CALIFORNIA STATE UNIVERSITY NORTHRIDGE
Multi-Criteria Decision Analysis for Placing a Concentrated Solar Power Plant in the Greater Los Angeles Area
A thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Geography, GIS Program
By
Michael Silviu Stere
May 2017
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Copyright by Michael Silviu Stere 2017
iii The thesis of Michael Silviu Stere is approved:
_________________________________________ ______________
Dr. Sanchayeeta Adhikari Date
_________________________________________ ______________
Dr. Soheil Boroushaki Date
_________________________________________ ______________
Dr. Regan M. Maas, Chair Date
California State University Northridge
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Acknowledgements
I want to acknowledge the entire professor staff in the Department of Geography at CSU Northridge which I had the privilege of meeting and/or was instructed by, including Dr. Soheil Boroushaki, Dr. Regan Maas, Dr. Sanchayeeta Adhikari, Dr. James Craine, Dr. Yifei Sun and Dr. Amelia Orne, along with Professor Adrian Youhanna from Pierce College for their dedication and knowledge, providing me with the tools,
foundation, discipline and insights to be successful in my career. I want to particularly acknowledge Dr. Soheil Boroushaki which I took the majority of classes with especially the G.I.S. core classes and Dr. Regan Maas which have helped me tremendously along the way from the first semester I attended California State University Northridge graduate school by being forthright, very knowledgeable and accessible.
I want to also acknowledge my family which has sacrificed a lot to get to the light at the end of the tunnel. I want to acknowledge my mother, Gabriela, which has helped tremendously with allowing me to work on my school work and thesis by babysitting our kids and generally being a great supportive mother. I acknowledge my wife, Kelly which has shown the most tremendous support, sacrifice and admiration any husband can ask for going back to school, and lastly my father, Vasile, my role model who guided me with his wisdom throughout most of my life, and which I know will see me down from Heaven when I walk during my graduation commencement.
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Dedication
I dedicate this piece of work to my kids, Hannah and Thomas, which are part of the future we will leave behind and have inspired me to look beyond the short term gains and losses of life. This work was completed so I may contribute even a little towards leaving them a better Earth.
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Table of Contents
Signature Page ii
Copyright Page iii
Acknowledgement iv
Dedication v
List of Tables viii
List of Figures ix
Abstract x
Chapter 1: Introduction 1
1.1 Background of Concentrated Solar Power in Relation to GIS 1
1.2 Scope of the Research 3
Chapter 2: Literature Review 6
2.1 History, Background and Applications of Solar Power Technologies 6
2.2 Needs, Benefits and Drawbacks of Solar Power Technologies 9
2.3 Multi Criteria Decision Analysis Application to Clean Energy Technologies 13
2.4 MCDA Methods 17
Chapter 3: Methodology 24
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Table of Contents (cont.)
3.1 Defining Project Objectives 24
3.2 Normalization and Weighting of the Raw Data 28
3.3 Applying techniques of MCDA to the research 32
Chapter 4: Summary and Explanation of Results 34
4.1 Criteria and Feasibility Results 34
4.2 Multi-Criteria Decision Analysis Results 38
4.3 Localized Assessment of the Site 43
Chapter 5: Discussion 44
5.1 Results Explained 44
5.2 Limitations and Future Research 45
5.3 Policy Implications 48
5.4 Conclusion 49
Bibliography 52
viii List of Tables
Table 1. Standard values of average random consistency index RI 21
Table 2. Pairwise Comparison Table 31
Table 3. Maximum Criteria Distances (in meters) 38
Table 4. Pairwise Comparison Group weights result 38
Table 5. Entropy based weights 40
ix List of Figures
Figure 1. Research Area encompassing Greater Los Angeles area (NOAA, 2017) 4
Figure 2. The types of CSP: a. parabolic, b. Fresnel, c. Stirling motor, d. solar tower 6
Figure 3. Scale for pairwise comparison comparability between two criteria 20
Figure 4. 50 mile buffer surrounding the study area (NOAA, 2017) 27
Figure 5. Total feasible land 34
Figure 6. Projected criterion layers (a. populated area, b. waterbodies, c.wildlife critical habitats, d. roads) 35-37 Figure 7. Projected DNI criterion layer 37
Figure 8. Simple Additive Weighting result 39
Figure 9. Simple Additive Weighting most suitable land (top 5%) 40
Figure 10. TOPSIS result 41
Figure 11. TOPSIS most suitable land (top 5%) 42
Figure 12. Proposed New CSP site location (NAIP, 2016) 42
Figure 13. Proposed Location Perspective View (Google Earth, 2017) 43
x Abstract
Multi-Criteria Decision Analysis for Placing a Concentrated Solar Power Plant in the Greater Los Angeles Area
By
Michael Silviu Stere
Master of Science in Geography, GIS Program
The threat of climate changehas started to become increasingly predominant.
Humans have had a proven environmental effect on the Earth through the use of fossil fuels as an energy source. The negative consequences of climate change need to be mitigated sooner by finding an alternative to fossil fuels for energy needs. The main cause of the human derived activity that affects the Earth’s climate has been rising carbon dioxide levels which trap heat and warm the planet, fossil fuels, being mostly the cause of this rise in carbon dioxide. One process to reduce fossil fuel usage has been to substitute fossil fuel power plants for renewable energy power plants.
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This study will focus on developing a scientific approach to placing a large concentrated solar power plant (CSP) in the Greater Los Angeles Area. The most suitable location was determined by using two Multi-Criteria Decision Analyses (MCDA), the Simple Additive Weighting (SAW) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Even though these methods have not been commonly used in CSP land suitability analysis, the convergence of MCDA maturity and the need for a comprehensive method of placing CSPs in Southern California due to the concentration of population have created an opportunity to rectify some pitfalls of prior CSP projects.
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Chapter 1: Introduction
1.1 Background of Concentrated Solar Power in Relation to GIS
Since 2006 a new awareness of climate change has revived the concentrated solar power industry, even though the technology is over 100 years old with the first parabolic trough demonstrator occurring in Egypt (Steinhagen and Trieb, 2004). The necessity to substitute fossil fuels for alternative energy sources like electric vehicles, solar power plants, wind power, or hydropower or even in some countries converting to nuclear generated power plants has accelerated (IRENA, 2012). This is especially true in the face of policy initiatives such as the Kyoto Accords, and the more recent Paris Climate
Change Summit.
Recognizing where the most good for the climate can be accomplished in terms of renewable energy with the least economic impact starts with electricity power generation.
Most of our electricity is still generated by fossil fuels, such as natural gas or coal. Such technologies, though inexpensive to utilize compared to concentrated solar power plants, have simply mortgaged our future for short term economic relief (Solomon et al, 2008).
