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Site suitability analysis for implementing tidal energy technology in southern California
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Site suitability analysis for implementing tidal energy technology in southern California
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Content
Copyright ã 2021 Drew Quenna Vagen
Site Suitability Analysis for Implementing Tidal Energy Technology in Southern California
By
Drew Quenna Vagen
A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)
May 2021
ii
Dedication
Dedicated to my closest friends and family.
iii
Acknowledgements
I would like to extend a special thank you to my closest friends and family. To my oceanography
friends: thank you for the kind words of encouragement and constant laughs. To my boyfriend:
thank you for being with me every step of the way. Finally, to my parents: thank you for
believing in me and providing your constant support.
I would also like to acknowledge the amazing support of the Spatial Sciences Institute at
the University of Southern California, and my professors and advisors who have guided me
through this process with kindness and support.
iv
Table of Contents
Dedication ii
Acknowledgements iii
List of Tables vi
List of Figures vii
List of Abbreviations ix
Abstract xi
Chapter 1 Introduction 1
1.1. Climate Change 1
1.2. The Use of Fossil Fuels 2
1.3. Renewable Energy 3
1.4. Hydrokinetic Energy 4
1.5. Thesis Structure 5
Chapter 2 Related Work 7
2.1. Ocean Energy 7
2.1.1. Wave Energy 7
2.1.2. Wave energy compared to tidal energy 9
2.2. Tides 10
2.2.1. Tidal Currents 12
2.2.2. NOAA Tidal Data Collection 13
2.3. Tidal Energy Technology 15
Chapter 3 Design and Methods 18
3.1. Methodologies Used to Assess Site Suitability 18
3.2. Data 20
3.2.1. Physical Data 21
3.2.2. Interpolation of Tidal Range 22
3.2.3. Government Regulation Data 26
3.2.4. Commercial Use Data 29
3.2.5. Power plants 31
3.3. Hard and Soft Criteria 32
3.4. Overlay Analyses 33
3.5. Weighted Overlay Analyses 34
3.5.1. Hard Criteria 34
3.5.2. Analysis 35
3.6. Fuzzy Membership 36
3.7. Fuzzy Overlay Analysis 37
3.8. Distance to Energy Facilities 38
3.9. Summary 38
Chapter 4 Results 39
4.1. Interpolation 39
v
4.2. Weighted Overlay Analysis 40
4.2.1. Eliminating Hard Criteria 41
4.2.2. Weighted Overlay Analysis 43
4.3. Fuzzy Overlay Analysis 47
4.4. Breakdown by County 51
4.5. Distance to Energy Facilities 52
Chapter 5 Discussion 55
5.1. Data 55
5.2. Differences in interpolation methods 56
5.3. Weighted Overlay 60
5.4. Fuzzy Overlay 61
5.5. Energy Facilities 63
Chapter 6 Conclusions 64
6.1. Project Expansion 65
6.2. Spatial Analysis 66
6.3. The Future of Tidal Energy 67
References 68
vi
List of Tables
Table 1. Data used in this study to define the study area. 21
Table 2. Data used in this study relating to the physical limitations of tidal energy technology. 26
Table 3. Data used in this study relating to the government use limitations of tidal energy
technology. 27
Table 4. Data used in this study relating to the commercial use limitations of tidal energy
technology. 29
Table 5. Scales and weights used for the weighted overlay analysis. 36
Table 6. Breakdown of cells from the weighted overlay analysis using the IDW interpolation
method. 44
Table 7. Breakdown of cells from the weighted overlay analysis using the RBF interpolation
method. 45
Table 8. Breakdown of suitable cells that were identified in the weighted overlay analysis using
each interpolation method. 47
Table 9. Breakdown of cells from the fuzzy overlay analysis using the IDW method. 49
Table 10. Breakdown of cells from the fuzzy overlay analysis using the RBF interpolation
method. 50
Table 11. Breakdown of suitable cells identified by each interpolation method. 51
Table 12. Breakdown of suitable cells by county comparing the weighed overlay analysis and
fuzzy overlay analysis. 52
Table 13. Breakdown showing the percent of cells that were within the specified distances to
energy facilities for each different interpolation method and analysis type. 54
vii
List of Figures
Figure 1. Map showing the tidal energy around the world calculated from the TOPEX/Poseidon
satellites. Red colors indicate the strongest tides, while blue indicates the smaller tides
(NASA 2019b) 11
Figure 2. Map depicting the tidal stations derived from CO-OPS used in this study. 15
Figure 3. Classification tree provided by ArcGIS Pro to help determine which type of
interpolation method to use. Esri (2020a). 22
Figure 4. Flowchart depicting the data organization for the physical datasets. 25
Figure 5. Flow chart depicting the data pre-processing for the government and commercial use
datasets. 31
Figure 6. Results of the IDW interpolation, showing the estimated average tidal ranges within the
study area. 39
Figure 7. Results of the RBF interpolation, showing the estimated average tidal ranges within the
study area. 40
Figure 8. The new study area, represented in red, created after elimination of the areas
disqualified with the hard criteria. 41
Figure 9. The new study area, represented in red, that excludes the areas disqualified with the
hard criteria in and near Long Beach, CA. 42
Figure 10. The new study area that excludes the areas disqualified with hard criteria in and near
San Diego, CA. 42
Figure 11. Weighted overlay analysis results using the IDW interpolation method. Top inset
image shows Long Beach, CA. Bottom inset image shows San Diego, CA. 43
Figure 12. Weighted overlay analysis results using the RBF interpolation method. Top inset
image shows Long Beach, CA. Bottom inset image shows San Diego, CA. 45
Figure 13. Results of the weighted overlay analysis comparing suitable cells identified by each
interpolation method. Top inset image shows Long Beach, CA. Bottom inset image shows
San Diego, CA. 46
Figure 14. Fuzzy overlay analysis results using the IDW interpolation method. Top inset image
shows Long Beach, CA. Bottom inset image shows San Diego, CA. 48
Figure 15. Fuzzy overlay analysis results using the RBF interpolation method. Top inset image
shows Long Beach, CA. Bottom inset image shows San Diego, CA. 50
Figure 16. Results of the fuzzy overlay analysis comparing each interpolation method. Top inset
image shows Long Beach, CA. Bottom inset image shows San Diego, CA. 51
Figure 17. Renewable power plants located near the study area. 53
Figure 18. Estimated tidal range values using the IDW interpolation method overlaid with
original tidal range data points. 57
Figure 19. Estimated tidal range values using the RBF interpolation method overlaid with
original tidal range data points. 57
viii
Figure 20. IDW interpolated tidal range values in San Diego County, CA. The values represent
the average tidal range. 58
Figure 21. RBF interpolated tidal range values in San Diego County, CA. The values represent
the average tidal range. 59
Figure 22. Weighted overlay analysis results in Los Angeles County, CA. 61
Figure 23. Results of the fuzzy overlay analysis in Los Angeles County, CA. 62
Figure 24. Suitable cells, represented in red, from both the weighted overlay and fuzzy overlay
analyses. The left map shows Los Angeles County and the right map shows San Diego
County. 65
ix
List of Abbreviations
ADCP Acoustic Doppler Current Profiler
ASBS Areas of Special Biological Significance
AWEA American Wind Energy Association
CO-OPS Center for Operational Oceanographic Products and Services
CSV Comma-Separated Values
EEZ Exclusive Economic Zone
EFH Essential Fish Habitat
EIA U.S. Energy Information Administration
EPA Environmental Protection Agency
EPRI Electric Power Research Institute
GIS Geographic Information System
IDW Inverse Distance Weighted
IPPC International Panel on Climate Change
IRENA International Renewable Energy Association
JSON JavaScript Object Notation
MPA Marine Protected Area
NAD North American Datum
NASA National Aeronautics and Space Administration
NCEI National Center for Environmental Information
NMS National Marine Sanctuary
NOAA National Oceanic and Atmospheric Administration
NREL National Renewable Energy Laboratory
x
NREA National Renewable Energy Association
NTSFL National Tide and Sea Level Facility
OIST Okinawa Institute of Science and Technology
RBF Radial Basis Function
ROMS Regional Ocean Modeling System
TXT ASCII-Text Formatted Data
UTM Universal Transverse Mercator
WEC World Energy Council
XML Extensible Markup Language
xi
Abstract
Traditional sources of energy are outdated and destructive. There is a clear need for new and
more sustainable energy sources, creating a huge market for innovative research and technology
within the energy industry. The use of the world’s oceans to generate energy is pioneering and
provides many enticing benefits including cleaner and more consistent energy generation. Tidal
energy converters harness the power of moving water generated by the tides and is a huge source
of untapped energy. This study analyzed the parameters most important to successful tidal
energy generation in order to determine the most suitable sites for implementing this technology
in southern California. Using data from various sources, two different interpolation methods
were used to create a continuous raster surface of tidal range values that, along with other
variable fields related to tidal energy, were used to perform two different types of analysis, a
weighted overlay and fuzzy overlay analysis. The results of these two different analysis methods
were compared and this methodology provided a conclusive map showing the most effective
sites for successful use of tidal energy converters in southern California. An analysis was also
completed to look at the distance of suitable sites to nearby onshore energy facilities. This
research builds upon previous renewable energy studies in southern California and provides
insight into how the region can reduce its fossil fuel emissions and convert to cleaner, more
sustainable energy sources.
1
Chapter 1 Introduction
This study sought to determine suitable sites for the implementation of tidal energy technology
off the southern coast of California. Tidal energy converters use new technology to harness
usable energy from the water motion and currents produced by the tides. This form of renewable
energy has many benefits not seen in traditional renewables such as wind and solar and is much
cleaner and better for the environment than current fossil fuel sources. This study uses GIS to
look at different parameters that influence the success of tidal energy converters such as tidal
range, strength of tidal currents, and seafloor bathymetry. Classifying these variables and then
performing a weighted analysis provides a detailed map showing the most suitable sites for
implementing tidal energy technology in southern California.
1.1. Climate Change
One of the largest problems the planet currently faces is the issue of climate change. Over
the period between 2006 and 2015, there was an increase in the global mean surface temperature
of approximately one degree Celsius, which can likely be attributed to human activity (IPCC
2018). Certain areas of the Earth such as the Arctic, and areas covering large amounts of land
have seen warming at even higher rates than the global average (IPCC 2018). The effects that
can be seen on the planet due to rising temperature are very serious. One of the main effects of
global temperatures increasing is sea level rise. Warmer temperatures cause thermal expansion of
water molecules, and also lead to melting of glaciers and ice sheets (Nuccitelli 2018). These
phenomena play a significant role in raising the sea level, with estimates that there could be an
increase of up to five feet by the year 2100 (Nuccitelli 2018). The effects of sea level rise include
flooding, coastal erosion, and habitat destruction, all of which are costly and dangerous (Nicholls
2
2011). Another destructive effect of global temperatures rising is the slow-down of global ocean
circulation. Thermohaline circulation plays a large role in global ocean circulation and includes
the mass flux of seawater that is caused by differences in density, mostly due to the heating and
cooling of water. After water is moved to the poles by wind-driven surface currents, it is cooled
at the high latitudes. Cooler water molecules are denser than warmer water molecules, which
causes the cold-water particles to sink, driving large-scale circulation (Wunsch 2002). When
global temperatures rise, the differences in ocean water at the equator and high latitudes
decreases, which leads to a decrease in the velocity of the global ocean circulation (NOAA
2019b).
1.2. The Use of Fossil Fuels
Rising global temperatures have been attributed to human activity, mostly due to the
emissions generated from using fossil fuels. Fossil fuels are considered to be any source of fuel
such as coal, oil, or natural gas that are formed from natural processes such as anaerobic
decomposition of ancient dead organisms. Greenhouse gasses are the resulting by-product of
burning these fossil fuels and are considered to be one of the largest contributors to global
climate change. Greenhouse gasses work by trapping heat in the Earth’s atmosphere, which
contributes to the warming of our planet (EPA 2019). The global warming trend, which has been
observed by many scientists, has already begun to affect our climate by increasing temperatures.
This, in turn, causes phenomena such as melting glaciers, warming oceans, and more extreme
weather (NASA 2019a). In 2017, the United States totaled 6,456.7 million metric tons of
emissions from carbon dioxide equivalents that stem from the use of fossil fuels.
Most of the electricity generated in the United States comes from fossil fuels. A report
provided by the U.S. Energy Information Administration (EIA 2019a) found that 62.9% of
3
electricity in the United States comes from these sources. The same study found that natural gas
provided the largest source of energy within the fossil fuel category, with coal and petroleum not
far behind (EIA 2019a). The report also mentioned the use of renewable energy, which found
that approximately 17% of energy in the U.S. came from renewable sources, mainly hydropower
plants.
