The ocean contains a variety of renewable energy resources, little of which has been exploited. Here, we review both tidal range and tidal stream energy, with a focus on the resource, feedbacks, and environmental interactions. The review covers a wide range of timescales of relevance to tidal energy, from fortnightly (spring-neap) and semi-diurnal variability, down to array, and device-scale turbulence. When simulating the regional tidal energy resource, and to assess environmental impacts, it is necessary to account for feedbacks between the tidal array and the resource itself. We critically review various methods for simulating energy extraction, from insights gained through theoretical studies of “tidal fences” in idealized channels, to realistic three-dimensional model studies with complex geometry and arrays of turbines represented by momentum sinks and additional turbulence due to the presence of rotors and support structures. We discuss how variability can be reduced by developing multiple (aggregated) sites with a consideration of the enhanced phase diversity offered by exploiting less energetic tidal currents. This leads to future research questions that have not yet been explored in depth at first-generation tidal sites in relatively sheltered channels (e.g., the interaction of waves with currents). Such enhanced understanding of real sea conditions, including the effects of wind and waves, leads to our other identified primary future research direction—reduced uncertainties in turbulence predictions, including the development of realistic models that simulate the interaction between ambient turbulence and the turbulence resulting from multiple wakes, and changes to system-wide hydrodynamics, water quality, and sedimentation.

Investment in emerging renewable energy technologies is essential if the global energy sector is to transition from fossil-based toward zero-carbon by the second half of this century, limiting the impacts of climate change.1 Many of these emerging technologies are based on a resource that surrounds us—the ocean. Although there are many forms of ocean energy conversion, including wave energy,2 Ocean Thermal Energy Conversion (OTEC),3 and ocean currents,4 one ocean energy resource has the dual advantage of high predictability and excellent opportunities for grid connectivity—tidal energy—the focus of this review.

Tides are predictable because of their origin in (astronomical) tide generating forces. These manifest as a number of tidal constituents, the dominant of which are the semi-diurnal lunar (M2) and solar (S2) constituents, with periods of 12.42 and 12 h, respectively (Table I). The combination of M2 and S2 leads to the fortnightly spring-neap cycle, with enhanced tidal range (spring tide) when the Earth–Moon–Sun system is in-line (either New Moon or Full Moon) and reduced tidal range (neap tide) when the Earth–Moon–Sun system is perpendicular (either First Quarter or Third Quarter Moon). It can therefore be seen that there is variability of the tides (and hence the potential of the tides for electricity generation) at both semi-diurnal and fortnightly time scales (Fig. 1). In addition to M2 and S2, other important tidal constituents that significantly affect the temporal variability of the tides are the lunar (O1) and lunisolar (K1) diurnal constituents, and the larger lunar elliptic semi-diurnal constituent (N2) (Table I). At longer timescales, the tidal resource is also influenced by the effect of the 18.6-year lunar cycle, which is mainly driven by the changes of the inclination of the Moon's orbital path relative to the plane of the Earth's equator.5 

TABLE I.

The main diurnal and semi-diurnal tidal constituents. The amplitude of each tidal constituent relative to M2 is calculated using mean amplitudes extracted from TPXO9 global tidal atlas152 for water depths less than 200 m (representative of shelf sea regions), between latitudes 66.5 °S and 66.5 °N.

SymbolNameSpeed (°/h)Period (h)Amplitude relative to M2
O1 Lunar diurnal 13.943 25.819 0.26 
K1 Lunisolar diurnal 15.041 23.935 0.37 
N2 Larger lunar elliptic semi-diurnal 28.44 12.658 0.21 
M2 Principal lunar semi-diurnal 28.984 12.421 
S2 Principal solar semi-diurnal 30 12 0.35 
SymbolNameSpeed (°/h)Period (h)Amplitude relative to M2
O1 Lunar diurnal 13.943 25.819 0.26 
K1 Lunisolar diurnal 15.041 23.935 0.37 
N2 Larger lunar elliptic semi-diurnal 28.44 12.658 0.21 
M2 Principal lunar semi-diurnal 28.984 12.421 
S2 Principal solar semi-diurnal 30 12 0.35 
FIG. 1.

Elevation time series based on four tidal constituents (M2, S2, K1, and O1) showing a typical spring-neap (fortnightly) cycle, in addition to semi-diurnal and diurnal variability. Reproduced with permission from Neill and Hashemi, Fundamentals of Ocean Renewable Energy: Generating Electricity from the Sea (Academic Press, 2018). Copyright 2018 Academic Press.

FIG. 1.

Elevation time series based on four tidal constituents (M2, S2, K1, and O1) showing a typical spring-neap (fortnightly) cycle, in addition to semi-diurnal and diurnal variability. Reproduced with permission from Neill and Hashemi, Fundamentals of Ocean Renewable Energy: Generating Electricity from the Sea (Academic Press, 2018). Copyright 2018 Academic Press.

Close modal

Global tidal dissipation is around 2.4 TW, with the majority of this, 1.7 TW, occurring in shelf sea environments.6 This represents an upper theoretical bound for tidal power, but due to interaction between tidal energy extraction and the resource (e.g., Ref. 7), in addition to technical and practical constraints, the available resource is likely to be considerably less. To put this in perspective, annual mean global electricity consumption is around 3 TW,8 and so even if 10% of the shelf sea resource was exploited, that is, 170 GW, tidal energy could have a substantial contribution to the global energy mix. Further, in some regions such as the UK, tidal dissipation is around 200 GW, whereas mean demand for electricity is currently around 40 GW; therefore in some areas, the potential contribution of the tides to the electricity mix significantly outweighs the global mean.

There are two main ways of converting the energy of the tides into electricity—tidal range power plants and tidal stream turbines. Tidal range power plants (Sec. II) rely on the potential energy of the tides. Water is impounded behind an artificial embankment and released through turbines when there is sufficient head difference between the artificial water level inside the barrage and the natural tidal level outside of the barrage. In contrast, tidal stream turbines (Sec. III) rely on the kinetic energy of the tides. In regions where the tidal streams are of sufficient magnitude, the flow is intercepted by an in-stream tidal turbine, which spins a generator and produces electricity. Although tidal stream turbines can be installed individually, it is only when they are deployed in arrays (i.e., like a wind array) that they can reach their full potential.

In this review, we consider these two forms of tidal energy conversion—tidal range energy and tidal stream energy—discussing the resource, projects, and research questions. We also discuss turbulence and wakes, and the environmental consequences of extracting tidal energy, particularly at significant scale, from the oceans. Finally, we identify future research areas that need to be addressed before the tidal energy resource can be exploited to its full potential.

Converting tidal range into other useful forms of energy has a history that extends back to the tide mills of the 6th Century.9 However, converting the potential energy of the tides into electricity began with the construction of La Rance power station (France) in 1966.10 La Rance comprises a 720-m-long barrage and impounds an area of approximately 22 km2 in a region that has a mean tidal range of around 8 m. The barrage houses 24 Kaplan bulb turbines, which provide a combined rated power output of 240 MW. Four other tidal range power plants have been constructed (Table II), but it is notable that no scheme has been constructed in the last 25 years, setting aside the complication that the embankment for the 254 MW Lake Sihwa barrage was constructed in 1994, and the power plant in 2011. Further, all of the existing tidal range power plants are of the barrage type; that is, they span the full width of an estuary or channel. It is generally considered that the next generation of tidal range power plants will be lagoons, which only partially span an estuary, since these are associated with lower capital cost and reduced environmental impacts.11 

TABLE II.

Characteristics of existing and proposed tidal range schemes (data from Neill and Robins156).

Power plantYearCapacity (MW)Basin area (km2)Operation mode
La Rance, France 1966 240 22 Two-way with pumping 
Kislaya Guba, Russia 1968 1.7 Two-way 
Annapolis Royal Generating Station, Canada 1984 20 Ebb only 
Jiangxia, China 1985 3.9 Two-way 
Lake Sihwa, Korea 1994a 254 30 Flood only 
Swansea Bay tidal lagoon, UKb Proposed 320 11.5 Two-way 
Power plantYearCapacity (MW)Basin area (km2)Operation mode
La Rance, France 1966 240 22 Two-way with pumping 
Kislaya Guba, Russia 1968 1.7 Two-way 
Annapolis Royal Generating Station, Canada 1984 20 Ebb only 
Jiangxia, China 1985 3.9 Two-way 
Lake Sihwa, Korea 1994a 254 30 Flood only 
Swansea Bay tidal lagoon, UKb Proposed 320 11.5 Two-way 
a

1994 is the date of the construction of the sea wall for flood mitigation, but the actual power station was constructed in 2011.

b

The Swansea Bay tidal lagoon is in italics as it is a proposed scheme.

A tidal range power plant is based on a number of components, the most important of which is the embankment. The embankment, which constitutes the majority of the capital cost of the power plant, is a barrier that is used to impound an area of sea, the filling or release of which is controlled by diverting the flow either through sluice gates (e.g., to transfer water into or out of the enclosed basin) or through turbines (i.e., to generate electricity). There are two main modes of operation of a tidal range power plant, and a third operation mode that is a combination of the other two. For ebb generation, the flooding tide enters the enclosed basin through sluice gates and idling turbines. Once the maximum level inside the lagoon is achieved, these gates are closed, until a sufficient head develops on the ebbing tide. Power is subsequently generated by the turbines and generators until a predetermined minimum head difference, when turbines are no longer operating efficiently. For flood generation, the process is reversed to produce electricity during the flood phase of the tidal cycle. Two-way generation is a combination of the two and can be used to reduce variability in the resulting power time series. Two-way generation can also be supplemented by pumping to further reduce variability.12 

Tidal waves propagate at a phase speed c=gh, where g is gravitational acceleration and h is water depth. Therefore, tidal waves travel much slower in shelf sea environments than they would in the deep ocean. For example, the mean depth of the world's oceans is 4000 m, and a tidal wave would travel at 200 m/s. In a shelf sea with water depth of 100 m, the tidal wave would propagate at 31 m/s. When a wave propagates along a channel that is relatively long compared to the tidal wavelength, at a distance of 1/4 of a wavelength from the head of the bay or estuary the crest of the incoming wave passes at the same time as the trough of the reflected wave (and vice versa). The two waves (incident and reflected) cancel at this point, known as a node, and so there is no tide at this location. Since the incoming and reflected waves combine, the tidal amplitude at the head of the bay is doubled in such a standing wave system.

Tidal resonance occurs when the length of a channel coincides with the quarter wavelength. In such a case, the tidal range at the entrance to the seaway is amplified at the head of the estuary. The most famous case of tidal resonance is the Bay of Fundy on the east coast of Canada, which has the highest tidal range in the world (16-m spring tidal range). With a mean depth of 66 m, the tidal wave propagates at a mean speed of 25 m/s along the channel. For quarter wavelength resonance with the semi-diurnal tide (T = 12.42 h—Table I), this represents a length of 280 km, which is very close to the actual length of the Bay of Fundy (290 km), that is, the channel is in near resonance with the semi-diurnal tide. Since tidal power is related to the basin area and the square of tidal range, these regions of high tidal range are sought for the placement of large tidal power plants; for example, the mean tidal range at La Rance is 8.2 m, and 5.6 m at Lake Sihwa.

Since the tidal range resource is a function of tidal range squared, the resource is discretely distributed across shelf sea regions (Fig. 2), with Australia alone containing 30% of the global resource, and Canada (Fundy) containing 23% due to its near resonance semi-diurnal tides. The UK and France each have an equal share of around 13% of the global tidal range resource,11 with much of this focused in the Severn Estuary (UK) and the Gulf of St. Malo (France). Since we know the speed of propagation of the tidal wave (a function of water depth), we know the timing of the tides at any single location, but also the relationship between the phase of the tides between multiple locations. It is therefore possible to stagger a series of tidal range power plants along a coastline to reduce variability in the aggregated power output.13 Tidal range power has more potential for this “tidal phasing” compared to tidal stream power (Sec. III), where the majority of energetic sites are in phase with one another.14 

FIG. 2.

Global distribution of the tidal range resource, calculated using five tidal constituents (M2, S2, N2, K1, and O1) from the TPXO9-v2 dataset, at a resolution of 1/30°×1/30°. Reproduced with permission from Neill et al., Renewable Energy 170, 683–692 (2021). Copyright 2021 Author(s), licensed under a Creative Commons Attribution (CC BY) license.

FIG. 2.

Global distribution of the tidal range resource, calculated using five tidal constituents (M2, S2, N2, K1, and O1) from the TPXO9-v2 dataset, at a resolution of 1/30°×1/30°. Reproduced with permission from Neill et al., Renewable Energy 170, 683–692 (2021). Copyright 2021 Author(s), licensed under a Creative Commons Attribution (CC BY) license.