An important role in the present and future power generating sector, will be played by concentrated solar power technologies as they use the unlimited and available daily energy source of the sun, an abundant energy source in California, especially due to the vast Mojave Desert location within (Tiana and Zhao, 2013). There are four types of concentrated solar power technologies for capturing the energy of the sun and
transforming it into electricity, including parabolic trough collector technology, the linear Fresnel collector, the solar tower power generator, and the dish Stirling engine system.
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The first model of the Stirling system was created in California and demonstrated in 1985. These technologies also have the ability to store the excess power generated in daylight operation then reuse it during the night.
As California population grows, so will the need for additional power generation capacity. Based on California’s Department of Finance population projections the state will add in 2020 1,660,190 from the estimated population of 39,059,809 in 2015. The state will reach 50 million by the year 2055 (CA DOF, 2017), an increase of more than 20 percent over 2015 levels. Conservation alone cannot mitigate the global climate change progression so a net reduction in greenhouse emissions would require a switch away from existing fossil fuel based power plants.
Part of the issue with the implementation of these renewable energy alternatives is a problem of space. It has been difficult in many situations to find suitable land for the construction of a solar power plant large enough to make a significant impact in energy production. It is also often difficult to replicate successful models in other regions due to the complexity of landscapes across the US (Mekhilefa et al, 2011). Some areas are more suitable than others, such as the southern United States, which has higher levels of solar insolation due to its arid climate and mild winter weather (Tiana and Zhao, 2013).
Using sophisticated decision techniques can pin point with efficiency the correct location for a new power plant. The connection between advanced computing systems and new research on climate change prompted the reinvigoration of work in solar power and other clean energy alternatives to fossil fuel derived energy. With the boom in smart technologies over the last decade, our society has shown an increased geospatial
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awareness. Access to large datasets and mobile technologies drove the increases in geospatial data usage and accelerated the adoption of Geographic Information Systems (GIS) in the decision making process. This in turn allowed for an increase in research that was more spatially precise and considered the complexities of site location for renewable energy (Voivontas et al, 1998).
1.2 Scope of the Research
The decision problem in this project was to identify the most suitable location for a future concentrated solar power plant development in the Greater Los Angeles area.
The Greater Los Angeles area counties included in the study were Ventura, Los Angeles, Orange, Riverside and San Bernardino (Figure 1). Los Angeles County alone has over 10 million people living within its borders based on 2016 Census estimates. The Los
Angeles metro area has almost double that amount at 18.6 million people a growth of almost 800,000 persons since the 2010 US Census (US Census Bureau, 2016).
Alleviating the ever growing power needs of this area requires adding new power generating plants.
Adding natural gas fired plants to the system would only add to increased carbon dioxide emissions. The main objective of this project was to develop techniques to find suitable locations for one concentrated solar power plant (CSP) similar in size and scope to the recently opened Ivanpah CSP, in the Greater Los Angeles area. Although
California was a pioneer in the early nineties with the world’s first commissioned CSP, the next large solar power plant was only recently commissioned in the state in 2014 near Ivanpah, in San Bernardino County near the Nevada state line. There was no indication in
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the literature that GIS-based MCDA techniques were used extensively to research
suitable locations for renewable energy; therefore, this research attempted to fill this void.
Figure 1. Research Area encompassing Greater Los Angeles area (NOAA, 2017)
In order to locate a contiguous location of sufficient size, an analysis of the top five percent most suitable land extracted from the analysis of two MCDA techniques, the Simple Additive Weighting (SAW) technique and the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) was conducted. The Mojave Desert is known to have one of the most suitable solar insolation ranges for placing a solar power
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plant in the Western Hemisphere, and it is no surprise that both SEGS (1990), and the Ivanpah facility (2014) are located within its boundaries (Quashning, 2004). It is expected that, due to low cloud cover on average, which increases the Direct Normal Irradiance (DNI) (a measure of solar energy intensity), as well as availability of open space, this study area would yield a suitable location for future CSP development. More specifically, cloud cover on average is greater in coastal regions, therefore, suitable areas were predicted to fall within the inland regions of the study area.
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Chapter 2: Literature Review
2.1 History, Background and Applications of Solar Power Technologies
2.1.1 Solar Power Technology History and Background
The history of the concentrated solar power plant is one which is barely 100 years old, however the innovation has accelerated tremendously in the past 25 years, and especially in the past 11 years (IRENA, 2012). Currently there are four types of CSP technologies. The first CSP technology, parabolic trough collector system (Figure 2a), was developed and demonstrated in Mehdi Egypt in 1913 just before World War I (Stinnesbeck et al, 14; Steinhagen and Trieb, 2004). The second technology developed
a. b.
c. d.
Figure 2. The types of CSP: a. parabolic, b. Fresnel, c. solar tower, d. Stirling motor
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was the linear Fresnel collector (Figure 2b), whose first prototype being demonstrated in Genoa, Italy in 1964, (Francia, 1968). The solar tower power system (Figure 2c), first built and the technology validated in Adrano, Italy in 1965, and labeled the EURELIOS generated 1 MW of energy (Gretz, 1982). Finally, the dish Stirling engine system (Figure 2d) was created in California and first demonstrated in 1987 (Mancini, 1997). The Solar One plant in California built in 1982 was the first plant to demonstrate the ability to produce large scale electricity production, over 10 MW, from solar tower power plant (Radosevich, 1988). The first commercial CSP plant worldwide also occurred in Southern California in 1984. SEGS I generated 14 MW of energy through parabolic trough technology. SEGS capacity increased to 354 MW through the construction of eight additional plants in the Mojave Desert by 1990 (EASAC, 2011).
After the SEGS project completion there was no new development of any solar power plants anywhere in the world for a significant period of time (IRENA, 2012).
However, due to recent technological innovations such as increased thermal efficiency and increased capacity, along with a continuous decrease in the cost of building such plants, there has been recent growth in this part of the energy industry (IRENA, 2012).
The development of CSPs has historically been concentrated on parabolic trough and power tower technologies due to their larger output potential per invested dollar
(Whitaker et al, 2013). Though the majority of installed capacity in US currently lies in parabolic trough plants, the majority of new construction projects are power towers (Tiana and Zhao, 2013).
