Integrated GIS and Fuzzy-AHP Framework for Suitability Analysis of Hybrid Renewable Energy Systems: A Case in Southern Philippines

Abstract

This study proposes an integrated framework for assessing the suitability of renewable energy systems, including wind, solar, hydro and hybrid wind–solar and hydro–solar, in the southern Philippines. The framework employs a combination of the Fuzzy-Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) techniques to evaluate various socio-environmental and techno-economic factors. Several suitability indices were developed and used in the analysis. The criteria used in the analysis are based on a comprehensive literature review and input from experts in renewable energy and micro-grid technology. The results indicate that energy production is the most important factor and the total suitable areas for hybrid wind–solar and hybrid hydro–solar systems are 126.60 and 629.02 square kilometers, respectively. This research provides valuable insights for decision-makers and potential investors in the renewable energy sector in the study area.

1. Introduction

Concerns about pollution and climate change caused by greenhouse gas emissions have prompted a global push towards renewable energy and a transition away from fossil fuels. According to the International Renewable Energy Agency (IRENA), renewable energy sources are projected to play a significant role in meeting future energy demand in both developed and developing countries [1]. In the Philippines, the shift towards renewable energy is imperative for various reasons, particularly due to the country’s vulnerability to the negative impacts of climate change. Its archipelagic nature, which includes numerous coastal towns, makes it particularly susceptible to rising sea levels and flooding as a result of increasing tides [2].

Despite the vast potential for renewable energy in the Philippines, as of 2018, renewable energy sources only accounted for 23.38 percent of the country’s energy needs, supplying 23,326 GWh of electrical energy against a total demand of 99,765 GWh [3]. For years, the Philippines has been a net importer of fossil fuels to meet its growing energy demand driven by economic and population growth. Recognizing this, the Philippine government has made efforts to advance the development and use of renewable energy sources to ensure energy security. The country has various renewable energy options such as hydropower, geothermal, solar photovoltaic and others [4].

To increase the country’s reliance on renewable energy, the Philippine government has implemented several policies. The Philippine Congress has enacted legislation to promote clean energy use, such as the 2001 Electric Power Industry Reform Act, the 2006 Biofuels Act, the 2008 Renewable Energy Act and the 2008 Climate Change Act (2009). With a goal of achieving a 70% reduction in carbon emissions, the government has committed to raising renewable energy’s share of the energy mix to 50% by 2030, with a total of 15.3 GW of generation capacity [5]. To achieve this transition, it is crucial to evaluate renewable energy potential and the suitability of renewable energy systems at different sites [6,7].

One of the major disadvantages of off-grid single renewable energy systems, such as wind and solar energy systems which are highly intermittent is their dependence on both short-term and long-term environmental and meteorological conditions, making it difficult to provide a reliable and stable energy supply. However, integrating multiple renewable energy sources into a hybrid system can address this constraint and decrease dependence on traditional energy sources. Hybrid renewable energy systems use more than one source of renewable energy to compensate for the limitations of a single source [8,9,10,11,12,13].

Finding a suitable location for renewable energy generation development poses several challenges. In the traditional approach, a comprehensive ground survey is required. Furthermore, It is not feasible to conduct a nationwide survey due to the costs, manpower and security restrictions [14,15]. Several factors are involved in the operation including the well-being of individuals, society and the environment. Geographic Information System (GIS) has developed into a rapidly growing technological field that is increasingly being used to assess real-world problems. It is capable of performing a wide variety of tasks, ranging from simple mapping to complex spatial modeling. Consequently, research on renewable energy resource suitability and site selection has increased dramatically in the last decade.

In the process of planning and evaluating renewable energy projects, GIS and Multi-Criteria Decision-Making (MCDM) are both extensively used, as shown in the literature survey summarized in

Table 1. Summary of literature survey.

		Renewable Energy System Considered
Authors	Year				Hybrid	Hybrid	Technique	Criteria Considered
		Wind	Solar	Hydro	Wind–Solar	Hydro-Solar
Aydin et al. [16]	2013	X	X		X		GIS-Fuzzy-OWA ${}^A$	Economic and environmental
Szurek et al. [17]	2014	X					GIS-AHP ${}^B$	Technical and socio-environmental
Noorollahi et al. [18]	2016	X					GIS-KLMM ${}^C$	Technical
Noorollahi et al. [19]	2016		X				GIS-Fuzzy-AHP	Techno-economic
Singh et al. [20]	2016			X			GIS-AHP	Techno-economic and socio-environmental
Gigovic et al. [21]	2017	X					GIS-DANP ${}^D$-MABAC ${}^E$	Technical and environmental
Díaz-Cuevas [22]	2018	X					GIS-AHP	Techno-economic and environmental
Yousefi et al. [23]	2018		X				GIS-Boolean-Fuzzy	Techno-economic and environmental
Ghorbani et al. [24]	2019			X			GIS-TOPSIS ${}^F$	Techno-economic
Rana et al. [25]	2020			X			GIS-AHP-WPM ${}^G$-TOPSIS	Techno-economic
Albraheem et al. [26]	2021		X				GIS-AHP	Techno-economic
Jafari et al. [27]	2021			X			GIS-BWM ${}^H$	Techno-economic
Görtz et al. [28]	2022			X			GIS	Technical
Zalhaf et al. [29]	2022	X					GIS-Fuzzy-AHP	Technical and environmental
Huang et al. [30]	2022	X					GIS-AHP	Techno-economic and socio-environmental
This study	2022	X	X	X	X	X	GIS-Fuzzy-AHP	Techno-economic and socio-environmental

X—Type of renewable energy system considered in the study; ^A OWA—Ordered Weighted Averaging; ^B AHP—Analytic Hierarchy Process; ^C CLIMM—Climate Model Maiz; ^D DANP—DEMATEL Analytic Network Process; ^E MABAC—Multi-Attributive Border Approximation Area Comparison; ^F TOPSIS—Technique for Order of Preference by Similarity to Idea Solution; ^G WPM—Weighted Product Method; ^H BWM—Best-Worst Method.