Close modal

As stated earlier, all of the existing tidal range power plants are barrages (Table II), but a newer concept is that of a tidal lagoon, where only part of an estuary is blocked off to create an artificial basin. A much studied location for a tidal lagoon is Swansea Bay in the Bristol Channel, UK,15 where the spring tidal range is 10.5 m. This site has also been the subject of a UK Government commissioned review, known as the Hendry Review, which investigated the role of tidal lagoons in the future UK energy mix.16 The proposed Swansea Bay tidal lagoon would comprise a 9.5 km embankment,17 enclosing an area of 11.5 km2. Using 16×7.2 m-diameter turbines, installed in 60 m draft tubes, the power plant would have a nameplate capacity of 320 MW. Using dual (flood/ebb) mode, the project is estimated to have a capacity factor of 19%. The turbine design is triple regulated,18 that is, adjustable guide vanes, blade pitch angle, and variable speed—the latter potentially reducing harm to fish. The Hendry Review supported the development of the Swansea Bay tidal lagoon, known as a “pathfinder” project; however, the review further recommended that the pathfinder project should be operational for a reasonable period of time before construction of any further (and potentially larger) tidal lagoon projects. This is to allow the full range of environmental impacts to be monitored over time. One important point about tidal range schemes, in comparison with other energy projects, is the lifetime of the embankment, which is generally estimated to exceed 100 years, and this could be accounted for when estimating the levelized cost of energy (LCoE).

Little research has been conducted into how climate change, particularly changes in mean sea level, will affect the tidal range resource. This is particularly relevant, since the embankment of a tidal range power plant is estimated to have a lifetime that is highly likely to witness significant changes in the climate. For example, an increase in sea level in a tidal basin will lead to an increase in the speed of propagation of the tidal wave, altering the resonance characteristics. A model study of the Bay of Fundy shows that a 1 m rise in mean sea level would lead to an increase in approximately 0.1 m in tidal amplitude due to the basin approaching true resonance.19 This may seem like a small change, but since the tidal range resource is related to the tidal range squared, it could lead to a significant change in the resource. Global tidal modeling under a more extreme scenario of 2 m sea-level rise leads to a complex picture of regional changes in tidal amplitude, with no consistent pattern emerging of increase or decrease in tidal amplitude over shelf sea regions.20 Further, such model studies, which examine future changes in tidal dynamics due to sea-level rise, should do so concurrently with changes in developed energy infrastructure.21 On non-tidal influences to the tidal range resource, it has been demonstrated that storm surges will not significantly impact the resource compared to that estimated by astronomical constituents alone.22 

Tidal streams are regions with concentrated tidal flows, posing an energy resource with significant potential. The kinetic energy may be extracted from these tidal streams using hydrokinetic turbines, analogous to wind energy. Similar to offshore wind, turbines are typically deployed in arrays, increasing system redundancy, and resilience. The vast majority of tidal turbines use rotors to convert the kinetic energy of the flow into mechanical energy, either with the horizontal axis parallel to the flow or with the axis perpendicular to the flow. However, there are examples of turbines using other means such as oscillating hydrofoils and underwater kites.23 Regardless of the specific type of technology being used, all projects require resource assessments to first determine project feasibility, then to design the turbine array layout, and finally to compute the project annual energy production (AEP). The International Electrotechnical Commission (IEC) has published a technical specification for performing tidal stream energy resource assessments for projects at both feasibility and design stages.24 

Starting with small-scale resource assessments, the theoretical power available for an individual turbine is computed as a cube of the free stream velocity

Pt=12ρV3,
(1)

where ρ is the density of the fluid and V the flow speed. Accounting for the efficiency of a particular turbine, this equation can be modified by multiplying by the power coefficient Cp,

Pd=12CpρV3,
(2)

where Pd is the electrical power density. Sometimes Cp is referred to as the performance coefficient and represents the shaft power rather than the electrical power. An upper bound (16/27, or 59.3%) for turbines in an unconstrained flow was derived using actuator disk theory and is formally referred to as the Lanchester–Betz–Joukowsky limit, but is most frequently called the Betz limit.25–27 This analysis was performed for wind turbines; however, it is generally accepted that it also applies to tidal energy, providing the turbine swept area is small relative to the channel depth and width.28 

The actual power output depends on the properties of the turbine. In particular, the power for the turbine (P) is found by multiplying the power density by the swept area of the turbine, As,

P=12CpρV3As.
(3)

The power coefficient can be a function of the velocity with variable efficiencies, including a minimum cut in speed below which the turbine does not operate and a rated speed at which the turbine does not generate more power for higher flow velocities. The IEC has a technical specification outlining the methodology for empirically determining the power coefficient as a function of the incoming current speed.29 Finally, the total energy converted by the turbine is found by integrating the power over the desired period of time, most commonly a year, to get the AEP. Optimal efficiencies of tidal power converters are generally lower than the Betz limit and are reported to be typically between 1650%.30,31 However, for many tidal streams, the Betz limit is irrelevant and may be exceeded under conditions with constrained flow32 or when using turbines with ducted intakes.33 

On a much broader scale, high-level reconnaissance studies are implemented at country or regional scales to identify regions with significant tidal energy resources by evaluating existing data or using numerical simulations of large regions. One example of a reconnaissance study is the geodatabase of tidal constituents developed using the Regional Ocean Modeling System (ROMS), which presents the regional assessment of tidal stream power resource and identifies locations with high kinetic power density in the USA.34 

Feasibility studies are used to compute a preliminary estimate of the upper bound for the potential available power for a project. These are generally done using simulations with depth-averaged 2D models that reasonably reproduce the general characteristics of the tidal flows. However, estimating the power for larger projects with the current velocities from model simulations without accounting for the effect of energy extraction can be erroneous. Vennell et al.35 suggested that the any project with a total swept area larger than 25% of the cross-sectional area may be considered large, requiring consideration of the effect of the turbines on the flow field. As illustrated in Fig. 3, starting with a small array with a few turbines, the inclusion of additional turbines will reduce the residual available kinetic power density, and each individual turbine will produce less overall power. However, the power produced by the array will still be increased. The rate of the increase in total power will be reduced as more turbines are included, reaching a threshold where adding further turbines will actually reduce the total power output from the full array, thereby leading to a maximum possible power that may be extracted for a given channel configuration.

FIG. 3.

Illustration of the diminishing return of additional turbines in a channel. Starting from the top left, as more turbines are added the residual kinetic power decreases, and the generated power increases. There is an optimal point where generated power is maximized, beyond which the generated power is decreased as further turbines are added.

FIG. 3.

Illustration of the diminishing return of additional turbines in a channel. Starting from the top left, as more turbines are added the residual kinetic power decreases, and the generated power increases. There is an optimal point where generated power is maximized, beyond which the generated power is decreased as further turbines are added.

Close modal

In order to provide an estimate of the maximum power available, Garrett and Cummins,36 hereafter referred to as G&C, identified that the backflow effect created by the obstructions in the channel would increase the pressure differential driving the flow and enhance the flow velocity. For a constricted channel connecting two large bodies of water in which the tides at both ends are assumed to be unaffected by the currents through the channel with a tidal fence consisting of turbines across the entire channel cross section, a general formula gives the maximum average power between 2024% of the peak tidal pressure head multiplied by the peak of the undisturbed mass flux through the channel. Maximum average tidal stream power, Pmax, is given as

Pmax=γρgaQmax,
(4)

where g is a parameter, a is the amplitude of the tidal water level constituent, and Qmax is the maximum corresponding tidal flow rate. The maximum average power may be estimated with an accuracy of 10% using g = 0.22, without any need to understand the basic dynamical balance.36 A multiplying factor is used to account for additional constituents (–, –) given as

1+(9/16)((r1)2+(r2)2+),
(5)

where r1=a1/a,r2=a2/a,. This upper bound on the available power ignores losses associated with turbine operation and assumes that turbines are deployed in uniform fences, with all the water passing through the turbines at each fence.

Using numerical simulations of tidal flows, Haas et al.37 applied Eqs. (4) and (5) to produce the United States national tidal energy reconnaissance resource assessment. Other studies have looked at various applications of G&C. The validity of the value of g in Eq. (4) was demonstrated by Sutherland et al.38 using numerical simulations of channel-wide energy extraction. However, they did find that for more complex channel geometries such as split channels, Eq. (4) overestimated the maximum power by up to 50%, demonstrating one of the limits to the applicability of this method. Recognizing the unlikely scenario of installing tidal fences across an entire channel, Garrett and Cummins32 analyzed partial fences across a channel. Analytically, it was found that the resultant maximum power was reduced by a factor of 1/3 to 2/3. This work was extended by Whelan et al.39 for the case of high flow blockage where the free surface drops immediately downstream of the turbine, and by Nishino and Willden40 who considered the case of a tidal fence partially blocking a wide channel.

Vennell41,42 also looked at the effects of partially blocked channels, finding that by adjusting the flow reduction (ratio of the wake velocity to the incoming velocity), the turbine array could be better optimized. Vennell argues that changes to individual turbines will affect the overall array drag and therefore found that flow reduction ratios could be tuned in the range from 1/3 to 1, in contrast to Garrett and Cummins32 where the optimal limit was 1/3. Vennell42 also discusses the need to tune multiple rows of turbines “in-concert” because even if the rows are widely separated such that the wake sufficiently recovers, they will still have interactions due to the full array's effect on the overall array drag coefficient. An excellent discussion comparing the results from Garrett and Cummins with the work by Vennell is found in Vennell.43 

The previously discussed studies generally focused on relatively simple channel geometry and idealized flow conditions. The comparison of array efficiency estimates obtained from analytical and numerical models suggests that analytical models underestimate the tidal energy yield potential, particularly as they do not realistically reproduce the flow structure in the vicinity of the turbines, especially three-dimensional effects and turbulent mixing.44 Further, tidal estuaries are frequently much more complex and may contain multiple channel branches and intertidal storage. Under these more complex scenarios, kinetic power density is sometimes still used to estimate the resource.45–47 However, additional work has been completed to extend the analytical approaches above to more complex geometries. Blanchfield et al.48 modeled a closed bay and open ocean, which was successfully verified by Yang et al.49 numerically. Polagye and Malte50 treated tidal networks like electrical circuits and found the most power efficient turbine deployment in networks required equally deploying turbines across sub-channels, or deploying prior to the channel bifurcation to a reduce flow diversion.

The project design process and final AEP calculations require much higher levels of resource assessments, using additional levels of complexity. Individual effects from turbines that must be considered can be small scale, where turbines interact directly (i.e., wake effects), or large scale, where the energy extraction from the turbines affects the tidal flow in the entire estuary. While it is not possible to resolve all these scales in a single model, it is necessary to resolve a broad range of scales, which is computationally challenging. Resource assessments utilized for siting considerations require a much higher model resolution than feasibility studies. This may be accomplished using models with unstructured grids, such as the resource assessment for New Jersey by Tang et al.51 or Yang et al.52 for the Western Passage. Another study by Ramos et al.53 coupled several structured grids with varying grid resolutions in a relatively simple estuary. In another example, Bomminayuni et al.54 used a model with an unstructured grid and therefore higher resolution in the region of interest to simulate the flows in tidal channels near Rose Dhu Island, Georgia. Recently, Lewis et al.55 simulated the Irish Sea with a structured grid model and determined that model resolution had a significant effect on the local resource assessment. They demonstrated that higher model resolutions (<500 m) are required for siting considerations. Yang and Haas56 used grid refinement with a structured grid to produce higher resolution within the region of interest for two different case studies.

In order to account for array effects, numerical simulations of the project site must quantify the effects of turbines on the flow field. Due to the necessity of resolving a large domain to capture the far field effects, simplified approaches to resolving the effect of turbines are generally utilized. While there are many options for incorporating the impacts of turbines into models, they generally have comparable approaches.49,57–61

One approach for simulating energy extraction incorporates an extra retarding force into the momentum equations. This force may be written as

F=12ρCext|V|V,
(6)

where Cext is an extraction coefficient. To evaluate far-field effects, the grid resolution may be relatively coarse where each grid point may represent multiple turbines, leading to larger overall extraction coefficients. At such a coarse resolution, the turbines within each grid cell cannot be individually tuned, and so the optimal grid layout cannot be determined. In order to optimize the turbine layout, individual turbines must be resolved by one or more grid cells. In these cases, three-dimensional (3D) models may also resolve the vertical structure of the flow with multiple layers resolving the swept area.62,63

Tidal turbines and their support structures will also have a pronounced effect on the turbulent characteristics of the flow field. Therefore, models must include modifications to the turbulence closure scheme.64 This includes an additional turbulent kinetic energy (TKE) source term, an additional term accounting for the transfer of large-scale turbulence to small-scale turbulence, and a term to model reduction in the spectrum of the turbulent length scales due to the partial generation of turbulence from fluid–structure interactions.