1.3. Renewable Energy
Renewable energy is a more sustainable and clean solution for the generation of
electricity and includes energy that comes from sources that are naturally replenished on human
timescales. This includes phenomena such as wind, sunlight, tides and waves, or geothermal
heat. Renewable energy research and implementation has made much progress in recent years as
more information about the negative effects of traditional energy sources has become available.
A study conducted by the National Renewable Energy Laboratory found that renewable energy
generation from today’s technologies and a few emerging technologies could adequately supply
80% of the U.S.’s energy needs by 2050 (NREL 2019). This would reduce greenhouse gas
emissions from electricity by almost 81%, which would help to reduce harmful emissions.
One of the most common forms of renewable energy comes from the wind. Wind
turbines are rotated by naturally occurring winds in the Earth’s atmosphere, which is converted
into mechanical energy that can be used for a variety of applications (AWEA 2019). In 2016,
wind energy accounted for 8% of the operating electric energy capacity of the United States,
which was more than any other renewable energy source (EIA 2019c). While not as
economically competitive as other traditional sources of energy, wind energy is one of the lowest
cost sources of renewable energy at approximately 2 to 6 cents per kilowatt-hour (U.S.
Department of Energy 2019a). One of the main disadvantages of wind energy is that the suitable
4
locations for implementing the technology are often far from areas that would benefit from the
energy production, such as large cities or residential neighborhoods. Another disadvantage is that
wind is not a constant phenomenon. Because of this, traditional energy sources must be kept in
place for reserve power when there is not an abundance of wind (OIST 2016). This practice is
costly and contradicts the positive aspects of using wind as a renewable energy source.
The second most common form of renewable energy comes from the sun. Solar
photovoltaic devices convert direct sunlight into mechanical energy that can be used to power
different applications (EIA 2019a). Solar power has become more accessible and affordable in
recent years, with prices for installing commercial solar panels dropping by nearly 50% since
2014 (U.S. Department of Energy 2019b). A report by the National Renewable Energy
Laboratory found that solar represented 22% of new electric generation capacity for the U.S. in
2018, with the state of California generating 19% of their energy from solar power (Feldman and
Margolis 2019). Similar to wind power, solar power has its drawbacks. Solar radiation is not
always constant and can produce varying levels of energy depending on the amount of cloud
cover, the location, or the season (EIA 2019a), all of which contribute to the amount of energy
that can be produced.
1.4. Hydrokinetic Energy
A newer from of renewable energy that has recently become more prevalent comes from
the ocean. Hydrokinetic energy encompasses a wide range of technologies that are able to
harness and convert energy from the ocean, whether it be through the oscillating motion of
waves, the flow of water through tidal currents and streams, or by thermal and osmotic processes
(Esteban and Leary 2012). The theoretical potential energy generation from the ocean is
estimated to be between 20,000 and 90,000 terawatt-hours per year, although it is unlikely that
5
any one technology will be able to extract the full potential (Soerensen and Weinstein 2008).
There are many types of technology that utilize processes from the ocean to generate energy,
with waves and tides being the most effective.
The greatest potential for harnessing renewable energy from ocean waves is located
where the surface winds are the strongest, which occurs between the latitudes of 40 and 60
degrees north and south of the equator, and on the eastern boundaries of oceans (Pelc and Fujita
2002). According to the World Energy Council, wave energy has the potential to provide two or
more terawatts of electricity, which is approximately one fifth of the world energy demand
(WEC 1993).
The main attraction of harnessing and converting energy from tidal systems versus wave
systems is predictability. Waves in the ocean are controlled by a variety of external factors such
as wind and seafloor bathymetry, which make them unpredictable and difficult to forecast. Tides,
on the other hand, are controlled by the consistent gravitational pull of the moon and sun, which
can be predicted with high accuracy years into the future. Because of the standard factors
controlling the tides, the flow of water from which energy is harnessed is forever in motion. This
allows for consistent harnessing of power that can be directly sold as firm power to an energy
grid, reducing the need for reserve power sources (Bedard et al. 2007). The potential of
implementing tidal energy is immense, hence the importance of continued exploration into the
development and use of this technology.
1.5. Thesis Structure
The following sections of this document provide a comprehensive review of related
literature, the methodology used in this analysis, and the results of the suitability analysis. The
literature review includes relevant information regarding renewable energy technologies, the use
6
of GIS in site suitability analyses, and how GIS is used in relation to tidal energy technology.
The methods section reviews the data manipulation and editing required for this analysis, as well
as the workflow used to perform two different kinds of overlays. The results section is followed
by a discussion of the results and their implications. This document concludes with an analysis of
the project and how it may be applied to future work.
7
Chapter 2 Related Work
To harness the full potential of tidal energy through appropriate site-suitability analysis, it is
important to understand: (1) the processes through which tidal energy is generated and how it is
utilized; and (2) the methodologies and results from similar studies looking to find sites that are
most appropriate for wave energy. The following review investigates research reports, academic
articles, and technical documents regarding tides, tidal energy technology, and site suitability
analyses of tidal energy in various locations conducted to date.
2.1. Ocean Energy
Ocean energy sources are increasingly becoming a part of the global renewable energy
mix. There are many types of technology that utilize processes from the ocean to generate
energy, with wave and tidal energy being the most effective. Ocean energy, alternatively called
hydrokinetic energy, encompasses a wide range of technologies that harness and convert energy
from the ocean, through the oscillating motion of waves, the flow of water through tidal currents
and streams, and thermal and osmotic processes (Esteban and Leary 2012). Theoretically, the
potential energy generation from the ocean could be between 20,000 and 90,000 terawatt-hours
per year, although it is unlikely that any one technology will be able to extract the maximum
amount possible (Soerensen and Weinstein 2008). Hydrokinetic energy will become an
increasingly important part of our energy future.
2.1.1. Wave Energy
Wave energy is the potential and kinetic energy harnessed from ocean waves that is
combined and then converted into electricity using wave energy converter (WEC) technologies
(Williams 2018). Over the last decade, the development of commercial wave farms has been
8
slow when compared to solar and wind farms. This is attributable to technological, financial, and
environmental concerns. Compared to other parts of the world, especially Europe, WEC
technology is still not widely recognized in North America as an alternative source of renewable
energy (Beyene and Wilson 2014). A lack of funding and little research support has kept the
estimated cost of installation and operation relatively high, which has kept interest in WECs very
low. Additionally, research on the environmental impacts of installing WECs is scarce, but initial
findings point to potential changes in sediment transport due to a change in wave
hydrodynamics, the creation of artificial habitats, and a change in migration routes for marine
mammals (Beyene and Wilson 2014).
The greatest potential for harnessing renewable energy from ocean waves is located
where the surface winds are the strongest, which occurs between the latitudes of 40 and 60
degrees north and south of the equator, and on the eastern boundaries of oceans (Pelc and Fujita
2002).
There are many different approaches to capturing wave energy in various stages of
development and deployment. WECs utilize the motion of surface ocean waves either at the
surface or at some depth in the water column. There are three different types of wave energy
converters (Rusu and Onea 2017). The first are point absorbers, which generate energy through
the vertical displacement of the device as it rises and falls with the passing waves. It is the
smallest WEC type and can capture wave power from any direction. The second are attenuators,
which are larger devices oriented parallel to the direction of wave travel that generate energy as
the wave passes along the length of the device. Lastly, the third option, a terminator is similar to
an attenuator but is positioned lengthwise facing the direction of the wave, generating energy as
each wave crashes into the device.
9
Theoretically, the wave energy potential on the west coast of the United States is more
than twice the wave energy potential of the east coast (Lehmann et al. 2017). The Electric Power
Research Institute (EPRI) reports the total available wave energy along the inner shelf of
California is 205 TWh/yr and the Pacific Gas and Electric Company (PG&E) reports that the
total energy consumption of California in 2005 was 272 TWh (Lehmann et al. 2017). Clearly
wave energy has the potential to generate large amounts of usable energy, but it is also limited by
various factors as discussed above.
2.1.2. Wave energy compared to tidal energy
Wave and tidal energy are two types of hydrokinetic energy, which are sometimes
confused with one another. The main difference is their requirements with respect to their
installation location, and both have benefits and drawbacks. Both types of hydrokinetic energy
show strong potential, but the focus of this study is tidal energy, which is relatively less
researched. However, since they contain many of the same criteria for determining site
suitability, a comparison of their characteristics and strengths and weaknesses is offered below.
With respect to wave energy potential, wave height is dependent on the strength of the
winds and seafloor bathymetry. Surface winds can only be predicted a few days in advance and
vary in strength. This poses challenges for wave energy, insofar as predicting strong waves well
in advance is challenging. Conversely, tides are dependent on the constant gravitational forces of
the sun and moon, which provide a continuous movement of water. This translates to constant
energy production that can be predicted years in advance.
Another limiting factor of wave energy converters are their physical locations. Most
wave energy converters operate at the surface of the ocean, making them susceptible to any
surface traffic from boats and other recreational activities. This reduces the areas in which wave
10
energy converters can be implemented, which negatively impacts their potential energy
production. Williams (2018) identified five locations for the implementation of wave energy
farms in southern California. These locations were generally far offshore, in areas where wave
power density was high. Tidal energy converters generally operate below the sea surface and are
anchored to the seafloor. This allows them to avoid any disruptions that may be caused at the
surface and also avoids the visual disruption that might occur. The differences in geography
allow for both tidal and wave energy technology to be operational in their own regions without
any overlap between the two.
2.2. Tides
Siting tidal energy turbines correctly is contingent on a detailed understanding of the
tides. Ocean tides are directly related to the gravitational pull of the Moon and Sun on the Earth
(NOAA 2019d). The gravitational forces create a “bulge” of water on the sides of the Earth
nearest and farthest from the Moon, which causes the differences in sea level associated with the
tides (NOAA 2019d). Tidal cycles vary by geographic location. One tidal cycle takes
approximately 24 hours and 50 minutes, with high tides occurring 12 hours and 25 minutes apart.
Diurnal cycles refer to a pattern of one high and one low tide per cycle, while semi-diurnal tidal
cycles produce two highs and two low tides per cycle. Mixed semi-diurnal tidal cycles, which are
prominent on the west coast of the United States, produce two high and two low tides per day
that differ in their height (NOAA 2019d). Choosing the appropriate type of tidal energy
technology and identifying the proper location must take these tidal patterns into account on a
location-specific basis.
The magnitude of the tidal range determines how much energy can be generated from the
tides. Tidal range refers to the difference in height between high and low tides. The height of the
11
tides is dependent on the locations of the Sun, Moon, and Earth, and differs throughout the lunar
cycle. Spring tides occur when the largest difference between high and low tide is observed,
usually occurring after a full moon when the Sun, Earth, and Moon are aligned. Neap tides occur
when the smallest differences between high and low tide are observed, which is usually in the
first or third quarter of the lunar cycle when the Sun, Earth, and Moon form a right angle
(NTSLF 2019). Other factors that dictate the height and extent of the tides include areas with
large continental margins, which magnify the tides, while narrow inlets and bays can also
exaggerate the tidal height (NOAA 2019d). Wind and weather patterns associated with different
climates can also magnify or reduce the magnitude of the tides. Figure 1 shows the tidal energy
across the globe, highlighting the differences in tides at different latitudes and longitudes. Any
sitting of tidal energy turbines must optimize the tidal trends in a particular area over time. The
suitability of tidal energy technologies such as tidal turbines will be heavily influenced by the
magnitude of the tides, as larger tidal ranges will produce a greater volume and flow of water
that can be optimized by the tidal energy converters.
Figure 1. Map showing the tidal energy around the world calculated from the
TOPEX/Poseidon satellites. Red colors indicate the strongest tides, while blue
indicates the smaller tides (NASA 2019b)
).
12
Southern California has a semi-diurnal tidal cycle, meaning that there are two high tides
and two low tides per day, each at differing heights. The tidal range between the lowest low tide
and highest high tide is between approximately 1 and 2 meters. The tidal range has increased
over the last century, which is consistent with mean sea level rise (Flick et al. 2003), indicating
that the tidal range in southern California has the potential to increase in magnitude in the future
which would be beneficial to tidal energy technologies. Southern California has varying tidal
ranges that are heavily influenced by geography, insofar as they follow a pattern of increasing
magnitude as the geographic location moves south, and also show large magnitudes in areas
where the topography is more enclosed.
