염상국
(Sang-Guk Yum)
1†
아드히카리마니크
(Manik Das Adhikari)
2
-
정회원․교신저자․강릉원주대학교 건설환경공학과 교수
(Corresponding Author․Gangneung-Wonju National University․skyeom0401@gwnu.ac.kr)
-
강릉원주대학교 건설환경공학과 연구원
(Gangneung-Wonju National University․rsgis.manik@gmail.com)
Copyright © 2021 by the Korean Society of Civil Engineers
키워드
스마트수소에너지, 공간 분석, GIS, 다기준의사결정분, 강원도석
Key words
Smart hydrogen energy, Spatial data, GIS, MCDA, Gangwon, South Korea
1. Introduction
The increasing global demand for clean and sustainable energy has led to a significant
increase in the use of hydrogen energy as a source of energy. However, implementing
a smart hydrogen energy plant (SHEP) requires careful location planning to ensure
optimal functioning and minimal environmental impact. It is critical to identify suitable
locations for hydrogen production and distribution in order to support the growth
of hydrogen energy as a clean energy source. In this study, we utilized GIS-based
site suitability analysis to determine the most suitable location for a smart hydrogen
energy plant in the Gangwon-Do region of South Korea.
Smart hydrogen is a key source of next-generation renewable energy, which is critical
for fuel cells and hydrogen energy in the automotive industry (Guleria and Bajaj, 2020; Kim et al., 2020; Wang et al., 2022). It is becoming increasingly viable for nations looking to decarbonize their energy
sector to use smart hydrogen energy generated by electrolysis from renewable energy
sources like wind, solar, and hydropower (Balta and Balta, 2022; Rezaei-Shouroki et al., 2017). To revitalize the hydrogen economy by 2030, the Korean government plans to build
safety and environmental infrastructure for green hydrogen technology. The government
intends for 40% of local governments to use hydrogen-powered vehicles by 2040, including
12,000 hydrogen buses and 825,000 hydrogen vehicles (Bergenson, 2021). The hydrogen market is projected to reach 70 trillion won by 2050, according to
the government's hydrogen road map (Korea Herald, 2019). Consequently, the Korean government has proposed developing a green hydrogen production
industry in Gangwon Province based on renewable energy. Thus, an accurate assessment
of potential is required to select a safe environment and a suitable site for a hydrogen
energy plant. Researchers and decision-makers have increasingly focused on hydrogen
energy in the last decade, particularly due to concerns about the site selection of
renewable energy sources (Alhamwi et al., 2017; Chrysochoidis-Antsos et al., 2020; Jung et al., 2019; Kabir and Sumi, 2014; Karipoglu et al., 2022). In the present study, we examine the site selection issues for smart hydrogen energy
production facilities in the presence of hydrogen generation potential and climatic
conditions, topographic and environmental problems, and extreme natural catastrophes.
It suggests a structured model for selecting the most appropriate SHEP site.
Smart hydrogen energy plant location planning is a complex process that involves multiple
criteria, such as the availability of renewable energy sources, the proximity to potential
customers, the transportation infrastructure, and the environmental impact. MCDA allows
decision-makers to consider all of these criteria in a structured and systematic way
and to assign relative importance to each criterion based on their priorities. On
the other hand, GIS-based MCDA is a popular site suitability analysis approach, allowing
decision-makers to consider multiple factors and criteria when selecting the most
suitable site (Scott et al., 2012). This approach involves developing a set of criteria and assigning weights to them
based on their relative importance in the decision-making process. Then, GIS-based
spatial analysis tools are used to map and analyze the spatial data related to each
criterion. The resulting maps are then combined using a weighted overlay analysis
to generate a final suitability map that indicates the most suitable location for
the proposed plant. Thus, GIS-based MCDA is a suitable method for site suitability
analysis for smart hydrogen energy plants because it allows spatial analysis, integration
of multiple data sources, weighting and ranking criteria, scenario analysis, handling
complex data, provides transparency and consistency and offers flexibility. In recent
years, there has been an increasing focus on GIS-based decision support systems for
hydrogen energy plant location planning (such as Ali et al., 2022; Karipoglu et al., 2022; Messaodi et al., 2019; Mrówczyńska et al., 2022; Noorollahi et al., 2022; Shorabeh et al., 2022; Vafaeipour et al., 2014; Zhou et al., 2022). For example, Ali et al. (2022) developed a GIS-based MCDA framework to identify suitable locations for solar-based
green hydrogen in southern Thailand. Wang et al. (2021) developed an MCDA to identify the optimal site for a solar photovoltaic (PV) power
plant in Taiwan. Kim and Cho (2023) developed a GIS-based decision support system to identify suitable locations for
hydrogen fueling stations in Korea. Subsequently, GIS-based multi-criteria spatial
decision support systems have shown great potential in aiding smart hydrogen energy
plant location planning.