There have been numerous tidal power assessments performed while accounting for the effects of tidal power extraction for different regions around the world. Tidal stream energy resources in northwest Spain were modeled numerically, and the impacts of tidal stream energy were assessed.45,53 The maximum tidal power potential of Johnstone Strait, British Columbia, Canada, was studied by Sutherland et al.38 using a 2D finite element model, and the maximum extractable power in northwestern Johnstone Strait was estimated to be about 1.3 GW. The available tidal power from in-stream turbines placed in the Minas Passage of the Bay of Fundy and the Passamaquoddy-Cobscook Bay located near the entrance to the Bay of Fundy has also been examined.65–67 Polagye et al.68 studied and characterized the in-stream tidal energy potential of Puget Sound, Washington, and quantified the far-field, barotropic effects of energy extraction. The Kennebec River of the central Maine coast was found to contain narrow passages where mean tidal energy capacity is sufficient to meet the consumption needs of about 150 homes.69 The Tanana River at Nenana, Alaska, was studied for its potential for installing and operating hydrokinetic turbines, and suitable locations were recommended.70 Coles et al.71 performed 2D model simulations around the Channel Islands and found benefits in developing multiple sites simultaneously. Hakim et al.72 modeled the Muskeget Channel and found modest impacts on the underlying hydrodynamics. Marsh et al.73 modeled the Clarence Strait in Australia both with and without the effects of energy extraction and found that modest sized arrays had a limited impact on the underlying hydrodynamics. In contrast, Coles et al.74 modeled the Alderney Race and documented a reduction in flow due to the impact of the turbines, resulting in a total power estimate 57% lower than the previous assessment without energy extraction.

Tidal stream energy sites are characterized by high Reynolds numbers. For instance, the order of magnitude is 2.5×107 for the Alderney Race, France.75 The flows are thus highly turbulent, and this has strong implications on the design of turbines. Whereas the smallest scales of turbulence increase the fatigue of the turbines by inducing vibrations, the larger scales are responsible for fluctuations of power76,77 and loads.78 Indeed, instantaneous velocities (and associated loads) result from the superposition of turbulence-induced velocity fluctuations and time-mean velocities. The characterization of turbulence is thus crucial for the design of tidal turbines. Turbulence is also known to have a significant effect on the wakes that form behind the turbines, as demonstrated experimentally79 and numerically.80 Basically, the higher the turbulence intensity, the greater the mixing behind the turbines and the faster the flow recovery between two consecutive rows of turbines in an array. This effect should thus be considered when optimally positioning turbines within an array, especially the longitudinal spacing between rows of turbines. Finally, as turbulence acts on the mixing processes and bed shear stress, it influences sediment transport and the transport of solute and suspended substances that pass through the array (e.g., biochemical particles). Wake turbulence should thus be considered when assessing the influence of tidal turbines on the physical conditions.

The turbulence at tidal energy sites results from a combination of processes that interact with each other. In this section, we first review studies dedicated to the characterization of the ambient turbulence (the turbulence that is naturally present in the flow). Then, we review investigations on the turbulence generated by the turbines.

Whereas measuring time-mean flows at tidal energy sites is relatively common today, measuring turbulence in fast currents is still technically challenging because of sensor limitations (sampling frequency) and difficulties in deploying and firmly fixing the sensors in harsh environments. Nevertheless, ambient turbulence measurements have been performed in the Fall of Warness, UK;81,82 Puget Sound, USA;83 the Sound of Islay, UK;84 Ramsey Sound, UK;85 and the Alderney Race, France.86–88 Such measurements are performed with acoustic Doppler velocimeters (ADV) that provide high-frequency data with low noise at a single location, acoustic Doppler current profilers (ADCP) that provide lower-frequency data along a profile (generally, the sensor is upward looking from a sea bed mooring), or two coupled ADCPs that allow the six components of the Reynolds stress tensor to be evaluated.86–88 The most popular metrics used to characterize turbulence are turbulence intensity (i.e., the turbulent velocity fluctuations normalized by the tidal currents), turbulent kinetic energy (TKE), the production and dissipation of TKE, and the time and length scales of the turbulence. Turbulence intensity (in percent) is

I=10013(uu¯+vv¯+ww¯)u¯2+v¯2+w¯2,

and turbulent kinetic energy (TKE) is

TKE=12(uu¯+vv¯+ww¯),

where, according to the Reynolds decomposition, the current velocity (u, v, w) is the sum of a time averaged velocity (u¯,v¯,w¯) and a fluctuating part (u,v,w).

The investigations mentioned above showed that (1) ambient turbulence intensity is of the order of 10% at hub height,83,86 and (2) turbulence is highly site-specific and strongly dependent on the (ebbing or flooding) tidal conditions because its characteristics are influenced by the (local and upstream) seabed features.85,86 They also highlighted that (3) turbulence is anisotropic with greater turbulence intensities and time-length scales in the streamwise direction than in the transverse or vertical directions.88In situ measurements are essential for understanding the turbulence characteristics at a given tidal energy site. However, the data are generally only available at one particular location (or along a profile if measurements are performed using an ADCP). Hence, numerical models are required to map the turbulence characteristics over the entire tidal stream energy site. There are two main ways of modeling the turbulence in tidal flows. The RANS (Reynolds-averaged Navier–Stokes) approach consists of resolving the time-mean variables and using closure schemes (such as the kε model) to simulate the effect of the turbulence on the flow. This is a temporal filtering of the Navier–Stokes equations. The other way is LES (large eddy simulation) and consists in resolving the greatest scales of the turbulence and in mimicking the effect of the smallest (dissipative) scales with subgrid models (e.g., Smagorinsky89). This is a spatial filtering of the Navier–Stokes equations. The RANS approach is generally used in regional models because it is suited to simulating the slowly varying tidal dynamics over large domains. Numerous regional models, such as Telemac, ROMS, or FVCOM (Finite Volume Community Ocean Model), have thus been applied to appraise the time-mean flow hydrodynamic characteristics of tidal energy sites as well as the resource.90 Several attempts were also undertaken to characterize turbulence from the turbulence closures of RANS models. Togneri et al.91 used the kε closure of the ROMS model to predict the turbulence properties at the West Anglesey Demonstration Zone off the coast of Wales (UK). The comparison with ADCP data showed good agreement in terms of TKE, especially in the lower part of the water column, where the influence of wave action was minimal. Dissipation agreed less well. Applying a comparable methodology to the Chacao Channel (Chile), Guerra et al.92 also showed that the regional model FVCOM can suitably predict the strength of turbulence at tidal energy sites. The comparison of the model predictions to ADV measurements showed good agreement in terms of dissipation rate. Nevertheless, TKE was significantly underestimated because the model was not able to capture the anisotropy of the low-frequency turbulence scales. LES models simulate the transient flow characteristics and thus enable more insights into the processes controlling the turbulence at tidal stream energy sites. LES has been performed in Ramsey Sound93 and the Alderney Race.94,95 The simulations of Zangiabadi et al.93 relied on the Fluent model and covered a 800×1800m2 domain. This highlighted the capabilities of LES to simulate flow patterns over complex seabed morphology, especially the dynamics of large eddies that form behind a large rock. Focusing on comparable length and time-scales, Mercier et al.95 characterized quantitatively the turbulence in a 240×960m2 zone of the Alderney Race with a LES model based on the Lattice Boltzmann method (Fig. 4). The model validation relied on measurements performed by two coupled ADCPs and showed a remarkable agreement for all components of the Reynolds stress tensor. Because of their high computational cost, LES focusses on small domains and is used to investigate only a particular snapshot of the tidal cycle (generally peak ebb or peak flood current). To cover larger temporal and spatial scales, Bourgoin et al.94 modified Telemac3D so that it solves both the RANS and the LES equations. In the outer domain, solving the RANS equations resolved the tidal propagation, whereas in the inner domain, application of the LES equations resolved the transient characteristics of the flow and the largest turbulence scales. This model was applied to simulate a tide cycle in the Alderney Race and was used to map the turbulence characteristics, investigating the differences in turbulence properties between the flood and ebb phases of the tidal cycle. Table III synthesizes the main characteristics of the models mentioned above.

FIG. 4.

Turbulent structures over a rocky seabed in the Alderney Race, France. (a) Without vortices. (b) with vortices represented with instantaneous iso-contour of the λ2 criterion.153 Domain dimensions are 70 × 150 m2. Reproduced with permission from Mercier et al., Appl. Ocean Res. 97, 102082 (2020). Copyright 2020 Elsevier.

FIG. 4.

Turbulent structures over a rocky seabed in the Alderney Race, France. (a) Without vortices. (b) with vortices represented with instantaneous iso-contour of the λ2 criterion.153 Domain dimensions are 70 × 150 m2. Reproduced with permission from Mercier et al., Appl. Ocean Res. 97, 102082 (2020). Copyright 2020 Elsevier.

Close modal
TABLE III.

Examples of modeling studies dedicated to the characterization of turbulence at tidal energy sites.

ReferenceSiteTurbulence modelModelDomain dimension/minimal resolution
Togneri et al.91  Anglesey (UK) kε (RANS) ROMS 600 × 300 km2/300 m 
Guerra et al.92  Chacao Channel (Chile) Mellor and Yamada155 and Smagorinsky89  FVCOM 66 400 km2/50 m 
Zangiabadi et al.93  Ramsey Sound (UK) kε (RANS) Fluent 1800 × 800 m2/0.25 m 
 Ramsey Sound (UK) Smagorinsky89 (LES) Fluent 1800 × 800 m2/0.25 m 
Bourgoin et al.94  Alderney Race (France) Spalart and Allmaras154 (RANS) and Anisotropic Minimum Dissipation model (LES) Telemac3D 150 × 120 km2 (RANS) and 1.8×2.5 km2 (LES)/3 m 
Mercier et al.95  Alderney Race (France) Smagorinsky89 (LES) Palabos (LBM) 240 × 960 m2/0.25 m 
ReferenceSiteTurbulence modelModelDomain dimension/minimal resolution
Togneri et al.91  Anglesey (UK) kε (RANS) ROMS 600 × 300 km2/300 m 
Guerra et al.92  Chacao Channel (Chile) Mellor and Yamada155 and Smagorinsky89  FVCOM 66 400 km2/50 m 
Zangiabadi et al.93  Ramsey Sound (UK) kε (RANS) Fluent 1800 × 800 m2/0.25 m 
 Ramsey Sound (UK) Smagorinsky89 (LES) Fluent 1800 × 800 m2/0.25 m 
Bourgoin et al.94  Alderney Race (France) Spalart and Allmaras154 (RANS) and Anisotropic Minimum Dissipation model (LES) Telemac3D 150 × 120 km2 (RANS) and 1.8×2.5 km2 (LES)/3 m 
Mercier et al.95  Alderney Race (France) Smagorinsky89 (LES) Palabos (LBM) 240 × 960 m2/0.25 m 

The turbulence induced by tidal stream turbines is the result of different processes. In the near wake (typically within five turbine diameters downstream of the device), the turbulence is mostly governed by the swirl (rotational motion of the flow generated by the blades) and by the vortices shed by the blades and the support structure. In the far wake (typically greater than five turbine diameters), turbulence results mainly from the shear between the accelerated flows that bypass the turbine and the slowly moving flows in the turbine wake. The interaction of these processes with the ambient turbulence determines the wake properties. To predict wake characteristics, different techniques can be applied. Here, we review measurements in real sea conditions, laboratory experiments of scaled studies, and numerical models.

In situ experiments can either be used to assess the influence of turbulence on turbine performance (thrust and power) or to map the wake characteristics (in terms of velocity deficit and turbulence intensity for instance). Whereas several in situ experiments highlighted the influence of ambient turbulence on the performance of full-scale tidal turbines,76,77,96,97 measurements of wake turbulence in real sea conditions are sparse in the scientific literature. The main reason is that acquiring data in the wake of a full-scale tidal turbine (in operating conditions) is highly challenging. Indeed, in addition to the technical limitations of current measuring devices, sensors are difficult to deploy close to the turbine, and multiple sensors are required to capture the shape and properties of the entire wake. In addition, data are generally not publicly available. The only existing studies on wakes found by the authors are those of Schmitt et al.98 and Verbeek et al.99 Schmitt et al.98 investigated the spatial distribution of the velocity deficit in the wake of a 4 m diameter turbine with two ADCPs and an ADV. Verbeek et al.99 measured, using two ADCPs, the velocity deficit, turbulence intensity, and the integral time-length scales behind a row of 5.3 m diameter turbines.

As wake data acquired in real sea conditions are sparse, the knowledge on the wakes of tidal turbines comes mainly from experiments on scaled turbines in laboratory flumes.100 The first measurements were carried out by Myers and Bahaj101 who mapped the near wake behind a 0.8 m diameter turbine with laser and acoustic Doppler velocimeters. Measurements in the far wake were also performed using an experiment in which the turbine was represented by a porous disk of diameter 0.1 m. Numerous experimental studies were then performed on the wake of a single turbine and showed, in particular, that the ambient turbulence has a strong influence on the flow recovery behind the turbine.79,102–105 Investigations were also conducted on the interactions between the wakes of two turbines,106 three turbines,107 and up to ten turbines.108 Finally, large arrays of tidal turbines were investigated by Coles et al.71 with porous fences representing several rows of devices. This experiment enabled an investigation of flow development through up to ten rows of turbines.