. This is because GIS offers a wide range of capabilities for the processing, management and analysis of geographical information. It also allows for the inclusion of various social, economic, technical and environmental criteria and constraints, making it a practical tool for multi-criteria analysis in site-selection problems.

However, previous research on site selection for renewable energy systems has primarily focused on single energy systems and has often used methods that are either too simplistic or too complex. These methods often do not consider the full range of factors that are important in site selection, such as technical, economic, social and environmental factors. Additionally, previous research has not adequately addressed the suitability analysis of hybrid renewable energy systems, which have the potential to increase energy efficiency and reduce costs.

Thus, this study aims to address these limitations by proposing an integrated framework for identifying suitable sites for the construction and development of single (solar, wind, hydro) and hybrid (wind–solar, hydro–solar) energy systems based on various socio-environmental and techno-economic objectives and criteria in Lanao del Norte, Philippines. The framework employs Fuzzy-AHP and GIS to conduct a comprehensive suitability analysis that takes into account a wide range of factors. The proposed framework is designed to be both simple and comprehensive, making it easy to use while still providing detailed and accurate results. Furthermore, other specific objectives of this study are:

• To develop and use various socio-environmental and techno-economic suitability indices for determining suitable sites for a variety of renewable energy systems.
• To identify the membership function of criteria and apply the fuzzification process to each layer that corresponds to it. To determine the weight of each layer, as computed after reaching a consistency ratio that is considered to be adequate.
• To determine the different suitability indices for single and hybrid renewable energy systems using Weighted Linear Combination (WLC), fuzzy AND operator and site selection rules.
• To provide detailed potential sites for single renewable energy plants such as hydropower, wind and solar-PV systems, as well as potential locations for hybrid hydro–solar and hybrid wind–solar energy systems.
• To develop a strategy for selecting sites for hybrid renewable energy systems that can assist decision-makers and future investors in choosing areas that are feasible from a techno-economic and socio-environmental standpoint.

The structure of this paper is as follows: Section 2 provides a detailed explanation of the methods and techniques used in the study, including the integrated framework for site selection, the socio-environmental and techno-economic criteria and indices and the use of Fuzzy-AHP and GIS. Section 3 presents the results of the analysis, including a discussion of the suitable sites identified for single and hybrid renewable energy systems and a sensitivity analysis of the results. Finally, Section 4 concludes the study with a summary of the main findings and their implications for decision-makers and future investors in the renewable energy sector.

2. Methodology

The proposed framework for suitability analysis of single and hybrid renewable energy systems shown in Figure 1 is a multi-step process that involves collection and processing of datasets, identification of restriction layers, objectives and criteria, fuzzification of criteria and calculation of Fuzzy-AHP weights, calculation of socio-environmental and techno-economic suitability indices and identification of suitable sites for single and hybrid renewable energy systems.

The first step of the framework is the collection and processing of datasets, including Digital Elevation Model and Slope Map, Streams and Rivers Network and Active Fault Line Maps, Road and Electrical Network, Land Use, Land Cover Maps and Renewable Energy Potential maps. These datasets are used to identify restriction layers, socio-environmental and techno-economic objectives and criteria which will be used to evaluate the suitability of different sites for renewable energy systems.

In the next step, the criteria and objectives are fuzzified and the Fuzzy-AHP weights are calculated, which allows for a more comprehensive assessment of the criteria. The socio-environmental and techno-economic suitability indices are then calculated for single renewable energy systems, including solar, wind and hydro energy systems.

Finally, the suitable sites for single and hybrid renewable energy systems are identified based on the calculated indices, taking into account various restriction layers, socio-environmental and techno-economic criteria. The framework provides a comprehensive and objective method for identifying suitable sites for renewable energy systems, ensuring that all important factors are taken into consideration. Further details of each step of the proposed framework are discussed in the succeeding subsections.

2.1. Study Area

The case study area for this research is situated in Lanao del Norte, a southern province in the Philippines, as shown in Figure 2. The province was chosen as the study area due to its high solar energy and hydropower potential, as well as a lack of previous suitability analysis for hybrid renewable energy systems in the region [31]. The province has a total land area of 4159.94 square kilometers and a population of 722,902 as of 2020. It is composed of 22 municipalities and one highly urbanized city. The province is known for its rich natural resources, including various water bodies such as streams, rivers and waterfalls, as well as hilly topography stretching from the coast to high plateaus and mountains in the south. The climate in Lanao del Norte is classified as Type III, with no clearly defined maximum rain period and a relatively brief dry season of 1–3 months from March to May. As of 2020, electricity is provided to 73.89% of the residents through two distribution companies, Iligan Light and Power Incorporated (ILPI) and Lanao del Norte Electric Cooperative (LANECO); however, only 149,379 out of 202,151 households have access to electricity [32,33].

2.2. Collection and Processing of Datasets

To perform suitability analysis, significant datasets from various sources were assembled. The ASTER Global Digital Elevation Model (ASTER GDEM), which has a spatial resolution of 30 m, was used to construct the digital elevation model (DEM) for this study [34]. For the renewable energy resource data, Global Solar Atlas, an online platform operated by the Energy Sector Management Assistance Program, was used to extract the long-term (2007–2018) annual average of global horizontal irradiation (GHI) in kilowatt-hour per square meter [35]. The wind speed data that were used for the analysis were extracted from the Global Wind Atlas [36]. Furthermore, the TIF raster format was used for both wind speed and GHI data. The Phil-LiDAR 2 tool was used to calculate the province’s hydropower resource data [37]. Phil-LiDAR 2 used a run-of-river setup with a minimum head of 20 m and a penstock length of 100 m to perform this resource assessment.

Land use, hazard maps, land cover and local political boundary vector data were collected using PhilGIS [38], while hazard maps were obtained from the Philippine Institute of Volcanology and Seismology (PHIVOLCS) [39]. Data on road networks, rivers, lakes and population centers were extracted from OpenStreetMap [40] while the data on transmission and distribution networks were from the Department of Energy (DOE) of the Philippines [41]. Both raster and vector datasets utilize the GCS _WGS _1984 geographic coordinate system. Figure 3 and Figure 4 show the input raster and vector layers used for the suitability analysis. Furthermore, the study area was divided into mesh grids of 300 m by 300 m, with each grid representing a separate potential installation site for single (hydro, solar and wind) and hybrid (wind–solar and hybrid hydro–solar) energy systems. The mesh grid size selected allows for the consolidation of individual cells into a land area large enough to support the development of a specific plant.

