Using Multi-Criteria Decision Making (MCDM) and Choosing by Advantages (CBA) to Determine the Optimal Location for Solar Photovoltaic (PV) Farms


 Solar energy is a critical component of the energy development strategy. The location of solar photovoltaic (PV) plants has a significant impact on the cost of power production. A favorable situation would result in significant cost savings and increased electricity generation efficiency. California is located in the United States of America's southwest region, a place blessed with an abundance of solar energy. In recent years, the state's economy and population have expanded quickly, resulting in increased need for power. The study examines south of California for an area that is well-suited for the building of large photovoltaic (PV) plants to meet local electricity needs. To begin, this article imposed some limits on the selection of three potential solar project locations (S1, S2, and S3). Then, a more systematic approach to solar plant site selection was presented, focusing on five major characteristics (economic, technological, social, geographical, and environmental). This is the first time that the Choosing by Advantages (CBA) approach has been used to determine the locations of photovoltaic (PV) plants, with possible locations ranked S2>S1>S3. It was compared to more classic methodologies such as Multi-Criteria Decision Making (MCDM). The findings of this study suggest that the CBA strategy not only streamlines the solar plant selection process, but also more closely aligns with the objectives and desires of investors.


Introduction
Historically, non-renewable energy sources such as fossil fuels were heavily relied upon. However, its usage results in an increase in harmful gas emissions, which has a detrimental effect on the long-term growth of society 1 . By contrast, renewable energy is vital for society's long-term growth. Solar energy is gaining prominence as a cost-effective and endless source of renewable energy 2 . Solar energy generation has also grown signi cantly due to its bene ts, which include ease of maintenance, low environmental effect, and a longer service life. Solar energy output is predicted to develop signi cantly in the near future, particularly in industrial areas such as California, which has the world's fth-largest economy 3 . California has huge potential for solar power generation. Governor Jerry Brown signed a bill setting the electricity target into law in 2018. California is required by law to generate 50% of its electricity from renewable sources by 2025, 60% by 2030, and 100% zero-carbon electricity by 2045 4

. This bill has advanced
California's renewable energy growth. According to the California Energy Commission's report, California became the rst state in the United States in 2014 to generate more than 5% of its utility-scale electricity from solar energy, increasing to 14% in 2019 5 . With the addition of small-scale solar, the state generates a fth of its overall electricity needs 6 . California generated 40% of the nation's solar photovoltaic energy and 70% of utility-scale solar thermal energy in 2019 7 . By November 2020, California would have approximately 13,000 megawatts of utility-scale solar capacity, the most of any state in USA 8 . California would roughly triple its existing electrical grid capacity and maintain a record rate of renewable energy capacity expansion over the next 25 years to ful ll the 2045 target while also powering other sectors and achieving the state's economy-wide climate goals 9 . This suggests that solar energy project construction in California will expand during the next decade. Solar project development is contingent upon the location of construction sites. Choosing appropriate locations for large solar energy systems has a substantial impact on the amount and quality of solar energy generated 10 . Additionally, investors would pro t economically and socially from a favorable geographic location for investment. However, due to the inherent environmental limits and con icts, locating an appropriate location for solar power plants in California presents certain obstacles. This is because California includes a variety of varied terrains, including deserts and coastlines; however, population distribution will also affect the accuracy of site selection. As a result, it is required to develop a new model and process for site selection that is tailored to the peculiarities of California.
The purpose of this study is to make a contribution to the eld of solar site selection. It is mostly used in California to determine the most cost-effective solar energy installations. Then, to close the research gap in California, a signi cantly more trustworthy model for solar plant site selection is offered. The following summarizes the work's key contributions: 1. To meet California's energy needs, it is vital to develop a sustainable energy supply position.
2. This is the rst time that the method of choosing by advantages (CBA) has been used to select locations for large solar photovoltaic (PV) plants. It instills a new way of thinking in decision makers.
3. To provide a more speci c and methodical framework for solar plant site selection, it includes ve basic criteria: economic, technological, social, geographical, and environmental, as well as various subcriteria for each primary criterion.
The remainder of the study is divided into the following sections: Section 2 provides an outline of how MCDM is used to select solar plant locations. Section 3 examines the criteria, parameters, and model for solar power plant location; it also includes speci cs on the CBA approach. Section 4 discusses the conclusions and the CBA sensitivity analysis. Finally, section 5 interprets the paper's conclusion.