Experimental studies are essential for understanding the processes controlling wake characteristics. However, the results are difficult to transpose to full scale, especially because the Reynolds similarity is not achievable experimentally. Numerical investigations are thus widely used to characterize the wakes of tidal stream turbines. Here, we present different types of wake models starting with the most sophisticated. Blade resolved models (e.g., Afgan et al.109) can be used to simulate the performance of turbines under different flow conditions. In such an approach, the rotating blades, the hub, and the support structure are physically represented in the model. Since they resolve the blade and support structure, they are able to simulate all turbulent processes generating turbulence in the near wake. However, due to their high computational cost, they cannot be applied to simulate an entire wake (or several interacting wakes). Blade element method (BEM) consists of applying on the fluid the forces experienced by different sections (elements) of the rotating blades (lift and drag forces). BEM is particularly suited to investigating turbine and array scales, and numerous investigations have been conducted to simulate isolated or superimposed wakes.110,111 The vortex method112 focuses on comparable scales and relies on a velocity–vorticity numerical implementation of the Navier–Stokes equations. In this approach, the vorticity is carried by particles emitted at the trailing edge of the turbine blades and advected in a Lagrangian framework. This method showed good agreement with experiments on a scaled turbine both in terms of performance and wake characteristics. The Actuator Line (AL),113 Actuator Disk (AD),80,114,115 or Actuator cylinder (for vertical-axis turbines) concepts represent turbines by applying on the fluid the force exerted by the turbines (thrust and eventually drag). This modeling approach has a lower level of fidelity then the methods mentioned above (especially because the rotational motion of the blades is neglected), but can successfully simulate far-field wakes (Fig. 5), provided the turbulence models are adequately tuned116 (and suitably mimic the effects of the unresolved turbulence processes, especially swirl and the shedding of vortices). The main interest of AD methods is that, due to their simplicity, they can be integrated into regional models (e.g., ROMS, Telemac3D). Hence, they enable wake studies to be performed under realistic sea conditions (e.g., Roc et al.,64 Michelet et al.,62 Thiébot et al.63), which are required to assess the energy conversion potential of tidal arrays.

FIG. 5.

Wake behind a vertical double-axis turbine. Transverse velocity is displayed along two vertical planes; Uy is the time-averaged velocity along the y-axis. Vortices are represented as instantaneous iso-contours of the λ2 criterion.153X/Deq and Y/Deq are x- and y-directions normalized by the equivalent turbine diameter Deq = 25 m. Reproduced with permission from Grondeau et al., Energies 12, 4273 (2019). Copyright 2019 Author(s), licensed under a Creative Commons Attribution (CC BY) license.

FIG. 5.

Wake behind a vertical double-axis turbine. Transverse velocity is displayed along two vertical planes; Uy is the time-averaged velocity along the y-axis. Vortices are represented as instantaneous iso-contours of the λ2 criterion.153X/Deq and Y/Deq are x- and y-directions normalized by the equivalent turbine diameter Deq = 25 m. Reproduced with permission from Grondeau et al., Energies 12, 4273 (2019). Copyright 2019 Author(s), licensed under a Creative Commons Attribution (CC BY) license.

Close modal

Tidal stream energy projects are generally located in coastal bays, estuaries, and tidal channels that are characterized by strong tidal currents. These coastal waterbodies present not only a great potential for electricity generation but also provide important nearshore habitats for many species of marine wildlife, including seabirds, fish, and marine mammals.117 The deployment of tidal turbine arrays in the coastal oceans will cause disturbances to the ambient ocean environments. The level of disturbance, or impact, depends not only on the amount of energy extraction and size of the tidal turbine arrays, but also the geometric features and volume of the tidal system where tidal turbine arrays are deployed.118 Therefore, to accelerate the development of tidal energy, it is important to assess, understand, and minimize the impacts of the deployment of tidal turbine arrays on coastal ecosystems, and to protect the marine wildlife while maximizing its contribution to the renewable energy portfolio. Environmental effects of tidal turbine array installations on tidal systems typically include the following areas:117,118

  • Physical processes

    • Effects on the far-field flows and water levels

    • Transport timescales such as residence time

  • Biogeochemical processes

    • Water quality

    • Sediment erosion and transport near the tidal turbine arrays

Common approaches for assessing the environmental impacts of tidal energy extraction include numerical modeling, laboratory experiments, field monitoring, and measurements. However, field monitoring and measurements are limited because very few tidal turbine arrays have been deployed in the world, and the associated costs are extremely high. Therefore, numerical simulations using advanced state-of-the-art models are often employed to evaluate the level of environmental impacts associated with tidal energy extraction in marine systems.35,63,119

The following two subsections (V A and V B) provide an overall review of the current state of research for the environmental impacts in each subject area.

Effects of tidal stream energy extraction on physical processes in marine systems, such as ambient flow fields, are the most evident phenomena in tidal energy projects. Assessment of the effects on physical processes generally can be divided into two groups—system-wide far-field effects and localized near-field effects. In a series of pioneering work on understanding and estimating the maximum power potential in tidal channels, Garrett and Cummins32,36,120 established a theoretical formula that relates the maximum extractable energy vs the maximum tidal flux across the channel. They discovered that the maximum extractable power is significantly less than the average kinetic energy flux in the undisturbed tidal channel, and the volume flux across the channel will be reduced by 42%. Subsequently, a number of modeling studies confirmed the conclusions by Garrett and Cummins,36 using numerical simulations in real-world cases and idealized test domains, and further extended the formula to a tidal channel connecting to a coastal bay case.38,48,49,66,121 It is important to note the previously mentioned studies were based on one-dimensional (1D) or two-dimensional (2D) governing equations. However, in reality, tidal energy extraction is a three-dimensional (3D) process. For example, Yang et al.49 found that the volume flux is reduced by 32.7% when maximum tidal energy is extracted in a 3D case, which is smaller than the 2D case and the results of Garrett and Cummins.36 Physics-based 2D and 3D models are often used to investigate the far-field effects of tidal energy extraction for real-world study sites.

Most studies showed that the effect of tidal energy extraction on water level was small.59,122–124 For example, Zhang et al.124 applied a 3D model to assess the impact of proposed tidal array with 115 turbines in the Zhoushan Archipelago of China and they found the impact of the tidal turbine array resulted in a water-level change of less than 3 cm. Similarly, Yang et al.123 also found that a hypothetical tidal array with 100 turbines in Tacoma Narrows, USA, resulted in a change in water level of less than 1 cm. Pacheco and Ferreira125 investigated the hydrodynamic impacts of a small array of thirty E35 turbines and found the change in a water level due to tidal turbine array operation was less than 2.5 cm. Hakim et al.72 studied the impacts of tidal stream turbines on hydrodynamics and sediment transport in the Muskeget Channel, Massachusetts, using an unstructured-grid model FVCOM. Model results suggested that water level was only affected by 0.8% when 9% of the natural tidal energy in the channel is extracted. However, some studies found that the presence of large arrays of turbines can potentially result in noticeable changes in water level, especially where tidal flats are present in the system or when tidal phase is affected. For example, Karsten et al.66 discovered a maximum of 7 GW of power can be extracted by turbines in the Minas Passage based on numerical simulations. However, such a large level of energy extraction could result in the system moving closer to true tidal resonance, increasing tidal amplitudes throughout the Bay of Fundy and Gulf of Maine. In a modeling study on the effect of tidal turbine arrays on the tidal regime, intertidal zones, and flushing time, Nash et al.126 pointed out the tidal range upstream of the tidal arrays could be affected significantly, varying from approximately 542% depending on the size of the tidal array. Similarly, intertidal mudflats could be affected by 1432% and thus result in loss of nearshore habitat.

In addition to currents and water level, another important parameter used to characterize the far-field, or system-wide effect of tidal stream energy extraction is the transport timescale, such as flushing time, residence time, and age, of a system where the tidal turbine arrays are installed. Information on the effects of tidal energy extraction on residence time and water renewal can be used to guide the optimal siting and layout of tidal turbine arrays during the permitting process. A detailed review on the methodologies of assessing the impact of tidal stream energy extraction on transport timescales was provided by Yang and Wang.127 For example, based on simulated tracer concentrations with and without tidal turbine arrays (Fig. 6), Yang and Wang127 showed that if a 10% change in flushing time is the acceptable upper limit for the environmental concern due to tidal energy extraction in Tacoma Narrows, USA, a smaller percentage reduction in volume flux must be considered because the change in flushing time is several times greater than the change in volume flux. Guillou et al.128 used model simulations to evaluate the effects of tidal turbine arrays on water renewal in a tidal stream energy site of the Fromveur Strait in northwestern coastal waters of Brittany, France. Their study demonstrated that the residence time in the tidal stream energy site was modified by less than 5.3% with an array of 207 tidal turbines. Nelson et al.129 developed a modeling framework for optimizing tidal turbine deployment in Cobscook Bay in Maine that maximizes the energy extraction with consideration of environmental constraints, such as criteria for the changes to flow fields and bed shear stress. James et al.130 applied a similar modeling approach to design turbine arrays in the Tanana River, Alaska, and evaluated the effects of different array layouts on power production and environmental impacts.

FIG. 6.

(a) Simulated instantaneous tracer concentrations without tidal turbines in the South Puget Sound (baseline condition). (b) Difference in tracer concentrations between the tidal turbine array (63 MW extraction) and baseline conditions in the South Sound. Tidal turbines were deployed in the Tacoma Narrows of Puget Sound. Reproduced with permission from Yang and Wang, Effects of tidal stream energy extraction on water exchange and transport timescales, in Marine Renewable Energy, pp. 259–278 (Springer, 2017). Copyright 2017 Springer.

FIG. 6.

(a) Simulated instantaneous tracer concentrations without tidal turbines in the South Puget Sound (baseline condition). (b) Difference in tracer concentrations between the tidal turbine array (63 MW extraction) and baseline conditions in the South Sound. Tidal turbines were deployed in the Tacoma Narrows of Puget Sound. Reproduced with permission from Yang and Wang, Effects of tidal stream energy extraction on water exchange and transport timescales, in Marine Renewable Energy, pp. 259–278 (Springer, 2017). Copyright 2017 Springer.

Close modal

Marine biogeochemical processes, such as water quality, sediment transport, nutrients, and marine habitats, are driven by coastal hydrodynamics. When the physical processes, including flow field, turbulence, vertical mixing, residence time, and bed shear stress, are modified due to the operation of tidal turbine arrays biogeochemical processes will also be affected.119,123,131,132 Changes to water quality such as dissolved oxygen, nitrogen, turbidity, and primary production in a marine system will ultimately affect the marine ecosystem services. Modification of turbulence characteristics and bottom shear stress within the vicinity of the tidal turbine array will also cause sediment erosion, re-suspension, and deposition. While there have been many studies conducted to investigate the effects of tidal energy extraction on the ambient hydrodynamics, studies on the effect of tidal energy extraction on biogeochemical processes are extremely limited. Therefore, there is a strong urgency to promote the research to assess the effects of tidal energy development on biogeochemical processes and minimize the negative impacts.

Wang et al.132 discovered that tidal stream energy extraction in a tidal channel can result in either positive or negative impacts to the water quality of a system, based on an idealized case of a tidal channel connecting with a coastal bay. Energy extraction by the tidal turbine array decreased flushing rates in the bay, which causes a negative impact on the water quality in the coastal bay. However, tidal energy extraction also enhanced vertical mixing, which could lead to higher bottom dissolved oxygen. Ashall et al.133 simulated tidal energy extraction in the Mina Passage of the Bay of Fundy, Canada, and found that a high power extraction case (5.6 GW) will cause 15% change in tidal amplitude, 10% change in velocity, and 37% change in suspended sediment concentration (Fig. 7). Nash et al.126 showed that the flushing time would be significantly altered if tidal turbine arrays were installed in the Shannon Estuary in the west of Ireland. Ahmadian et al.134 applied a hydro-environmental model to investigate the impacts of tidal energy extraction on water levels, tidal currents, and sediment and fecal bacteria levels in the Severn Estuary and Bristol Channel, UK. Their study found that 200 tidal turbines with 20 m diameter would cause noticeable changes of current speed and suspended sediment concentrations within 15 and 10 km from the turbine array, respectively. Shapiro135 applied a 3D coastal ocean model to simulate the effect of tidal energy extraction on far-field current distribution and transport process in the neighboring Celtic Sea. By simulating the presence of a hypothetical tidal array, current speeds in the vicinity of the array were significantly reduced due to tidal energy extraction and the backwater effect. In particular, residual tidal currents and pollutant transport were affected as far as a hundred kilometers away from the tidal turbine array.