2.3. Identification of Restriction Layers, Socio-Environmental and Techno-Economic Objectives and Criteria

Suitability analysis of single and hybrid renewable energy systems requires the identification of different social, environmental, technical and economic restrictions, objectives and criteria. Prior to the selection of these restrictions, objectives and criteria, existing laws and regulations regarding the installation of renewable energy facilities such as wind farms, solar-PV plants and small run-of-river hydropower plants in the Philippines and other previously published related research from other countries were examined and considered. Experts in renewable energy technology and microgrid design and installation from academe and industry were also interviewed to filter out and narrow down the set of restrictions, objectives and criteria that were used in the study.

Table 2. Restriction layers for the suitability analysis of hybrid renewable energy systems.

Restriction Layer	Criteria	References
R01—Distance from active fault kines	Less than 100 m	[42,43]
R02—Distance from protected areas and ancestral domains	Less than 1 km	[44,45,46]
R03—Distance from urban areas	Less than 2 km	[18,47]
R04—Distance from streams/rivers *	Greater than 2 km	[24,28]

* This restriction layer is only applied for the suitability analysis of hydropower plants and hybrid hydro–solar energy systems.

shows the restriction layers used for the site selection and suitability analysis. For example, R01, which is the layer containing the active fault lines in the study area, was given a buffer distance of 100 m to be avoided in the analysis based on the guidelines from PHILVOLCS. Other prohibited areas, such as ancestral domains and protected areas in R02, were given a 1 km buffer. All of the restriction layers, with the exception of R04, were applied to the suitability analysis and site selection of wind, solar and hydro energy systems. These buffered, restricted areas were given a Boolean value of “0” so they can be excluded from the site selection. For R04, areas that were more than 2 km away from the streams and rivers were excluded from the land suitability analysis and from finding areas that are suitable for hydro energy and hybrid hydro–solar systems.

Socio-environmental objectives and criteria for wind, solar and hydro energy systems derived from legislation and previous studies are shown in

Table 3. Socio-environmental suitability objectives and criteria for construction of wind energy facilities.

Socio-Environmental Suitability Objectives	Criteria	References
C01—Distance from airports	Greater than 3 km	[18,21,29]
C02—Distance from protected and conservation areas	Greater than 2 km	[17,18,30]
C03—Distance from urban areas	Greater than 2 km	[22,29,48]
C04—Distance from rural settlements	Greater than 1 km	[18,22,30]
C05—Distance from coastlines	Greater than 400 m	[22,29,48]

,

Table 4. Socio-environmental suitability objectives and criteria for construction of solar energy facilities.

Socio-Environmental Suitability Objectives	Criteria	References
C06—Distance from coastline	Greater than 100 m	[16,49]
C07—Distance from airports	Greater than 3 km	[16,49]
C08—Distance from lakes and wetlands	Greater than 2.5 km	[16,23,50]
C09—Distance from agricultural areas	Greater than 1 km	[19,23,26]

and

Table 5. Socio-environmental suitability objectives and criteria for construction of hydropower facilities.

Socio-Environmental Suitability Objectives	Criteria	References
C10—Distance from protected and conservation areas	Greater than 1 km	[27,51,52]
C11—Distance from urban areas	Greater than 3 km	[25,27]
C12—Distance from agricultural areas	Greater than 1 km	[25,27,52]
C13—Distance from residential areas	Greater than 1 km	[20,27,51,52]

, respectively. These lists of objectives and criteria are the combined social and environmental factors that were considered for the construction of renewable energy systems. For example, in C01, which is the “distance from airports” criteria, previous studies dictate that, pertaining to flight protection, shiny structures such as PV farms are prohibited in the first 3 km zone. Although they have some similarities, the socio-environmental objectives for solar energy systems have some differences from those for wind energy systems. Philippine renewable energy regulations, for example, permit the building of wind turbines near agricultural areas. Solar power plants, on the other hand, necessitate vast installation areas. This means that the construction of solar power plants would result in the loss of large areas for agriculture.

When determining where to construct and develop hybrid renewable energy systems, the techno-economic feasibility of wind, solar and hydropower projects must be considered in addition to socio-environmental concerns. For renewable energy projects to have feasible operation, technical and economic factors should be considered prior to development and installation. These two types of factors were fused and termed “Techno-Economic Objectives” in this study.

Table 6. Techno-economic suitability objectives and criteria for construction of wind farms.

Techno-Economic Suitability Objectives	Criteria	References
C14—Wind Speed	Greater than 4.5 m/s	[22,29,48]
C15—Slope	Lesser than 30%	[17,22,29,48]
C16—Distance from transmission lines	Lesser than 10 km	[16,29,48]
C17—Distance from main roads	Lesser than 10 km	[16,22,29,48]

,

Table 7. Techno-economic suitability objectives and criteria for construction of solar energy facilties.

Techno-Economic Suitability Objectives	Criteria	References
C18—Solar energy generation	Greater than 4 kWh/m${}^2$ (yearly)	[16,19,26,53]
C19—Slope	Lesser than 3%	[23,53,54,55]
C20—Distance from transmission lines	Lesser than 10 km	[23,26,53,55]
C21—Distance from main and minor roads	Lesser than 10 km	[19,53,55,56]

and

Table 8. Techno-economic suitability objectives and criteria for construction of hydropower facilities.

Techno-Economic Suitability Objectives	Criteria	References
C22—Slope	Greater than 2°	[27,51,57]
C23—Distance from transmission network	Lesser than 20 km	[25,27,57,58]
C24—Distance from main and minor roads	Lesser than 10 km	[25,58,59]
C25—Distance from site with identified energy potential	Lesser than 3 km	[25,27,57,58]

list all the techno-economic objectives and criteria for wind, solar and hydro energy systems, respectively.