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2.1.2 Concentrated Solar Power Towers: the future of commercial solar plants
Power towers have had some advantages over parabolic trough power plants as they have had higher operating temperatures and lower cost solar collectors which led to the potential of lower cost of producing electricity than existing parabolic designs (Tiana and Zhao, 2013). There have been two types of power tower designs with the main difference underlined in the type of heat transfer fluid they have used. Direct steam towers, such as in the Ivanpah, CA facility, boil water which is housed at the top of the receiver tower to produce steam. The created steam turns a turbine to generate electricity and are similar to conventional power plants. However, molten salt towers, such as Crescent Dunes Solar Energy Project in Nevada, use salt to boil the water in a separate heat exchanger (Whitaker et al, 2013). The main advantage of direct steam towers has been the elimination of this separate heat exchanger. Molten salt towers have the advantage of creating electricity around the clock regardless of solar availability due to the excellent preservation of heat in the salt which allows it to be stored to provide power later, such as night operation (Whitaker et al, 2013). The energy storage efficiency in molten salt towers had been estimated to be greater than 98% (Whitaker et al, 2013).
The Ivanpah, Mojave Desert facility, commissioned in 2014 with a gross capacity of 394 MW, is currently the largest single complex CSP plant in the world by capacity (Saqib et al, 2014). It deploys 173,000 heliostats, flat mirrored devices with a reflective surface of 151.2 sq. feet in this case, which turn to compensate for the sun's motion during the day in order to continuously reflect sunlight towards the receiver (Saqib et al, 2014). In the Ivanpah facility, the heliostats focus power on receivers in three centralized solar power towers which house boilers (Saqib et al, 2014). This project cost 2.2 billion
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dollars of which 1.6 billion was a US Federal loan guarantee and was built on public land (Brown, 2014). However, there are no new CSP plans in the near future in California likely due to some of the shortcomings on the Ivanpah facility, including an
underperformance of the facility on its wattage capacity (Brown, 2014). Due to this issue the facility almost declared bankruptcy within its first year of operation (Brown, 2014).
2.2 Needs, Benefits and Drawbacks of Solar Power Technologies
2.2.1 A Changing Climate
The increasingly positive outlook for alternative energy has been driven by rises in environmental awareness as well as by increases in fossil fuel prices and fears of a depletion in current energy stockpiles. Additionally, burning of fossil fuels has
contributed to accelerated climate change by increasing carbon dioxide concentrations in the atmosphere. This has led to a change in natural equilibrium of the environment by introducing harmful substances into the atmosphere, which, has created an urgent need to reduce our dependence on fossil fuels (Madden et al, 1980). This has, in turn, increased sourcing of renewable energy that use solar power, hydro-power and wind-power to generate electricity in order to avoid a major global catastrophe in the future.
According to ongoing temperature analyses the increase in global temperature on Earth since 1880 has been 1.4 degrees Fahrenheit (Hansen et al, 2010). The main cause of climate change has been rising levels of carbon dioxide which have caused measured rising levels of the world’s oceans due to the melting of the polar ice cap in the Arctic and the melting of the polar ice sheets from the Antarctic (Van De Wal et al, 2011).
Irreversible rainfall declinesin the dry season of several regions in the United States
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caused by weather fluctuations have been another cause of alarm as they could lead to weather events similar to the Dust Bowl era, such as dry and windy weather which
affected much of the Central and Midwest United States in the 1930s (Meehl et al, 2000).
Though a large part of the carbon dioxide in the atmosphere comes from natural
processes such as oceans, soils, plants, animals and volcanoes, human activity accounts for most of the rise in carbon dioxide levels over the last hundred years since
industrialization (Vitousek et al, 1997). Substituting the fossil fuel consumed within the energy sector can go a long way toward alleviating this climate change problem (Madden et al, 1980).
The increase in carbon dioxide (CO2), which helps trap heat in our atmosphere, has been primarily caused by burning of fossil fuels such as oil, coal and gas as well as deforestation (Solomon et al, 2008). The burning of fossil fuels burning account for 87%
of all human introduced CO2 (Saqib et al, 2014). Most of the electricity generated today in the US is from fossil fuels. A substantial drop in CO2 emissions is predicted by substituting fossil fuel plants with cleaner electric plants powered by renewable energy sources (Solomon et al, 2008). The Ivanpah facility was estimated to bring a reduction of CO2 emissions of more than 400,000 tons annually (Saqib et al, 2014).
2.2.2 Energy Conservation in California
Rainy season wet starts do not always translate to normal precipitation for the whole year due to climate change, as witnessed in California during the 2012-2013 season. The 2012-2013 season saw an extremely torrential start to the point where an additional powerful storm during late 2012 would have likely led to severe flooding
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throughout Northern California (Griffin and Anchukaitis, 2014). However the rain storms stopped from December until May of 2013. In fact it was the driest start of the year in 118 years, with no precipitation occurring at all in several areas of the state. It was a dramatic disparity when considering the extremely wet conditions experienced before December 2012 (Griffin and Anchukaitis, 2014).
California has shown leadership in clean utility growth, influenced by its culture of environmental friendliness in terms of public opinion and public policy (Weber et al, 2011). This is all the more impressive considering that California is also the most
populous state in the nation based on the US Census Bureau’s Decennial Census in 2010.
California delivered impressive energy conservation results with the launch of an energy conservation initiative designed to overcome the energy crisis in 2001. In 2002, instead of returning to pre-crisis electricity consumption levels, Californians adopted many conservation behaviors which decreased energy consumption levels by 25 percent compared to the levels before the crisis (CEC, 2003). This has been all the more
impressive because California’s level of per capita electricity consumption was already forty percent lower than the nation’s average (Bachrach, 2003). This was the most
successful statewide energy conservation movement in history up to that point (Bachrach, 2003).
2.2.3 Concentrated Solar Power Plant Needs and Drawbacks
CSP plants used large amounts of water, thus accessibility to water has been paramount for location analyses (Krpan et al, 2012). Additionally, ease of connecting to the power grid has been a strong consideration in the placement of power plants (Krpan
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et al, 2012). The most important factor however was the solar insolation or irradiance capacity of a given area (Krpan et al, 2012). The North American Southwest exhibits comparatively high solar irradiance capacity levels throughout the Western Hemisphere, specifically within the Mojave Desert. Hence this is one of the main reasons the original SEGS and the Ivanpah facilities were located within the Mojave (Tiana and Zhao, 2013).
However, the problem with the Mojave Desert remains a scarcity of water sources.
Water conservation in California, has been of greater importance since severe drought started in 2013. Thus, the dry cooling of steam back to water has seen an
increased importance in the design of the power towers. Water consumption reduction via dry cooling systems accounts for a 90% savings in water consumption of the wet cooling system, though the construction of a dry cooling tower has been significantly higher due to the far larger sizes necessitated by the dry cooling system (Whitaker et al, 2013).
Another concern for molten salt solar power plants has been the salt disposition after the plants end their life-cycle. However, it is of far less concern than the nuclear waste generated from nuclear power plants. Waste salt has not been directly disposed, which could increase efficiencies during disposition in time and cost because currently the waste salt is restrained by a ceramic waste system and stored (Whitaker et al, 2013).