Literature Review
Due to the complexity of solar power station location selection and the numerous elements that may in uence it, researchers may consider using multi-criteria decision making to make a location an option during the study process. MCDM is a well-known decision approach in operations research that encompasses a variety of techniques. Among the most often used are the analytic hierarchy process (AHP), the preference rating organization system for enrichment evaluations (PROMETHEE), and multiattribute utility theory (MAUT) 11 . These methods are capable of analyzing and evaluating discrepancies between criteria and decision maker's opinions 12 .
Multi-criteria analysis has been effectively utilized internationally in the eld of solar plant site selection due to this property. Dejan Doljak et al. identi ed suitable locations for solar energy production in Serbia using the spatial suitability index 13 17 used fuzzy logic and weighted linear combination (WLC) to resolve con icting elements and determine the optimal location for solar energy development in Iran. This concept addressed the problem of insu cient information being available throughout the site selection process. However, the site selection criteria are not thoroughly considered. Similarly, Shaimaa Magdy Habib used only two criteria -technical and economic -to locate solar plants on Egypt's northwest coast, utilizing remote sensing (RS) and AHP techniques 19 . H.S. Ruiz et al. considered climate, geography, and technical factors in selecting the ideal place for solar energy installation in Indonesia, but ignored local economic and social development. 20 . As illustrated in Table 1, present researches on solar site selection frequently overlook social aspects, which is irrational. Social factors signi cantly affect the investment and bene t of a project. Taking into account social factors is crucial for promoting sustainable development. supports sound decision-making using alternative advantage comparisons 21 . It can convert qualitative aspects to quantitative ones in order to mitigate subjective effect caused by the relative bene ts of various factors. Due to the CBA method's superior performance in terms of optimal project selection, it has been widely adopted in the architectural, engineering, and construction (AEC) business [22][23][24][25][26] . Based on these features, the CBA technique is deemed suitable for solar energy site selection. To demonstrate CBA's superiority, this article also employs two standard multi-criteria decision-making methodologies. TOPSIS and PROMETHEE are used to conduct comparative analyses.

Establish the criteria and variables
Following a comprehensive review of the relevant literature and consultation with industry experts, this paper suggests sixteen essential site selection considerations. However, at some point throughout the site selection process, the characteristics of variables may have an effect on the output's accuracy. To ideally solve this problem, the variables in this study can be classi ed as useful or negative, based on whether or not to enhance photovoltaic power plant production as their values increase. Visual impact, solar irradiation potential, land types, geological disaster, policies, public attitude, and local development planning are considered bene cial criteria in this paper; payback period, investment cost, rainfall, temperature, humidity, distance to roads, distance to substations, and population density are considered detrimental criteria. This treatment would advocate for simplifying the MCDM model and outlining the CBA model's decision rules. The justi cation and explanation for the selection of each factor will be discussed in greater detail below: Visual impact The building of solar farms would have an effect on the daily life of animals and humans 27 . To maintain the long-term viability of the ecosystem, the visual impact of the solar farm must be considered throughout the design stage.

Solar irradiation potential
It's fair to say that this is a key indicator for whether solar farms will be built. Solar farms' ability to produce and save money is directly impacted by the amount of available solar energy. The more solar radiation there is, the more electricity is generated, and the more e cient the electric eld is 28 .

Land types
In some places, land types and availability might be a factor in determining the location of a solar power plant. Numerous countries have regulations regarding the sorts of land that can be used for solar projects. Generally, it is preferable to employ building land rather than agricultural land, as this would contravene the principle of sustainable growth.

Geological disaster
This is a critical geographical factor in the development of photovoltaic power plants. If an area is prone to geological disasters such as tsunamis and earthquakes, investors will face signi cant risks, and there is no value in installing solar farms in this area.

Policy
It is critical to consider local policies while selecting a location. Solar energy generation is expensive due to technical constraints. When a country or municipal government reduces taxes while increasing energy prices, investors are relieved and the investment rate increases.

Social bene t
The photovoltaic power station is built to meet the interests of investors while also contributing positively to society. It will assist in the promotion of local businesses, the creation of more jobs, and the impact on local education and culture 29 .

