Location Suitability Analysis

Problem Statement

As interest in renewable energy sources and reducing greenhouse gas emissions has increased globally, offshore wind energy is seen as having some of the highest growth potential for electricity generation in part due to the extensive areas of ocean available (Costoya et al. 2020). Offshore wind farms can also be less impacted by the current impediments to onshore wind farms, which include land use contention, threats to wildlife, and overall public acceptance. A primary challenge in developing offshore wind farms is identifying sites that are suitable, as there is no general consensus approach (Mahdy & Bahaj, 2018). This study aims to apply two location suitability analysis methods, Boolean Overlay and Weighted Linear Combination, to determine feasible sites for offshore wind farms in the Pacific Ocean off the coast of Oregon.

Data

The Pacific region of the U.S. is currently seen as underutilized for offshore wind energy production primarily due to water depths and technological constraints. Worldwide, the vast majority of offshore wind farms are in fixed locations in waters with depths of under 60 meters (Vasileiou et al. 2017). Although the exclusive economic zone in which the U.S. can utilize natural resources extends to 200 nautical miles from the coast, much of this area exceeds the practical limits of wind energy production. As 97% of what is considered viable offshore wind resources in Oregon are in deeper waters, advancement in the development of floating wind farms is seen as paramount (Musial et al. 2019). The Oregon coast is known to have favorably high wind speeds, a number of ports for potential wind farm installation and maintenance, and major shipping routes from the city of Portland. The Oregon Islands National Wildlife Refuge also spans the entire length of the coast to protect the habitat of birds, fish, and marine mammals. 

Data for the evaluation criteria for this project include summary statistics of offshore wind resources for the Pacific coast from the Wind Integration National Dataset Toolkit, a depth raster from the General Bathymetric Chart of the Oceans, the locations of Marine Highways as designated by the U.S. Department of Transportation, and the locations of global maritime ports (Figure 1). Marine and coastal protected areas from the World Database on Protected Areas are also used as a boundary constraint. Descriptions of all data sources used can be found in Table 1.

Methods

The projected coordinate system used for this analysis is the NAD 1983 UTM Zone 10N. Unlike State Plane systems that are centered on the land mass of the state, UTM Zone 10N extends into the Pacific Ocean which should serve to minimize distortions where this analysis occurs.

Selection Criteria

The four criteria used in this analysis to evaluate suitable areas for wind farm development are wind speed, ocean floor depth, distance from Marine Highways, and distance from ports. Table 2 shows the cutoff values for the Boolean Overlay analysis, while Table 3 shows the ranked selection criteria for the Weighted Linear Combination method. Without consistently adequate wind speeds to drive the turbines, there will not be enough energy generation to justify the costs of farm development. Although wind speeds of 7 m/s are typically considered adequate according to Musial et al. (2019), the cutoff value of 8 m/s was chosen for the Boolean Overlay method to better account for any fluctuations at a finer timescale than the annual data used in this analysis. For the ranked suitability, higher wind speeds were considered more favorable. As previously described, ocean floor depth is currently a major limiting factor in offshore wind development especially in the Pacific Ocean where the continental shelf drops off sharply. Depths of 1,000 m were set as the Boolean cutoff, as this is currently seen as the feasible limit for floating wind farm technology (Musial et al. 2019). The highest suitability rank was set at the typical limit for fixed wind farms at depths of 5 to 60 m, with suitability decreasing at greater depths that would necessitate floating wind farms.

The established north-south Marine Highway along the coast is important as a boundary to the analysis. Diverting the routes of ships along the Marine Highway as well as having any maintenance ships from the shore crossing the shipping lane could prove costly or dangerous. Although it is not at a fixed width, a cutoff value of 5 km on either side of the Marine Highway was chosen with further distances ranked as more highly suitable. Installing as well as maintaining wind farms off the coast would require access to ports on the shore. A cutoff value of 75 km was chosen as being a reasonable distance that was also within the extent of the analysis, with closer distances being ranked as more suitable.