Power towers’ impact on the natural environment has been less severe due to less required grading of the land than the other types of commercial grade CSP types such as photovoltaic, parabolic trough or Fresnel collector solar power plants due to the use of heliostats (Tiana and Zhao, 2013). However, the facilities were often fenced off to keep some wild life out disrupting some habitats (Turney and Fthenakis, 2011). Additionally, birds have been known to fly and collide with heliostat mirrors or burned in the solar flux
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created by the mirror field (Kagan et al, 2014). Worse, the lights generated by the receivers on the towers have attracted insects which lead to birds chasing them into the solar flux, near the epicenter of the fields, which has also led to predatory birds chasing the smaller birds into the solar flux, a unique occurrence associated with the power towers (Kagan et al, 2014). Another common environmental effect of power tower systems is encroachment into the endangered species habitat (Turney and Fthenakis, 2011). For example, the Ivanpah facility was scaled back from 440 MW capacity as to not overstep into endangered desert tortoise habitat (Tiana and Zhao, 2013).
2.3 Multi Criteria Decision Analysis application to Clean Energy Technologies 2.3.1 Decision maker’s tool background
Multi Criteria Decision Analysis (MCDA) also known as Multi Criteria Decision Making (MCDM), was first developed by Stanley Zionts in 1979 (Zionts et al, 2013). In 1770, Jean-Charles de Borda, a French mathematician, physicist and political scientist defined the Borda count, a ranked preferential voting system which is still used today in a handful of small countries to determine the winner of an election (Vainikainen et al, 2008). The Borda count is a form of weighting which is a critical part of MCDA as it calculates how much weight out of a given range (usually between 0 and 1) the criterion is given in various ways to determine the final analysis. MCDA explicitly evaluates multiple conflicting criteria in decision-making. Often criteria are contradictory, like quality versus price of a product, thus importance of one over the other had to be determined through weighting of said criterion from most important to least important (Malczewski, 1999).
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MCDA in relation to Geographic Information Systems (GIS) involves multiple spatial criteria for the existing options with geographic location data attached, which has led to many existing MCDA methods having also been adjusted for these kinds of problems. In summary, this process describes a dissimilar consecutive step procedure which comprises of identifying relevant criteria, normalizing values of the criteria, stating preferences and choosing the decision rule to create a one-dimensional score by
aggregating the normalized criteria values. Spatial decision analysis requires capabilities of both MCDA and GIS tools, thus this process for resolving spatial decisions is defined as GIS-based MCDA (Malczewski, 1999).
2.3.2 Structural decisions
CSP plants have been expensive to build and have not yet delivered as promised (Whitaker et al, 2013). Yet their benefits cannot be ignored. Formulating a
comprehensive model based in multi-criteria decision analysis is crucial to the success of future CSP projects (Khan, Rhati, 2014). MCDA is a field still in its infancy as it was only defined and formulated in the late 1990s by Malczewski (1999) thus the availability of peer-reviewed studies using this methodology are scarce, especially related to the locating of CSP plants. Thus the concentration of the available research reviewed will focus more on a theoretical adaptation of MCDA.
Being part of the operations research management science (OR/MS) tradition, CSP management decisions have used GIS-MCDA in the past and this technique is increasingly becoming popular in the sustainable energy domain (Datta et al, 2007). CSP MCDA would be evaluated using raster based data since most of the data that is pertinent
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to CSP is in raster datasets such as land use and solar radiance levels. The selected criteria would be spatially explicit and spatial implicit criteria, such asspatial distribution of landscape features, sincelocation mattered to the process versus spatially implicit alternatives. According to Malczewski’s own 2006 survey, 70 percent of the studies involved a combination of spatially implicit alternatives and spatially explicit criteria. In Pereira and Duckstein (1993), the authors identified the ideal reference point method of MCDA for tackling Environmental planning evaluations as multi-objective optimization.
However, depending on the criterion, a spatial objective optimization method might be more suitable. This method of spatial decision evaluates alternatives which have multiple geographic orders operating simultaneously, such as location, distance, or connectivity.
The structure of the decision models also analyzes goal structure whether it has a multiple or single goal structures. The main difference is the goal-preference structure here. This means, if the goal structure is single in nature, such as the goal to substitute fossil fuel energy with sustainable energy regardless of any other factors such as cost, the decision is individual in nature. Whether, the decision maker is single or multiple in count is not taken into account. If the goal-preference is different or multiple, such as taking into account additional goals the decision making structure dictates a group decision type. Using also a GIS based Multi Attribute Decision Analysis is the preferred way to find the most suitable area for a CSP (Pereira and Duckstein, 1993). The CSP problem is a group decision problem thus, this goal structure will be applied in this study, since there has been an increasing shift towards the group decision making structure (Omitaomua et al, 2012).
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The goals assigned to the problem can be defined by a number of sub-objectives.
To make the sub-objectives measurable we have to assign to each of them at least one attribute or criteria. MCDA can also be categorized according to the amount of information about the decision making process that is available to the decision maker which are in nature deterministic, probabilistic and fuzzy. The model for this spatial decision was evaluated under a condition of uncertainty. The methods of weighing being limiting as the criteria grows under the pairwise weighing method caused insufficient data due to the limitation of choosing a restricted number of criteria. The pairwise method cannot be completed efficiently with more than seven criteria and keep the consistency required in the process (Saaty, 1980).
Most of the MCDA studies fall under a deterministic category. The uncertainty of data would be addressed by choosing the best and most encompassing criteria however the data used in this MCDA evaluation would be of a more certain category, thus the study was not under probabilistic, which meant limited information uncertainty, or fuzzy, (imprecise) category as the data that was relevant was deemed reliable. However, not all decisions have a similar impact, as the impact varies from decision to decision. Offering an alternative site to the Mojave sites while addressing some limitations of the existing plants. These limitations, such as the high mortality of birds, would begin to resolve the large shortfall in actual power production of the Ivanpah site. By combining tactical and strategic level decisions an alternative can be pinpointed.
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2.4.1 Normalization of Criteria
Linear scale transformation methods convert the data into standardized criterion scales which can be then assessed side by side later in the Multi Criteria Decision Making process. There are multiple linear scale transformation methods including range score normalization, min-max score range normalization, and vector normalization (Voogd, 1983; Massam 1988). The main advantage of the range score normalization method was that it always scales values between 0 and 1, with 0 being least desirable and 1 most desirable (Malczewski, 1999). This scaling is advantageous as model variables are not scaled with respect to the units of the inputs, thus are treated the same in the model, making it easier to compare criteria. This process does not create a proportional range like in the min-max score range method. However unlike min-max score range, the lowest standardized value always equals zero. This makes the interpretation of the lowest score criterion difficult for min-max normalization. This was a clear advantage of range score normalization. An alternative to linear scale normalization is the vector
normalization method, which comes with different advantages and disadvantages.