FIG. 7.

Predicted time- and depth-averaged fine suspended sediment concentrations (mg L−1) at five sites (S2–S6) in the Minas Passage over the period June 6–13, 2013. Bar plots indicate the observed (red) and predicted (black = no turbines; dark gray = low power extraction case; light gray = high power extraction case) time- and depth-averaged SSC. Reproduced with permission from Ashall et al., Coastal Eng. 107, 102–115 (2016). Copyright 2016 Elsevier.

FIG. 7.

Predicted time- and depth-averaged fine suspended sediment concentrations (mg L−1) at five sites (S2–S6) in the Minas Passage over the period June 6–13, 2013. Bar plots indicate the observed (red) and predicted (black = no turbines; dark gray = low power extraction case; light gray = high power extraction case) time- and depth-averaged SSC. Reproduced with permission from Ashall et al., Coastal Eng. 107, 102–115 (2016). Copyright 2016 Elsevier.

Close modal

A detailed review was provided by Neill et al.136 on the impact of tidal (and wave) energy extraction on sediment dynamics. Neill et al.137 first demonstrated that sediment impacts of tidal energy arrays are not confined to the site of energy extraction, but can extend over distances of up to 10 km. Other factors on the siting of tidal arrays are important, such as increased impacts in regions of tidal asymmetry137,138 and the proximity to headland systems (regions of strong tidal flow) affecting the morphodynamics of associated headland sand banks139—large sedimentary features that protect the neighboring coastline from the impact of waves. Goh et al.140 investigated the effect of tidal energy extraction on flow field and sediment erosion adjacent to headlands along Negeri Sembilan (Malaysia) coastlines using numerical modeling. Their study showed that the deployment of a tidal energy array resulted in reduced velocity and increased sediment deposition within the vicinity of the tidal array, and increased velocity accompanied by sediment erosion along either side of the tidal array. It is also important to consider the impacts on sediments in relation to natural variability. For example, during summer months, when natural variability of waves and hence sediments is lower, the impact of tidal energy extraction will be relatively greater than during the energetic winter months.141 Prior to the construction of large arrays of tidal energy devices, impacts on sediments can only be assessed via numerical models. There are various techniques to incorporate energy extraction into numerical models139,141,142 and no standardized methodology, and therefore, in situ validation of such models and benchmarking against laboratory experiments is an important step. Finally, tidal range schemes (Sec. II) are likely to have significant impacts on the sediment dynamics of a region, due to the presence of the artificial embankment and the operation of the turbines. The region inside the lagoon will be characterized by reduced energy, especially during periods of holding,136 leading to reduced sediment concentrations in the water column and increased sedimentation.143 Closely spaced turbines in one section of the lagoon wall will lead to strong wake effects, further disturbing the sediment regime—increasing the spacing of these components of the lagoon along the embankment as much is practically feasible will reduce such impacts.144 

One of the major advantages of tidal energy is predictability. However, this predictability is associated with variability at two main timescales: semi-diurnal and fortnightly (spring/neap cycle).

It could be possible to phase tidal power plants along a coastline to minimize the semi-diurnal variability of the aggregated power signal,14 but it has been demonstrated that for shelf sea regions, the currents at the most energetic “hot spot” sites are either in phase with one another or 180° out-of-phase,145 and hence so would be the aggregated power signal. However, by diversifying tidal stream site selection to incorporate less energetic tidal current amplitudes in the energy mix, the resulting increased phase diversity has considerable advantages in minimizing variability at semi-diurnal timescales.13 At fortnightly timescales, since the tides everywhere on Earth are simultaneously affected, it is not possible to significantly reduce variability. However, by selecting sites that exhibit larger ratios of M2:S2 amplitudes, it is possible to optimize multiple site selection, reducing fortnightly variability.90 Such a reduction could also be achieved by optimizing the joint development of a range of semi-diurnal, diurnal, and mixed sites, considering the K1 and O1 constituents alongside the principal semi-diurnal constituents M2 and S2.146 However, to counteract the spring/neap cycle, other forms of low carbon (renewable) energy conversion, or significant grid storage, will likely be required.

The development of less energetic tidal sites (to increase phase diversity) comes with its own set of challenges.55 Such locations tend to be in deeper water, further from the coast (and hence grid connections), are potentially more exposed to waves, and the currents are less rectilinear. Therefore, the development of such sites requires a new type of device (e.g., a floating technology with yaw capabilities) and a new set of research questions (e.g., the interaction of waves with currents at such sites).147 However, in addition to the advantage of increased phase diversity, there is also considerably more sea space available at such sites (Fig. 8). Taking an M2 current amplitude of 2.5 m/s as the current technology standard,55 lowering the M2 current amplitude to 2 m/s approximately doubles the available sea space. Further reduction of the threshold to 1.5 m/s increases sea space by a factor of 8 compared to sites suitable for current technology. Note also that less energetic tidal sites, far offshore and so not suitable for grid connection, could be used to power the “blue economy,” meeting the power demands of, for example, weather buoys, tsunami warning devices, communication, defense, and aquaculture applications.148 

FIG. 8.

Availability of UK shelf sea area (for water depths less than 100 m) for a range of M2 current amplitude thresholds.

FIG. 8.

Availability of UK shelf sea area (for water depths less than 100 m) for a range of M2 current amplitude thresholds.

Close modal

As seen in this review, numerous investigations have been performed that rely on numerical models and/or measurements performed in real sea conditions or in the laboratory. Despite these efforts, the understanding of processes controlling turbulence of tidal-stream energy sites is still incomplete for various reasons: (i) the difficulty with transposing experimental results to realistic sea conditions (which is partially due to similitude problem), (ii) the limitations of actual CFD (Computational Fluid Dynamics) models that cannot resolve the entire range of scales, and (iii) the scarcity of measurements in real sea conditions (for both ambient turbulence and wake-added turbulence). Future research is thus necessary to reduce uncertainties in turbulence predictions. The challenge will be to reliably model the interactions between ambient turbulence and the turbulence resulting from multiple (superimposed) wakes, and to predict the characteristics of wakes in real sea conditions, that is, with irregular sea bed, complex flow structure, and under the combined influence of waves and wind. Furthermore, existing studies show that the turbulence at tidal-stream energy sites is highly variable in space. To explain this heterogeneity, it is necessary to investigate the links between the local characteristics of turbulence and the (local and upstream) seabed morphology. In particular, it is not clear yet how different sea bed features (e.g., micro or macro seabed roughness, the presence of large boulders, abrupt changes of bathymetry) influence the turbulence. Finally, turbulence measurements are only available at specific locations, and so spatial coverage is incomplete. Although numerical models allow us to investigate the spatial distribution of turbulence, computational constraints mean that these models only cover short periods of time (ranging from minutes to hours), thus preventing investigation of the wide range of realistic tidal flow conditions (e.g., spring/neap tidal cycles). It is thus important to combine different methodologies (numerical, laboratory, and in situ measurements) in order to achieve a better understanding of processes controlling the generation and the transport of turbulence.

An ongoing challenge for the industry is how to best perform resource assessments and characterizations in a cost-effective manner, particularly for smaller scale projects. One option for addressing this challenge is the application of a measurement-based approach for resource assessments. The IEC technical specification for tidal energy resource assessment24 specifies a methodology in which a fixed current profiler can be used to collect 90 days of continuous data. However, additional approaches using short-term high-resolution measurements need to be developed to eliminate the requirement for long-term measurements at the location of each turbine. These approaches can adopt recent developments in remote sensing applications for measuring tidal currents. For example, initial analysis of different measurement approaches for reconstructing volumetric currents including vessel mounted profilers, X-band radar, and surface floats has recently been completed.149 Ongoing work needs to further develop these methodologies, combining short-term and long-term measurements to create the velocity probability distributions necessary for performing accurate AEP calculations.

Over the last decade, many studies have investigated the potential environmental impacts of tidal energy extraction on physical and biogeochemical processes in coastal bays and estuaries. Most of these studies are based on numerical simulations. However, model validation of the biogeochemical processes was rarely performed because of lack of field measured environmental data, especially pre- and post-tidal array installations. Therefore, conclusions drawn from unvalidated model simulations may be associated with high uncertainties. To increase model confidence in predicting the environmental impacts due to tidal energy extraction, it is important to collect field data relevant to environmental impacts to support model validation. Environmental impact modeling and related field measurements should be considered as an integrated element throughout the course of tidal array development. It is important to understand that tidal energy extraction is a 3D problem since tidal turbines are typically located at certain depths (hub height) in the water column, in the form of either free-floating or bottom-moored systems. Therefore, the environmental impacts associated with tidal energy extraction are also three-dimensional. To simulate the far-field and localized environmental impacts using numerical models, selection of models and specification of model resolutions are important.150 It is not sufficient to use depth-averaged 2D numerical models in the context of tidal energy extraction, since 2D models are not able to simulate vertical variability in the flow field and the associated water quality and suspended sediment dynamics in the water column. To accurately assess the environmental impacts associated with tidal energy extraction, 3D models should be used. More importantly, water temperature, salinity, vertical mixing, stratification, and estuarine circulation induced by density gradients should be considered.151 The biogeochemical processes must be dynamically coupled with the coastal circulation models with the capability of simulating tidal energy extraction. It is worth noting that the effects of tidal energy extraction on biogeochemical processes may not always be negative, because of the complex interplay of multiple factors. For example, increased mixing in the water column as a result of tidal energy extraction could potentially reduce the hypoxia problem in an estuary.132 Furthermore, because estuarine biogeochemistry and sediment transport are long-term processes, model simulation periods should be of sufficient length to cover the seasonal variabilities that are governed by other factors such as river discharge, heat flux, and longer-term sea-level rise.

Tidal energy, consisting of tidal range and tidal stream, has huge global potential, but there are several research challenges that must be addressed before realizing even a fraction of this potential. In this article, we have identified several evolving research areas that will help enable this goal. These are:

  • Exploitation of the phasing between multiple tidal range or tidal stream locations to minimize variability in the aggregated power output. Since there is more phase diversity offered by less energetic sites, the unique site characteristics of these locations lead to additional related research topics, including the interaction of waves and currents at such exposed sites.

  • Improvements in simulating the interactions between the ambient turbulence and the turbulence resulting from multiple (superimposed) wakes, and in predicting the characteristics of wakes in real sea conditions, that is, irregular sea bed, complex flow structure, and under the combined influence of wind and waves.

  • Improved resource assessment and characterization through novel approaches using short-term high-resolution measurements (e.g., using X-band radar and vessel mounted profiles) to eliminate the requirement for long-term measurements at the location of every turbine within an array.

  • Increased confidence in predicting the environmental impacts of tidal energy extraction, by collecting appropriate field data relevant for validating numerical models that are used to assess impacts to a range of physical and biogeochemical processes. Environmental impact modeling and supporting field measurements should be considered as an integrated element throughout the entire course of tidal power plant development.

Simon Neill acknowledges the support of SEEC (Smart Efficient Energy Centre) at Bangor University, part-funded by the European Regional Development Fund (ERDF), administered by the Welsh Government. Jérôme Thiébot acknowledges the financial support of the Tidal Stream Industry Energiser project (TIGER), co-financed by the European Regional Development Fund through the Interreg France (Channel) England Programme. Zhaoqing Yang acknowledges the funding support by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Water Power Technologies Office under Contract No. DE-AC05–76RL01830 to Pacific Northwest National Laboratory (PNNL). Kevin Haas acknowledges the support from the PNNL for the funding of this work under Grant No. AWD-001313.