Public attitude
The development of a huge solar energy system is a massive and time-consuming endeavor. It frequently has a detrimental effect on nearby inhabitants, for example, noise. It is critical and appropriate to perform extensive research to ascertain whether the local populace supports solar energy generation.

Local development planning
It serves as the foundation for investment and commercial decision-making. If the local economy and social system have remained stagnant and saturated, the viability and hazards of investing in photovoltaic power stations must be evaluated.

Payback period
This is a critical factor to examine when determining if a project is worth investing in, and it is also a benchmark for decision makers when determining a project's pro tability. When selecting a solar power plant, a project with a lengthy payback period is inappropriate and should not be prioritized.

Investment cost
Investment cost is a critical factor to consider while undertaking any project. It weighs the project's expenses and bene ts. Consideration of this factor in the analysis of the optimal site of solar farms will result in a more cost-effective and dependable location outcome. The expenditure mostly encompasses the costs associated with land acquisition in this paper.

Rainfall
Rainwater may cause damage to photovoltaic (PV) and other construction equipment, reducing their useful life. Solar power plants should be constructed with extreme caution in places prone to excessive precipitation.

Temperature
Temperature can have an effect on the longevity of solar power generation devices. Increased temperature can reduce the e ciency of solar panel energy conversion devices, resulting in decreased output 30 . When the average temperature is maintained at a steady and acceptable level, solar power plants can operate at maximum capacity.

Humidity
Increased humidity does result in less solar radiation, lowering the performance of photovoltaic energy and increasing the cost of power generation 31 .

Distance to roads/substations
The technical strategy must account for the distance between solar farms and roadways and substations. Solar farms built near transformer substations will help reduce equipment transportation costs and enable the construction of new infrastructure.

Population density
This is an illustration of how metropolitan systems evolve. The distribution and number of populations are even more critical variables throughout the solar plant site selection process.
All of the factors stated above were determined with the assistance of experts and relevant institutions from around the world to bolster the viability of the site selection system and the data's dependability.
Experts include local governments, government agencies, consultants, renewable energy specialists, project managers, quantity surveyors, engineers, architects, scientists, and developers. Their knowledge and abilities ensure the logic and dependability of the system.

3.2
The procedure for determining the optimal location for a solar power plant This research evaluates the economic, environmental, geographical, and social factors of the study region, as well as the potential for solar energy growth, in order to maximize the site of a solar plant.
Developed a more precise approach for determining the location of solar energy plants. Figure 1 illustrates the process of choosing an alternate site for a solar farm. The speci c steps are described as follows: Step 1 Created a site selection model based on 16 factors and suggested some constraints to help de ne possible alternatives (S1, S2, S3).
Step 2 Collect and evaluate relevant data for each alternative in accordance with the site selection method. All collected data will be processed and used as input parameters for the CBA model.
Step 3 Determining the optimal site by using CBA methods.
This approach would improve the precision and objectivity of the site selection process's outcome.
Notably, due to the low slope angle of the land in the study eld, the slope and orientation of the land are not included in this research.

Study area and data collection
This study focused on the southern California counties of San Bernardino and Riverside (Fig .2), which are mostly desert, sparsely populated, and bountiful in solar energy. As a result, the majority of California's solar projects are located in those two counties to supply electricity to western California's metropolitan clusters. To begin, the constraints indicated in Fig. 1 were used to select three suitable solar project locations (S1, S2, S3). Following that, speci cs about possible places are provided. Prior to analyzing the alternatives, this study's data were collected and show in Table 2. All data and statistics are derived from a variety of sources, including the National Renewable Energy Laboratory (NREL), the Weather Atlas (WA) website, and the Bureau of Land Management (BLM).