The present work aims to determine a suitable location for smart hydrogen energy plants,
particularly solar and wind power plants. Spatial analysis tools like GIS have been
extensively used to analyze the potential sites for renewable energy sources like
solar, wind, hydropower, and biomass (such as Cradden et al., 2016; Mezaei et al., 2021; Pamučar et al., 2017; Yousefi et al., 2018; Yushchenko et al., 2018; Zhao et al., 2022). These methods are critical for developing a smart hydrogen sector and hydrogen energy
roadmap (Ali et al., 2022; Kim et al., 2020; Mostafaeipour et al., 2020; Rezaei et al., 2020; Tavana et al., 2017; Vafaeipour et al., 2014; Yousefi et al., 2018). Several studies have examined potential hydrogen station locations based on economic,
technical, environmental, social, and geographic factors (Ali et al., 2022; Dagdougui et al., 2011; Gye et al., 2019; Lin et al., 2020; Messaoudi et al., 2019; Rezaei et al., 2021; Yousefi et al., 2018). However, the influence of natural disasters such as landslides, coastal hazards,
typhoons and forest fires play a crucial role in developing smart hydrogen energy
plants. In the present study, to assess the viability of a smart hydrogen energy plant
in the Gangwon-do region, we used a geographic information system (GIS) and the MCDA
method known as the analytical hierarchy process (AHP). We determined 20 factors,
i.e., horizontal irradiation, direct normal irradiation, potential photovoltaic electricity
production, mean wind speed, mean wind power density, average air temperature, altitude,
slope, distance from public service/facilities, distance from sub-stations, distance
from transmission lines, distance from road networks, distance from the residential
area, distance from water bodies, population density, landuse/land cover, distance
from the forest fire, distance from landslide potential, distance from shorelines,
and distance from typhoon track for selecting a suitable site for a smart hydrogen
energy plant. Following that, an AHP pair-wise comparison matrix was created. Finally,
a GIS-based spatial analysis tool was used to determine the suitable locations for
SHEP based on each factor's weights and normalized ranks. As a result, the Gangwon-do
region was divided into extremely suitable, high, moderate, low and unlikely/unsuitable
areas for the smart hydrogen energy plant.
2. Data and Methods
The Korean government intends to build infrastructure for green hydrogen technology
that is both safe and environ- mentally friendly to revitalize the hydrogen sector
by 2030. Consequently, the government has proposed a green hydrogen production industry
linked to renewable energy in Gangwon Province. In the present study, developing a
green hydrogen production system has considered renewable energy sources such as solar
and wind power and other geospatial data. Therefore, MCDA-based spatial analysis techniques
evolved into a cost-effective, quick, and dependable tool for assessing the potential
of renewable energy sources (Martínez-Gordón et al., 2021). Several models and methodologies were developed to plan and construct the future
hydrogen supply chain, such as evaluation plans, GIS-based decision support systems,
and mathematical optimization techniques (Messaoudi et al., 2019; Karipoglu et al., 2022). However, due to its analytical strength, the multi-criteria approach in a geographical
information system was widely used for the SHEP project. Several researchers were
conducted on this topic to determine the suitable locations for solar, wind, or hybrid
systems (Ali et al., 2022; Atwongyeire et al., 2022; Jung et al., 2019; Rezaei et al., 2021; Yousefi et al., 2018). The GIS-based MCDA approach for site suitability analysis of a smart Hydrogen energy
production plant involved integrating spatial data, including hydrogen generation
potential and climatic factors, environmental and topographic factors, and the influence
of natural disasters. All potential locations were analyzed based on their important
and competing factors to determine the best location for utilizing renewable energies.