There have been a few advantages of vector normalization compared to score range. First, it can simplify calculations of angles and distances, such as normalizing angles between large numbers of vector pairs, in which case the denominator is equal to 1. Another important benefit is stability for repetitive procedures. A method operation can obtain large values which are implemented in the vector matrix, yet if the normal vector is multiplied by a constant it would not change. The main drawback is that it does
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not lead to a scale with equal measurement lengths. Therefore, a straight forward comparison would be difficult due to the non-linear transformation. This form of normalization is used in the TOPSIS method commonly.
2.4.2 Basic Weighting of the Criteria
Changes in the range of variation to each criterion, and different degrees of importance assigned to these ranges of variation account for allocating weights of importance to allocation criteria (Kirkwood, 1997). The derivation of weights is an important step in constructing the decision maker’s preferences. They are split into largely subjective weighting methods such as pairwise comparison, rank sum, or ratio weighting methods, and objective methods such as entropy based weights which strictly derive from the data with no preference input from the decision method or maker. The calculated weights of importance are all directly related to the decision-maker’s
perceptions, background, and knowledge of the spatial problem. Entropy based weights are largely used in the ELECTRE, which is a French acronym meaningelimination and choice expressing reality, a hierarchy based MCDA process, and TOPSIS, while more subjective methods tend to use the weighted linear combination method.
Weights are a set of values which indicate the importance of a criteria within the context of the project and in relation to the other criteria analyzed. The criterion’s
importance directly correlates with the value of its weight however it cannot exceed one;
the criteria weights’ sum equals one. A set of weights is defined in the following example when there are n criteria under consideration (Malczewski, 1999):
y (y1, y2, ..yj, ..yn), y 1
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The most basic way to calculate weights is the rank order method, where the evaluated criteria is ranked in the preferential order chosen by the decision maker.
Several ways are available to compute weights using the rank order method, once the rank is established for each criteria, with the rank sum method being a popular approach.
The standardized weight yj is for the jth criterion, rj is the rank position of each criteria, while n is the total number of criteria in the project (h=1, 2,.., n) where the weights are calculated accordingly (Malczewski, 1999):
𝑦𝑗 = 𝑛−𝑟𝑗+1
∑(𝑛−𝑟ℎ+1)
2.4.3 Pairwise Comparison Weighting
The pairwise comparison method is the most used weighting method in MCDA, and the main advantage of using this method is its ease of application. The name is derived after the method of criteria comparison it uses, as each criteria is compared with only one other criteria at a time, hence in pairs. The main goal of the pairwise comparison method is to identify objectives and rank them against each other, thus it is more accurate than rank order which ranks the criteria generally from top to bottom in order of
importance. The main drawback of this weighting is that it is limited in its usefulness in MCDA by the number of criteria to be ranked. Though only two criteria are evaluated at a time, this can also limit the size of the evaluation criteria as 10 criteria for example would require 45 different comparisons, which all have to be consistent to each other.
Therefore, it is recommended not to use the pairwise comparison method to weigh more than 7 criteria. (Saaty, 1980).
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Another drawback of pairwise comparison is it only states which criterion is preferred and does not refer to the criteria scales on which they are measured, hence it does not define the relative importance of criteria in relation to the other criteria under evaluation (Goodwin and Wright, 1998). This leads to fuzziness which means that the same questions can be construed differently, or even erroneously, by decision makers.
For example, two different persons can have different ratings for criteria based on their knowledge of the topic of the comparison, sometimes with completely mistaken results such as misrepresenting the real importance a factor can have in an evaluation, an example being the importance of solar insolation to solar power.
Figure 3. Scale for pairwise comparison comparability between two criteria
The main scale of pairwise comparison in MCDA is from 9 or most important, to 1/9 or least important (Figure 3) (Saaty, 1980). Another important aspect of this method is that a measure of inconsistency follows from the calculations performed on the
pairwise judgment. To keep the inconsistency within acceptable limits, Saaty introduced the consistency ratio (CR) (Table 1). When all judgements are perfectly consistent the CR is zero (Kent and Mardia, 1988).
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Table 1. Standard values of average random consistency index RI.
2.4.4 Entropy Based Weights
There have been several reviews and surveys on subjective weighting methods in the MCDA literature (Hobbs 1980; Barron and Barrett 1996). These reviews and
comparisons all are showing that decision-makers cannot always provide consistent value judgments when comparing evaluation criteria and accordingly will be creating different weighting directions utilizing unrelated weighting procedures. These assessments present that there is no subjective scheme that can produce even more precise or robust results, due to the very nature of subjective weighting methods (Diakoulaki et. al 1995).
Objective weighting methods determine the weights of the evaluation criteria based on the amount of information contained within the decision matrix and criterion values. In complex spatial decision problems providing a consistent and exact numerical judgment is extremely difficult due to uncertainties and large numbers of criteria.
One common method of applying objective weighting is the entropy weighting method. This objective method analyzes the relative intensities of criterion significance to indicate the average essential data the decision maker evaluates and provide consistent judgments based on the changes in the variation of criterion values. Information entropy, or Shannon entropy, is a measure of the degree of order or disorder within a system
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(Hwang and Yoon 1981; Malczewski and Rinner 2015). Considering the classic game of
“rock, paper, scissors”, the probabilities of coming up rock, paper or scissors is known, though not necessarily equal. This is due to introducing personal bias. For example, some might prefer “rock” over the other two choices from the get go, or think that the other player would always show “rock”. The entropy of the unknown result of the next choice by the player is maximized if the probability of landing either of the three choices is equal. In the context of MCDA, if the entropy measure is small, the dissimilarity of the amount of information between the criterion and the other criteria is higher and
accordingly more information was contained in the evaluated criterion thus it was more important. This, in turn, indicated that the criterion provided more useful decision information for the problem at hand and the criterion would have a higher importance weight within the decision procedure which was a clear advantage of entropy based weighting. The main disadvantage was the more mathematically complex calculations applied.
2.4.5 Decision Rules Background
The simple additive weighting method (SAW) is a more subjective analytical MCDA decision rule when two or more criteria are considered. The land decision
analysis has been one of the more common uses of the SAW method. Criteria are defined as the attributes that are evaluated. A weight is allocated to each criterion based on its significance. The results are land features with final values which can be evaluated in the context of suitability analysis. (Drobne and Lisec, 2009). This method is considered an overlay in GIS systems and can be performed by any application that has overlay capabilities, such as ArcMap or QGIS. These applications are capable to evaluate and
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sum the attribute map layers in accordance to the applied weights in order to determine the output merged layer.