CBA method
CBA's tabular approach is utilized to determine solar photovoltaic (PV) plant installation locations in this study. As illustrated in Fig. 3, the tabular CBA technique comprises of six steps 33 .
1. Determine possible alternatives; in this study, three possible alternatives (S1, S2, and S3) are ultimately produced by imposing some constraints on the investigation. These are the alternatives that are used to conduct the evaluation.
2. De ning criteria and factors. Section 2 discusses the criteria and elements that in uence the location of solar energy plants. It's worth emphasizing that the majority of characteristics and variables are quantitative, which makes the CBA method's decision-making outputs more objective and reliable.
3. Enumerating the characteristics of each alternative. This process involves experts and stakeholders developing choice rules for each criterion and element, as well as summarizing the qualities of each alternative.
4. Assessing advantages of each alternative. This step requires stakeholders to evaluate the merits of each alternative based on speci ed characteristics and considerations, which should be a straightforward undertaking.
5. Deciding the importance of each advantage. Decision makers should prioritize each advantage. Participants used a scale ranging from 1 to 100 in order to assign varying degrees of importance. To begin, the "most critical bene t" should receive a score of 100. The following goal is to utilize the "most signi cant bene t" as a baseline against which to compare the remaining advantages. The nal stage is to determine each alternative's Total Importance Advantages (IofAs).
6. Choosing the best alternative. Calculate the cost of each alternative scheme to obtain the cost -IofAs curve. The alternative that gives the most value for money should be chosen by stakeholders and decision makers Result And Discussion

CBA results
In contrast to the standard MCDM method, the CBA method places a premium on the relative advantages of factors rather than their relative importance. The goal of this study is to determine the suitability of a location for solar project construction using the CBA decision-making framework. To con rm the accuracy of the data and the method's viability, experts from around the world were enlisted to de ne criteria and weigh the relative merits of each choice. As a consequence, 15 decision-making elements and criteria (left column of Table 2) were found, with the exception of investment cost. Fig. 4 illustrates the score assigned by experts to each factor's advantage. Clearly, professionals prefer solar radiation potential, which has a maximum score of 100 and corresponds to a basic understanding of solar energy generation. Additionally, the overall score for technical and social variables is high, showing that decision makers place a premium on the bene ts of these two factors when deciding on solar farm locations. Table 2 demonstrates how CBA can be used to organize data in a way that makes selecting the ideal solar plant location easier for experts and stakeholders.

S1
The area is classi ed as a high desert and is zoned for construction.
Solar projects are more suited to construction land.

S2
The land is primarily used to build tourist amenities. --

S3
This area is primarily made up of building land and desert wasteland.
Desert wasteland has a cheap cost of land.

S1
There are no signi cant natural disasters or looming oods. --

S2
There have been no signi cant natural catastrophes.
The solar facility's life will be extended, and the project's development will be safer.

S3
The location is located on a fault line, which means that mild earthquakes are possible.
Minor tremors have little effect on the stability of solar-powered equipment.

Policies
Criteria: The more positive the impact on the construction of solar farms, the better.

S1
There is little demand for electricity in this area. --

S2
This area's electricity is mostly used to support local tourism growth.
This location has a certain demand for electricity.

S3
In fact, this area will serve as a power transmission link between California and Arizona.
Bene t of promoting the solar energy project's development. To facilitate analysis of the results, the IofAs (Importance of Advantages) values in this study were divided by 100. Fig. 5 illustrates a cost analysis of each alternative in terms of IofAs. S2 had the second lowest cost and the highest IofAs value when compared to S1 and S3. S1 and S3 have similar IofAs values, however S3 is substantially less expensive. In conclusion, S2 is the optimal location for solar plant construction using the CBA technique due to its higher cost performance, and the nal ranking is S2>S3>S1.