2.2.1. Tidal Currents
Tidal currents affect the suitable locations for tidal energy turbines depending on how
strong the flow of water is at a specific site. Tidal currents occur with the rise and fall of the
tides, which results in a sub-surface flow of water (NOAA 2019a). When the tides are rising and
moving towards the shore, the resulting current has a net flow towards land and is considered to
be “flooding”. When the tides recede and move back towards the ocean, the resulting current has
a net flow towards the ocean and is considered to be “ebbing” (NOAA 2019a). Tidal currents
vary in strength depending on the relationship between the Sun, Moon, and Earth. During neap
tides, tidal currents are weaker and during spring tides tidal currents are stronger. In a standing
wave model, slack water will occur at the same time as the high and low tides, with the
maximum flood and ebb currents occurring between the high and low tides. In a progressive
wave system, which characterizes most coastal areas, the tidal current will be strongest at the
high and low tides with little or no current occurring between the two extremes (NOAA 2019a).
The relationship between tides and tidal currents is unique to every location on Earth, so while
13
the two can be measured and derived using similar methods, they are most often calculated
separately to account for any differences in physical environment. With respect to southern
California specifically, tidal currents off of the coast are relatively slow with an average velocity
of 0.1 to 0.5 meters per second. These tidal currents are barotropic, meaning the flow travels in
the same direction throughout the water column. This type of flow is beneficial for tidal turbines
because it allows for the full capture of water flow, which can result in higher energy capture.
Due to the velocity of water flow they create, tidal currents are important for tidal energy
technologies such as tidal turbines. Tidal currents are the source of water movement, which turns
the arms of the turbine. Faster tidal currents will spin the tidal turbines quicker, which will result
in higher energy production. Since tidal currents move both towards and away from the shore,
they can move tidal turbines in both directions meaning a greater potential for energy production.
The implications of slower tidal currents in southern California indicate less suitability for tidal
turbines, but there is still potential for these technologies in the region.
2.2.2. NOAA Tidal Data Collection
This study used data from the National Oceanic and Atmospheric Administration
(NOAA). NOAA is the leading producer of oceanographic data and sets many of the standards
used for collecting tidal data. The Center for Operational Oceanographic Products and Services
(CO-OPS) at NOAA operates a network of buoys and water level stations across the United
States that are used by NOAA to create accurate tidal predictions. There are over 3,000 locations
where tidal information is collected, with 27 stations located within the study site in Southern
California. Each station is designated as harmonic or subordinate, meaning the station predicts
tidal levels using either a harmonic constant that physically measures the tide (harmonic) or a
14
mathematical prediction based on a separate harmonic constant (subordinate). Each station
produces a chart containing the times and heights of the two high and two low tides per day.
This free data set includes a variety of metadata. Variables include date, day of the week,
time, predicted height, and high or low tide. To use these data, a date range must be specified in
addition to the units for each parameter. The data can be downloaded in 30-day increments. The
website also includes options for historical data, which can be downloaded at larger temporal
scales. Export options include XML, TXT, JSON, or CSV format. This data set is not only
comprehensive, but also well vetted and easily imported into ArcGIS. The National Ocean
Survey, a component of NOAA and the U.S. Department of Commerce, maintains a continuous
control network of approximately 140 tide gauges which are located along the coasts and within
the major embayments of the United States, ensuring the accuracy of CO-OPS tidal data (NOAA
2019c).
NOAA also produces tidal current predictions for a number of locations in the United
States including sites in southern California (Figure 2). This information is calculated from the
CO-OPS stations and is available for download. The data shows the ebb and flow tidal current
speeds over a weeklong period for each location.
Additionally, NOAA and CO-OPS host a GIS data portal. The CO-OPS Station map
service includes current and historical spatial data, which includes most of the products and data
produced by the CO-OPS program. This information can be easily accessed in ArcGIS
JavaScript, ArcMap, and ArcGIS Online. This simplified the data importing process in ArcGIS
while working on this project.
15
Figure 2. Map depicting the tidal stations derived from CO-OPS used in this study.
2.3. Tidal Energy Technology
Tidal energy has evolved significantly over the last 20 years, increasing its potential for it
to be a core part of the alternative energy portfolio. Currently, there are two main technologies
used to harness and convert tidal energy, each of which has applicability in different scenarios.
The first, tidal range technology, uses barriers or dams to generate power from the height
difference between high and low tides. Tidal turbines are installed in the barrier, which rotate
and generate power when water flows through them during the ebb and flow tides (IRENA
2014). Currently, the largest tidal barrage system is located in South Korea at the Sihwa Lake
Tidal Power Station, which generates up to 255 megawatts of electricity (EIA 2019b). The
drawbacks of implementing tidal barrage systems is that they create barriers for natural
16
migration of marine wildlife and increase sediment deposits which is detrimental to many
organic processes (Uihlein and Magagna 2016). There are certain areas where a tidal barrage
system may function efficiently in southern California, such as at entrances to bays or at river
mouths, but because of the drawbacks mentioned above, and the many protected areas on the
southern California coast, tidal barrage systems were not considered for this study.
The second type of technology is called tidal stream or tidal current technology. This
technology utilizes turbines similar to wind turbines that are moved by the currents that result
from tides. The flow of water rotates the arms of the turbine, which turns kinetic energy into
mechanical energy that can be harvested and used (IRENA 2014). Tidal turbines are more
expensive to develop due to the harsh nature of the ocean but cause less damage to the ecology
and potentially have higher energy outputs (EIA 2019b). Tidal turbines also have higher power
densities that allow them to produce higher energy outputs while maintaining a smaller size
(Roberts et al. 2016), which in comparison to wind turbines makes them much more efficient.
Tidal turbines are an ideal candidate for further study in southern California. In addition
to expansive miles of coastline in which these turbines can operate, the bathymetry and tidal
range prove ideal for tidal turbines. Tidal turbines are very similar to wind turbines, which are
common in southern California. The existing wind energy grids in the region can be used as a
foundation for implementing tidal turbines. Additionally, California has strong environmental
regulations, so tidal turbines are ideal due to their reduced environmental impact. Due to their
efficiency and minimal environmental impact, tidal turbines were the focus of this study.
The connection between wind and tidal energy may not be readily apparent, but they
work in conjunction. A critical aspect of the success of tidal technologies is their ability to be
integrated into the energy grid. Tidal energy technologies can build upon previous work on wind
17
turbines as they contain similar mechanisms. Further, the integration strategies can be easily
transferred to the newer technologies (Blavette et al. 2011). The existing wind energy grids in the
southern California region can be used as a foundation for the implementing tidal turbines and
their resulting energy grids.
18
Chapter 3 Design and Methods
The goal of this study was to identify suitable areas off of the coast of southern California for
implementing tidal energy technology. Southern California was selected because of the potential
for tidal energy development, the state’s interest in renewable energy, and the large expanse of
coastline available for installation. The northern end of the study area was Point Conception,
California (34.447490, -120.471715) and the southern boundary was the California-Mexico
border (32.534425, -117.122903). Between the northern and southern points, a 2,000 m buffer
from the coastline constituted the study area. The methodology included data classification,
weighted overlay analysis, fuzzy overlay analysis, and distance analysis.
3.1. Methodologies Used to Assess Site Suitability
Multiple studies have explored the suitability of various sites for harvesting tidal and
wave energy. In South Carolina, physical data observation was used with 3-D modeling to
perform a site assessment for tidal stream energy (Work et al. 2012). The authors used the
Regional Ocean Modeling System (ROMS) to numerically simulate tidal flows, but also took
physical measurements of tidal velocities using Acoustic Doppler Current Profilers (ADCPs).
Using both the simulated and the physical data allowed the authors to validate the data model.
This study demonstrates the use of multiple data sources to assess tidal energy technology
potential. While this study focused on one specific location, the methodologies could be
expanded to larger spatial areas.
Defne et al. (2011) examined the potential of tidal stream power in Georgia using a GIS-
based multi-criteria assessment. The authors gathered geospatial data that was categorized into
three layers: the physical realization layer, the environmental constraints layer, and the
socioeconomic constraints layer. Breaking up the data into these three categories allowed for the
19
separation of parameters into like categories, which aided in identifying suitable areas. This
study selected areas based upon the three categorical layers but did not utilize overlay
techniques. The methods used in the Georgia study were modified for this study namely through
the categorization of data into a physical layer, an environmental layer, and a socioeconomic
layer.
The issue of locating alternative energy facilities, specifically in the marine environment,
has been addressed in studies. Williams (2018), for example, conducted a site suitability analysis
for wave energy farms off the coast of southern California. The study identified five different
sites that were suitable for wave energy farms based on the natural environment, government and
legal constraints, and cost effectiveness. The methodology considered Marine Protected Areas,
military zones, and wave power density, and used a weighted overlay analysis to delineate five
areas in which wave energy could be harvested. This study showed that hydrokinetic energy is
well suited to many locations in California. Using a very similar methodology to Williams
(2018), this study utilizes similar approaches to data management and data type but applies it to
tidal energy. Many of the same parameters are utilized but modified to better represent the
specific requirements of tidal energy technology.
Another study that was conducted in Greece used multi-criteria decision-making methods
as well as GIS to locate suitable marine areas for the deployment of Hybrid Offshore Wind and
Wave Energy Systems (Vasileiou et al., 2017). The authors of this study developed a GIS
database that produced thematic maps representing exclusion criteria based on economic,
technical and social constraints. They next used these maps to analyze eligible marine areas not
satisfying exclusion criteria and ranked them using a hierarchy process developed from the
evaluation of criteria related to economic, technical and socio-political factors. The use of GIS to
20
create databases relating different factors that impact wind and wave energy systems in a marine
environment was beneficial in creating spatial representations of suitable areas that could support
these technologies.
Each of the studies reviewed here contributed to a better understanding of the siting of
energy technologies. However, none of them have applied geospatial technologies to the siting of
tidal energy turbines in Southern California. This study provides an initial attempt to address this
research question.
3.2. Data
The data were broken up into three categories: physical, government regulation, and
commercial use. Each category consisted of various datasets that were derived from different
sources and had different spatial resolutions. The data were manipulated so that each dataset had
the same spatial extent and scale and the classification and subsequent overlay analyses would be
accurate. For this analysis, each dataset was projected, edited, and formatted using the North
American Datum of 1983 and Universal Transverse Mercator (UTM) Zone 11. The data was also
set to a uniform cell size of 100 m.
Once all of the data had been classified and set to the correct spatial extents, it was
further categorized into hard and soft criteria. Mierzwiak and Calka (2017) outlined the use of
multi-criteria analysis to assess solar farm location suitability. The use of hard and soft criteria
was implemented to define areas that strictly met or did not meet the criteria (hard) or to define
areas that had varying levels of suitability based on specific criteria (soft). Applying this logic to
the data related to tidal energy technology allowed for further classification of the data resulting
in a better suitability analysis.
21
3.2.1. Physical Data
To define the study area, national shoreline data for the California region were
downloaded from the NOAA National Shoreline Data Explorer. These data were collected by the
National Geodetic Survey. Shoreline data was imported into ArcGIS Pro and clipped to the
extent of the study area running from the California-Mexico border north to Point Conception. A
2,000 m buffer was then applied to the clipped shoreline polygon to create a new polygon
consisting of the study area. Then, a land feature polygon dataset from the U.S. Census Bureau
was used to clip and eliminate all land areas. Table 1 lists the different data sources.
Table 1. Data used in this study to define the study area.
Data Source Year Spatial
Reference
Information
Description
National Shoreline NOAA / National
Geodetic Survey,
2011
2011 NAD 1983 Vector, line data mapping
the national shoreline.
U.S. Country
Boundary
Esri, Living Atlas 2018 WGS 1984 Vector, Polygon data
defining USA country
boundary.
Physical data for this analysis consisted of tidal range and bathymetry data. Tidal range
quantifies the extent of high and low tides within the study area and is the most relevant
parameter to tidal energy technology as the flow of water produced by the tides is what powers
the technology. NOAA’s CO-OPS produces an interactive data map containing every tidal
station in the United States. For each station within the study area, annual prediction tide tables
for a two-year period between January 1
st
, 2018 and December 31
st
, 2019 were downloaded as a
TXT file. As mentioned in Chapter 2, tidal range does not change much over small time scales,
22
so a two-year data duration was deemed appropriate. Once the data were downloaded, each
station’s file was imported into Excel. A simple formula was used to calculate the distance in
meters between each high and low tide (high tide minus low tide) during the two-year period.
The calculated distances were then averaged to obtain the average tidal range for the two-year
period. This methodology was applied to each of the 27 stations. The average tidal range
calculations were compiled into a spreadsheet containing the station name, latitude and longitude
coordinates, and the average tidal range of each station. This Excel spreadsheet was imported
into ArcGIS Pro as XY point data.
3.2.2. Interpolation of Tidal Range
Using ArcGIS Pro and NOAA’s tidal range data set, interpolation was used to create a
continuous surface depicting tidal range within the study area. Data was taken from tidal stations,
averaged, and then interpolated to create a continuous surface. There are different types of
interpolation methods that are available in ArcGIS Pro. Each method type has different qualities
and provides different information.