While SAW is a subjective method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is objective. TOPSIS, developed by Hwang and Yoon (1981), compares each alternate choice to its positive and negative ideal solution through calculating geometric distance, where the shortest distance applied to the positive solution and the longest distance to the negative ideal solution. TOPSIS assumes that the criteria have monotonic increases or decreases in value. When criteria have incompatible units, normalization is performed. TOPSIS is a compensatory method of aggregation since it allows trade-offs between criteria since a good result in one
criterion can counterbalance a poor result in another criterion. Since it does not include or exclude alternative solutions based on hard limits, it provides a more realistic analysis than non-compensatory approaches. The problem with this decision rule is the less complete the data, the more inaccurate it becomes.
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Chapter 3: Methodology
3.1 Defining Project Objectives
3.1.1 MCDA Decision Tools Implementation
Every Multi-Criteria Decision Analysis starts with defining the problem by recognizing evaluation criteria, identifying feasible alternatives or the locations, normalizing of the criteria, estimating relative importance of the criteria by assigning weights, and applying decision rules. The decision problem involved a number of considerations, which were criteria that defined what needs to be integrated in the decision making process, and constrained the choices of feasible locations (Malczewski, 1999). For example, it would not be possible to build a large CSP facility on top of a mountain, thus these tracts of land would be deemed infeasible regardless of the modeled criterion. The following land uses were not feasible: water bodies, urban, forest, critical habitat, agriculture, wetlands, and steep slopes above 10%.
Therefore, this project limited feasibility based on the following considerations:
1) accessibility to infrastructure, 2) solar radiation energy received for a given area, 3) population/worker accessibility and 4) environmental conditions at the location. All were evaluated one by one to see whether it was appropriate to maximize or minimize the data within the model. Also, since the data can be analyzed in varying ways, two differing methods were applied, SAW and TOPSIS processes. Entropy weights method was utilized in the TOPSIS decision rule while SAW decision rule applied pairwise
comparison method to weigh the data. Data was normalized using the score range method for both decision rule applications.
25 3.1.2 Criteria
Accessibility to infrastructure was defined in terms of two criteria: 1) accessibility to transportation infrastructure, and 2) accessibility to water infrastructure. Data to
measure these objectives included major roads and highways (US Census Tiger Line 2016), and major waterbodies (USGS NHD 2012). Both of these criteria were defined using raster-based Euclidean distance measures. These were minimizing criteria as they minimized construction costs where proximity to existing primary roads and highways was modelled as more desirable. Additional proximity to water was more desirable as to reduce costs of transporting water to the facility. A CSP necessitates water to maintain it properly in large quantities in order to create steam to generate electricity.
Solar radiation was the main driver of the location, as power cannot be generated without direct sunlight. Thus the goal was to maximize this criterion. Solar radiation was defined specifically as solar insolation, which was acquired from the National Renewable Energy Laboratory (NREL 2014) at a 4 km resolution. Additionally, due to the intense light emitted from the CSP tower, placement was modelled at a reasonable distance from any populated land cover, as defined in the National Land Cover Dataset (NLCD 2011).
The environmental condition goal was disaggregated into four criteria or constraints: 1) slope constraint, 2) land use conflicts characterized as a constraint, 3) threatened and endangered species habitat and 4) bird habitat. Slope was calculated from a 30m digital elevation model (USGS 2016). It was limited to a maximum of ten percent rise as solar tower systems do not need as much grading of land due to the heliostats’
bases ability to be inserted in the ground regardless of slope. If a lower percent rise value would be chosen the feasible land would be far more limited. The study would want to
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retain some flexibility in areas available if more than one suitable site is found. All other slope above ten percent was excluded. All non-open land use as defined by NLCD was marked as infeasible and excluded from the model, given that the main goal of the project was to find open, unused land to accommodate construction of the CSP.
Wildlife critical habitat was the final criterion. Bird habitat was often
intermingled with other species and had a more fluid range in some cases, therefore it was used as a maximizing criterion where the model defined areas furthest from possible dense bird populations (Bird Life 2016) or threatened species zones as most feasible (CFW 2016). The locations of bird habitats and threatened wildlife were deemed infeasible. Reducing or limiting bird casualties will not only be beneficial to the environment, but also reduces repair costs onto the large heliostat fields that are often damaged by falling birds injured by flying into the solar flux field. Additionally, the endangered or threatened species habitat served a similar function as bird habitat. Since they often overlap, the habitats were aggregated (critical habitat). The criteria were represented by layers in ArcMap.
All decision rule calculations were concluded in ArcMap using raster resolution of 30m (1 arc second approximately). After the feasible area and the criteria layers were extrapolated, the infeasible area was removed from each of the criteria layers. The distances were then calculated using the Euclidian distance tool. Euclidean distance is a way to define space between two points using the straight-line distance. The limit of the Euclidian distance tool was the distance was calculated on a defined specific area. If the calculation area was restricted to the study area defined in the project, this calculation would cause distortions in the distance calculation thus show inaccurate distances at the
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edges. In order to rectify these shortcomings, a buffer of 50 miles surrounding the study area (Figure 4) was used to calculate the distances then the distance result was then extrapolated from only the study area. All the distance data gathered was extended into Nevada and Arizona given than San Bernardino and Riverside counties border these states. Certain land was deemed infeasible for this problem and not considered in any calculations of the model. Examples of fundamentally infeasible areas include water bodies and urban land use where building a CSP would not be possible.
Figure 4. 50 mile buffer surrounding the study area (NOAA, 2017).
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The criteria and constraints were chosen based on three land protection models, which defined why each criterion was included in the study and why each constraint was excluded. The type of land to be selected was correlated to the important consideration of what type of criterion was chosen. This type of model was called the Space
Attractiveness Model. The Space Vulnerability Model evaluated based on three aspects of land protection. The first safeguard was applied to natural environment, which defined the vulnerability of land flora and fauna. The second protection was applied to human environment, which categorized the vulnerability of cultural and visual qualities of land.
Lastly, vulnerability of physical space regarded as a resource for forestry, agriculture, and water industry was considered. The Space Convenience Model coalesced to the
possibility of space to accept development of this activity and of everything the implementation of this activity implies. All three Models could be summarized as the terrain must have access to high solar irradiation, be contiguous enough and flat for a large enough lot to house the CSP and not be protected under environmental law. It should not be close to populated or coastal areas (Krpan et al, 2012).