Comparative analysis of conventional MCDM method and CBA
To validate the CBA results, this study used the TOPSIS and PROMETHEE comparative analysis methodologies. There are two primary reasons for selecting these two methods. The rst is that they are the most often utilized methods for solar farm site selection, making them highly representative. Second, the decision-making philosophy of these two systems is diametrically opposed to that of the CBA method, implying a sharp comparison. The ranking of solar farm site selection alternatives based on standard MCDM and CBA approaches is shown in Table 3. S2=0.488> S1=0.564> S3=0.473 while using TOPSIS, however S2=0.045 > S1=0.029 > S3=-0.073 when using PROMETHEE. The rankings produced by these two approaches are identical. This demonstrates that the classic MCDM method has the same decision performance. Clearly, this result is not the same as the one reached using the CBA technique (S2>S3>S1). The fundamental reason for this is that the investment cost was factored into the TOPSIS and PROMETHEE method evaluations at the beginning, and as a result, potential site S3 is scored better than S1 due to its superior social and environmental performance. In other words, the disadvantage of S3's investment cost is outweighed by its other bene ts. As a result, when traditional MCDM methods are used to make a choice, the investment cost is weighed against other criteria. Unlike the typical MCDM technique, the CBA model incorporates the predicted investment cost of each choice as an independent parameter. That is, despite the fact that S1 performed brilliantly in this study and achieved a high score in a multitude of areas, Due to the high estimated investment cost, investors and decision makers would continue to shun it. To emphasize this property of CBA, the cost of the three site selection possibilities is maintained constant for analysis in this study. Tables 4 and 5 detail the ndings when the total cost of all alternatives is set to the maximum and minimum values, respectively. Clearly, the CBA approach and the classic MCDM method provide the same outcomes when all participating options have the same investment cost.
However, in practice, the location of solar farms will be in uenced by a variety of factors, and the investment costs of different projects will vary signi cantly as a result of the in uence of various surroundings and legislation. As a result, the CBA technique outperforms the classic MCDM method when it comes to making decisions about projects with signi cant cost discrepancies.  Additionally, in comparison to the traditional MCDM method, decision makers can use CBA to make decisions based on their own needs in response to cost changes. Table 6 and Fig. 6 illustrate the decision-making outcome when the costs in S1 vary proportionately. Clearly, as the cost of S1 is reduced, its cost performance improves. When the cost of S1 is lowered by approximately 20%, the cost performance index (the value of IofAs divided by the cost) of S1 is greater than the cost performance index of S3. This signi es that S1 outperforms S3 in terms of cost performance, and the ndings of the CBA method will be changed to S2>S1>S3. As can be seen, CBA facilitates more transparent and adaptable decision-making.
In conclusion, while all three approaches are designed to help decision makers analyze their preferences, the CBA method gives a more complete cost analysis and a simpler process. When considering considerations, the CBA requires decision makers to assess the relative value of each bene t in the context in which it is being considered. By contrast, the standard MCDM method places a premium on the signi cance of components when evaluating projects.

Sensitivity analysis
To ensure the stability of CBA results, two scenarios are designed to investigate how the results uctuate when one aspect's advantages change. In our situation, we altered the relative advantages of technology and social elements. This is because technical and social factors are weighted greater than other factors.
In other words, the advantages of these two factors outweigh the disadvantages of the others. These two scenarios are detailed in Table 7.

Conclusions
This paper begins by discussing the development of solar energy in California, the United States of America, and points out that with the expansion of solar energy projects, location selection for solar power farms will become more di cult. As a result, this paper will examine solar farm site in order to give technical support for solar energy projects in California.
Additionally, this study discusses the process of solar energy plant site selection. The conclusion is then drawn that the typical MCDM approach is incapable of handling the site selection di culties associated with solar energy production. As a result, it is important to develop a new way of thinking and acting in order to meet the needs for new energy deployment.
In this vein, this research created a more precise and detailed method for large solar plant site selection that takes into account economic, technological, social, geographic, and environmental elements, as well as a number of factors based on essential criteria. According to the decision-making outcomes, a region's solar project potential is clearly determined by its high prospective solar radiation, policies, and investment prices. Then, utilizing CBA, an optimum strategy for solar plant site selection in southern California, USA, was presented. To begin, limits on the selection of three possible choices are placed. After that, the CBA model was utilized to classify those potential locations. The CBA ndings indicate that S2 is the optimal location for solar plant construction.
To demonstrate CBA's advantages, the standard approaches TOPSIS and PROMETHEE are compared to CBA. The results indicate that when the costs of all alternatives are very variable, CBA can provide a more accurate and exible cost analysis. As a result, the solar industry's adoption of CBA enables the decisionmaking team to make an accurate assessment of the project's importance and risk tolerance. Cost analysis enables the project's management team to make more informed and smart choices.

Declarations Data availability
The data used in the publication were made from meteorology and geography. It is widely mentioned in the "Methodology" section of the article.  Figure 1 Solar plant location selection framework.

Figure 2
Map of the study area32.  The score distribution of each factor's advantage.
Page 25/27 The nal result of CBA method when the costs in S1 change.

Figure 7
Result analysis based on scenario A.

Figure 8
Result analysis based on scenario B.