Figure 3. Classification tree provided by ArcGIS Pro to help determine which type of
interpolation method to use. Esri (2020a).
23
It was decided that to create a surface of tidal range data, an interpolation method that
provides one prediction per location would be necessary. The Inverse Distance Weighted (IDW)
and Radial Basis Function (RBF) methods were selected as the most appropriate interpolation
types. RBFs are used to produce smooth surfaces from a large number of data points. The
functions produce good results for gently varying surfaces and pass through the original data
points making it an exact interpolator (Esri 2020d). IDW is also an exact interpolator and uses
the measured values surrounding the prediction location to predict a value for any unmeasured
location (Esri 2020c). The measured values closest to the prediction location have more
influence on the predicted value than those farther away. The IDW method is prone to clustering
but is a simple method for predicting values. Both of these interpolation methods are suitable for
producing a raster dataset containing a surface of tidal range values and are used in the analysis.
To create a raster dataset of tidal range values, two different interpolations were used.
First, an IDW interpolation was performed using the tidal range point data. This method was
chosen because it uses the proximity of surrounding values to predict a new location’s value,
which seemed appropriate for this type of data. For the interpolation, the input point features
constituted the tidal range data, the Z value field was set to ‘average’, which was the average
tidal range of each station, and then the remainder of the parameters were kept as default values
to ensure a classic IDW interpolation. The extent of the interpolation matched the study area
polygon. To further clip the interpolated data to the study area, and to eliminate any “no data”
cells, the “extract by mask” tool was used with the study area polygon as the mask.
An RBF interpolation was also performed to create a different raster surface of tidal
range data. As mentioned in Chapter 2, the IDW interpolation does have limitations on data that
is clustered, so another interpolation method was used to see how the results would change. An
24
empty raster dataset was created to house the RBF interpolation. The Radial Basis Function tool
in ArcGIS was used with the tidal range data as the input feature, the Z value set to ‘average’,
and the output raster set as the empty raster. The extent and snap rasters were set to the IDW
raster and default values were used for the remainder of the parameters. The multi-quadratic
radial basis function was selected because it could predict values that may be higher or lower
than the original data’s maximum and minimum value in the source dataset. This allowed for a
greater field of values to be estimated and for a smoother surface to be created. To further clip
the interpolated data to the study area, and to eliminate any no data cells, the “extract by mask”
tool was used with the study area polygon as the mask.
The two tidal range rasters were classified into 5 different classes based on their
suitability for tidal energy technology. Using the reclassify tool, natural breaks were used to
separate the data into 5 sections of tidal range, and then a value of ‘1’ was assigned to the
smallest tidal range class and a value of ‘5’ was assigned to the largest tidal range class.
Another piece of physical data needed for this analysis was bathymetry. Bathymetry
refers to the measurement of the depth of the ocean relative to sea level. Data were downloaded
from NOAA’s National Center for Environmental Information (NCEI) coastal relief model. This
model combines bathymetry with land topography to provide detailed measurements along the
coast. Data can only be extracted at certain maximum extents, so four custom grids were
downloaded as GeoTIFF files with a resolution of 1 arc-second encompassing the study area.
The four GeoTIFF files were imported into ArcGIS Pro and then the mosaic tool was used to
combine the data into one continuous raster. This raster used the same NAD 1983 datum and
UTM Zone 11 projection as the other rasters in this study, the pixel type was set to 16-bit signed
to account for negative bathymetry values, and the minimum mosaic operator type was adopted.
25
The new mosaic raster was then clipped using the extract by mask tool with the study area
polygon as the mask and the IDW tidal range raster as the extent and snap raster parameters.
For the bathymetry data, the reclassify tool was used to divide the data into three classes.
The ideal class range was set to a range of -50 to -15, accounting for the water depth in which
tidal energy technology can be installed. This range was chosen based on the pre-feasibility
assessment of tidal energy systems performed by Khare et al. (2019). The ranges accompanying
either side of the ideal range were broken into two classes, one being -322 to -51 and the other
being -14 to +435. These ranges encompassed the entirety of the bathymetry data. The ideal
bathymetry range was given a classification of ‘1’ while the two less than ideal ranges were
given a classification of ‘0’ so that the ideal bathymetry values could be easily identified and be
applied in the suitability analysis. Figure 4 illustrates the workflow used in manipulating the
bathymetry and tidal range data while Table 2 highlights the data properties.
Figure 4. Flowchart depicting the data organization for the physical datasets.
26
Table 2. Data used in this study relating to the physical limitations of tidal energy technology.
Physical Data Source Year Datum Description
Tidal Range NOAA / Center for
Operational
Oceanographic Products
and Services, 2018 - 2019
2018-
2019
WGS 1984 Vector, Point data consisting of
tidal stations. Each station has an
average tidal range measurement.
Bathymetry NOAA / National Center
for Environmental
Information, 2003
2003 WGS 1984,
1 arc-second
Raster, Coastal relief model
combining bathymetry and
topography. Southern California
region was used.
3.2.3. Government Regulation Data
Building upon work by Williams (2018), government regulation data were processed and
edited so that it had the correct spatial scale and extent, by projecting the data to the NAD 1983
datum and UTM Zone 11 projection and clipping it to the study area. The government regulation
data included Marine Protected Areas, Areas of Special Biological Significance, and Military
Use Zones as noted in Table 3. Each of these datasets was imported into ArcGIS Pro in their
original formats. A system was established to score each dataset based on its suitability for tidal
energy technology. Each feature was given a suitability score consisting of a value of 1 through
5, with 1 being least suitable and 5 being the most suitable for tidal energy technology. Some
features were given a restricted score of zero accounting for technical limitations or legal
regulations, which would warrant classifying these areas as completely unsuitable for tidal
energy technology.
The scoring system used to give each dataset a suitability score was chosen based on the
extensive background research completed for this project. The scoring system for government-
regulation data is described below:
27
Table 3. Data used in this study relating to the government use limitations of tidal energy
technology.
Government
Regulation Areas
Source Year Datum Description
Marine Protected
Area
NOAA Marine
Protected Areas
Inventory, 2017
2017 WGS 1984 Vector, polygon data outlining areas
designated as Marine Protected
Areas.
Areas of Special
Biological
Significance
California Water
Boards GIS Rest
Services, California
Government, 2005
2005 WGS 1984 Vector, Polygon data showing
boundaries of areas of special
biological significance
Military Use Zones Department of
Commerce, NOAA
Office for Coastal
Management
2017 NAD 1983 Vector, Polygon data showing
boundaries of danger zones and
restricted areas used by the U.S.
Military
California State
Parks
California Department
of Parks and
Recreation
2020 WGS 1984 Vector, Polygon data showing
boundaries of state parks in
California.
• Marine Protected Areas (MPAs): Federal and state regulated waters that serve to
achieve the long-term conservation of nature with associated ecosystem services and
cultural values, which are managed by legal or other affective means (Day et al.,
2008). MPAs prohibit all commercial activities but do allow some pre-approved
research operations. Tidal energy installation is not considered a pre-approved
activity therefore MPAs were given a suitability score of 0 as well.
• Areas of Special Biological Significance (ASBS): A subset of state water quality
protection areas that protect important ecosystems from any undesirable alteration in
natural water quality (NOAA 2019c). These areas prohibit pollution from water
runoff as well as dredging of the seafloor. Because tidal turbines are most often
secured to the seafloor, ASBS were given a suitability score of 0.
• Military Use Zones: Danger zones and restricted areas within coastal and marine
waters used by the armed forces. Restricted areas generally provide security for
Government property and/or protection of the public from the risks of damage or
28
injury arising from the Government's use of that area (NOAA OCM 2017). Because
of these heavy restrictions it was logically assumed that tidal energy technology could
not be installed in these areas and was therefore given a suitability score of 0.
• California State Parks: State parks are managed by the California Department of
Parks and Recreation. State parks have many allowances for recreational use,
although there are some restrictions regarding the disturbance of wildlife and natural
habitats. No literature could be found about the prospective installation of energy
systems within state parks, therefore it was inferred that government permission could
be sought out for the implementation of tidal energy technology in these areas. A
suitability score of 3 was chosen to reflect these findings, and also to account for the
high level of recreational use in these areas.
This project omitted other protected areas that could limit the installation of tidal energy
technology. Essential Fish Habitats (EFH), National Marine Sanctuaries (NMS), and Exclusive
Economic Zones (EEZ) are federally and state regulated areas that would impose restrictions on
projects such as tidal energy installation. Because there were no instances of these areas within
the study area, they were omitted from the analysis. Table 3 highlights the data sources and their
properties for all of the Government Regulation data.
A new attribute field called “suitability” was created in each government data layer
(MPA, ASBS, Military Use Zones, State Parks) and was populated with the corresponding
suitability score as described above. Once scored, each of these government regulation data
layers were individually dissolved into a single feature using the Dissolve tool in ArcGIS Pro.
The datasets were then clipped to the extent of the study area. Then, the dataset was converted
into a raster using the Polygon to Raster tool. The extent and snap rasters were set to the tidal
29
range raster to ensure that the new raster would overlay the others perfectly. Each cell was
populated with its corresponding suitability score as described above. Areas considered ‘no data’
were reclassified to a value of 0 to represent areas that were not identified by the parameter. This
process was repeated for each dataset in the government use category resulting in a raster for
each parameter.
3.2.4. Commercial Use Data
Commercial use data were also processed and converted to the NAD 1983 datum and
clipped to the study area. Commercial use data included shipping lanes, oil pipelines, and
submarine cables (Table 4). While oil pipelines are considered in the analysis, oil platforms were
excluded from this study because they are located very far from the study area, and also would
have little impact on tidal turbines installed on the seafloor.
Table 4. Data used in this study relating to the commercial use limitations of tidal energy
technology.
Commercial Use
Data
Source Year Datum Description
Shipping Lanes NOAA, Office of Coast
Survey
2015 WGS 1984 Vector, Polygon data showing
boundaries of major shipping lanes
in the U.S.
Oil Pipelines NOAA, Office of Coast
Survey
2018 WGS 1984 Vector, Line data mapping the
location of oil pipelines.
Submarine Cables NOAA 2019 WGS 1984 Vector, polygon data showing the
locations of various submarine
cables.
Marinas California Department
of Fish and Game,
Marine Region GIS Lab
2020 NAD 1983 Vector, Point Data consisting of
coastal and marine related boat
marinas.
Similar to the government use data, each dataset in the commercial use category was
scored based on its suitability for tidal energy technology. Below is the description and reasoning
for the suitability scores for commercial use data.
30
• Shipping Lanes: Designated lanes for commercial and private vessel traffic. Blocking
these lanes is strictly prohibited. Because tidal turbines are installed on the seafloor, they
would not directly block shipping lanes. Subsequently, this category does not need to be
restricted. However, movement from large shipping vessels on the surface could have an
impact on the water flow beneath, interfering with the tidal flow that powers turbines.
Because of this, the shipping lane data was given a suitability score of 2.
• Oil Pipelines: Pipelines running along the seafloor for the transport of oil. Because these
pipelines are installed into the seafloor, they would directly impact the installation of tidal
turbines. For this reason, oil pipelines were given a restricted suitability score of 0.
• Submarine Cables: Cables running along the seafloor used for a variety of commercial
and governmental purposes. Similar to oil pipelines, these cables would directly impact
the installation of tidal turbines and were therefore given a restricted score of 0.
• Marinas: Areas consisting of public and private marinas. While no specific literature
discusses the limitation of marinas, an assumption that tidal energy technology would not
be allowed for installation in these areas was made. A restricted suitability score of 0 was
given. A 100 m buffer was applied to all marina data points to account for these areas.
A new attribute field called “suitability” was created in each data layer, following the
same methodology as the government regulation data described in section 3.1.2, and was
populated with the corresponding suitability score. Once scored, each of these commercial use
data layers were individually dissolved into a single feature using the Dissolve tool in ArcGIS
Pro. The datasets were then clipped to the extent of the study area. Then, the datasets were
converted into a raster using the Polygon to Raster tool. The extent and snap rasters were set to
the tidal range raster to ensure that the new raster would overlay the others perfectly. Each cell
31
was populated with its corresponding suitability score as described above. Areas considered ‘no
data’ were reclassified to a value of 0 to represent areas that were not identified by the parameter.
This process was repeated for each dataset in the commercial use category resulting in a raster
for each parameter. Figure 5 depicts the workflow used to process this data while Table 4 lists
the data properties.
Figure 5. Flow chart depicting the data pre-processing for the government and commercial use
datasets.