The availability of suitable wind speed and solar energy generation within the study area is one of the most important techno-economic suitability objectives since techno-economic feasibility is strongly reliant on energy potential. Furthermore, previous studies identify adequate hydro, solar and wind energy potential as highly important, if not the most important, criteria to be considered in the site selection and suitability analysis. Areas must have average wind speed greater than 4.5 m per second to be suitable for installation wind farms while for solar-PV farms, suitable areas must have a yearly solar energy generation based on global horizontal irradiance (GHI) greater than 4 kWh/m${}^2$. Additionally, distances from existing roads and transmission networks were considered for the analysis. Renewable energy project developers prefer sites that are closer to roads and transmission networks since longer distances incur higher construction and project development costs. For solar-PV farms, steep slopes are avoided wherever possible in order to cut down on construction costs and minimize the negative effect that development has on the local geomorphology.

2.4. Fuzzification of Socio-Environmental and Techno-Economic Criteria and Calculation of Fuzzy-AHP Weights

Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8 show that socio-environmental and techno-economic suitability objectives involve fuzziness and ambiguity (criteria values in range instead of crisp discrete values), which are common features of many complex decision-making problems. By expressing previously socio-environmental and techno-economic objectives and criteria characteristics for wind, solar and hydro energy systems as fuzzy sets, Fuzzy Set Theory is utilized to deal with complexity and ambiguity. The process of transforming raw data from identified socio-environmental and techno-economic suitability objectives into fuzzy membership values is referred to as “fuzzification.” This conversion occurs on the basis of a decisively specified fuzzy membership function for each component. A fuzzy set can be described mathematically as follows [60]:

$$FS=\left\{X,{\mu }_{FS}\right\}\mathrm{for}\mathrm{each}x\in X$$

(1)

where X is a group of objects represented by the symbol x. The membership function (MF) that specifies the degree of membership of X in a fuzzy set is represented by ${\mu }_A$. The MF can take on any value between 1 and 0, inclusive of both of those values, for any given $FS$, where having an ${\mu }_{FS}$ value of 0 indicates that the value x does not belong to $FS$ and having an ${\mu }_{FS}$ value of 1 indicates that it belongs totally to $FS$. Alternatively, a value of ${\mu }_{FS}$ that is greater than 0 but less than 1 suggests that x is related to A to some degree. In the event when $X=\left\{x_1,x_2,...,x_n\right\}$, the previously stated equation can be rewritten as follows:

$$FS=\left\{\left[x_1,{\mu }_{FS}\left(x_1\right)\right]+\left[x_2,{\mu }_{FS}\left(x_2\right)\right]+\dots +\left[x_n,{\mu }_{FS}\left(x_n\right)\right]\right\}$$

(2)

Figure 5 shows the different types of fuzzy membership (FM) functions used in fuzzification, while the summary of the fuzzy membership functions for each criteria selected in this study are shown in

Table 9. Summary of FM function type for different socio-environmental and techno-economic suitability objectives and criteria.

Suitability Criteria	FM Type	a-Value	b-Value
Wind (Socio-Environmental)
C01—Distance from airports	Linear-Ascending	3 km	6 km
C02—Distance from protected and conservation areas	Linear-Ascending	2 km	8 km
C03—Distance from urban areas	Linear-Ascending	2 km	6 km
C04—Distance from rural settlements	Linear-Ascending	1 km	7 km
C05—Distance from coastlines	Linear-Ascending	400 m	1 km
Solar (Socio-Environmental)
C06—Distance from coastline	Linear-Ascending	100 m	1 km
C07—Distance from airports	Linear-Ascending	3 km	6 km
C08—Distance from lakes and wetlands	Linear-Ascending	2.5 km	6 km
C09—Distance from agricultural areas	Linear-Ascending	1 km	2 km
Hydropower (Socio-Environmental)
C10—Distance from protected and conservation areas	Linear-Ascending	1 km	6 km
C11—Distance from urban areas	Linear-Ascending	3 km	8 km
C12—Distance from agricultural areas	Linear-Ascending	1 km	5 km
C13—Distance from residential areas	Linear-Ascending	1 km	5 km
Wind (Techno-Economic)
C14—Wind Speed	Linear-Ascending	4.5 m/s	8 m/s
C15—Slope	Linear-Descending	10%	30%
C16—Distance from transmission lines	Linear-Descending	5 km	10 km
C17—Distance from main roads	Linear-Descending	2 km	10 km
Solar (Techno-Economic)
C18—Solar energy generation	Linear-Ascending	4 kWh/m${}^2$	5 kWh/m${}^2$
C19—Slope	Linear-Descending	1%	3%
C20—Distance from transmission lines	Linear-Descending	3 km	10 km
C21—Distance from urban areas	Linear-Descending	3 km	10 km
Hydropower (Techno-Economic)
C22—Slope	Linear-Ascending	2°	10°
C23—Distance from transmission network	Linear-Descending	3 km	10 km
C24—Distance from major and minor roads	Linear-Descending	3 km	10 km
C25—Distance from site with identified energy potential	Linear-Descending	500 m	3 km

. In simple terms, Equations (1) and (2) mean that for every x that belongs to the set X, there is an MF that describes the degree of ownership of x in $FS$. For socio-environmental and techno-economic suitability objectives and criteria with increasing values for wind, solar and hydro energy systems, the following Linear-Ascending MF was used.

$${\mu }_{FS}\left(x\right)=f\left(x\right)=\left\{\begin{array}{cc}0& x\le a\\ x-a/b-a& a<x<b\\ 1& x\ge b\end{array}\right\}$$

(3)

where x is the input data and a, b are the limit values.

