(d) and (e). Inter-annual variability has a significant influence on lifetime performance of WEC farms and, thus, should be considered when determining deployment sites and design capacity ratings [87, 89, 90]. For instance, the wave power on the west coast of Ireland has seen a significant increase in the 20 th century, which shows a power surplus of 15% within the lifespan of a point absorber (PA) or an oscillating wave surge converter (OWSC) [87, 91]. However, extreme events also increase, requiring more focus on WEC survivability [87]. In addition, increase in off-limit events ( H s ≥ 5 m ) can significantly reduce the capture width ratio of an OWSC in the Irish sea, up to a level of 20% [92]. Thus, long-term trends of wave climate should be considered for commercial planning [29, 73]. However, long-term variability can, in general, be only hindcasted rather than forecasted [83]. Statistical methods are generally used to quantify the temporal variability of wave resource. One simple measure is the coefficient of variation (CoV) [93], given as C o V ( J ) = J ( t ) − J ( t ) ¯ 2 J ( t ) ¯ , (14)where t is the time interval used for computing the wave power level J, ranging from 30 min to decades. Thus, the CoV can be used to describe medium- and long-term variability. Based on the C o V definition in Equation (14), a new world map for wave power with long-term variability is studied in [94], and shown in Figure 4. For various selected sites, the average value of ( C o V , J) is marked by the red dot, which also divides Figure 4 into four regions. The sites in the green region (top left corner) are ideal for WEC installation, as the wave power level is high and the CoV is low. FIGURE 4Open in figure viewerPowerPoint The average wave power level and the coefficient of variation for various typical sites, data from [94]. In this figure, the mean value for these sites is marked by the red dot; 'S', 'N', 'W', 'C' represent 'south', 'north', 'west' and 'central', respectively Intra-annual variability can be quantified by the monthly variation index (MVI) and seasonal variation index (SVI) [93], while inter-annual variability is represented by the annual variation index (AVI) [86], as M V I = J M , max − J M , min J Y , (15) S V I = J S , max − J S , min J Y , (16) A V I = J Y , max − J Y , min J Y ¯ , (17)where the suffixes M, S, and Y represent month, season and year, respectively, while the suffixes max and min represent maximum and minimum values, respectively. These measures are generally adapted to quantify temporal variability of wave power resource globally, regionally, and/or locally [4, 14, 16, 20, 29, 73, 86]. 2.5 Influence of wave climate on commercialisation For commercialising WEC technology, the first step is to select a deployment site, mainly according to annual mean wave power level and temporal variability. Sites with a high wave power level but low variability are preferred, and WECs should be selected accordingly. Variation in wave climate is a strong cost driver in both CapEx and OpEx [94-96]. Short- and medium-term variability can be handled by real-time control and power management, along with wave climate prediction. However, long-term variability is difficult to forecast but can significantly affect the lifetime performance of WEC farms. In addition, extreme wave conditions, characterised by maximum wave height and storm occurrence, have significant influence on accessibility and availability of wave power, and survivability of WEC devices. More R&D activities are needed to improve WEC survivability in extreme waves. To ease the installation and operation of WEC farms, other key factors should be considered, including water depth, distance to the coast, wind and tidal climate, existing infrastructure, and environmental and spatial constraints [89, 97]. 3 WAVE ENERGY TECHNOLOGY PRINCIPLES This section gives an overview of working principles for various WEC concepts and their classification, followed by an overview of hydrodynamic modelling of WECs, based on these principles. 3.1 Wave energy conversion concepts and classification A WEC device converts the kinetic and/or potential energy contained in moving waves to useful energy (mainly electricity), comprising a set of floating or submerged bodies, a PTO unit, a control system, power electronics and other accessories. Since wave energy conversion concepts diverge, with over 1000 devices reported [10], there is no unique categorisation method to cover all possible WEC systems. In general, WECs can be classified according to their deployment locations, working principles, operation modes and device geometries [32, 34, 35, 38]. In this study, the classification method detailed in [35] is adapted and shown in Figure 5. In Figure 5, WECs are classified into three types, including oscillating water columns (OWCs), wave activated bodies and overtopping devices. For each type, the exemplified prototypes are pre-commercial and have been tested in the open ocean. FIGURE 5Open in figure viewerPowerPoint Classification of WEC devices, inspired by [35] An OWC utilises a hollow structure with an open inlet below the still water level to trap air in its chamber above the inner free-surface; wave action alternately compresses and decompresses the trapped air, which forces air to flow through a turbine coupled to a generator [36], principally using the kinetic wave energy. As listed in Figure 5, OWCs can be further catalogued into two subclasses: (i) fixed OWCs, for example, the Pico and LIMPET devices, and (ii) floating OWCs, for example, Masuda's navigation buoy, and the Spar-buoy OWC. A comprehensive review, with a specific focus on OWCs and their PTO systems, is summarised in [36]. Overtopping devices are exemplified by fixed prototypes such as the TAPCHAN and OBREC devices, or the floating Wave Dragon (WD) device. Overtopping WECs mainly use potential wave energy, with electricity generated via somewhat conventional unidirectional (low head) hydro-turbines. Most R&D activities focus on wave-activated WEC concepts, which can make use of the potential or/and kinetic wave energy to generate electricity [11, 35, 36, 62]. Wave-activated WECs can be further classified as (i) floating or submerged subclasses, according to wave-WEC interaction, (ii) rotating or translating subtypes according to the essential degrees of freedom (DoFs) exploited, or (iii) PAs, attenuators and terminators according to WEC geometry, with respect to wavelength and propagating direction. PAs refer to WEC devices whose characteristic dimensions are much smaller than the incident wavelength. PAs may operate in heave, pitch or multiple DoFs, and can be situated nearshore or offshore. Attenuators are floating WEC devices, oriented parallel to the wave direction, usually composed of multiple floating bodies connected by hinged joints, with relative motion between two connected bodies used to generate electricity via PTO systems. Terminators are oriented perpendicular to the wave direction, typically including duck-like devices, or OWSCs. Some typical wave-activated type WECs, at pre-commercial scales, are listed in Figure 5. 3.2 Hydrodynamic modelling Mathematical modelling of WEC dynamics gives a foundation for WEC R&D activities, and an accurate mathematical model can be used for evaluating WEC performance, developing control strategies and optimising system design according to a specific wave climate. In the literature, a considerable amount of R&D activities have been devoted to WEC hydrodynamics, summarised in [38, 40, 41, 44-47, 98]. One main challenge of WEC modelling is how to represent the wave-structure interaction (WSI) in a proper way. Typically, the motion of a WEC structure is governed by Newton's 2 nd Law, as M ξ ̈ ( t ) = f h ( t ) + f g ( t ) + f pto ( t ) + f ext ( t ) , (18)where M is the inertial matrix and ξ is the displacement of the WEC. f h , f g , f pto and f ext are the hydrodynamic, gravity, PTO (or control), and external forces, respectively. There is no unique representation of f ext , but it may contain mooring and other potentially non-linear forces, for example, end-stop force [99-101]. Hydrodynamic fluid/body force can be computed as the integral of the pressure p on the wetted surface S, given as f h = − ∫ ∫ S p n d S , (19)where n is the normal vector on the wetted surface. Thus, the key of hydrodynamic modelling is to compute the pressure p in the fluid. Fluid dynamics is governed by the Navier–Stokes equations (NSEs), which cannot be solved analytically, and computational fluid dynamics (CFD) methods are generally applied to obtain a numerical solution. By assuming an ideal fluid (incompressible, inviscid and irrotational), the NSEs can be simplified to the Laplace