3.2.5. Power plants
Information about power plant locations in southern California was also utilized. The
California Energy Commission runs a Critical Infrastructure GIS data portal that has an ArcGIS
feature layer containing information about all of the power generating facilities located in
California and their source of energy. This layer was imported into ArcGIS Pro where it was
then clipped to the study area. Next, all locations that were appointed as hydro, wind, or solar
energy were selected to identify renewable energy sites. These were chosen because hydro, wind,
32
and solar energy processes are similar in nature to tidal energy, meaning these facilities will be
somewhat more prepared to handle and convert tidal energy since they are already processing
renewable energy.
3.3. Hard and Soft Criteria
The ways in which the data themes were reclassified to fit the needs of this study in
Section 3.2 provided two groups of variables comprised of hard and soft criteria. The themes
with Boolean outcomes (0/1) served as hard criteria because the areas rated 0 would not support
the implementation of tidal energy technology. There was six such variables as follows:
• Marine Protected Areas
• Areas of Special Biological Significance
• Military Use Zones
• Marinas
• Oil Pipelines
• Submarine cables
The second group behaved like soft criteria because they supported varying levels of suitability
in terms of the implementation of tidal energy technology. There were four such
variables as follows:
• Tidal Range
• Bathymetry
• California State Parks
• Shipping Lanes
The conceptualization of the criteria in these two ways meant that the computational effort
required to perform the various kinds of overlays described in Sections 3.4 and 3.5 could be
33
minimized by introducing each criterion in the aforementioned order because the first set could
be used iteratively to reduce the area of interest.
3.4. Overlay Analyses
Two different analysis types were used to determine site suitability. The first was a
weighted overlay analysis. A weighted overlay analysis uses a workflow in which all data is first
reclassified to a scale based on suitability or preference, then each cell is multiplied by its weight
of importance, and finally all of the cell values are added together to produce a raster dataset
(Esri 2020e). This method allows for more impactful parameters to be given a larger importance
in determining which areas are suitable. Weighted overlay is appropriate for investigating tidal
energy technology because the inputs, such as tidal range, can be more heavily weighted to
influence the overall results.
A fuzzy overlay analysis has a similar workflow to weighted overlay analysis but differs
in how it classifies the data. Fuzzy overlay is based on the fuzzy membership, which transforms
a dataset into a 0 to 1 scale indicating how likely a cell belongs to a specified set (Esri 2020b).
This classification principle allows for more leniency when deciding how a cell is classified,
operating under the assumption that boundaries between different classes may not be so strict.
Fuzzy membership has many different types including Gaussian, Large, Linear, Small, Near, MS
Large, and MS Small. Each type dictates how the data is transformed and is appropriate for
particular types of data. Once data has been classified using the fuzzy membership principles, it
is used in a fuzzy overlay. The fuzzy overlay process uses all of the input values to determine
what sets the phenomenon belongs to. There are many different overlay types to use with the
fuzzy overlay process. For this study the ‘And’ operator was chosen. This operator type returns
the minimum value of the sets the cell location belongs to, so for tidal energy technology the
34
analysis will result in suitable locations that have a high probability of matching all of the input
criteria.
3.5. Weighted Overlay Analyses
The weighted overlay analysis was performed in ArcGIS Pro and was separated into two
phases. The first phase of the weighted overlay used the hard criteria defined in Section 3.2 to
eliminate all areas that were not available for the implementation of tidal energy technology.
This resulted in a map of restricted and non-restricted areas. The second phase of the weighted
overlay analysis used the soft criteria defined in Section 3.2 that used physical, government
regulation, and commercial use data to perform a weighted overlay analysis on the non-restricted
areas to identify suitable sites for tidal energy technology.
3.5.1. Hard Criteria
The first step in the weighted overlay analysis process was to eliminate all of the areas
that were completely off-limits for tidal energy technology. Using the parameters assigned to the
hard criteria class defined in Section 3.2, the union tool was used to dissolve all of the features
into a single class. Areas of overlapping cells were given the lowest suitability value, which was
0 since all of the parameters from the hard criteria class had a suitability score of 0. The resulting
feature showed all areas within the study area that were completely off-limits for the
implementation of tidal energy technology. To isolate areas that were excluded from the hard
criteria classification, the study area raster was clipped using the new hard criteria raster,
resulting in an area that did not include restricted cells.
35
3.5.2. Analysis
The next part of the analysis used the soft criteria to perform a weighted overlay analysis
on the remaining study area that was identified by removing the hard criteria area. Using the
weighted overlay tool, the soft criteria parameters were given varying weights. The input rasters
used in the weighted overlay analysis were the classified bathymetry raster, classified tidal range
raster, the state park raster, and the shipping lanes raster (Table 5). The tidal range raster was
given a weight of 50%, and the scale was set so that classifications of ‘1’, ‘2’, ‘3’, ‘4’, and ‘5’
were set to 1, 1, 3, 5, and 5, respectively. These numbers were chosen so that the highest two
classes of tidal range were given a higher suitability score while the lowest classes of tidal range
values were given low suitability scores. A 50% weight was chosen because tidal range directly
impacts the flow of water generated, which is what powers tidal turbines. This factor will have
the most impact on how successful tidal energy technology would be if it was implemented. The
bathymetry raster was given a weight of 25%, and the scale was set so that a classification of ‘0’
was restricted and the classification of ‘1’ was set at 5. This scale was set with the intention of
restricting non-ideal ranges of ocean depth, and the weight was chosen based on previous
research for this project that indicated bathymetry was less important to tidal energy technology
compared to the tidal range and was therefore given a lower weight. The state park raster was
given a weight of 15%, and the classification value of ‘3’ was set to 3. This weight was chosen
because the restrictions regarding state parks are somewhat unknown, so their impact on tidal
energy technology implementation is smaller. Lastly, the shipping lane raster was given a weight
of 10%, and the classification of ‘2’ was set to 2. Shipping lanes were given the smallest weight
because they impact a very small area of the study area, and generally do not affect tidal energy
technology on larger scales. For both the parks and shipping lane parameters, the classification
36
value of 0 was given a scale value of 5. Values of 0 represented cells in each raster that were not
identified as either a park or shipping lane, meaning they had no restrictions regarding tidal
energy implementation. For that reason, they were given a high scale value of 5. Once the
weighted overlay analysis was run, a raster was generated showing how suitable each cell within
the study area was for the siting of tidal energy technology based upon the soft criteria
parameters described above. This exact process was completed two times, the first time using the
IDW interpolated tidal range raster and then a second time using the RBF interpolated tidal range
raster.
Table 5. Scales and weights used for the weighted overlay analysis.
Weight Classification Scale
Tidal Range 50% 1 1
2 1
3 3
4 5
5 5
Bathymetry 25% 0 Restricted
1 5
State Parks 15% 0 5
3 3
Shipping Lanes 10% 0 5
2 2
3.6. Fuzzy Membership
In order to perform the Fuzzy Overlay Analysis, the rasters used in the analysis must be
first classified using the fuzzy membership technique. Fuzzy membership uses a 0 to 1 scale that
indicates the possibility of a cell being a member of a specified set. To classify each raster into
the 0 to 1 scale, the fuzzification process was followed. To apply fuzzy logic to both the IDW
37
and RBF tidal range rasters, the fuzzy membership tool in ArcGIS Pro was used with a specified
membership type ‘large’ and an auto-generated midpoint of 1.16. The large membership type
was chosen because the largest tidal range values are ideal, so the fuzzy membership process will
allocate a higher value to the larger tidal range values. A similar logic was used for the
bathymetry raster. The raster was run through the fuzzy membership tool used with a
membership type of ‘Near’, a midpoint of -30 and a spread of 0.01. The Near membership type
was chosen because the ideal bathymetry range is specific, represented by a midpoint of -30,
which translates to a 30 m depth, so the fuzzy membership prioritizes bathymetry values near the
midpoint and then reduces the outlying values towards 0.
For the remaining soft criteria parameters, state parks and shipping lanes, the fuzzy
membership tool was applied using the large type. This allowed for areas not identified to be a
state park or shipping lane to be considered higher suitability. The auto generated midpoint was
used
3.7. Fuzzy Overlay Analysis
Similar to the weighted overlay analysis, the fuzzy overlay analysis was performed on the
study area that excluded the areas that were restricted by the hard criteria in Section 3.3.2. The
analysis was based on the tidal range, bathymetry, state parks, and shipping lane rasters that had
been classified using the fuzzy membership principle described in Section 3.4 as inputs. The
‘and’ operator was used in the analysis and the snap raster and extent were set to match the new
study area raster that excluded the hard criteria areas to ensure the correct spatial extent and a
perfect overlay. The resulting raster generated values between 0 and 1 indicating how suitable
each cell is for implementing tidal energy technology based on the input data. Values closer to 1
represent higher likelihood of suitability while values of 0 represented no suitability. This
38
process was repeated two times, first using the IDW interpolated tidal range raster and then a
second time using the RBF tidal range raster.
3.8. Distance to Energy Facilities
Once both the weighted and fuzzy overlay analyses had been completed, it was important
to calculate the distance to energy facilities for the most suitable areas. Since there is no specific
literature on the ideal distance between tidal energy installations and energy facilities, three
different distances were analyzed. Three buffers were created around the selected energy
facilities as described in Section 3.1.4 with a radius of 15, 10, and 5 km. Then the extract by
mask tool was used on both the weighted and fuzzy overlay rasters. This produced a result
showing all of the suitable areas for tidal energy technology identified in the previous analyses
that were within 15, 10, and 5 km to an energy facility.
3.9. Summary
The methodology of this study followed a simple workflow, relying heavily on the initial
data manipulation and methods used in Williams (2018). After reviewing and editing all of the
data from each source, the datasets were classified based on ideal values. These classifications
were then used in both a weighted and fuzzy overlay analysis, and finally connected with
neighboring energy facilities based on the distance thresholds.
39
Chapter 4 Results
A weighted and fuzzy overlay analysis were conducted using ArcGIS Pro to search for suitable
areas in southern California to implement tidal energy technology. The results from these
analyses are examined in detail below, highlighting the results from both approaches as well as
the proximity to renewable energy power plants.
4.1. Interpolation
The foundation for both the weighted overlay and fuzzy overlay analysis was built on the
success of interpolating the tidal range data. The IDW interpolation method was performed first
(Figure 6) and the RBF interpolation was performed second (Figure 7). While the two different
interpolation methods produced similar results with regard to estimated tidal range values, they
differ in their spatial extents. For that reason, both of the interpolation types were used in the
following overlay analyses, and their effect on the results is examined and discussed later in this
chapter.
Esri, HERE, Garmin, USGS, NGA, EPA, USDA, NPS
IDW Interpolation
Value
1.02938
1.31022
0 23 46 11.5 Mi l e s
Figure 6. Results of the IDW interpolation, showing the estimated average
tidal ranges within the study area.
40
When comparing the tidal range data produced by the two different interpolation
methods, it is noticeable that the IDW method produces more of a clustered result with similar
values of tidal range being seen over more condensed areas. The RBF method shows a larger
spatial range of where similar tidal range values are clustered. Both interpolation methods
showed the largest tidal range values in San Diego, which is consistent with the raw data before
interpolation.
4.2. Weighted Overlay Analysis
The weighted overlay analyses were performed in two phases. The first phase took the
hard criteria that were established in section 3.2 to identify areas within the study area that were
completely unsuitable for tidal energy technology based on physical, government, and
commercial limitations. These areas were then removed from the study area. The second phase
of the weighted overlay analysis used the soft criteria that were established in section 3.2 to
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
RBF Interpolation
1.03181
1.317
0 21 42 10.5 Mi l e s
Figure 7. Results of the RBF interpolation, showing the estimated average
tidal ranges within the study area.
41
perform the weighted overlay analysis on the new and smaller study area that excluded the areas
disqualified with hard criteria.
4.2.1. Eliminating Hard Criteria
After creating a raster data set that included all of the hard criteria, including MPAs,
ASBS, Military Use Zones, Oil Pipelines, Submarine Cables and Marinas, a new study area was
created. The new study area ensured that the weighted overlay could be applied to areas that
were free of complete restrictions to tidal energy technology implementation. Figure 8 shows the
new study area after removing all of the areas disqualified with the hard criteria. This new area,
represented in red, is potentially suitable for the siting of tidal energy technology.
Figure 8. The new study area, represented in red, created after elimination of the areas
disqualified with the hard criteria.
Esri, HERE, Garmin, USGS, NGA, EPA, USDA, NPS
Study Area
0 19 38 9.5 Mi l e s
42
The new study area now had many areas that are excluded. Cells disqualified using the
hard criteria accounted for approximately 25.4% of the study area. Figures 9 and 10 show the
new study area, represented in red, that excludes the hard criteria in the Long Beach and San
Diego areas. This new study area is potentially suitable for the siting of tidal energy technology.
Figure 9. The new study area, represented in red, that excludes the areas disqualified with the
hard criteria in and near Long Beach, CA.
Figure 10. The new study area that excludes the areas disqualified with hard criteria in and near
San Diego, CA.