3.2 Normalization and Weighting of the Raw Data
3.2.1 Normalization
Criteria are usually measured in different units on different scales making comparison difficult. Finding a common scale of the criteria, was achieved using normalization techniques. It was a necessary step in order to evaluate to one suitability value for each alternative choice before the comparison method was applied. All criteria was normalized in a positively correlated way in relation to the suitability of the land as
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to create a uniform comparison model. There have been developments of several
normalization techniques like vector normalization or linear scale standardization, which itself has many sub processes to choose from such as range score or ratio score
normalization (Malczewski, 1999).
The scales of the criteria varied greatly, thus they were normalized to a common scale to be compared appropriately. Distances used meters while solar irradiance
measured insolation value over a year. What truly mattered was the range of the scale had to be similar. All the data was standardized for better comparison. Due to the size of the data available when evaluating 5 counties of data, a vector normalization could not be calculated with available computers. The calculation of said data was memory intensive.
Consequently, a score range normalization was used in both methods of MCDA performed in this analysis.
In order to apply correctly the normalization, the criteria had to come under scrutiny as whether to maximize the normalization or account for the highest value being the best, or minimize the normalization or account for the lowest value being the best.
The evaluation of the criteria deemed necessary to place the CSP as close as possible to a road or water source, so the minimal distance values were regarded as better values thus, the minimization score range method was applied to these criteria where xsta was the standardized value of raw value xj:
𝑥𝑠𝑡𝑎 = 𝑥𝑚𝑎𝑥 − 𝑥𝑗 𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛
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This calculation resulted in a scale from 1 to 0 with 1 being the best value. Lastly, the plant must be placed as far away as possible from critical habitats and population centers and have as high a DNI value as possible, thus, the maximization score range method was utilized to normalize these criteria as more importance was placed in higher raw values.
The maximization method calculation was:
𝑥𝑠𝑡𝑎 = 𝑥𝑗− 𝑥𝑚𝑖𝑛 𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛
3.2.2 Weighting of data
Pairwise comparison was conducted using a group decision method using six classmates in the Multi-Criteria Decision Analysis class which are familiar to both the MCDA techniques and a brief explanation was completed in order to familiarize them with the needs of CSP. A seventh assessment was completed by the author of the study.
The average of each criterion weight then was computed using a geometric mean
calculation. The pairwise method comprised of four major steps devised by Saaty (1980).
First the criteria to be ranked was identified. Next step was to arrange the criteria in a table and compare the criterion to the matching criteria in each row from the column above (Table 2). Each comparison between two criteria corresponds to a cell in the table.
It was necessary to complete one triangle (yellow) of the table only, because comparing DNI to roads was the same as comparing roads to DNI in reverse. Furthermore, a criterion when compared to itself was regarded as itself in the comparison table.
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Table 2. Pairwise Comparison Table
The third step in the pairwise comparison process was to assign weights. A new table was created to the right of the first keeping the rows in line, containing the results of each cell from the first table divided by the sum of its column. Then the rows of the second table were averaged which yielded the weight of the criterion.
Lastly, consistency of the initial table was verified. The individual weights in the second table were multiplied by the corresponding weight of the criterion in the second table it was compared to in the first then all five calculations were summed up. The result was divided by its criteria weight then all 5 results were averaged, then subtract from it the number of criteria under consideration then divided it by the number of criteria less one. This result was the Consistency Index (CI). The CI was divided by the RI which was predefined by Saaty (1980) for up to 500 criteria (Table 1). This result was be the
Consistency Ratio. If the CR was smaller or equal to 10 percent then the inconsistency was acceptable. If the CR was greater than 10 percent, the subjective judgement had to be revised.
Entropy based weights had two major calculations. In first equation where Pij was the normalized criterion values based on the sum of the value, E the information entropy of attribute j was calculated:
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𝐸𝑗 = −∑ 𝑃𝑖𝑗.ln(𝑃𝑖𝑗)
𝑚 𝑖=1
ln(𝑚)
Then the weight was calculated using the degree of divergence (1 – Ej) as follows:
𝑤𝑗 =∑ 1− 𝐸𝑗
(1−𝐸𝑗) 𝑛𝑗=1
3.3 Applying techniques of MCDA to the research
3.3.1 Decision Rules Implemented
The SAW decision rule application was a multi-step evaluation process. First, the criteria was normalized using the score range method. The weights were then applied to the tool corresponding to the criterion. Each option had a score which was calculated by multiplying the weight assigned to each criteria by its normalized value and then
summing the results over all five criteria. The final score was a spatial feature. The higher the SAW score, the more suitable the area. However, as simple as the SAW rule was, the TOPSIS rule was far more complex and calculation heavy.
The TOPSIS method was also a multistep process comprised of seven stages of sets of calculation. First step in the process was to create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria given as xij, therefore a matrix (xij)m*n was built. The next two steps were previously explained in the normalization section (vector normalization) and weighting section (entropy weighting method). The fourth and fifth step comprised of determining the best alternative (Ay) and the worst alternative (Az), then calculated the distance between alternative i and Ay and also the distance between alternative i and Az. Sixth step
calculated the rank to the best alternative solution using 1 or 0, where 1, the best solution,
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was closer to the point that was evaluated and 0, the worst solution, was farther. Last step ranked the alternatives. Both linear normalization (score range) and vector normalization could be used however score range was chosen for this project.
3.3.2 Localized research.
In order to determine the best site one must not just look at one point, but at a series of points contiguous enough to give us a large enough area for a CSP. To this point the most suitable land was extracted through evaluating the top five percent best value cells in both SAW and TOPSIS method results. The area large enough was defined as 1600 acres which was similar in size to the Ivanpah facility. Since there was no actual way to compare the existing Ivanpah CSP area to the study area result, a comparison of two overview photos with the Ivanpah facility side by side was conducted at the same approximate height to check for contiguous enough land to construct the CSP plant.
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Chapter 4: Summary and Explanation of Results 4.1 Criteria and Feasibility Results
The project comprised an area of 35,315 square miles of California land. By and large San Bernardino County, the largest county in the United States, comprises a surface area of 20,105 square miles making it 57% of the study area. After removing the
infeasible land which had the following land uses: water bodies, urban, forest, critical habitat, agriculture, wetlands, and slopes above 10%, San Bernardino County had 8651 sq. miles of feasible land, followed by Riverside with 2393 sq. miles, Los Angeles with 486 sq. miles, Ventura with 98 sq. miles and Orange County with 11 sq. miles (Figure 5).
This accounted for 11,639 square miles of feasible land or 33% of the total study area.
Figure 5. Total feasible land
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San Bernardino County accounted for 74.3% of the feasible land mass. Orange County and Ventura County accounted together for less than one percent (0.94%) of the suitable land. It should be noted that Orange County provided no sufficiently large continuous land area in which to construct a solar power plant.