Boolean Overlay

Map overlay is the process in which information from different map layers can be combined. In the Boolean Overlay method, binary logic is used to disqualify areas based on intersections of the selection criteria until only suitable areas remain (O’Sullivan & Unwin, 2014). For the wind speed criterion, the wind speed points were first spatially joined to the aliquot grid cell that contained them. Each area with an annual average wind speed greater than or equal to 8 m/s was then selected (Figure 2). The raster of ocean floor depths was first reclassified into areas above and below 1000 m (Figure 3). These areas were then converted to vector polygons to represent the areas meeting the depth criterion or not.

A buffer of 5 km was first placed around the Marine Highways (Figure 4), which was then erased from the overall study area to create the suitable area meeting the distance criterion (Figure 5). A buffer of 75 km was also created around the Oregon ports to create the area that was suitable according to the distance criterion (Figure 6). Finally, the Boolean intersection (AND) of all four criteria was found to determine the area that is suitable for wind farm development (Figure 7).

Methods

Weighted Linear Combination

The Weighted Linear Combination method instead assigns ranked classes to the selection criteria. Weights are assigned to each criterion based on their relative importance, which are then combined and standardized to create a total suitability score. As opposed to a binary score of each criterion, the Weighted Linear Combination method can result in a high score in one factor balancing out a low score in another factor (Malczewski, 2004). The wind speed areas were first reclassified into the four rankings from Not Suitable to High Suitability, as given in Table 3 and shown in Figure 8. Similarly, the depth raster was reclassified based on its four rankings (Figure 9) before being converted to  polygons.

Non-overlapping buffers of the nearest distance to Marine Highways rankings were created (Figure 10), and then their union with the overall study area was found to create the furthest distance ranking (Figure 11). These distance areas were then reclassified into four rankings (Figure 12). The distance to ports suitability rankings were found in a similar way, by first buffering (Figure 13), finding the union with the study area (Figure 14), and finally reclassifying the areas into the four rankings (Figure 15). The intersection of the four rankings for all four criteria was then found (Figure 16). Finally, weights (found from Tables 4 and 5 and further described below) were applied to the four criteria and summed to determine the ranked suitability areas for wind farms.

Assumptions

The extent of this analysis was determined by both physical constraints as well as data availability. The Oregon Islands National Wildlife Refuge extends approximately 3 to 4 miles from the coast. As it would not be suitable to build wind farms in these protected areas (as well as due to shallow depths), this served as the eastern boundary. A buffer of 80 km was created to west of this boundary. This ensured that the study did not extend much beyond the indefinite width of the Marine Highway nor beyond the availability of the wind speed data. This buffer was then clipped based on lines drawn parallel from the northern and southern state boundaries of Oregon to create the overall target area in Figure 1.

In determining the criteria weights, wind was the most important factor as wind turbines cannot operate without it. Wind speed was thought to be twice as important as depth. There are two different methods available in fixed versus floating wind farms as well as potential improvements in floating wind farm technology in the future. Wind speed was seen as three times as important as the distance from Marine Highways. Unlike highways on land, the Marine Highways are not of an actual finite width so some flexibility in the paths of traveling ships seems reasonable. The distance to ports was deemed to be least important factor, with wind speeds being four times as important. If the distance from ports were truly the limiting factor in wind farm placement, then larger boats with greater fuel capacities would seem to be an accessible solution. From these initial judgements, the rest of the pairwise comparisons were made between each criterion as shown in Table 4. Each value was then normalized by dividing by the sum of its criterion column to fill the matrix in Table 5. These normalized values were then averaged across rows to determine the final weight for each criterion, which sum to a value of one (final column of Table 5).

Results

Boolean Overlay

The area that is suitable for offshore wind farms according to the Boolean Overlay method is shown in Figure 18. This area, which represents approximately 27.4% of the total target area, is concentrated near the coast and towards the southern end of the study area. The buffer around the Marine Highway on the western side of the target area had the least effect on the overall result, as this area is already generally too deep. The northern portion was deemed as not suitable mainly due to the wind speeds being too low. The remaining suitable area is thus primarily where the shallower depths and smaller distances to ports overlap.