Esri, HERE, Garmin, USGS, NGA, EPA, USDA, NPS
Study Area
0 6.3 12.6 3.15 Mi l e s
SanGIS, Bureau of Land Management, Esri, HERE, Garmin, USGS, NGA, EPA, USDA, NPS
Study Area
0 6.3 12.6 3.15 Mi l e s
43
4.2.2. Weighted Overlay Analysis
The weighted overlay analysis was first performed using the IDW interpolation method
and the soft criteria on the new study area that was created by excluding the hard criteria. The
results from this analysis show that areas near Long Beach and San Diego have higher numbers
of cells that scored in the suitable range compared to other areas. This can be attributed to these
areas having larger tidal ranges, which are more beneficial to tidal energy technology. For this
analysis, scores of 0 indicated low suitability while scores of 5 indicated the highest suitability.
Figure 11 shows the results of the weighted overlay analysis.
Figure 11. Weighted overlay analysis results using the IDW interpolation method. Top inset
image shows Long Beach, CA. Bottom inset image shows San Diego, CA.
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
Esri, HERE, Garmin, USGS, NGA,
EPA, USDA, NPS
Esri, HERE, Garmin, USGS, NGA,
EPA, USDA, NPS
Weighted Overlay Analysis
Value
0 - Least Suitable
3
4
5 - Most Suitable
44
From this analysis, no cells received a score of 1 or 2. The majority of cells (60.04%) had
a suitability score of 0. The most suitable cells, with suitability scores of 4 and 5, accounted for
12.62% of cells. Table 6 shows the breakdown of cells by suitability score.
Table 6. Breakdown of cells from the weighted overlay analysis using the IDW interpolation
method.
Value Count Percent
0 50,435 60.04
1 0 0
2 0 0
3 22,976 27.35
4 10,550 12.56
5 48 0.05
Totals 84,009 100
A second weighted overlay analysis was performed using the RBF interpolation method,
with results seen in Figure 12. These areas showed the highest number of cells scoring in the
suitable range. This analysis used the RBF interpolated tidal ranges to locate suitable areas for
implementing tidal energy technology based on the soft criteria. The resulting cells were given
scores that reflected the inputs and how suitable they were for tidal energy technology. A score
of 0 indicated no suitability while a score of 5 represented the most suitable cells. In this analysis
no cells received a score of 1 or 2. The results are similar to the IDW results, especially when
looking at the breakdown of how many cells were scored in which category. Table 7 shows the
number of cells scored for each category using the RBF method. The RBF method identified
approximately 5.8% fewer cells in the suitable categories compared to the IDW method.
45
Figure 12. Weighted overlay analysis results using the RBF interpolation method. Top inset
image shows Long Beach, CA. Bottom inset image shows San Diego, CA.
Table 7. Breakdown of cells from the weighted overlay analysis using the RBF interpolation
method.
Value Count Percent
0 50,435 60.04
1 0 0
2 0 0
3 27,912 33.22
4 56,19 6.69
5 43 0.05
Totals 84,009 100
Esri, HERE, Garmin, USGS, NGA, EPA, USDA, NPS
Esri, HERE, Garmin, USGS, NGA,
EPA, USDA, NPS
Esri, HERE, Garmin, USGS, NGA,
EPA, USDA, NPS
Weighted Overlay Analysis
Value
0 - Least Suitable
3
4
5 - Most Suitable
46
To compare the differences between the IDW and RBF interpolation methods when
performing the weighted overlay analysis, a map was created showing the suitable areas
identified by both interpolation methods. Suitable cells were considered to be any cell that scored
either a 4 or 5 in the weighted overlay analysis. Figure 13 shows cells identified by the IDW
method, RBF method, and both methods. Table 8 shows the breakdown of those classifications
with 50.74% of suitable cells identified with the IDW interpolation method, 7.8% with the RBF
method, and 36.4% of cells with both methods. This indicates that most of the cells identified by
the RBF method were also identified by the IDW method, showing that the suitable results from
the IDW method have a larger spatial extent.
Figure 13. Results of the weighted overlay analysis comparing suitable cells identified by each
interpolation method. Top inset image shows Long Beach, CA. Bottom inset image shows San
Diego, CA.
Esri, HERE, Garmin, USGS, NGA, EPA, USDA, NPS
Esri, HERE, Garmin, USGS, NGA,
EPA, USDA, NPS
SanGIS, Bureau of Land
Management, Esri, HERE, Garmin,
USGS, NGA, EPA, USDA, NPS
Weighted Overlay Analysis
Value
IDW
RBF
Combine
47
Table 8. Breakdown of suitable cells that were identified in the weighted overlay analysis using
each interpolation method.
Weighted Overlay Count Percent
IDW 5,834 50.75
RBF 898 7.81
Both 4,764 41.4
Totals 11,496 100
4.3. Fuzzy Overlay Analysis
The second analysis type used to look at suitable areas for tidal energy technology was
the fuzzy overlay analysis. Similar to the weighted overlay analysis, the fuzzy overlay analysis
was performed on the new study area that was created after eliminating the areas disqualified
with the hard criteria as described in Chapter 3. Using the same study area as the weighted
overlay analysis ensured accurate results in the fuzzy overlay analysis that could then be
compared with the weighted overlay results.
The fuzzy overlay analysis was first performed with the IDW interpolation method using
the soft criteria which included tidal range, bathymetry, state parks, and shipping lanes. The
analysis resulted in each cell having a score on a 0 to 1 scale, with higher values representing a
better likelihood of those cells being suitable based on the input parameters. Figure 14 shows the
results of the fuzzy overlay analysis using the IDW method. Darker colors indicate cells with
higher suitability scores while lighter colored cells indicate lower suitability.
48
Figure 14. Fuzzy overlay analysis results using the IDW interpolation method. Top inset image
shows Long Beach, CA. Bottom inset image shows San Diego, CA.
Compared to the weighted overlay analysis, the fuzzy overlay analysis has a much larger
spatial range. Whereas the weighted overlay did not identify many suitable cells north of the Los
Angeles metropolitan area, the suitable cells identified with the fuzzy overlay extended as far
north as the Santa Barbara area. The fuzzy overlay analysis identified 34.45% of cells as suitable
while the majority of cells were considered unsuitable. Suitable cells were determined to be any
cell that scored in the top 50% of the fuzzy value range. Table 9 shows the breakdown of cells
from the fuzzy overlay analysis using the IDW method.
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
Esri, HERE, Garmin, USGS, NGA, EPA,
USDA, NPS
Esri, HERE, Garmin, USGS, NGA, EPA,
USDA, NPS
Fuzzy Overlay Analysis
Value
Not Suitable
Most Suitable
49
Table 9. Breakdown of cells from the fuzzy overlay analysis using the IDW method.
Value Count Percent
Not Ideal 55,070 65.55
Ideal 28,939 34.45
Totals 84,009 100
The fuzzy overlay analysis was performed a second time using the RBF interpolated tidal
range data. The results have a similar spatial distribution to the IDW method, again with a larger
spatial range compared to the weighted overlay analysis. Figure 15 shows the results of the fuzzy
overlay analysis using the RBF interpolation method. From this analysis, only 36.6% of cells
were considered suitable. This was an increase (2.16%) compared to the IDW method. Table 10
shows the breakdown of cells from the fuzzy overlay analysis using the RBF interpolation
method.
The results for both interpolation method analyses and the fuzzy overlay were compared
against each other to characterize the spatial variation in suitable cells. Table 11 shows the
distribution of cells showing that the majority, approximately 85.2% of cells, was identified as
suitable using interpolation methods. Only a small percentage of suitable cells, 4.6% were solely
identified by the IDW method, meaning that this method had a much smaller spatial extent
compared to the RBF method, which identified 10.2% of the cells as suitable. Table 11
summarizes suitable cells identified by the different interpolation methods. The spatial
distribution of suitable cells identified by each interpolation type is shown in Figure 16.
50
Figure 15. Fuzzy overlay analysis results using the RBF interpolation method. Top inset image
shows Long Beach, CA. Bottom inset image shows San Diego, CA.
Table 10. Breakdown of cells from the fuzzy overlay analysis using the RBF interpolation
method.
Value Count Percent
Not Ideal 53,253 63.39
Idea 30,756 36.61
Totals 84,009 100
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
Esri, HERE, Garmin, USGS, NGA, EPA,
USDA, NPS
SanGIS, Bureau of Land
Management, Esri, HERE, Garmin,
USGS, NGA, EPA, USDA, NPS
Fuzzy Overlay Analysis
Value
Not Suitable
Most Suitable
51
Table 11. Breakdown of suitable cells identified by each interpolation method.
Fuzzy Overlay Count Percent
IDW 1,476 4.58
RBF 3,293 10.22
Both 27,463 85.20
Totals 32,232 100
Figure 16. Results of the fuzzy overlay analysis comparing each interpolation method. Top inset
image shows Long Beach, CA. Bottom inset image shows San Diego, CA.
4.4. Breakdown by County
Of the cells identified as suitable in the weighted overlay analyses, the majority (46.7%)
were located in Los Angeles County. These numbers were calculated using the total number of
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
Esri, HERE, Garmin, USGS, NGA, EPA,
USDA, NPS
SanGIS, Bureau of Land
Management, Esri, HERE, Garmin,
USGS, NGA, EPA, USDA, NPS
Fuzzy Overlay Analysis
Value
IDW
RBF
Combine
52
suitable cells and all interpolation methods. Ventura and Santa Barbara Counties had no suitable
cells. Orange and San Diego Counties had 15.1% and 38.18%, respectively. The fuzzy overlay
analysis also identified the majority of suitable cells in Los Angeles County with 33.5%. This
was a decrease of 13% compared to the suitable cells identified by the weighted overlay analysis.
Comparatively, the fuzzy overlay analysis identified suitable cells in both Ventura and Santa
Barbara Counties whereas the weighted overlay analysis did not. Orange and San Diego
Counties had relatively low numbers of suitable cells which were 7.05% and 8.54% less than that
for the weighted overlay, respectively. These results show that the weighted overly approach
identified more suitable cells in San Diego, Los Angeles, and Orange Counties while the fuzzy
overlay approach identified more suitable cells in Santa Barbara and Ventura Counties. Table 12
shows the breakdown of the analysis results by county.
Table 12. Breakdown of suitable cells by county comparing the weighed overlay analysis and
fuzzy overlay analysis.
San Diego Orange LA Ventura Santa
Barbara
Total
Weighted Count 4,389 1,736 5,371 0 0 11,496
% 38.18 15.10 46.72 0 0 100
Fuzzy Count 6,838 2,595 10,796 6,862 5,141 32,232
% 21.22 8.05 33.49 21.29 15.95 100
4.5. Distance to Energy Facilities
The last portion of the analysis completed for this study examined the distance to energy
facilities. Energy facilities in southern California that processed wind, solar, and hydro energy
types were selected from a California Power Plants database and then clipped to the study area.
A 15, 10, and 5 km buffer was applied to the data points to analyze how many suitable cells were
53
within these varying ranges. Figure 17 shows a map of renewable power plant locations relative
to the study area.
Figure 17. Renewable power plants located near the study area.
The results vary greatly between analysis types as seen in Table 13. When looking at
cells identified as suitable by both interpolation methods, the weighted overlay analysis
identified more cells than the fuzzy overlay analysis at each distance. For the 15 km distance,
each analysis type had a majority of its cells fall within the range. The weighted overlay had
95.6% of cells fall within this 15 km distance, which was 16.0% more than the fuzzy overlay.
For the shortest distance, 5 km, 24% of the weighted overlay cells fell within the range while the
fuzzy overlay only had 21.5%. California has an abundance of energy facilities, especially along
54
the coast, so it is not surprising to see the variation of suitable cells that fall within the different
distance ranges of these facilities.
Table 13. Breakdown showing the percent of cells that were within the specified distances to
energy facilities for each different interpolation method and analysis type.
15 km 10 km 5 km
Weighted Combined Count 10,988 9,763 2,797
% 95.58 84.92 24.33
Fuzzy Combined Count 25,656 21,093 69,27
% 79.59 65.44 21.49
55
Chapter 5 Discussion
The results of this study are discussed in detail in this chapter, starting with a review of the data
procedures and the two different interpolation methods. The results of the weighted and fuzzy
overlay analyses are then analyzed with a focus on areas determined to be the most suitable for
the potential siting of tidal energy technology. The discussion of the results is also broken down
by county, as well as including a review of nearby potential energy facilities. Given the results of
the weighted and fuzzy overlay analyses, areas within Los Angeles County were found to be the
most suitable for implementing tidal energy technology.