In the present study, we considered solar radiation, wind power density, elevation,
topographic slope, population density, landuse, air temperature, proximity to transmission
lines and sub-stations, proximity to water bodies, residential areas, and the influence
of natural disasters, as the potential factor for SHEP. The overall methodology is
depicted in Fig. 1.
In order to determine the appropriate sites for constructing smart hydrogen energy
plants, three main factors and 20 sub-factors were evaluated using GIS and AHP. Establishing
green hydrogen production facilities at some locations may not be feasible due to
a lack of hydrogen demand, lack of hydrogen potential, or being too far away from
roads, communities, or water. Thus, to address this issue, we determine the influencing
factors based on the literature review and experts' judgment, as presented in Fig. 1 and illustrated in Table 1.
Fig. 1. GIS-MCDA-Based Framework for Site Suitability Analysis of the Smart Hydrogen Energy (SHE) Plant Establishment
Table 1. List of Data Considered for the Site Suitability Analysis for Smart Hydrogen Energy Plant (SHEP)
|
Factors
|
Sub-factors
|
Sources
|
|
Hydrogen generation potential and Climatic factors
|
Horizontal Irradiation (HI)
|
www.esmap.org
|
|
Direct Normal Irradiation (DNI)
|
|
Potential Photovoltaic Electricity Production (PVOUT)
|
|
Average Air Temperature (TEMP)
|
|
Mean Wind Speed (WS)
|
|
Mean Wind Power Density (WPD)
|
|
Environmental and Topographic factors
|
Elevation (E)
|
National Geographic Information Institute (NGII)
|
|
Slope (S)
|
|
Distance from public services/facilities (PS)
|
|
Distance from sub-stations (SS)
|
KPX (2016)
|
|
Distance from transmission lines (TL)
|
|
Distance from road (R)
|
Open street map
|
|
Population Density (PD)
|
https://data.humdata.org/
|
|
Distance from the residential area (RA)
|
https://data.humdata.org/
http://nationalatlas.ngii.go.kr/
|
|
Distance from Waterbodies (W)
|
Google Earth
|
|
Landuse/landcover (LC)
|
https://livingatlas.arcgis.com/landcover/
|
|
Natural catastrophic factors
|
Distance from Forest Fire (FF)
|
Ministry of Environment, http://me.go.kr/
|
|
Distance from Landslide Potential Areas (LP)
|
Lee et al. (2022)
|
|
Distance from Shorelines (SL)
|
Google Earth
|
|
Distance from Typhoon Tracks (TR)
|
https://www.ncdc.noaa.gov/ibtracs/
|
2.1 Data Description
2.1.1 Hydrogen Generation Potential and Climatic Factors
Hydrogen generation potential and climatic factors are important factors to consider
when determining the suitability of a site for a smart hydrogen energy plant. Hydrogen
generation potential is affected by the availability of renewable energy sources such
as wind and solar power. The ability to generate hydrogen depends on the availability
and reliability of these sources to provide sufficient energy to power the hydrogen
production process. Climatic factors such as temperature, rainfall, and humidity can
also affect the efficiency of the hydrogen production process. Temperature can affect
the rate at which hydrogen is produced, and humidity can affect hydrogen storage.
The annual hydrogen energy production, such as solar radiation and wind and average
temperature data, were collected from the available information and reports. In the
present study, the mean wind power density, wind speed, horizontal irradiation, direct
normal irradiation, potential photovoltaic electricity production and average air
temperature were considered and processed from solar resource data as illustrated
in Table 2 (www.esmap.org). The average potential photovoltaic electricity production and mean
wind power density distribution of the Gangwon region are depicted in Fig. 2.