The authors have no conflicts to disclose.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

1.
L. E.
Clarke
,
K.
Jiang
,
K.
Akimoto
,
M.
Babiker
,
G. J.
Blanford
,
K.
Fisher-Vanden
,
J.-C.
Hourcade
,
V.
Krey
,
E.
Kriegler
,
A.
Loschel
 et al, “
Assessing transformation pathways
,” in
Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
[
Pacific Northwest National Lab (PNNL)
,
Richland, WA
,
2015
].
2.
M.
Lehmann
,
F.
Karimpour
,
C. A.
Goudey
,
P. T.
Jacobson
, and
M.-R.
Alam
, “
Ocean wave energy in the United States: Current status and future perspectives
,”
Renewable Sustainable Energy Rev.
74
,
1300
1313
(
2017
).
3.
J. H.
VanZwieten
,
L. T.
Rauchenstein
, and
L.
Lee
, “
An assessment of Florida's ocean thermal energy conversion (OTEC) resource
,”
Renewable Sustainable Energy Rev.
75
,
683
691
(
2017
).
4.
K.
Haas
,
X.
Yang
,
V.
Neary
, and
B.
Gunawan
, “
Ocean current energy resource assessment for the Gulf stream system: The Florida current
,” in
Marine Renewable Energy
(
Springer
,
2017
), pp.
217
236
.
5.
J.
Thiébot
,
S.
Guillou
, and
E.
Droniou
, “
Influence of the 18.6-year lunar nodal cycle on the tidal resource of the Alderney Race, France
,”
Appl. Ocean Res.
97
,
102107
(
2020
).
6.
G.
Egbert
and
R.
Ray
, “
Significant dissipation of tidal energy in the deep ocean inferred from satellite altimeter data
,”
Nature
405
,
775
778
(
2000
).
7.
T. A.
Adcock
,
S.
Draper
,
G. T.
Houlsby
,
A. G.
Borthwick
, and
S.
Serhadloğlu
, “
The available power from tidal stream turbines in the Pentland Firth
,”
Proc. R. Soc. A
469
,
20130072
(
2013
).
8.
Enerdata
,
Global Energy Statistical Yearbook
(
Enerdata
,
2019
).
9.
T.
McErlean
,
Harnessing the Tides: The Early Medieval Tide Mills at Nendrum Monastery, Strangford Lough, Northern Ireland Archaeological Monographs
(
The Stationery Office
,
2007
).
10.
R. H.
Charlier
, “
Forty candles for the Rance River TPP tides provide renewable and sustainable power generation
,”
Renewable Sustainable Energy Rev.
11
,
2032
2057
(
2007
).
11.
S. P.
Neill
,
A.
Angeloudis
,
P. E.
Robins
,
I.
Walkington
,
S. L.
Ward
,
I.
Masters
,
M. J.
Lewis
,
M.
Piano
,
A.
Avdis
,
M. D.
Piggott
 et al, “
Tidal range energy resource and optimization-past perspectives and future challenges
,”
Renewable Energy
127
,
763
778
(
2018
).
12.
N.
Yates
,
I.
Walkington
,
R.
Burrows
, and
J.
Wolf
, “
The energy gains realisable through pumping for tidal range energy schemes
,”
Renewable Energy
58
,
79
84
(
2013
).
13.
S. P.
Neill
,
M. R.
Hashemi
, and
M. J.
Lewis
, “
Tidal energy leasing and tidal phasing
,”
Renewable Energy
85
,
580
587
(
2016
).
14.
A.
Iyer
,
S.
Couch
,
G.
Harrison
, and
A.
Wallace
, “
Variability and phasing of tidal current energy around the United Kingdom
,”
Renewable Energy
51
,
343
357
(
2013
).
15.
S.
Waters
and
G.
Aggidis
, “
A world first: Swansea Bay tidal lagoon in review
,”
Renewable Sustainable Energy Rev.
56
,
916
921
(
2016
).
16.
C.
Hendry
, “
The role of tidal lagoons
,” Final Report (
2016
).
17.
It is interesting to note the considerable length of this embankment for a 320 MW scheme, compared to the length of the 240 MW La Rance barrage (720 m).
18.
S.
Waters
and
G.
Aggidis
, “
Tidal range technologies and state of the art in review
,”
Renewable Sustainable Energy Rev.
59
,
514
529
(
2016
).
19.
H. E.
Pelling
and
J.
Mattias Green
, “
Sea level rise and tidal power plants in the Gulf of Maine
,”
J. Geophys. Res.: Oceans
118
,
2863
2873
, (
2013
).
20.
M.
Pickering
,
K.
Horsburgh
,
J.
Blundell
,
J.-M.
Hirschi
,
R. J.
Nicholls
,
M.
Verlaan
, and
N.
Wells
, “
The impact of future sea-level rise on the global tides
,”
Cont. Shelf Res.
142
,
50
68
(
2017
).
21.
B.
Kresning
,
M. R.
Hashemi
,
S. P.
Neill
,
J. M.
Green
, and
H.
Xue
, “
The impacts of tidal energy development and sea-level rise in the Gulf of Maine
,”
Energy
187
,
115942
(
2019
).
22.
M.
Lewis
,
A.
Angeloudis
,
P.
Robins
,
P.
Evans
, and
S.
Neill
, “
Influence of storm surge on tidal range energy
,”
Energy
122
,
25
36
(
2017
).
23.
A.
Roberts
,
B.
Thomas
,
P.
Sewell
,
Z.
Khan
,
S.
Balmain
, and
J.
Gillman
, “
Current tidal power technologies and their suitability for applications in coastal and marine areas
,”
J. Ocean Eng. Mar. Energy
2
,
227
245
(
2016
).
24.
International Electrotechnical Commission
,
TS 62600-201:2015 Marine Energy—Wave, Tidal and Other Water Current Converters—Part 201: Tidal Energy Resource Assessment and Characterization
(
International Electrotechnical Commission
,
2015
).
25.
A.
Betz
, “
Das maximum der theoretisch möglichen ausnutzung des windes durch windmotoren
,”
Z. Gesamte Turbinenwesten
26
,
307
309
(
1920
).
26.
F. W.
Lanchester
, “
A contribution to the theory of propulsion and the screw propeller
,”
J. Am. Soc. Naval Eng.
27
,
509
510
(
1915
).
27.
N.
Joukowsky
, “
Windmill of the NEJ type
,”
Trans. Central Inst. Aero-Hydrodyn. Moscow
1
,
57
(
1920
).
28.
L.
Blunden
and
A.
Bahaj
, “
Tidal energy resource assessment for tidal stream generators
,”
Proc. Inst. Mech. Eng., Part A
221
,
137
146
(
2007
).
29.
International Electrotechnical Commission
,
TS 62600-200:2013 Marine Energy—Wave, Tidal and Other Water Current Converters—Part 200: Electricity Producing Tidal Energy Converters—Power Performance Assessment
(
International Electrotechnical Commission
,
2013
).
30.
A. N.
Gorban'
,
A. M.
Gorlov
, and
V. M.
Silantyev
, “
Limits of the turbine efficiency for free fluid flow
,”
J. Energy Resour. Technol.
123
,
311
317
(
2001
).
31.
S. B.
Elghali
,
M.
Benbouzid
, and
J. F.
Charpentier
, “
Marine tidal current electric power generation technology: State of the art and current status
,” in
2007 IEEE International Electric Machines & Drives Conference
(
IEEE
,
2007
), Vol.
2
, pp.
1407
1412
.
32.
C.
Garrett
and
P.
Cummins
, “
The efficiency of a turbine in a tidal channel
,”
J. Fluid Mech.
588
,
243
251
(
2007
).
33.
C.
Lawn
, “
Optimization of the power output from ducted turbines
,”
Proc. Inst. Mech. Eng., Part A
217
,
107
117
(
2003
).
34.
Z.
Defne
,
K. A.
Haas
,
H. M.
Fritz
,
L.
Jiang
,
S. P.
French
,
X.
Shi
,
B. T.
Smith
,
V. S.
Neary
, and
K. M.
Stewart
, “
National geodatabase of tidal stream power resource in USA
,”
Renewable Sustainable Energy Rev.
16
,
3326
3338
(
2012
).
35.
R.
Vennell
,
S. W.
Funke
,
S.
Draper
,
C.
Stevens
, and
T.
Divett
, “
Designing large arrays of tidal turbines: A synthesis and review
,”
Renewable Sustainable Energy Rev.
41
,
454
472
(
2015
).
36.
C.
Garrett
and
P.
Cummins
, “
The power potential of tidal currents in channels
,”
Proc. R. Soc. A
461
,
2563
2572
(
2005
).
37.
K. A.
Haas
,
H. M.
Fritz
,
S. P.
French
,
B. T.
Smith
, and
V.
Neary
, “
Assessment of energy production potential from tidal streams in the United States
,”
Report No. DE-FG36-08GO18174
(
Georgia Tech Research Corporation
,
Atlanta, GA
,
2011
).
38.
G.
Sutherland
,
M.
Foreman
, and
C.
Garrett
, “
Tidal current energy assessment for Johnstone strait, Vancouver Island
,”
Proc. Inst. Mech. Eng., Part A
221
,
147
157
(
2007
).
39.
J. I.
Whelan
,
J.
Graham
, and
J.
Peiro
, “
A free-surface and blockage correction for tidal turbines
,”
J. Fluid Mech.
624
,
281
291
(
2009
).
40.
T.
Nishino
and
R. H.
Willden
, “
The efficiency of an array of tidal turbines partially blocking a wide channel
,”
J. Fluid Mech.
708
,
596
606
(
2012
).
41.
R.
Vennell
, “
Tuning turbines in a tidal channel
,”
J. Fluid Mech.
663
,
253
267
(
2010
).
42.
R.
Vennell
, “
Tuning tidal turbines in-concert to maximise farm efficiency
,”
J. Fluid Mech.
671
,
587
604
(
2011
).
43.
R.
Vennell
, “
Realizing the potential of tidal currents and the efficiency of turbine farms in a channel
,”
Renewable Energy
47
,
95
102
(
2012
).
44.
J.
Thiébot
,
N.
Djama Dirieh
,
S.
Guillou
, and
N.
Guillou
, “
The efficiency of a fence of tidal turbines in the Alderney Race: Comparison between analytical and numerical models
,”
Energies
14
,
892
(
2021
).
45.
R.
Carballo
,
G.
Iglesias
, and
A.
Castro
, “
Numerical model evaluation of tidal stream energy resources in the Ría de Muros (NW Spain)
,”
Renewable Energy
34
,
1517
1524
(
2009
).
46.
G.
Iglesias
,
M.
Sánchez
,
R.
Carballo
, and
H.
Fernández
, “
The TSE index—A new tool for selecting tidal stream sites in depth-limited regions
,”
Renewable Energy
48
,
350
357
(
2012
).
47.
B.
Polagye
and
R.
Bedard
,
Tidal in-Stream Energy Resource Assessment for Southeast Alaska
(
Electric Power Research Institute
,
2006
).
48.
J.
Blanchfield
,
C.
Garrett
,
P.
Wild
, and
A.
Rowe
, “
The extractable power from a channel linking a bay to the open ocean
,”
Proc. Inst. Mech. Eng., Part A
222
,
289
297
(
2008
).
49.
Z.
Yang
,
T.
Wang
, and
A. E.
Copping
, “
Modeling tidal stream energy extraction and its effects on transport processes in a tidal channel and bay system using a three-dimensional coastal ocean model
,”
Renewable Energy
50
,
605
613
(
2013
).
50.
B. L.
Polagye
and
P. C.
Malte
, “
Far-field dynamics of tidal energy extraction in channel networks
,”
Renewable Energy
36
,
222
234
(
2011
).
51.
H.
Tang
,
S.
Kraatz
,
K.
Qu
,
G.
Chen
,
N.
Aboobaker
, and
C.
Jiang
, “
High-resolution survey of tidal energy towards power generation and influence of sea-level-rise: A case study at coast of New Jersey, USA
,”
Renewable Sustainable Energy Rev.
32
,
960
982
(
2014
).
52.
Z.
Yang
,
T.
Wang
,
Z.
Xiao
,
L.
Kilcher
,
K.
Haas
,
H.
Xue
, and
X.
Feng
, “
Modeling assessment of tidal energy extraction in the Western Passage
,”
J. Mar. Sci. Eng.
8
,
411
(
2020
).
53.
V.
Ramos
,
R.
Carballo
,
M.
Álvarez
,
M.
Sánchez
, and
G.
Iglesias
, “
A port towards energy self-sufficiency using tidal stream power
,”
Energy
71
,
432
444
(
2014
).
54.
S.
Bomminayuni
,
B.
Bruder
,
T.
Stoesser
, and
K.
Haas
, “
Assessment of hydrokinetic energy near Rose Dhu island, Georgia
,”
J. Renewable Sustainable Energy
4
,
063107
(
2012
).
55.
M.
Lewis
,
S.
Neill
,
P.
Robins
, and
M.
Hashemi
, “
Resource assessment for future generations of tidal-stream energy arrays
,”
Energy
83
,
403
415
(
2015
).
56.
X.
Yang
and
K. A.
Haas
, “
Improving assessments of tidal power potential using grid refinement in the coupled ocean-atmosphere-wave-sediment transport model
,”