5.1. Data
The foundation for all of the results from both types of analyses was from the data. Each
data set that was used as an input for the different overlay analyses was originally defined on
vastly different scales and projections and represented spatial areas much larger than what was
needed for this study. The process of manipulating the data so that everything was cohesive was
an extensive process but resulted in a workflow that could be applied to each parameter. Each
dataset had to first be converted to the correct datum (NAD 1983) and projection (UTM Zone
11), and then trimmed to the correct extent for this study. While doing this, it was important to
use a carefully selected snap raster and extent parameter in which to align the data correctly. The
same snap raster was used for each dataset resulting in a perfect alignment between raster
datasets. It was also important to resample each dataset to the same spatial scale. Each parameter
was set to a 100 m cell size. Having each dataset on the same spatial scale and with perfect
alignment set up the remainder of the analysis for success.
56
5.2. Differences in interpolation methods
There were two different types of interpolation methods used to create the raster dataset
containing tidal range values. In order for the weighted overlay and fuzzy overlay analyses to
work, the tidal range data needed to be a continuous surface that had the same spatial scale and
extent as the remainder of the datasets. The first interpolation method used was the IDW method.
This method uses the proximity of surrounding values to predict a new location’s value while
still using the original data points, making it an exact interpolator. It works on the assumption
that nearby values contribute more to the estimation than farther values. Because of this, the
IDW method is prone to clustering. Areas with a low density of data points exert an influence in
all directions that diminishes evenly which results in concentrated patterns of estimated values.
This can be seen in Figure 18 in the tidal range data that was estimated using the IDW method.
Where the original data points are more spread out there is less variance in estimated value,
while in areas such as San Diego where there is a concentration of data points the values are
much more diverse. It could be assumed that if more sample points containing tidal data were
included in the interpolation, there would be greater variability in the resulting raster which
could help to refine the overall results of this study.
The second interpolation method used for estimating the tidal range values was the RBF
method. The multi-quadratic function was used in this study. RBFs are used to produce smooth
surfaces from a large number of data points. The functions produce good results for gently
varying surfaces and pass through the original data points making it an exact interpolator, similar
to the IDW method. RBFs can predict values that are larger or smaller than the minima and
maxima of the original data, which allows them to produce smoother surfaces. Figure 19 shows
the RBF interpolation compared to the original data points.
57
Figure 18. Estimated tidal range values using the IDW interpolation method overlaid with
original tidal range data points.
Figure 19. Estimated tidal range values using the RBF interpolation method overlaid with
original tidal range data points.
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
Ti d a l R a n g e D a t a
Ti d a l R a n g e D a t a
ID W
1.02938
1.31022
0 21 42 10.5 Mi l e s
Esri, HERE, Garmin, FAO, USGS, NGA, EPA, NPS
Ti d a l R a n g e D a t a
Ti d a l R a n g e D a t a
RB F
1.03181
1.317
0 21 42 10.5 Mi l e s
58
Figures 20 and 21 show the comparison of estimated values from the IDW and RBF
interpolations in the San Diego region. The maps show how the different interpolation methods
affect the spatial distribution of estimated values. The RBF method shows a wider spatial range
of similar values and a more spatially diverse series of estimates compared the IDW method.
These differences can be attributed to the mathematical models each interpolation type uses and
could be one of the causes of varying results produced by the weighted and fuzzy overlay
analyses.
Figure 20. IDW interpolated tidal range values in San Diego County, CA. The values represent
the average tidal range.
59
Figure 21. RBF interpolated tidal range values in San Diego County, CA. The values represent
the average tidal range.
After performing both the weighted overlay and fuzzy overlay analyses, differences in
the interpolation method used are quite large. In the weighted overlay analysis, 50.75% of
suitable cells were identified by the IDW method compared to 7.8% identified by the RBF
method. The remaining cells were identified by both methods (41.4%). The large difference
shows how the IDW method could be favored by the weighted overlay because of the clustering
of similar cell values produced by the IDW interpolation. Comparatively, the fuzzy overlay
analysis found more suitable cells from the RBF interpolation method (10.2%) compared to the
IDW method (4.6%). The majority of suitable cells in the fuzzy overlay (85.2%) were identified
by both interpolation methods. This indicates that the fuzzy overlay favored the RBF
interpolation method which provided greater spatial variability of tidal ranges. It is important to
60
note that the tidal ranges created by interpolation are not the only factor in determining suitable
cells, and that other restrictions did influence the results of the overlay analyses.
5.3. Weighted Overlay
Looking at the general trends from the results of the weighted overlay analysis it can be
seen that there are many more suitable cells located in the southern half of the study area, in Los
Angeles, Orange, and San Diego Counties. This could be attributed to the spatial variance of
tidal ranges in southern California. The initial tidal range data used in this study showed that
average tidal ranges were larger in the southern regions. Los Angeles acted as a midpoint in
which lower tidal ranges were seen north of Los Angeles up to Conception Point, and larger tidal
ranges were seen south of Los Angeles down to the California-Mexico border. Because the
weighted overlay analysis placed a large weight on the tidal range values, the results predicted
more suitable cells in the southern half of the study area where tidal ranges are larger.
This factor drove the conclusion that tidal energy technology would be most suitable off
of the coast of Los Angeles County. This area contained 46.8% of cells that were identified as
suitable from the weighted overlay analysis. This result was generated using the total number of
suitable cells predicted with both interpolation methods. Figure 22 highlights the suitable cells in
Los Angeles County from the weighted overlay analysis.
While tidal range plays a large role in determining where are the most suitable areas for
tidal energy technology, it is not the only factor. As seen in the initial tidal range data, the largest
average tidal ranges within the study area occur in San Diego County. Even though this area had
the highest tidal ranges, it only contained 38.2% of the predicted suitable cells, a difference of
8.5% compared to Los Angeles County. This indicates that there were likely more restrictions in
the San Diego region that limit the identification of suitable cells. Specifically, San Diego
61
County has many Military Use Zones as well as protected areas and government regulation
areas. These limiting factors contributed greatly to the results produced by the weighted overlay
analysis and the differences seen between Los Angeles and San Diego Counties.
Figure 22. Weighted overlay analysis results in Los Angeles County, CA.
5.4. Fuzzy Overlay
The results of the fuzzy overlay analysis show that Los Angeles County again had the
highest number of suitable cells (33.5%). This is a smaller percentage compared to the weighted
overlay analysis (46.7%). This can be attributed to the spatial variation in cells produced by the
fuzzy overlay analysis. Analyzing the results from the fuzzy overlay analysis shows that there
62
were more cells overall that were identified as suitable compared to the weighted overlay
analysis, although these results show a larger spatial variability compared to the weighted
overlay analysis, most notably in the northern half of the study area. While the weighted overlay
analysis found no suitable cells in both Ventura and Santa Barbara Counties, the fuzzy overlay
analysis found 21.3% and 15.9% of the cells were suitable in those areas, respectively. The
larger spatial variation can be attributed to the method by which the cells were classified, as the
fuzzy method implements all parameters using a 0 to 1 scale. This allowed for a more fluid
classification compared to the crisp logic used by the weighted overlay method. A more fluid
classification would allow for more cells to be considered suitable, which is seen in the results of
the fuzzy overlay analysis in Los Angeles County (Figure 23).
Figure 23. Results of the fuzzy overlay analysis in Los Angeles County, CA.
63
5.5. Energy Facilities
Energy facilities within close proximity to tidal energy technology sites are important so
that once tidal energy is eventually harnessed it can be added into the energy grid and used
efficiently. In the results of the weighted overlay analysis, almost every suitable cell (95.6%) was
located within 15 km of an appropriate energy facility. This is an excellent statistic indicating
that once tidal energy technology is implemented it can almost always be connected to the grid
even if it is at a greater distance. Almost a quarter of all suitable cells (24.3 %) were located
within 5 km of an appropriate energy facility, which would allow for even greater efficiency in
adding usable energy to the grid.
The fuzzy overlay analysis found slightly fewer cells located within similar distances to
energy facilities: 79.6% of suitable cells were located within 15 km and 21.5% of suitable cells
were located within 5 km of an energy facility. While slightly less than the results from the
weighted overlay, the statistics still indicate that a connection to the energy grid from tidal
energy technology sites would be possible and efficient. The differences between the results of
the different overlay methods can be attributed to the spatial variability of suitable cells produced
by each method.
64
Chapter 6 Conclusions
The results of this study show that tidal energy technology is suitable and can be implemented in
southern California. The coastal areas in Los Angeles County are the most suitable areas for the
implementation of tidal energy technology based upon a weighed overlay and fuzzy overlay
analysis. The implementation of tidal energy technology in southern California could have a
positive impact in reducing the population’s carbon footprint as well as mitigating some of the
effects of climate change. A study conducted by the National Renewable Energy Laboratory
found that renewable energy generation from today’s and a few emerging technologies could
adequately supply 80% of the U.S.’s energy needs by the year 2050 (NREL 2017). This would
reduce greenhouse gas emissions from electricity by almost 81%, which would help to reduce
harmful emissions. The potential for implementing tidal energy is immense; hence, the
importance of continued exploration into the development and use of this technology.
The areas around Los Angeles and San Diego Counties were identified as the most
suitable for tidal energy technology in both the weighted overlay and the fuzzy overlay analyses
(Figure 24). The city of Los Angeles has an action plan to achieve 100% renewably generated
electricity by 2045 (City of Los Angeles, 2020). Based on the results of this study, tidal energy
technology development could potentially help the city of Los Angeles reach the renewable
energy goals, with more in-depth research, planning and commitment on the part of vested
interests. Other cities in California also have their own renewable energy goals, such as the city
of San Diego, which has a Climate Action Plan that includes a goal of 100% renewable energy
citywide by 2035 (City of San Diego 2019). Implementing tidal energy technology in areas
identified by this analysis could potentially help cities reach their emissions goals.
65
Figure 24. Suitable cells, represented in red, from both the weighted overlay and fuzzy overlay
analyses. The left map shows Los Angeles County and the right map shows San Diego County.
6.1. Project Expansion
One major drawback in this study, and in southern California in general, is that the
southern west coast of the United States does not have a large tidal range compared to other parts
of the country. The average tidal range in the study area was approximately 1.2 m, whereas the
east coast of the United States can see tidal ranges up to 2.5 m and Alaska experiences tidal
ranges up to 9 m (U.S. Department of Commerce, 2008). Having a larger tidal range would be
more beneficial in the long run when trying to produce tidal energy because a larger volume of
water being moved through the converters results in higher rates of energy conversion. It is
important to consider the fact that southern California does not have large tidal ranges when
looking at the suitability of implementing tidal energy technology. While areas of southern
California were found to be suitable based upon the analysis conducted in this study, California
may not be the most suitable location in general for implementing this type of renewable energy.
An examination of the cost efficiency between different types of renewable energy compared to
66
tidal energy technology, could be used to identify the best solution for California. Additionally, a
larger study that utilizes more areas throughout the United States could be of use to find the most
suitable areas for tidal energy technology.
By applying the methodology used in this analysis of tidal energy technology to other
parts of the United States, a larger database can be created of suitable locations for tidal energy
technology installation. This could be useful for interested developers as well as local and federal
governments that may have an interest in this type of renewable energy. There are many
initiatives across the country with important renewable energy goals, and tidal energy technology
could help in reaching those milestones.
6.2. Spatial Analysis
The use of spatial analysis within this study proved to be successful in identifying
suitable areas for the implementation of tidal energy technology. The use of two interpolation
methods for estimating tidal range values across the study area was also very successful. The
IDW and RBF interpolation methods both provided strong predictions and had a wide spatial
range of values. The results of these interpolations as well as the pre-processing of data also
resulted in the creation of valuable spatial databases that store important information related to
tidal energy in the state of California.
Spatial analysis has been used to study tidal and wave energy technologies in the past
(Defne et al. 2011; Work et al. 2012; Vasileiou et al., 2017; Williams 2018) with great success.
These studies utilized overlay analysis techniques with the application of spatial data. These
principles were built upon successfully in this study of tidal energy technology by utilizing
spatial data processing techniques and spatial data analysis. Spatial sciences proved very useful
67
for identifying suitable sites for renewable energy development and should be applied further to
advance the use of GIS techniques in the renewable energy industry.
6.3. The Future of Tidal Energy
Tidal energy technology is still relatively new and in the early stages of development.
Many forward strides within the industry will come within the next few years, which will help to
expand the number of areas that can host these kinds of technologies. The world’s first subsea
offshore tidal array, known as the Paimpol-Bréhat Tidal Array, has been deployed 16 km off-
shore at a depth of 35 m with a second unit about to be deployed (REW 2019). Once completed,
the Paimpol-Bréhat Tidal Array is expected to produce of 1 MW of power. Another company,
Magallanes Renovables, is looking to design tidal turbines that attach to the underside of floating
platforms (Givetash 2019). This idea would potentially reduce the costs of installation and
maintenance compared to other types of tidal turbines. Companies like these represent the future
development of tidal energy technology, proving that big gains are being made that will make
tidal technology more efficient and cost-effective.