Results

Weighted Linear Combination

The ranked suitability areas for offshore wind farms from the Weighted Linear Combination method are shown in Figure 19. The 8.8% of the total target area that is considered High Suitability is concentrated in the southeast region and appears to be most directly influenced by the high wind speeds in this area. The 31.5% that is Low Suitability is along the western and northern edges where the distance from Marine Highways and deeper depths dominate. The 59.7% that are Medium Suitability areas are located throughout the rest of the target area mainly in the shallower depths that are closer to ports. As the Not Suitable area for each criterion was given a rank of 1, only the single area that was assigned a rank of 1 for all of the criteria could result in the overall Not Suitable area in the northern region that was only 0.03% of the target area.

Conclusion

The general spatial trends tend to agree across both location suitability analysis methods. Suitable areas for offshore wind farms are most directly influenced by average wind speeds, followed by both shallow ocean depths and nearness to ports. Areas that were not suitable were closest to the shipping lanes. The primary differences lie in the capability of rankings to offset each other within the Weighted Linear Combination method, as well as determining the weights to be applied to them. For the Weighted Linear Combination method, if the intersected areas are selected before weights are applied where none of the four criteria rankings are equal to one then the result exactly matches the suitable area from the Boolean Overlay method. In the remainder of the target area, having at least one criterion that does not have a ranking of one would allow for that area to at least be considered to have Low Suitability. Selecting the weights to apply to the criteria is highly influential and can introduce significant uncertainty. Any stakeholder in the development of offshore wind farms could make a different determination on the relative importance of each factor. When external knowledge is not available or agreed upon, other probabilistic methods that rely on weights of evidence could be used (O’Sullivan & Unwin, 2014). However, it should also be accepted that generally there is not a single correct answer to an overlay analysis question.

Other limitations of this analysis should be noted. For the wind speeds, a single value that was averaged over the seven-year period of data collection was used. This could thus not take into account daily or seasonal fluctuations, nor could it necessarily be representative of future wind speeds in climate change scenarios. There is also uncertainty surrounding the development of floating wind farm technology that affects the ocean depth rankings. All ports along the Oregon coast were used in the analysis, without determining if their size was appropriate for supporting installation and maintenance ships. Additionally, the analysis was limited to Oregon only and did not consider any cooperation or port access with California or Washington. As described earlier, the typical widths of shipping lanes in the area are unknown. Public perceptions were also not considered, as the suitable areas could be deemed to be within the viewshed and too close to shore. There are numerous other criteria that could have been studied, in particular population density, existing electric grid infrastructure, and total costs.

There are suitable areas off the Oregon coast where offshore wind farms could be feasible in the absence of costs and other relevant factors, especially as the technology of floating wind farms continues to improve.

References

Costoya, X., deCastro, M., Carvalho, D., & Gómez-Gesteira, M. (2020). On the suitability of offshore wind energy resource in the United States of America for the 21st century. Applied Energy, 262, 114537. https://doi.org/10.1016/j.apenergy.2020.114537

Mahdy, M., & Bahaj, A. S. (2018). Multi criteria decision analysis for offshore wind energy potential in Egypt. Renewable Energy, 118, 278–289. https://doi.org/10.1016/j.renene.2017.11.021

Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65. https://doi.org/10.1016/j.progress.2003.09.002

Musial, W., Beiter, P., Nunemaker, J., Heimiller, D., Ahmann, J., & Busch, J. (2019). Oregon Offshore Wind Site Feasibility and Cost Study (NREL/TP--5000-74597, 1570430; p. NREL/TP--5000-74597, 1570430). https://doi.org/10.2172/1570430

O’Sullivan, D., & Unwin, D. J. (2014). Geographic information analysis (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/9780470549094

Vasileiou, M., Loukogeorgaki, E., & Vagiona, D. G. (2017). GIS-based multi-criteria decision analysis for site selection of hybrid offshore wind and wave energy systems in Greece. Renewable and Sustainable Energy Reviews, 73, 745–757. https://doi.org/10.1016/j.rser.2017.01.161