For socio-environmental and techno-economic suitability objectives and criteria with decreasing values, the following Linear-Descending MF was used:

$${\mu }_{FS}\left(x\right)=f\left(x\right)=\left\{\begin{array}{cc}0& x\le a\\ b-x/b-a& a<x<b\\ 1& x\ge b\end{array}\right\}$$

(4)

Fuzzy-AHP

After the fuzzification of different socio-environmental and techno-economic suitability criteria, Fuzzy-AHP was used to determine the weights of each criterion, which are then used to calculate different suitability indices. Fuzzy-AHP, which was first proposed by van Laarhoven [61] and further developed by Chang [62], is a technique for making decisions based on multiple criteria that combines Analytic Hierarchy Process (AHP) [63] with Fuzzy Sets. This approach is used to establish the relative importance of various criteria and alternatives. In contrast to AHP, Fuzzy-AHP replaces precise numbers with fuzzy numbers that reflect linguistic expressions. This tolerates ambiguous judgments by giving membership degrees to specific numbers in order to reflect the extent to which these numbers belong to an expression [64].

Table 10. Saaty’s Scale for Decision-Making using Fuzzy-AHP.

Linguistic Scales for Importance	Triangular Fuzzy Number (TFN)	Triangular Fuzzy Reciprocal Numbers
Equally Important (EI)	(1,1,1)	(1,1,1)
Intermediate 1 (IM1)	(1,2,3)	(1/3,1/2,1)
Moderately Important (MI)	(2,3,4)	(1/4,1/3,1/2)
Intermediate 2 (IM2)	(3,4,5)	(1/5, 1/4, 1/3)
Important (I)	(4,5,6)	(1/6,1/5,1/4)
Intermediate 3 (IM3)	(5,6,7)	(1/7,1/6/1/5)
Very Important (VI)	(6,7,8)	(1/8,1/7,1/6)
Intermediate 4 (IM4)	(7,8,9)	(1/9,1/8,1/7)
Absolutely Important (AI)	(9,9,9)	(1/9,1/9,1/9)

shows Saaty’s Scale for Decision-Making using Fuzzy-AHP [65].

Figure 6 shows the overall process of the calculation of criteria weights using Fuzzy-AHP. First, the problem is structured in a hierarchy. For example, to determine the socio-environmental suitability index of wind energy systems, criteria C01, C02, C03, C04 and C05 were considered in the decision-making process shown in Figure 7. These criteria were used to create a comparison matrix.

After creating the hierarchical structure of the problem, a fuzzy pairwise comparison matrix based on AHP is established [63]. The decision-maker judges or decides the relative importance between each criteria based on the Triangular Fuzzy Numbers (TFNs) in Table 10. Mathematically, the decision-maker’s quantified judgements are expressed in an n × n matrix as follows:

$$A={\left(a_{ij}\right)}_{nxn}=\left(\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ ·& ·& \cdots & ·\\ ·& ·& \cdots & ·\\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right)$$

(5)

$$a_{ij}=\left(l_{ij},m_{ij},u_{ij}\right)=a_{ji}^{-1}=\left(\frac{1}{u_{ji}},\frac{1}{m_{ji}},\frac{1}{l_{ji}}\right)$$

(6)

where n is the number of criteria, $i,j=1,\cdots ,n$ and $i\ne j$.

After constructing the pairwise comparison matrix using TFNs, Chang’s extent analysis [62] approach is utilized to eliminate uncertainty. Based on this approach, fuzzy synthetic extent $S_i$ with respect to the ith criterion is computed using the following formula:

$$S_i=\left(\frac{{\sum }_{j=1}^n{l}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^n{u}_{ij}},\frac{{\sum }_{j=1}^n{m}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^n{m}_{ij}},\frac{{\sum }_{j=1}^n{u}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^n{l}_{ij}}\right)$$

(7)

After calculating the synthetic value, the degree of possibility is then determined for each convex TFN. Supposing $M_2=(l_2,m_2,u_2)$ and $M_1=(l_1,m_1,u_1)$ are two TFNs as shown in Figure 8, the degree of possibility that $M_1$ is greater than or equal to $M_1$ which is represented by $V(M_1\ge M_2)$ is defined as follows:

$$V\left(M_2\ge M_1\right)=sup\left[min\left({\mu }_{M1}\left(x\right),{\mu }_{M2}\left(y\right)\right)\right],y\ge x$$

(8)

$$V(M_1\ge M_2)=\left\{\begin{array}{c}1\mathrm{if}{m}_1\ge m_2\hfill \\ 0\mathrm{if}{l}_2\ge u_1\hfill \\ \frac{l_2-u_1}{\left(m_1-u_1\right)-\left(m_2-l_2\right)}\mathrm{Otherwise}\hfill \end{array}\right.$$

(9)

where x and y are the x- and y-axis values of the membership function of each criterion, respectively, and d is the ordinate of D, which is the highest intersection between $M_1$ and $M_2$. The values for $m_1$ and $m_2$ are required for the comparison of the two TFNs. In addition, the following equations are used to calculate the degree of possibility for a convex fuzzy number to be greater than k convex fuzzy numbers:

$$\begin{array}{cc}\hfill & V(M\ge M_1,M_2,M_3,\dots ,M_k)\hfill \\ \hfill & =V[(M\ge M_1)\mathrm{and}(M\ge M_2)\mathrm{and}(M\ge M_3)\mathrm{and}\hfill \\ \hfill & \cdots \mathrm{and}(M\ge M_k)]\hfill \end{array}$$

(10)

$$V(M\ge M_1,M_2,M_3,\dots ,M_k)=minV(M\left.\ge M_i\right),(i=1,2,3,\dots ,k)$$

(11)

With the assumption that $d^{\prime }\left(a_i\right)=minV(M_i\ge M_k)$ for $k=1,2,3\cdots n;k\ne i$, the weight vector is obtained using the following equation:

$$W^{\prime }={\left(d^{\prime }\left(A_1\right),d^{\prime }\left(A_2\right),d^{\prime }\left(A_3\right),\dots ,d^{\prime }\left(A_n\right)\right)}^T$$

(12)

where $A_i(i=1,2,3\dots n)$ are n elements. The weight vector is then normalized to obtain the non-fuzzy weight using the following equation:

$$W={\left(d\left(A_1\right),d\left(A_2\right),d\left(A_3\right),\dots ,d\left(A_n\right)\right)}^T$$

(13)