With newer developments in tidal energy technology, it is important to look at what will
need to change in order to host these new technologies. This study was conducted using current
tidal turbine technologies as a reference, and the results therefore do not reflect newer
developments. Continuing this suitability study of tidal energy technology will be important
moving forward, especially as the technology becomes more advanced.
68
References
AWEA (American Wind Energy Association). 2019. “Basics of wind energy.” Washington, D.C:
American Wind Energy Association.
Bedard, R., Previsic, M., Hagerman, G., Polagye, B., Musial, W., Klure, J., von Jouanna, A.,
Mathur, U., Collar, C., Hopper, C., Amsden, S. 2007. North American Ocean Energy
Status – March 2007. In Proceedings of the Seventh European Wave and Tidal Energy
Conference, Porto, Portugal.
Beyene, A., Wilson, J. 2014. “Challenges and Issues of Wave Energy Conversion.” Ocean
Energy Council. https://www.oceanenergycouncil.com/challenges-issues-wave-energy-
conversion/.
Blavette, A., O’Sullivan, D., Lewis, A., Egan, M. 2011. “Grid integration of wave and tidal
energy.” In Proceedings of the 30th International Conference on Ocean, Offshore and
Artic Engineering, Rotterdam, The Netherlands.
City of Los Angeles. 2020. “PLAn.” pLAn, 2020. https://plan.lamayor.org/.
City of San Diego. 2019. “Climate Action Plan - Annual Report.”
https://www.sandiego.gov/sites/default/files/2019_cap_digital_version.pdf
City of San Diego. 2020. “Clean and Renewable Energy.”
https://www.sandiego.gov/sustainability/clean-and-renewable-energy
Day, J., Dudley, N., Hockings, M., Stolton, S., Wells, S., Wenzel, L. 2008. “Guidelines for
Applying the IUCN Protected Area Management Categories to Marine Protected Areas,”
https://www.europarc.org/wp-content/uploads/2019/12/IUCN_Guidelines_MPAs.pdf
Defne, Z., Haas, K.A., Fritz, H.M. 2011. “GIS Based Multi-Criteria Assessment of Tidal Stream
Power Potential: A Case Study for Georgia, USA.” Renewable and Sustainable Energy
Reviews 15(5): 2310–2321.
EIA (U.S. Energy Information Administration). 2019a. “Electricity in the United States.”
https://www.eia.gov/energyexplained/electricity/electricity-in-the-us.php
EIA (U.S. Energy Information Administration). 2019b. “Hydropower explained.”
https://www.eia.gov/energyexplained/hydropower/tidal-power.php
EIA (U.S. Energy Information Administration). 2019c. “Wind turbines provide 8% of U.S.
generating capacity, more than any other renewable source.”
https://www.eia.gov/todayinenergy/detail.php?id=31032
69
EPA (Environmental Protection Agency). 2019. “Inventory of U.S. Greenhouse Gas Emissions
and Sinks.” https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-
and-sinks
Esri. 2020a. “Classification Trees of the Interpolation Methods Offered in Geostatistical
Analyst.” https://pro.arcgis.com/en/pro-app/help/analysis/geostatistical-
analyst/classification-trees-of-the-interpolation-methods-offered-in-geostatistical-
analyst.htm
Esri. 2020b. “How Fuzzy Overlay Works.” https://pro.arcgis.com/en/pro-app/tool-
reference/spatial-analyst/how-fuzzy-overlay-works.htm
Esri. 2020c. “How Inverse Distance Weighted Interpolation Works.”
https://pro.arcgis.com/en/pro-app/help/analysis/geostatistical-analyst/how-inverse-
distance-weighted-interpolation-works.htm
Esri. 2020d. “How Radial Basis Functions Work.” https://pro.arcgis.com/en/pro-
app/help/analysis/geostatistical-analyst/how-radial-basis-functions-work.htm
Esri. 2020e. “How Weighted Overlay Works.” https://pro.arcgis.com/en/pro-app/tool-
reference/spatial-analyst/how-weighted-overlay-works.htm
Esteban, M., Leary, D. 2012. “Current developments and future prospects of offshore wind and
ocean energy.”. Applied Energy, 90(1): 128-136.
Feldman, D., Margolis, R. 2019. Solar Industry Update (Slide Presentation). Retrieved from
https://www.nrel.gov/docs/fy19osti/73992.pdf
Flick, R., Murray, J., Ewing, L. 2003. “Trends in United States Tidal Datum Statistics and Tide
Range.” Journal of Waterway, Port, Coastal, and Ocean Engineering, 129(4): 155-164.
Givetash, L. 2019. “Tidal Energy Pioneers See Vast Potential in Ocean Currents' Ebb and Flow.”
NBCNews.com. NBCUniversal News Group. March 27.
https://www.nbcnews.com/mach/science/tidal-energy-pioneers-see-vast-potential-ocean-
currents-ebb-flow-ncna981341
Iglesias, G., R. Carballo. “Wave farm impact: The role of farm-to-coast distance.”
Renewable Energy, 69(2014): 375-385.
IPPC (International Panel on Climate Change). 2018. “Global warming of 1.5° C: Summary for
Policy Makers.” https://report.ipcc.ch/sr15/pdf/sr15_spm_final.pdf
70
IRENA (International Renewable Energy Agency). 2014. “Tidal energy technology brief.”
https://www.irena.org/documentdownloads/publications/tidalenergyv4web.pdf
Khare, V., Khare, C., Nema, S., Baredar, P. 2019. “Prefeasibility Assessment of a Tidal Energy
System.” Tidal Energy Systems, 2019, 115–188.
Lehmann, M., Karimpour, F., Goudey, C.A., Jacobson, P.T., Alam, M.R.. 2017. “Ocean Wave
Energy in the United States: Current Status and Future Perspectives.” Renewable and
Sustainable Energy Reviews, 74: 1300-1313.
Lynge, B.K. “High resolution tidal models for the Norwegian coast.” Doctoral Dissertation,
University of Oslo, 2011.
Mierzwiak, M., Calka, B. 2017. “Multi-Criteria Analysis for Solar Farm Location
Suitability.” Reports on Geodesy and Geoinformatics 104(1): 20-32.
NASA (National Aeronautics and Space Administration). 2019a. “The causes of climate
change.” https://climate.nasa.gov/causes/
NASA (National Aeronautics and Space Administration). 2019b. “Scientific Visualization
Studio.” https://svs.gsfc.nasa.gov/stories/topex/index.html
NOAA (National Oceanic and Atmospheric Administration). 2019a. “Currents.”
https://oceanservice.noaa.gov/education/tutorial_currents/02tidal1.html
NOAA (National Oceanic and Atmospheric Administration). 2019b. “Effects of Climate
Change: Currents.”
https://oceanservice.noaa.gov/education/tutorial_currents/05conveyor3.html
NOAA (National Oceanic and Atmospheric Administration). 2019c. “MPA and ASBS Sites in
the MBNMS.” MBNMS: MPA and ASBS Sites in the MBNMS.
https://montereybay.noaa.gov/materials/mappages/mpaasbssitesmap.html
NOAA (National Oceanic and Atmospheric Administration). 2019d. “Tides and Water Levels.”
https://oceanservice.noaa.gov/education/tutorial_tides/welcome.html
NOAA OCM (Office of Coastal Management). 2017. “Danger Zones and Restricted Areas -
NOAA Data Catalog.” https://data.noaa.gov/dataset/dataset/danger-zones-and-restricted-
areas1
NREL (National Renewable Energy Laboratory). 2019. “Renewable electricity futures study.”
https://www.nrel.gov/analysis/re-futures.html
71
NTSLF (National Tidal and Sea Level Facility). 2019. “All about tides.”
https://www.ntslf.org/about-tides/tides
Nicholls, R. 2011. “Planning for the Impacts of Sea Level Rise.” Oceanography; 24(2): 144-157.
Nuccitelli, D. 2018. “How much and how fast will global sea level rise?” Bulletin of the Atomic
Sciences, 74(3): 139-141.
OIST (Okinawa Institute of Science and Technology) Graduate University. 2016. "New insights
into fluctuations of wind energy, with implications for engineering and policy."
www.sciencedaily.com/releases/2016/12/161231184935.html
Pelc, R., Fujita, R. 2002. “Renewable energy from the ocean.” Marine Policy, 26(6): 471-479.
Renewable Energy World. 2019. “Moving Offshore-The Future of Tidal Energy.”
https://www.renewableenergyworld.com/2016/05/16/moving-offshore-the-future-of-
tidal-energy/#gref
Roberts, A., Thomas, B., Sewell, P., Khan, Z., Balmain, S., Gillman, J. 2016. “Current tidal
power technologies and their suitability for applications in coastal and marine areas.”
Journal of Ocean Engineering and Marine Energy, 2: 227-245.
Rusu, L., Onea, F. 2017. “The Performance of Some State-of-the-Art Wave Energy Converters
in Locations with the Worldwide Highest Wave Power.” Renewable and Sustainable
Energy Reviews, 75: 1348–1362.
Soerensen, H.C., Weinstein, A. 2008. "Ocean energy: position paper for IPCC." In IPCC
Scoping Meeting on Renewable Energy Sources–Proceedings, pp. 93-102.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.362.1202&rep=rep1&type=pdf
#page=109
Uihlein, A., Magagna, D. 2016. “Wave and tidal current energy – A review of the current state of
research beyond technology.” Renewable and Sustainable Energy Reviews, 58: 1070-
1081.
Union of Concerned Scientists. 2017. “Benefits of renewable energy use.”
https://www.ucsusa.org/clean-energy/renewable-energy/public-benefits-of-renewable-
power#references
72
U.S. Department of Commerce, and National Oceanic and Atmospheric Administration. 2008.
“Where Is the Highest Tide?” NOAA's National Ocean Service.
https://oceanservice.noaa.gov/facts/highesttide.html
U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. 2019a.
“Advantages and challenges of wind energy.”
https://www.energy.gov/eere/wind/advantages-and-challenges-wind-energy
U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. 2019b. “Solar
energy in the United States.” https://www.energy.gov/eere/solarpoweringamerica/solar-
energy-united- states
U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. 2019c. “Solar
explained.” https://www.eia.gov/energyexplained/solar/
Vasileiou, M., Eva L., Vagiona, D.E. 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.
WEC (World Energy Council). 1993. Energy for Tomorrow’s World. London, UK: World
Energy Council.
Williams, R.R. 2018. “Suitability Analysis for Wave Energy Farms off the Coast of Southern
California: An Integrated Site Selection Methodology.” Master’s Thesis, University of
Southern California.
Work, P.A., Haas, K.A., Defne, Z., Gay, T. 2013. “Tidal Stream Energy Site Assessment via
Three-Dimensional Model and Measurements.” Applied Energy, 102: 510–519.
Wunsch, C. 2002. "Oceanography: What Is the Thermohaline Circulation?" Science 298 (5596):
1179-1181.
Abstract (if available)
Abstract
Traditional sources of energy are outdated and destructive. There is a clear need for new and more sustainable energy sources, creating a huge market for innovative research and technology within the energy industry. The use of the world’s oceans to generate energy is pioneering and provides many enticing benefits including cleaner and more consistent energy generation. Tidal energy converters harness the power of moving water generated by the tides and is a huge source of untapped energy. This study analyzed the parameters most important to successful tidal energy generation in order to determine the most suitable sites for implementing this technology in southern California. Using data from various sources, two different interpolation methods were used to create a continuous raster surface of tidal range values that, along with other variable fields related to tidal energy, were used to perform two different types of analysis, a weighted overlay and fuzzy overlay analysis. The results of these two different analysis methods were compared and this methodology provided a conclusive map showing the most effective sites for successful use of tidal energy converters in southern California. An analysis was also completed to look at the distance of suitable sites to nearby onshore energy facilities. This research builds upon previous renewable energy studies in southern California and provides insight into how the region can reduce its fossil fuel emissions and convert to cleaner, more sustainable energy sources.
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Asset Metadata
Creator
Vagen, Drew Quenna
(author)
Core Title
Site suitability analysis for implementing tidal energy technology in southern California
School
College of Letters, Arts and Sciences
Degree
Master of Science
Degree Program
Geographic Information Science and Technology
Publication Date
02/26/2021
Defense Date
12/21/2020
Publisher
University of Southern California
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Tag
California,OAI-PMH Harvest,renewable energy,suitability analysis,tidal energy
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English
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Bernstein, Jennifer (
committee chair
), Duan, Leilei (
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), Sedano, Elisabeth (
committee member
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drewvagen@gmail.com,vagen@usc.edu
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