J. Renewable Sustainable Energy
7
,
043107
(
2015
).
57.
Z.
Defne
,
K. A.
Haas
, and
H. M.
Fritz
, “
Numerical modeling of tidal currents and the effects of power extraction on estuarine hydrodynamics along the Georgia coast, USA
,”
Renewable Energy
36
,
3461
3471
(
2011
).
58.
P. A.
Work
,
K. A.
Haas
,
Z.
Defne
, and
T.
Gay
, “
Tidal stream energy site assessment via three-dimensional model and measurements
,”
Appl. Energy
102
,
510
519
(
2013
).
59.
T.
Wang
and
Z.
Yang
, “
A modeling study of tidal energy extraction and the associated impact on tidal circulation in a multi-inlet bay system of Puget Sound
,”
Renewable Energy
114
,
204
214
(
2017
).
60.
L.
Flores Mateos
and
M.
Hartnett
, “
Hydrodynamic effects of tidal-stream power extraction for varying turbine operating conditions
,”
Energies
13
,
3240
(
2020
).
61.
S. C.
James
,
E. L.
Johnson
,
J.
Barco
, and
J. D.
Roberts
, “
Simulating current-energy converters: SNL-EFDC model development, verification, and parameter estimation
,”
Renewable Energy
147
,
2531
2541
(
2020
).
62.
N.
Michelet
,
N.
Guillou
,
G.
Chapalain
,
J.
Thiebot
,
S.
Guillou
,
A. G.
Brown
, and
S.
Neill
, “
Three-dimensional modelling of turbine wake interactions at a tidal stream energy site
,”
Appl. Ocean Res.
95
,
102009
(
2020
).
63.
J.
Thiébot
,
N.
Guillou
,
S.
Guillou
,
A.
Good
, and
M.
Lewis
, “
Wake field study of tidal turbines under realistic flow conditions
,”
Renewable Energy
151
,
1196
1208
(
2020
).
64.
T.
Roc
,
D. C.
Conley
, and
D.
Greaves
, “
Methodology for tidal turbine representation in ocean circulation model
,”
Renewable Energy
51
,
448
464
(
2013
).
65.
D. A.
Brooks
, “
The tidal-stream energy resource in Passamaquoddy–Cobscook Bays: A fresh look at an old story
,”
Renewable Energy
31
,
2284
2295
(
2006
).
66.
R. H.
Karsten
,
J.
McMillan
,
M.
Lickley
, and
R.
Haynes
, “
Assessment of tidal current energy in the Minas Passage, Bay of Fundy
,”
Proc. Inst. Mech. Eng., Part A
222
,
493
507
(
2008
).
67.
R. A.
Walters
,
M. R.
Tarbotton
, and
C. E.
Hiles
, “
Estimation of tidal power potential
,”
Renewable Energy
51
,
255
262
(
2013
).
68.
B.
Polagye
,
M.
Kawase
, and
P.
Malte
, “
In-stream tidal energy potential of Puget Sound, Washington
,”
Proc. Inst. Mech. Eng., Part A
223
,
571
587
(
2009
).
69.
D. A.
Brooks
, “
The hydrokinetic power resource in a tidal estuary: The Kennebec River of the central Maine coast
,”
Renewable Energy
36
,
1492
1501
(
2011
).
70.
H.
Toniolo
,
P.
Duvoy
,
S.
Vanlesberg
, and
J.
Johnson
, “
Modelling and field measurements in support of the hydrokinetic resource assessment for the Tanana river at Nenana, Alaska
,”
Proc. Inst. Mech. Eng., Part A
224
,
1127
1139
(
2010
).
71.
D.
Coles
,
L.
Blunden
, and
A.
Bahaj
, “
Turbulent flow characterisation within large multi-row tidal turbine array simulators
,” in
Proceedings of the 12th European Wave and Tidal Energy Conference (EWTEC)
(
2017
).
72.
A. R.
Hakim
,
G. W.
Cowles
, and
J. H.
Churchill
, “
The impact of tidal stream turbines on circulation and sediment transport in Muskeget Channel, MA
,”
Mar. Technol. Soc. J.
47
,
122
136
(
2013
).
73.
P.
Marsh
,
I.
Penesis
,
J.
Nader
,
C.
Couzi
, and
R.
Cossu
, “
Assessment of tidal current resources in Clarence Strait, Australia including turbine extraction effects
,”
Renewable Energy
179
,
150
162
(
2021
).
74.
D.
Coles
,
L.
Blunden
, and
A.
Bahaj
, “
The energy yield potential of a large tidal stream turbine array in the Alderney race
,”
Philos. Trans. R. Soc. A
378
,
20190502
(
2020
).
75.
M.
Ikhennicheu
,
G.
Germain
,
P.
Druault
, and
B.
Gaurier
, “
Experimental investigation of the turbulent wake past real seabed elements for velocity variations characterization in the water column
,”
Int. J. Heat Fluid Flow
78
,
108426
(
2019
).
76.
J.
McNaughton
,
R.
Sinclair
, and
B.
Sellar
, “
Measuring and modelling the power curve of a commercial-scale tidal turbine
,” in
Proceedings of 11th European Wave and Tidal Energy Conference (EWTEC)
,
Nantes, France
,
2015
.
77.
A.
Sentchev
,
M.
Thiébaut
, and
F. G.
Schmitt
, “
Impact of turbulence on power production by a free-stream tidal turbine in real sea conditions
,”
Renewable Energy
147
,
1932
1940
(
2020
).
78.
T.
Blackmore
,
L. E.
Myers
, and
A. S.
Bahaj
, “
Effects of turbulence on tidal turbines: Implications to performance, blade loads, and condition monitoring
,”
Int. J. Mar. Energy
14
,
1
26
(
2016
).
79.
P.
Mycek
,
B.
Gaurier
,
G.
Germain
,
G.
Pinon
, and
E.
Rivoalen
, “
Experimental study of the turbulence intensity effects on marine current turbines behaviour. Part I: One single turbine
,”
Renewable Energy
66
,
729
746
(
2014
).
80.
J.
Thiébot
,
S.
Guillou
 et al, “
Modelling the effect of large arrays of tidal turbines with depth-averaged Actuator Disks
,”
Ocean Eng.
126
,
265
275
(
2016
).
81.
E.
Osalusi
,
J.
Side
, and
R.
Harris
, “
Structure of turbulent flow in EMEC's tidal energy test site
,”
Int. Commun. Heat Mass Transfer
36
,
422
431
(
2009
).
82.
E.
Osalusi
,
J.
Side
, and
R.
Harris
, “
Reynolds stress and turbulence estimates in bottom boundary layer of Fall of Warness
,”
Int. Commun. Heat Mass Transfer
36
,
412
421
(
2009
).
83.
J.
Thomson
,
B.
Polagye
,
V.
Durgesh
, and
M. C.
Richmond
, “
Measurements of turbulence at two tidal energy sites in Puget Sound, WA
,”
IEEE J. Oceanic Eng.
37
,
363
374
(
2012
).
84.
I. A.
Milne
,
R. N.
Sharma
,
R. G.
Flay
, and
S.
Bickerton
, “
Characteristics of the turbulence in the flow at a tidal stream power site
,”
Philos. Trans. R. Soc. A
371
,
20120196
(
2013
).
85.
M.
Togneri
and
I.
Masters
, “
Micrositing variability and mean flow scaling for marine turbulence in Ramsey Sound
,”
J. Ocean Eng. Mar. Energy
2
,
35
46
(
2016
).
86.
M.
Thiébaut
,
J.-F.
Filipot
,
C.
Maisondieu
,
G.
Damblans
,
R.
Duarte
,
E.
Droniou
,
N.
Chaplain
, and
S.
Guillou
, “
A comprehensive assessment of turbulence at a tidal-stream energy site influenced by wind-generated ocean waves
,”
Energy
191
,
116550
(
2020
).
87.
M.
Thiébaut
,
J.-F.
Filipot
,
C.
Maisondieu
,
G.
Damblans
,
R.
Duarte
,
E.
Droniou
, and
S.
Guillou
, “
Assessing the turbulent kinetic energy budget in an energetic tidal flow from measurements of coupled ADCPs
,”
Philos. Trans. R. Soc. A
378
,
20190496
(
2020
).
88.
M.
Thiébaut
,
J.-F.
Filipot
,
C.
Maisondieu
,
G.
Damblans
,
C.
Jochum
,
L. F.
Kilcher
, and
S.
Guillou
, “
Characterization of the vertical evolution of the three-dimensional turbulence for fatigue design of tidal turbines
,”
Philos. Trans. R. Soc. A
378
,
20190495
(
2020
).
89.
J.
Smagorinsky
, “
General circulation experiments with the primitive equations: I. The basic experiment
,”
Mon. Weather Rev.
91
,
99
164
(
1963
).
90.
P. E.
Robins
,
S. P.
Neill
,
M. J.
Lewis
, and
S. L.
Ward
, “
Characterising the spatial and temporal variability of the tidal-stream energy resource over the northwest European shelf seas
,”
Appl. Energy
147
,
510
522
(
2015
).
91.
M.
Togneri
,
M.
Lewis
,
S.
Neill
, and
I.
Masters
, “
Comparison of ADCP observations and 3D model simulations of turbulence at a tidal energy site
,”
Renewable Energy
114
,
273
282
(
2017
).
92.
M.
Guerra
,
R.
Cienfuegos
,
J.
Thomson
, and
L.
Suarez
, “
Tidal energy resource characterization in Chacao Channel, Chile
,”
Int. J. Mar. Energy
20
,
1
16
(
2017
).
93.
E.
Zangiabadi
,
M.
Edmunds
,
I. A.
Fairley
,
M.
Togneri
,
A. J.
Williams
,
I.
Masters
, and
N.
Croft
, “
Computational fluid dynamics and visualisation of coastal flows in tidal channels supporting ocean energy development
,”
Energies
8
,
5997
6012
(
2015
).
94.
A. C.
Bourgoin
,
S. S.
Guillou
,
J.
Thiébot
, and
R.
Ata
, “
Turbulence characterization at a tidal energy site using large-eddy simulations: Case of the Alderney Race
,”
Philos. Trans. R. Soc. A
378
,
20190499
(
2020
).
95.
P.
Mercier
,
M.
Grondeau
,
S.
Guillou
,
J.
Thiebot
, and
E.
Poizot
, “
Numerical study of the turbulent eddies generated by the seabed roughness. Case study at a tidal power site
,”
Appl. Ocean Res.
97
,
102082
(
2020
).
96.
J.
MacEnri
,
M.
Reed
, and
T.
Thiringer
, “
Influence of tidal parameters on SeaGen flicker performance
,”
Philos. Trans. R. Soc. A
371
,
20120247
(
2013
).
97.
Y.
Li
,
J.-H.
Yi
,
H.
Song
,
Q.
Wang
,
Z.
Yang
,
N. D.
Kelley
, and
K.-S.
Lee
, “
On the natural frequency of tidal current power systems—A discussion of sea testing
,”
Appl. Phys. Lett.
105
,
023902
(
2014
).
98.
P.
Schmitt
,
B.
Elsaesser
,
S.
Bischof
, and
R.
Starzmann
, “
Field testing a full-scale tidal turbine Part 2: In-line wake effects
,” in
Proceedings of the 11th European Wave and Tidal Energy Conference (EWTEC)
,
Nantes, France
(EWTEC,
2015
), pp.
6
11
.
99.
M.
Verbeek
,
R.
Labeur
,
W.
Uijttewaal
, and
P. D.
Haas
, “
The near-wake of horizontal axis tidal turbines in a storm surge barrier
,” in
Proceedings of 12th European Wave and Tidal Energy Conference (EWTEC)
,
Cork, Ireland
,
2017
.
100.
A.
Bahaj
,
A.
Molland
,
J.
Chaplin
, and
W.
Batten
, “
Power and thrust measurements of marine current turbines under various hydrodynamic flow conditions in a cavitation tunnel and a towing tank
,”
Renewable Energy
32
,
407
426
(
2007
).
101.
L.
Myers
and
A.
Bahaj
, “
Near wake properties of horizontal axis marine current turbines
,” in
Proceedings of the 8th European Wave and Tidal Energy Conference (EWTEC)
(
Uppsala
,
Sweden
,
2009
), pp.
558
565
.
102.
F.
Maganga
,
G.
Germain
,
J.
King
,
G.
Pinon
, and
E.
Rivoalen
, “
Experimental characterisation of flow effects on marine current turbine behaviour and on its wake properties
,”
IET Renewable Power Gener.
4
,
498
509
(
2010
).
103.
S.
Rose
,
A.
Good
,
M.
Atcheson
,
G.
Hamill
,
C.
Johnstone
,
P.
MacKinnon
,
D.
Robinson
,
A.
Grant
, and
T.
Whittaker
, “
Investigating experimental techniques for measurement of the downstream near wake of a tidal turbine
,” in
Proceedings of the 9th European Wave and Tidal Energy Conference (EWTEC) (
2011
).
104.
L.
Chamorro
,
C.
Hill
,
S.
Morton
,
C.
Ellis
,
R.
Arndt
, and
F.
Sotiropoulos
, “
On the interaction between a turbulent open channel flow and an axial-flow turbine
,”
J. Fluid Mech.
716
,
658
670
(
2013
).
105.
A.
Vinod
and
A.
Banerjee
, “