The Fuzzy-AHP also includes mathematical measures that can be used to determine whether or not judgments are consistent. A consistency ratio (CR) can be calculated using the properties of reciprocal matrices. The largest eigenvalue, ${\lambda }_{max}$, is always greater or equal to the number of rows or columns, n, in a reciprocal matrix. If there are no inconsistencies in a pairwise comparison, ${\lambda }_{max}=n$ is used. The computed ${\lambda }_{max}$ value will be closer to n if the comparisons are more consistent.

$$\left(A-{\lambda }_{max}I\right)\times W=0$$

(14)

The inconsistencies of pairwise comparisons are measured via a consistency index, CI, which is written as:

$$CI=\left(\frac{{\lambda }_{max}-n}{n-1}\right)$$

(15)

The consistency ratio (CR) is then used to determine the degree to which the pairwise comparisons are coherent shown by the equation:

$$CR=\frac{CI}{RI}$$

(16)

where RI is the random consistency index, which is the average consistency index of the randomly produced comparisons. As a rule of thumb, a CR value of 10% or less is regarded as acceptable. Otherwise, a re-evaluation of the comparison matrix must be carried out [63].

2.5. Calculation of Socio-Environmental and Techno-Economic Feasibility Indices for Single Renewable Energy Systems

Several indices were developed to determine the socio-environmental and techno-economic suitability for single renewable energy systems. The socio-environmental suitability indices for wind, solar and hydro energy systems are Socio-Environmental Suitability Index for Wind Energy (SEI-W), Socio-Environmental Suitability Index for Solar Energy (SEI-S) and Socio-Environmental Suitability Index for Hydro Energy (SEI-H), while the techno-economic suitability indices are Techno-Economic Suitability Index for Wind Energy (TEI-W), Techno-Economic Suitability Index for Solar Energy (TEI-S) and Techno-Economic Suitability Index for Hydro Energy (TEI-H). The different socio-environmental and techno-economic suitability indices were calculated using Weighted Linear Combination (WLC) based on their corresponding fuzzified objectives and criteria from Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8 and Fuzzy-AHP weights using the following equation:

$$SI=\sum w_i{C}_i$$

(17)

where $w_i$ is the Fuzzy-AHP weight of the ith fuzzified criterion $C_i$. For example, in the case shown in Figure 7, the SEI-W is calculated using the fuzzified criteria C01–C05 and their corresponding Fuzzy-AHP weights.

2.6. Identification of Suitable Sites for Individual Renewable Energy Systems

In a GIS environment, spatial analysis is used to find sites that are suitable for the construction and development of single renewable energy systems. In this study, ArcGIS was used to carry out the spatial analysis. Separate layers in the GIS environment were generated to reflect each criterion associated with socio-environmental and techno-economic suitability objectives, as previously discussed. After calculating the different socio-environmental and techno-economic indices, the overall suitability indices were determined and mapped using the fuzzy AND operator, as shown in Figure 9. The overall suitability indices for wind, solar and hydro energy systems are referred to as the Overall Suitability Index for Wind Energy (OSI-W), the Overall Suitability Index for Solar Energy (OSI-S) and the Overall Suitability Index for Hydro Energy (OSI-H), respectively.

The suitable sites for single renewable systems are then identified using the site selection rules shown in

Table 11. Site selection rules for single and hybrid renewable energy systems based on Fuzzy AND operator.

Suitability Index Value—SI1 (SEI-W/SEI-S/SEI-H)	Suitability Index Value—SI2 (TEI-W/TEI-W/WPI)	Resulting Index Value Individual (OSI-W/OSI-S/OSI-H) Hybrid (OSI-WS/OSI-HS)	Decision
0 ≤ $S{I}_1$ < 0.5	0 ≤ $S{I}_2$ < 0.5	0	Reject (Unsuitable)
0.5 ≤ $S{I}_1$ < 1.0	0 ≤ $S{I}_2$ < 0.5	0	Reject (Unsuitable)
0 ≤ $S{I}_1$ ≤ 0.5	0.5 ≤ $S{I}_2$ ≤ 1.0	0	Reject (Unsuitable)
0.5 ≤ $S{I}_1$ ≤ 1.0	0.5 ≤ $S{I}_2$ ≤ 1.0	minimum[$S{I}_1$,$S{I}_2$]	Accept (Suitable)

. The first and second columns in Table 10 reflect the two different suitability indices used to calculate the overall suitability index and determine the suitable site. For example, the first and second columns for wind energy systems are SEI-W and TEI-W, respectively. Suitable sites for wind energy systems are those with suitability indices that have values of at least 0.5 for both SEI-W and TEI-W. For solar and hydro energy systems, the same technique is used to establish the overall suitability index and the suitable site maps.

2.7. Identification of Suitable Sites for Hybrid Renewable Energy Systems

To identify sites that are suitable for the construction and development of hybrid renewable energy systems, overall suitability indices for individual renewable energy systems are overlaid using the fuzzy AND operator and the same site selection rules as in Table 11 are applied. Figure 10 shows the overall flowchart for determining sites for hybrid renewable energy systems. As shown in this figure, the overall suitability indices for hybrid wind–solar and hybrid hydro–solar energy systems are OSI-WS and OSI-HS, respectively. The map for OSI-WS is created by using fuzzy AND on OSI-W and OSI-S. The same process is used in creating the map for OSI-HS using OSI-S and OSI-H.

3. Results and Discussion

This section discusses the details of the results of the performed suitability analysis.

3.1. Fuzzy-AHP Weights

The weights for each socio-environmental and techno-economic criteria which are calculated using Fuzzy-AHP are summarized in

Table 12. Summary of calculated socio-environmental criteria weights using Fuzzy-AHP.

Suitability Criteria	Calculated Weight
Wind (Socio-Environmental)
C01—Distance from airports	4.60%
C02—Distance from protected and conservation areas	37.83%
C03—Distance from urban areas	9.82%
C04—Distance from rural settlements	13.14%
C05—Distance from coastlines	34.61%
Solar (Socio-Environmental)
C06—Distance from coastline	10.31%
C07—Distance from airports	18.75%
C08—Distance from lakes and wetlands	23.98%
C09—Distance from agricultural areas	46.96%
Hydro (Socio-Environmental)
C10—Distance from protected and conservation areas	45.80%
C11—Distance from urban areas	8.83%
C12—Distance from agricultural areas to	28.31%
C13—Distance from residential areas	17.06%

and

Table 13. Summary of calculated techno-economic criteria weights using Fuzzy-AHP.