The populated area in the research (plus a 50 mile buffer) consisted of 3661 square miles (Figure 6a), waterbodies entailed 4249 sq. miles (Figure 6b), and wildlife critical habitat area involved 17077 sq. miles (Figure 6c). Primary roads were line features (Figure 6d). The Direct Normal Irradiance (Figure 7) had a range between 0 and 8.62 DNI. A heat map was used in this instance to better illustrate the contrasting
extremes. The Euclidean distances had a range between a max of 151,429 meters for waterbodies to a max of only 60,839 meters for roads (Table 3), while the minimum value for all four distance criteria was 0. When normalized using the score range method, all five criteria were adjusted to a range between 0 and 1.
a.
36 b.
c.
37 d.
Figure 6. Projected criterion layers (a. populated areas, b. waterbodies, c. wildlife critical habitats, d. roads)
Figure 7. Projected DNI criterion layer
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Criterion Layer Critical Habitat Population Roads Waterbodies
Distance 147,025 143,712 60,839 151,429
Table 3. Maximum Criteria Distances (in meters)
4.2 Multi-Criteria Decision Analysis Results
4.2.1 Simple Additive Weighting Outcome
The weights from the group decision matrix after calculating the geometric mean were then applied to the SAW model, the heaviest weight being applied to the DNI criterion with 36.7% weight, while the least weighted criterion was proximity to water at 6.5% (Table 4). The consistency ratios of all the pairings were within the 10% threshold thus any inconsistencies were deemed acceptable.
Table 4. Pairwise Comparison Group weights result
The result of the SAW method (Figure 8) showed a range between 0.131 at its lowest aggregated result to 0.835 at its highest result. Most of the best suitable land was
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Figure 8. Simple Additive Weighting result
located in San Bernardino County Riverside County has a smaller area with potential than San Bernardino County however it was substantially larger than the area in Los Angeles, Ventura and Orange Counties. When looking for the top five percent result, the cut off was calculated at 0.800 SAW value. All cells that were higher or equal to said number were included in the most suitable SAW method land analysis and had an area of 42.68 square miles (Figure 9).
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Figure 9. Simple Additive Weighting most suitable land (top 5%)
4.2.1 Technique for Order of Preference by Similarity to Ideal Solution Results
The weights from the entropy based method were then applied to the TOPSIS model, the heaviest weight being assigned to the DNI criterion with 36.2% weight while proximity to populated areas weighted the least at 11.8%. (Table 5).
Table 5. Entropy based weights
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The TOPSIS method result (Figure 10) showed a range between 0.235 at its lowest calculated result to 0.809 at its highest value. Very similarly to SAW, the best suitable land was located in San Bernardino County. When looking for the top five percent result, the cut off was calculated at 0.780 TOPSIS value. All points that were higher or equal to said number were included in the most suitable TOPSIS method land analysis while the suitable area was 17.56 square miles (Figure 11). When superimposed with shading to compare both suitable areas, as results significantly overlapped, the location was identified containing 14.89 square miles of suitable land. Considering the Ivanpah plant was built on less than three square miles (1600 acres), the most suitable area included 1844 acres of contiguous suitable land, which would allow construction for a CSP plant with a gross capacity of 394 megawatts, which would be same gross capacity as Ivanpah plant (Figure 12).
Figure 10. TOPSIS result
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Figure 11. TOPSIS most suitable land (top 5%)
Figure 12. Proposed New CSP site location (NAIP, 2016)
43 4.3 Localized Assessment of the Site
The final suitable site was located to the north of San Bernardino County, ten miles south of the Inyo County border, and about 35 miles north of the US Army installations at Fort Irwin, CA, also known as the National Training Center. The area in question appeared to be more secluded than the Ivanpah facility which was only several miles away from the I15 highway close to the border between California and Nevada, near Prim, NV. The location had considerable open space, especially to the south (Figure 13). To the north, there are soft rolling inclines, possibly suitable to a large power tower installation based on the elevation as these solar fields do not require as much grading as other options.
Figure 13. Proposed Location Perspective View (Google Earth, 2017)
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Chapter 5: Discussion 5.1 Results Explained
This analysis confirmed earlier studies that deemed coastal areas largely
unsuitable for solar power generation due to their general low DNI. The result could be easily explained as coastal areas have a lot more cloud cover reducing their DNI
dramatically (Figure 6.). In order to show the importance of this criterion, its weight was appropriately deemed the most important in the SAW method by a significant margin.
Six of the seven respondents in the pairwise comparison survey which ultimately resulted with the weights in Table 5 assessed that DNI was most important criterion thus had the heaviest weights in their evaluations. The seventh evaluator deemed it as the second most important criterion only edged out by proximity to critical habitats. Solar insolation drives the capability of the power plant to operate at a higher efficiency, allowing for more power to be generated.
Neither model used in this analysis was perfect. The SAW method was prone to over-judgement or under-judgement from the author. Judging a criteria with limited information or knowledge could over-miss or under-miss the ideal calibration of the weight of the criteria since it was entirely subjective. The TOPSIS method may have overlooked some other factors which a more subjective weighting could have considered, or additional data might have made the TOPSIS model more accurate. In this case, the SAW method was more compensatory, as the weights were judged with conflicting information about the said criteria, thus a more accurate portrayal was gathered due to
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that factor. However TOPSIS did allow for a multitude of criteria to be handled easily, although, calculations were intensive and burdensome in terms of hardware resources.
5.2 Limitations and Future Research
5.2.1 Research Limits Discovered
One of the biggest obstacles in creating an appropriate Multi-Criteria Decision Analysis was lack of data. One of the most limiting factors in this research was the lack of raw Direct Normal Insulation data outside of the available data from the National Renewable Energy Laboratory data. The one benefit of this data was however that it was recent and it included raw data on the ground, yet the resolution of 4km was very large.
Even though part of the data was modeled to fill in the gaps, the model was derived from actual ground data. Since the project sought a large area to build a CSP, the DNI
resolution did not require a lesser resolution since typically the DNI increases or decreases less dramatically from raw data cell to cell. This data set was specifically created by NREL for usage by solar power energy sector or for research and has been widely used in these roles (NREL, 2014). Additionally, if the plant location was more desirable to be closer or within an urban center, then the resolution would not be
acceptable, as urban centers would invariably have a larger range of variation of the DNI.
Population centers also largely correlated with the DNI values being lower due to the coastal nature of urban development in the Greater Los Angeles area.
One specific limitation about water data was that there was no definitive fluvial water network of permanent rivers that was accurate enough for this study in Southern California, as the author has mapped the National Hydrological Department hydrological