Fig. 2. Spatial Distribution of (a) Average Potential Photovoltaic Electricity Production (PVOUT) and (b) Mean Wind Power Density Map of the Gangwon Region (Data Source: www.esmap.org)
2.1.2 Environmental and Topographic Factors
Various environmental and topographic factors can influence the suitability of a location.
This study explored the impact of environmental and topographic factors on site suitability
analysis for smart hydrogen energy plant location planning. The environmental and
topographic criteria considered for this study are distance to transmission lines/sub-stations,
distance to urban utilized services, distance from the residential area, distance
from water bodies, landuse/landcover, distance from road networks, slope, and elevation
(Table 2).
For smart hydrogen energy plants (SHEPs) to succeed, power plants must be located
near industrial and urban centers because the distance between power plants and customers
directly affects the cost of transmitting and distributing electricity and the network
losses. Therefore, transmission lines and power stations must be located near residential
and industrial areas to reduce grid energy waste. Topographic factors such as slope
and elevation can impact the ease of transportation of goods and equipment necessary
for the plant's operation. Therefore, considering slope and elevation also effectively
lowers the civil costs of SHEPs. Constructing SHEPs in mountainous or high-altitude
areas is more difficult and expensive.
Large areas are typically required for solar and wind power plants, which can have
a negative impact on the environment and surrounding communities. SHEP construction
is unsuitable in some areas, including protected areas, forests, wetlands, and water
resources such as lakes and rivers (Ali et al., 2022). A landuse map was employed to sustainably manage and alter the natural environment
to create constructed environments such as cities, factories, and other industrial
areas. Additionally, the land should be suitable for the plant and should not cause
any harm to the environment or nearby communities. Since the increase in residents
generally results in an increase in cars, which increases dangerous emissions. A city
with a higher population density should be ranked higher than one with a lower population.
This statement refers to the fact that the population criterion is positive. Therefore,
site suitability analysis for smart hydrogen energy plant location planning should
consider the potential impact of these environmental and topographic factors to ensure
the sustainability and resilience of the plant. Fig. 3 depicts the spatial distribution of power sub-stations and the population distribution
map of the Gangwon region.
Fig. 3. (a) Spatial Distribution of Power Sub-Stations, and (b) Population Distribution Map of the Gangwon Region (Data Source: KPX , 2016; https://data.humdata.org/ )
2.1.3 Natural Catastrophic Factors
Natural disasters can significantly impact the suitability of a location for a smart
hydrogen energy plant. The suitability of a location can be influenced by natural
catastrophic factors such
as floods, earthquakes, landslides, forest fires, and typhoons. The extreme catastrophic
events can damage the infrastructure and equipment necessary for the plant's operation
and production, leading to potential shutdowns and economic losses. Additionally,
these factors can cause environmental pollution, significantly impacting nearby communities
and ecosystems. Therefore, to ensure the longest possible lifetime for the SHEPs,
the impact of natural catastrophic factors on site suitability analysis for smart
hydrogen energy plant location planning was considered. This factor indicates that
a less vulnerable region to natural disasters is more suitable. Here, forest fires,
distance from the shoreline, distance from landslide inventory and distance from past
typhoon tracks were considered for selecting suitable sites for SHEPs (Table 2). This region is frequently affected by forest fires and landslides, which pose a
significant risk to the development of any SHEP, as shown in Fig. 4.
