Wave resource assessments are conducted with two major goals in mind: 1) provide the necessary quantitative data to identify optimal sites for future WEC deploy-ments and 2) provide an accurate and reliable assessment of wave and environ-mental conditions at the identified location.
Determining the best locations for future WEC deployment requires a consistent and easily repeatable methodology that exposes technical, environmental, and political constraints. In this section, analyses will focus on the technical constraints of the identification process; the environmental and political aspects are left for later chapters and authors. Examples of technical constraints include the magnitude of the wave energy resource, distance to existing electrical infrastructure, water depth, and probability of extreme wave conditions. Examples of environmental and political aspects include biological species identification, marine protected areas, commercial fishing, or military use areas. The relative impact of the technical aspects are acknowledged to be dependent on the local industry, political landscape, and WEC architecture of choice. Nevertheless, a baseline methodology and pro-cedural framework are valuable for providing guidance for global efforts to identify optimal deployment locations for individual WECs and future WEC farms.
The proposed identification methodology sequentially filters the wave resource assessment data until only regions that satisfy a series of constraints are identified.
First, given the high costs associated with procurement and laying of seafloor electrical cables, up to∼USD $1.7 M/km (Kim et al.2012), it is unlikely that WEC deployment in the far-offshore region will be financially viable initially (Dykes et al.2002). The added operating and maintenance costs associated with deep water
28 B. Robertson
and far flung sites make these sites increasingly unattractive. Under this constraint, the a priori application of a maximum offshore deployment distance (say, 15 km) greatly reduces the computational effort associated with detailed statistical analysis of the wave climate and wave resource characteristics.
Within the resulting 15 km coastal region, water depth has been used as a proxy for the differing WEC operating and energy extraction principles and fiscal suit-ability (Boelen et al. 2010). Under this broad generalization, the global suite of WEC architectures can be categorized by their operating depths, and the 15-km-wide coastal region can be divided into distinct depth-correlated regions.
The use of this depth/WEC architecture proxy allows the optimal deployment site within each region to be identified. For illustration purposes, Fig.16used 0–0 m, 30–150 m, and greater than 150-m-depth regions for characterizing WEC operating depths and as a proxy for WEC architectures.
Within each of these regions, the spatial distribution and magnitude of nearshore wave energy vary dramatically because of bathymetric and coastal topographic features. Ranking the omnidirectional wave energy transport (J) values associated with each computational grid point in each depth region allows for the identification of percentile-based wave energy transport values. For example, Robertson et al.
(2002) only used the 90th percentile of wave energy transport grid nodes in their Fig. 16 Possible WEC farm deployment locations on the west coast of Canada. Green identifies farm locations; the color map provide percentile wave energy transport; and the depths regions are noted
Wave Energy Assessments: Quantifying the Resource… 29
siting exercise. The analysis will identify not only the value for the 90th percentile wave energy transport, but also the associated spatial area and coast-parallel length.
The area and length can be used as secondary proxies for the maximum number of individual WEC deployments within the farm and the rated generation capacity of the farm. Figure16illustrates the WEC farm locations identified for the three depth regions off the west coast of Canada, while Table3presents the details associated with the differing locations. Note that the annual wave energy transport percentile ranking values in Fig.16 are depth-region dependent and cannot be compared across regions. In this example, and contrary to conventional wisdom, the deepest site does not have the highest mean wave energy transport and the highest potential for electricity generation. The middle depth site has the highest mean annual wave energy transport and features the largest spatial area and a moderate depth for mooring configurations. The statistics presented in Table3 provide the baseline information required to help WEC technology and project developers objectively assess the suitability and comparative performance of prospective deployment locations.
The proposed methodology provides a first-pass and rough estimate of prospective WEC deployment sites and the rated capacity of the associated WEC farms. This allows for quick side-by-side comparisons of the costs vs generation potential for potential future WEC farm deployment locations. Note that this method does not take into account the impact of the actual WEC or WEC farm on the ambient wave climate, and, depending on the WEC and number of WECs deployed, will generally result in an overestimation of WEC power production. The modeling of WEC farms is an active area of research (Ruehl2013; Luczko et al.
2016; Folley and Whittaker2011) and, undoubtedly, will result in the development of numerical models that can optimize farm layouts and maximize power produc-tion for a specific site.
Additional fidelity can be added to the siting methodology through numerous mechanisms. If a specific WEC and associated performance matrix are available, the site can be identified through a percentile analysis of WEC power production, rather than the suggested bulk omnidirectional wave energy transport metric. If multiple prospective sites are identified, the application of an extreme wave analysis will identify the respective probabilities of large and destructive wave events within the prospective regions and provide quantitative technical data for use in filtering unsuitable locations.
Table 3 Statistics for identified prospective WEC farm locations Water depth
(m)
Farm area (km2)
Mean J (kW/m)
Distance to shore (km)
Water depth (m)
0–30 8 35.42 3.26 16.88
30–150 33 39.03 7.58 41.03
>150 19 36.72 13.17 175.96
30 B. Robertson