Suitability Criteria	Calculated Weight
Wind (Techno-Economic)
C14—Wind speed	52.16%
C15—Slope	20.91%
C16—Distance from transmission lines	19.00%
C17—Distance from main roads	7.93%
Solar (Techno-Economic)
C18—Solar energy generation	51.17%
C19—Slope	25.91%
C20—Distance from transmission lines	17.08%
C21—Distance from urban areas	5.84%
Hydro (Techno-Economic)
C22—Slope	10.05%
C23—Distance from transmission network	17.06%
C24—Distance from major and minor roads	23.36%
C25—Distance from site with identified energy potential	49.53%

, respectively. Based on Table 12, criteria C02, C09 and C10 with weights of 37.83%, 46.96% and 45.80%, the most important factors for considering the socio-environmental suitability of wind, solar and hydro energy systems, respectively. Table 13 shows that the criteria that are considered the most important for determining techno-economic suitability of single renewable energy systems are the criteria associated with energy production. For solar energy systems, C18 which is the criterion associated with solar energy generation was given the most importance with a calculated weight of 51.17% while for wind and hydro energy systems the criteria C14 and C25 have the highest weights of 52.15% and 49.53%, respectively. In real-life practice, renewable energy project developers determine areas that have high energy potential first before considering other factors, which is a confirmation of the previous studies reviewed and discussed in the earlier section.

3.2. Suitable Sites for Wind, Solar and Hydro Energy Systems

Using the proposed framework shown in Figure 1, the overall suitability index and suitable site maps for wind, solar and hydro energy systems in the study area, Lanao del Norte, were created as shown in Figure 11 and Figure 12, respectively. As shown in these figures, the areas that are considered suitable for the construction and development of renewable energy systems have an overall suitability index of at least 0.5, with 1 as the highest value. Sites that are considered unsuitable have an overall suitability index value of less than 0.5, with 0 as the lowest value. These unsuitable sites include areas that are covered by the restriction layers and areas that have a high socio-environmental suitability index but low techno-economic suitability index and vice versa. The spatial analysis performed in ArcGIS for the site selection process is driven by the site selection rules shown in Table 11. For example, in determining sites that are suitable for solar energy systems, some areas that have a high socio-environmental suitability index are eliminated due to a low techno-economic suitability index or because they have inadequate energy potential.

The results of the site selection for single renewable energy systems are summarized in

Table 14. Summary of total suitable areas for the construction of renewable energy facilities for each municipality/city.

Municipality/City	Wind (km${}^2$)	Solar (km${}^2$)	Hydro (km${}^2$)	Wind–Solar (km${}^2$)	Hydro–Solar (km${}^2$)
Bacolod	0	46.89	43.84	0	37.23
Baloi	0	2.48	10.74	0	0
Baroy	4.13	50.36	5.09	2.97	4.60
Iligan City	0	333.56	316.31	0	203.26
Kapatagan	58.25	119.62	68.71	50.35	50.27
Kauswagan	0	40.74	30.39	0	30.34
Kolambugan	8.00	63.87	35.30	8.00	35.26
Lala	17.05	29.13	2.79	8.13	1.40
Linamon	0	17.13	7.38	0	7.38
Magsaysay	4.97	59.59	66.45	3.17	46.28
Maigo	0	75.22	77.66	0	44.07
Matungao	0	26.52	38.69	0	24.50
Munai	1.06	8.01	97.87	0	3.38
Nunungan	0	31.23	61.28	0	18.76
Pantao Ragat	0	8.29	47.08	0	7.01
Pantar	0	5.50	2.07	0	0.61
Poona Piagapo	0	43.22	81.60	0	41.58
Salvador	0	19.94	0.90	0	0.90
Sapad	3.25	10.97	7.44	1.26	2.01
Sultan Naga Dimaporo	49.29	123.63	43.61	47.48	34.61
Tagoloan	0	1.90	21.62	0	1.90
Tangcal	4.46	9.35	104.38	0	6.40
Tubod	5.47	103.59	34.79	4.84	27.26
Total	155.93	1230.73	1206.00	126.20	629.02

. The table shows the total area suitable for wind, solar and hydro energy systems (in km${}^2$) for each municipality in Lanao del Norte. Furthermore, based on the results, the province has total areas of 155.93 km${}^2$, 1230 km${}^2$ and 1206 km${}^2$ for wind, solar and hydro energy systems, respectively. Most of the areas which have the highest potential for construction of solar power plants are located near the coastline. This is due to solar energy potential and suitability factors having the highest values in these areas. Municipalities near Iligan Bay also have the highest total number of potential areas with high suitability for solar plant construction. Out of the 23 municipalities, only 10 have areas suitable for wind farms. Since most of the areas in the province have wind speeds lower than 5 m/s, only a few municipalities have areas that are suitable for construction of the facilities. Municipalities with the highest potential areas are either located near Ilana Bay or at the part of Iligan Bay where the wind funneling effect occurs. For suitable hydro energy potential sites in the province, Iligan City has the highest number of suitable sites. This is because Mandulog River and Agus River, two of the major rivers in the province, flow through the city.