Fig. 4. Spatial Distribution of (a) Forest Fire Inventory and (b) Landslide Potential Sites of the Gangwon Region (Data Source: Ministry of Environment, http://me.go.kr/;Lee et al., 2022)
2.2 MCDA Approach for Site Suitability Analysis of Smart Hydrogen Energy Production
Plant
Locating the most suitable locations for a smart hydrogen energy production plant
is a challenging feat. The GIS-based MCDA approach is a widely useful tool for site
suitability analysis of a smart hydrogen energy production plant (Ali et al., 2022; Rezaei et al., 2021). It requires taking into account several potential alternatives
and assessment criteria. Researchers and decision-makers commonly use the MCDA methods
to solve this problem (Karipoglu et al., 2022; Messaodi et al., 2019; Rezaei et al., 2020). The following steps were followed to apply the GIS-based MCDA approach for site
suitability analysis of a smart hydrogen energy production plant: (a) define the criteria,
(b) collect and prepare spatial and non-spatial data, (c) assign weights to the criteria,
(d) standardize the criteria, (e) analyze and combine the criteria, (f) mapping of
suitable sites, and (g) decision-making.
The required spatial and non-spatial data were collected from various sources such
as literature reviews, field studies, satellite images, existing maps, census data
and statistical data, as discussed in Section 2.1. In the present study, we used ArcGIS
software to integrate and analyze multiple factors related to site suitability, such
as horizontal irradiation, direct normal irradiation, potential photovoltaic electricity
production, mean wind speed, mean wind power density, average air temperature, altitude,
slope, distance from public service/ facilities, distance from sub-stations, distance
from transmission lines, distance from road networks, distance from the residential
area, distance from water bodies, population density, landuse/land cover, distance
from the forest fire, distance from landslide potential, distance from shorelines,
and distance from typhoon tracks. Assigning weights to the criteria based on their
relative importance to the site suitability analysis is crucial for the MCDA approach.
In the present study, the weight of each sub-criterion was calculated based on a pair-wise
comparison matrix (Saaty, 1980). We calculated the consistency index (CI), which is a significant feature of the
AHP that enables the rating inconsistencies to be determined (Saaty, 1980). Consequently, it is important to check the consistency ratio (CR) value when using
a weighted decision-making method to ensure that the weights assigned to each criterion
are feasible. The CR is calculated by dividing the CI and the random index (RI). The
RI values can be found in AHP tables (Alonso and Lamata, 2006; Saaty, 1980). The obtained CR value is < 0.1, which meets the AHP criteria. On the other hand,
expert opinions and existing literature were used to rank the selected criteria. After
that, we standardize the criteria to ensure they are on the same scale using a min-max
normalization method (Table 2). Finally, we combine the criteria using a weighted overlay method (Eq. (1)).
Where w represents the factor weight, βi represents the normalized ranks of factor
attributes, and ISHEP represents the site suitability index of the smart hydrogen
energy production plant.
The outcome exhibits a suitability map that shows the most suitable areas for a smart
hydrogen energy production plant. The identified suitable sites will be used to build
a smart hydrogen energy plant in the Gangwon-do region. The findings of this study
will greatly assist government organizations, decision-makers, and private investors
in making the most reliable decision about constructing a hydrogen station.