Performance and near-wake characterization of a tidal current turbine in elevated levels of free stream turbulence
,”
Appl. Energy
254
,
113639
(
2019
).
106.
P.
Mycek
,
B.
Gaurier
,
G.
Germain
,
G.
Pinon
, and
E.
Rivoalen
, “
Experimental study of the turbulence intensity effects on marine current turbines behaviour. Part II: Two interacting turbines
,”
Renewable Energy
68
,
876
892
(
2014
).
107.
B.
Gaurier
,
C.
Carlier
,
G.
Germain
,
G.
Pinon
, and
E.
Rivoalen
, “
Three tidal turbines in interaction: An experimental study of turbulence intensity effects on wakes and turbine performance
,”
Renewable Energy
148
,
1150
1164
(
2020
).
108.
T.
Stallard
,
R.
Collings
,
T.
Feng
, and
J.
Whelan
, “
Interactions between tidal turbine wakes: Experimental study of a group of three-bladed rotors
,”
Philos. Trans. R. Soc. A
371
,
20120159
(
2013
).
109.
I.
Afgan
,
J.
McNaughton
,
S.
Rolfo
,
D.
Apsley
,
T.
Stallard
, and
P.
Stansby
, “
Turbulent flow and loading on a tidal stream turbine by LES and RANS
,”
Int. J. Heat Fluid Flow
43
,
96
108
(
2013
).
110.
R.
Malki
,
A.
Williams
,
T.
Croft
,
M.
Togneri
, and
I.
Masters
, “
A coupled blade element momentum–computational fluid dynamics model for evaluating tidal stream turbine performance
,”
Appl. Math. Modell.
37
,
3006
3020
(
2013
).
111.
B.
Elie
,
G.
Oger
,
P.-E.
Guillerm
, and
B.
Alessandrini
, “
Simulation of horizontal axis tidal turbine wakes using a weakly-compressible Cartesian hydrodynamic solver with local mesh refinement
,”
Renewable Energy
108
,
336
354
(
2017
).
112.
G.
Pinon
,
P.
Mycek
,
G.
Germain
, and
E.
Rivoalen
, “
Numerical simulation of the wake of marine current turbines with a particle method
,”
Renewable Energy
46
,
111
126
(
2012
).
113.
M.
Grondeau
,
S.
Guillou
,
P.
Mercier
, and
E.
Poizot
, “
Wake of a ducted vertical axis tidal turbine in turbulent flows, LBM actuator-line approach
,”
Energies
12
,
4273
(
2019
).
114.
M.
Harrison
,
W.
Batten
,
L.
Myers
, and
A.
Bahaj
, “
Comparison between CFD simulations and experiments for predicting the far wake of horizontal axis tidal turbines
,”
IET Renewable Power Gener.
4
,
613
627
(
2010
).
115.
V.
Nguyen
,
S. S.
Guillou
,
J.
Thiébot
, and
A.
Santa Cruz
, “
Modelling turbulence with an Actuator Disk representing a tidal turbine
,”
Renewable Energy
97
,
625
635
(
2016
).
116.
S. S.
Olson
,
J. C.
Su
,
H.
Silva
 III
,
C. C.
Chartrand
, and
J. D.
Roberts
, “
Turbulence-parameter estimation for current-energy converters using surrogate model optimization
,”
Renewable Energy
168
,
559
567
(
2021
).
117.
A. E.
Copping
,
L. G.
Hemery
,
D. M.
Overhus
,
L.
Garavelli
,
M. C.
Freeman
,
J. M.
Whiting
,
A. M.
Gorton
,
H. K.
Farr
,
D. J.
Rose
, and
L. G.
Tugade
, “
Potential environmental effects of marine renewable energy development-the state of the science
,”
J. Mar. Sci. Eng.
8
,
879
(
2020
).
118.
Z.
Yang
and
A.
Copping
,
Marine Renewable Energy: Resource Characterization and Physical Effects
(
Springer
,
2017
).
119.
M.
Kadiri
,
R.
Ahmadian
,
B.
Bockelmann-Evans
,
W.
Rauen
, and
R.
Falconer
, “
A review of the potential water quality impacts of tidal renewable energy systems
,”
Renewable Sustainable Energy Rev.
16
,
329
341
(
2012
).
120.
C.
Garrett
and
P.
Cummins
, “
Limits to tidal current power
,”
Renewable Energy
33
,
2485
2490
(
2008
).
121.
R.
Vennell
, “
Estimating the power potential of tidal currents and the impact of power extraction on flow speeds
,”
Renewable Energy
36
,
3558
3565
(
2011
).
122.
T.
Wang
and
Z.
Yang
, “
A tidal hydrodynamic model for Cook Inlet, Alaska, to support tidal energy resource characterization
,”
J. Mar. Sci. Eng.
8
,
254
(
2020
).
123.
Z.
Yang
,
T.
Wang
,
A.
Copping
, and
S.
Geerlofs
, “
Modeling of in-stream tidal energy development and its potential effects in Tacoma Narrows, Washington, USA
,”
Ocean Coastal Manage.
99
,
52
62
(
2014
).
124.
D.
Zhang
,
X.
Liu
,
M.
Tan
,
P.
Qian
, and
Y.
Si
, “
Flow field impact assessment of a tidal farm in the Putuo-Hulu channel
,”
Ocean Eng.
208
,
107359
(
2020
).
125.
A.
Pacheco
and
Ó.
Ferreira
, “
Hydrodynamic changes imposed by tidal energy converters on extracting energy on a real case scenario
,”
Appl. Energy
180
,
369
385
(
2016
).
126.
S.
Nash
,
A.
Olbert
,
M.
Hartnett
 et al, “
Modelling the far field hydro-environmental impacts of tidal farms—A focus on tidal regime, inter-tidal zones and flushing
,”
Comput. Geosci.
71
,
20
27
(
2014
).
127.
Z.
Yang
and
T.
Wang
, “
Effects of tidal stream energy extraction on water exchange and transport timescales
,” in
Marine Renewable Energy
(
Springer
,
2017
), pp.
259
278
.
128.
N.
Guillou
,
J.
Thiébot
, and
G.
Chapalain
, “
Turbines' effects on water renewal within a marine tidal stream energy site
,”
Energy
189
,
116113
(
2019
).
129.
K.
Nelson
,
S. C.
James
,
J. D.
Roberts
, and
C.
Jones
, “
A framework for determining improved placement of current energy converters subject to environmental constraints
,”
Int. J. Sustainable Energy
37
,
654
668
(
2018
).
130.
S. C.
James
,
S. S.
Olson
,
S.
McWilliams
,
C. A.
Jones
, and
J. D.
Roberts
, “
Modeling current-energy-converter devices in the Tanana River, Alaska
,” in
Offshore Technology Conference
(
OnePetro
,
2020
).
131.
N.
Guillou
,
J.-F.
Charpentier
, and
M.
Benbouzid
, “
The tidal stream energy resource of the Fromveur Strait—A review
,”
J. Mar. Sci. Eng.
8
,
1037
(
2020
).
132.
T.
Wang
,
Z.
Yang
, and
A.
Copping
, “
A modeling study of the potential water quality impacts from in-stream tidal energy extraction
,”
Estuaries Coasts
38
,
173
186
(
2015
).
133.
L. M.
Ashall
,
R. P.
Mulligan
, and
B. A.
Law
, “
Variability in suspended sediment concentration in the Minas Basin, Bay of Fundy, and implications for changes due to tidal power extraction
,”
Coastal Eng.
107
,
102
115
(
2016
).
134.
R.
Ahmadian
,
R.
Falconer
, and
B.
Bockelmann-Evans
, “
Far-field modelling of the hydro-environmental impact of tidal stream turbines
,”
Renewable Energy
38
,
107
116
(
2012
).
135.
G.
Shapiro
, “
Effect of tidal stream power generation on the region-wide circulation in a shallow sea
,”
Ocean Sci.
7
,
165
174
(
2011
).
136.
S. P.
Neill
,
P. E.
Robins
, and
I.
Fairley
, “
The impact of marine renewable energy extraction on sediment dynamics
,” in
Marine Renewable Energy
(
Springer
,
2017
), pp.
279
304
.
137.
S. P.
Neill
,
E. J.
Litt
,
S. J.
Couch
, and
A. G.
Davies
, “
The impact of tidal stream turbines on large-scale sediment dynamics
,”
Renewable Energy
34
,
2803
2812
(
2009
).
138.
S. P.
Neill
,
M. R.
Hashemi
, and
M. J.
Lewis
, “
The role of tidal asymmetry in characterizing the tidal energy resource of Orkney
,”
Renewable Energy
68
,
337
350
(
2014
).
139.
S. P.
Neill
,
J. R.
Jordan
, and
S. J.
Couch
, “
Impact of tidal energy converter (TEC) arrays on the dynamics of headland sand banks
,”
Renewable Energy
37
,
387
397
(
2012
).
140.
H.-B.
Goh
,
S.-H.
Lai
,
M.
Jameel
, and
H.-M.
Teh
, “
Potential of coastal headlands for tidal energy extraction and the resulting environmental effects along Negeri Sembilan coastlines: A numerical simulation study
,”
Energy
192
,
116656
(
2020
).
141.
P. E.
Robins
,
S. P.
Neill
, and
M. J.
Lewis
, “
Impact of tidal-stream arrays in relation to the natural variability of sedimentary processes
,”
Renewable Energy
72
,
311
321
(
2014
).
142.
J.
Thiébot
,
P. B.
Du Bois
, and
S.
Guillou
, “
Numerical modeling of the effect of tidal stream turbines on the hydrodynamics and the sediment transport—Application to the Alderney Race (Raz Blanchard), France
,”
Renewable Energy
75
,
356
365
(
2015
).
143.
A.
Cornett
,
J.
Cousineau
, and
I.
Nistor
, “
Assessment of hydrodynamic impacts from tidal power lagoons in the Bay of Fundy
,”
Int. J. Mar. Energy
1
,
33
54
(
2013
).
144.
R. A.
Falconer
,
J.
Xia
,
B.
Lin
, and
R.
Ahmadian
, “
The Severn Barrage and other tidal energy options: Hydrodynamic and power output modeling
,”
Sci. China Ser. E
52
,
3413
3424
(
2009
).
145.
S. P.
Neill
,
M. R.
Hashemi
, and
M. J.
Lewis
, “
Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function
,”
Energy
73
,
997
1006
(
2014
).
146.
S. P.
Neill
,
M.
Hemer
,
P. E.
Robins
,
A.
Griffiths
,
A.
Furnish
, and
A.
Angeloudis
, “
Tidal range resource of Australia
,”
Renewable Energy
170
,
683
692
(
2021
).
147.
M.
Lewis
,
S.
Neill
,
M. R.
Hashemi
, and
M.
Reza
, “
Realistic wave conditions and their influence on quantifying the tidal stream energy resource
,”
Appl. Energy
136
,
495
508
(
2014
).
148.
A.
LiVecchi
,
A.
Copping
,
D.
Jenne
,
A.
Gorton
,
R.
Preus
,
G.
Gill
,
R.
Robichaud
,
R.
Green
,
S.
Geerlofs
,
S.
Gore
 et al,
Powering the Blue Economy; Exploring Opportunities for Marine Renewable Energy in Maritime Markets
(
US Department of Energy, Office of Energy Efficiency and Renewable Energy
,
Washington
,
DC
,
2019
).
149.
T.
Harrison
,
K. M.
Thyng
, and
B.
Polagye
, “
Comparative evaluation of volumetric current measurements in a tidally dominated coastal setting: A virtual field experiment
,”
J. Atmos. Oceanic Technol.
37
,
533
552
(
2020
).
150.
A.
Avdis
,
A. S.
Candy
,
J.
Hill
,
S. C.
Kramer
, and
M. D.
Piggott
, “
Efficient unstructured mesh generation for marine renewable energy applications
,”
Renewable Energy
116
,
842
856
(
2018
).
151.
Z.
Yang
and
T.
Wang
, “
Modeling the effects of tidal energy extraction on estuarine hydrodynamics in a stratified estuary
,”
Estuaries Coasts
38
,
187
202
(
2015
).
152.
G. D.
Egbert
and
S. Y.
Erofeeva
, “
Efficient inverse modeling of barotropic ocean tides
,”
J. Atmos. Oceanic Technol.
19
,
183
204
(
2002
).
153.
J.
Jeong
and
F.
Hussain
, “
On the identification of a vortex
,”
J. Fluid Mech.
285
,
69
94
(
1995
).
154.
P.
Spalart
and
S.
Allmaras
, “
A one-equation turbulence model for aerodynamic flows
,” in
30th Aerospace Sciences Meeting and Exhibit
(
American Institute of Aeronautics and Astronautics
,
1992
), p.
439
.
155.
G. L.
Mellor
and
T.
Yamada
, “
Development of a turbulence closure model for geophysical fluid problems
,”
Rev. Geophys.
20
,
851
875
, (
1982
).
156.
S. P.
Neill
and
P. E.
Robins
, “
Global and regional tidal range resource
,” in
Proceedings of the 13th European Wave and Tidal Energy Conference (EWTEC)
,
Napoli, Italy
, 1–6 September
2019
.