3.3. Suitable Sites for Hybrid Renewable Energy Systems

For hybrid wind–solar and hybrid hydro–solar energy systems, the overall suitability index and suitable site maps are shown in Figure 13. The suitable sites for hybrid renewable energy systems based on the Overall Suitability Index for Hybrid Wind–Solar Energy (OSI-WS) and Overall Suitability Index for Hybrid Hydro-Solar Energy (OSI-HS) were then selected using same site selection rule shown Table 11. Hybrid wind–hydro energy systems were not considered in this study since only a small area of the northwestern part of the province has an acceptable wind speed for wind farms and there are no suitable areas for the combined suitable wind energy and hydro energy. Based on the results of the suitability analysis, suitable sites for hybrid wind–solar energy systems are the areas that have a high overall suitability index for wind energy since these areas also have a high overall suitability index for solar energy. Furthermore, as shown in Table 14, the total area that is suitable for hybrid wind–solar and hybrid hydro–solar are 126.60 km${}^2$ and 629.02 km${}^2$, respectively. The installation and development of hybrid renewable energy systems in these identified sites can aid in increasing the rural electrification of the province while addressing the issue of single renewable energy systems’ intermittency problems.

3.4. Model Validation

To validate the results of the analysis, the locations of existing hydropower plants in the study area (Agus IV, Agus V, Agus VI and Agus VII Hydropower Plants) were compared to the identified suitable sites for hydropower or hydro energy systems as identified in the model’s suitability analysis, shown in Figure 11. This comparison was conducted using a combination of Google Maps and on-site surveys. The results, presented in Figure 14, indicate a high degree of correlation between the locations of existing hydropower plants and the model’s identified suitable sites, providing strong evidence for the accuracy of the model’s suitability analysis and site selection for hydropower plants. It is worth noting that validation for suitable sites for solar, wind and hybrid renewable energy systems was not conducted in this study, as there were no existing plants of those types in the area at the time of analysis.

3.5. Sensitivity Analysis

A sensitivity analysis was performed to evaluate the effect of using Fuzzy-AHP weights on the results of the suitability analysis for hybrid renewable energy systems (wind–solar and hydro–solar). In this analysis, the results of the suitability analysis were compared using equal weights for all socio-environmental and techno-economic objectives and criteria to the results obtained using the Fuzzy-AHP weights calculated in the previous section.

For the suitability analysis without Fuzzy-AHP, equal weights were assigned to all objectives and criteria. These equal weights were then used to calculate the different suitability indices (SEI-W, SEI-S, SEI-H, TEI-W, TEI-S, TEI-H). Using the same spatial analysis and site selection rule from the previous section, the different suitable sites for single and hybrid renewable energy systems were then selected.

The results of the sensitivity analysis (Figure 15 for hybrid hydro–solar and wind–solar systems) indicate that the use of Fuzzy-AHP weights in the suitability analysis leads to a more accurate identification of suitable sites for renewable energy systems. As shown in the figure, there is an increase in the number of identified suitable sites when Fuzzy-AHP weights were not applied in the analysis; however, some of these areas have low energy production and may not be preferred by experts in the field.

Table 15. Total suitable areas using Fuzzy-AHP Weights and Equal Weights.

Renewable Energy System Type	Suitable Area Fuzzy-AHP Weights (km2)	Suitable Area Equal Weights (km2)
Wind	155.93	1762.66
Solar	1230.73	1149.14
Hydro	1206.00	1610.22
Wind–Solar	126.20	1116.76
Hydro-Solar	629.02	750.12

summarizes the total areas identified using equal weights and Fuzzy-AHP weights. The sensitivity analysis highlights the importance of using Fuzzy-AHP weights in the suitability analysis of hybrid renewable energy systems as it allows for a more comprehensive evaluation of the different objectives and criteria and results in a more accurate identification of suitable sites.

3.6. Managerial Implications

Based on the results of the study, the following are key managerial implications for the construction and development of renewable energy systems in Lanao del Norte:

1. The use of the proposed framework for identifying suitable sites for renewable energy systems can aid decision-makers in the Philippine government and energy industry in achieving their goals of diversifying the energy mix, increasing rural electrification using local renewable resources, mitigating the negative impacts of climate change and attaining sustainable energy development.

2. The results of the study provide detailed potential sites for single renewable energy plants such as hydropower, wind and solar-PV systems, as well as potential locations for hybrid hydro–solar and hybrid wind–solar energy systems. These results can be used by investors and developers to identify areas with high potential for renewable energy development and prioritize their investments accordingly.

3. The study highlights the importance of considering socio-environmental and techno-economic factors when identifying suitable sites for renewable energy systems. By taking into account a wide range of factors, the proposed framework ensures that all important considerations are taken into account when selecting sites for renewable energy development, which can help to minimize negative impacts and ensure the long-term sustainability of the projects.

4. The results of the study demonstrate that municipalities near Iligan Bay have the highest potential for solar energy development, while municipalities with the highest potential for wind energy are either located near Ilana Bay or at the part of Iligan Bay where the wind funneling effect occurs. Furthermore, Iligan City has the highest number of suitable sites for hydro energy systems, which is due to the presence of major rivers such as Mandulog River and Agus River.

5. By identifying suitable areas for renewable energy development, the proposed framework can also help to identify areas where other forms of development may be less appropriate, such as in environmentally sensitive or culturally significant areas. This can aid in sustainable land use planning and decision-making by government and private sectors.

4. Conclusions

In conclusion, this study proposed an integrated framework for identifying suitable sites for the construction and development of single and hybrid renewable energy systems in Lanao del Norte, Philippines. The framework utilizes Fuzzy-AHP and GIS to consider various socio-environmental and techno-economic objectives and criteria in determining the suitability of different areas. The results of the study provide detailed potential sites for single renewable energy plants such as hydropower, wind and solar-PV systems, as well as potential locations for hybrid hydro–solar and hybrid wind–solar energy systems. The strategy for selecting sites for hybrid renewable energy systems that has been developed can be of assistance to decision-makers and future investors in choosing areas that are feasible from a techno-economic and socio-environmental standpoint.

However, it is important to note that this study had some limitations. The data used in the analysis are based on information that was available during the carrying out of this research and thus may not be up to date. Additionally, the study only considered the province of Lanao del Norte and the results may not be generalizable to other regions.

Future works that can be done include the application of the proposed framework and objectives and criteria in other study areas with similar geographic and environmental limitations and criteria for suitability. Additionally, other types of single and hybrid renewable energy systems can also be considered in the analysis. Furthermore, additional objectives and criteria may also be added in the analysis based on the availability of datasets to further improve results of the suitability analysis and site selection.