Table 2. Normalized Ranks and Weights Assigned to Respective Factors Were Used for Site Suitability Analysis for SHEP and the Factor Characteristics Thereof for GIS Integration
|
Criteria
|
Sub-criteria
|
Attributes
|
Rank
|
Normalized Rank
|
Weight
|
|
Hydrogen generation potential and climatic factors
|
Horizontal irradiation (HI)
[kWh/m2]
|
2.46 - 3.00
|
1
|
0.000
|
0.086
|
|
3.01 - 3.20
|
2
|
0.167
|
|
3.21 - 3.40
|
3
|
0.333
|
|
3.41 - 3.60
|
4
|
0.500
|
|
3.61 - 3.80
|
5
|
0.667
|
|
3.81- 4.00
|
6
|
0.833
|
|
4.01- 4.07
|
7
|
1.000
|
|
Direct normal irradiation (DNI)
[kWh/m2]
|
1.36 - 2.00
|
1
|
0.000
|
0.090
|
|
2.01 - 2.25
|
2
|
0.167
|
|
2.26 - 2.50
|
3
|
0.333
|
|
2.51 - 3.00
|
4
|
0.500
|
|
3.01 - 3.25
|
5
|
0.667
|
|
3.26 - 3.50
|
6
|
0.833
|
|
3.51 - 3.89
|
7
|
1.000
|
|
Potential photovoltaic electricity production (PVOUT)
[kWh/kWp]
|
2.83 - 3.00
|
1
|
0.000
|
0.095
|
|
3.01 - 3.10
|
2
|
0.091
|
|
3.11 - 3.20
|
3
|
0.182
|
|
3.21 - 3.30
|
4
|
0.273
|
|
3.31 - 3.40
|
5
|
0.364
|
|
3.41 - 3.50
|
6
|
0.455
|
|
3.51 - 3.60
|
7
|
0.545
|
|
3.61 - 3.70
|
8
|
0.636
|
|
3.71 - 3.80
|
9
|
0.727
|
|
3.81 - 3.90
|
10
|
0.818
|
|
3.91 - 4.00
|
11
|
0.909
|
|
4.01 - 4.06
|
12
|
1.000
|
|
Average air temperature (TEMP)
[°C]
|
3.7 - 5.0
|
1
|
0.000
|
0.081
|
|
5.1 - 6.0
|
2
|
0.125
|
|
6.1 - 7.0
|
3
|
0.250
|
|
7.1 - 8.0
|
4
|
0.375
|
|
8.1 - 9.0
|
5
|
0.500
|
|
9.1 - 10.0
|
6
|
0.625
|
|
10.1 - 11.0
|
7
|
0.750
|
|
11.1 - 12.0
|
8
|
0.875
|
|
12.1 - 13.1
|
9
|
1.000
|
|
Hydrogen generation potential and climatic factors
|
Mean wind speed (WS)
[m/s]
|
0.0977 - 2.03
|
1
|
0.000
|
0.076
|
|
2.04 - 3.0
|
2
|
0.125
|
|
3.1 - 4.0
|
3
|
0.250
|
|
4.1 - 5.0
|
4
|
0.375
|
|
5.1 - 6.0
|
5
|
0.500
|
|
6.1 - 7.0
|
6
|
0.625
|
|
7.1 - 8.0
|
7
|
0.750
|
|
8.1 - 10.0
|
8
|
0.875
|
|
10.1 - 17.1
|
9
|
1.000
|
|
Mean wind power density (WPD)
[w/m2]
|
0.003 - 10.0
|
1
|
0.000
|
0.071
|
|
10.1 - 100
|
2
|
0.111
|
|
101 - 200
|
3
|
0.222
|
|
201 - 300
|
4
|
0.333
|
|
301 - 400
|
5
|
0.444
|
|
401 - 500
|
6
|
0.556
|
|
501 - 750
|
7
|
0.667
|
|
751 - 1000
|
8
|
0.778
|
|
1001 - 1500
|
9
|
0.889
|
|
> 1501
|
10
|
1.000
|
|
Environmental and topographic factors
|
Elevation (E)
[m]
|
-9 - 100
|
8
|
1.000
|
0.057
|
|
100.1 - 200
|
7
|
0.857
|
|
200.1 - 300
|
6
|
0.714
|
|
300.1 - 400
|
5
|
0.571
|
|
400.1 - 500
|
4
|
0.429
|
|
500.1 - 750
|
3
|
0.286
|
|
750.1 - 1000
|
2
|
0.143
|
|
>1000
|
1
|
0.000
|
|
Slope (S)
[Degree]
|
0 - 5
|
8
|
1.000
|
0.062
|
|
5.01 - 10
|
7
|
0.857
|
|
10.1 - 15
|
6
|
0.714
|
|
15.1 - 20
|
5
|
0.571
|
|
20.1 - 25
|
4
|
0.429
|
|
25.1 - 30
|
3
|
0.286
|
|
30.1 - 45
|
2
|
0.143
|
|
>45
|
1
|
0.000
|
|
Distance from public service/facilities (PS) [m]
|
0 - 250
|
7
|
1.000
|
0.067
|
|
250.1 - 500
|
6
|
0.833
|
|
500.1 - 1000
|
5
|
0.667
|
|
1,000.1 - 2,000
|
4
|
0.500
|
|
2,000.1 - 3,000
|
3
|
0.333
|
|
3,000.1 - 5,000
|
2
|
0.167
|
|
>5000
|
1
|
0.000
|
|
Environmental and topographic factors
|
Distance from sub-stations (SS)
[km]
|
0 - 5
|
7
|
1.000
|
0.048
|
|
5.1 - 10.0
|
6
|
0.833
|
|
10.1 - 15.0
|
5
|
0.667
|
|
15.1 - 20.0
|
4
|
0.500
|
|
20.1 - 25.0
|
3
|
0.333
|
|
25.1 - 30.0
|
2
|
0.167
|
|
>30.0
|
1
|
0.000
|
|
Distance from transmission lines (TL)
[m]
|
0 - 500
|
8
|
1.000
|
0.052
|
|
500.1 - 1000
|
7
|
0.857
|
|
1000.1 - 2000
|
6
|
0.714
|
|
2000.1 - 3000
|
5
|
0.571
|
|
3001 - 5,000
|
4
|
0.429
|
|
5,001 - 10,000
|
3
|
0.286
|
|
10,001 - 15,000
|
2
|
0.143
|
|
>15,000
|
1
|
0.000
|
|
Distance from the road (R)
[km]
|
0 - 0.5
|
8
|
1.000
|
0.043
|
|
0.51 - 1.0
|
7
|
0.857
|
|
1.1 - 1.5
|
6
|
0.714
|
|
1.51 - 2.0
|
5
|
0.571
|
|
2.1 - 2.5
|
4
|
0.429
|
|
2.51 - 3.0
|
3
|
0.286
|
|
3.1 - 3.5
|
2
|
0.143
|
|
>3.5
|
1
|
0.000
|
|
Distance from the residential area
(RA) [km]
|
0 - 0.5
|
1
|
0.000
|
0.033
|
|
0.51 - 1.0
|
2
|
0.250
|
|
1.1 - 2.0
|
3
|
0.500
|
|
2.1 - 3.0
|
4
|
0.750
|
|
3.1 - 4.9
|
5
|
1.000
|
|
Distance from waterbodies (W)
[km]
|
0 - 0.5
|
5
|
1.000
|
0.029
|
|
0.51 - 2.0
|
4
|
0.750
|
|
2.1 - 5.0
|
3
|
0.500
|
|
5.1 - 7.5
|
2
|
0.250
|
|
>7.5
|
1
|
0.000
|
|
Landuse/landcover (LC)
|
Water
|
8
|
1.000
|
0.038
|
|
Bare Land
|
7
|
0.857
|
|
Flooded Vegetation
|
6
|
0.714
|
|
Trees
|
5
|
0.571
|
|
Dense Forest
|
4
|
0.429
|
|
Crops Land
|
3
|
0.286
|
|
Built Area
|
2
|
0.143
|
|
Snow
|
1
|
0.000
|
|
Population density (PD)
[Sq. km]
|
0 - 50
|
1
|
0.000
|
0.005
|
|
51 - 100
|
2
|
0.167
|
|
101 - 200
|
3
|
0.333
|
|
201 - 500
|
4
|
0.500
|
|
501 - 750
|
5
|
0.667
|
|
751 - 1,000
|
6
|
0.833
|
|
>1,000
|
7
|
1.000
|
|
Natural catastrophic
factors
|
Distance from forest fire (FF)
[km]
|
0 - 5.0
|
1
|
0.000
|
0.024
|
|
0.51 - 2.0
|
2
|
0.250
|
|
2.1 - 5.0
|
3
|
0.500
|
|
5.1 - 10.0
|
4
|
0.750
|
|
>10.0
|
5
|
1.000
|
|
Distance from landslide potential areas (LP) [km]
|
0 - 5.0
|
1
|
0.000
|
0.019
|
|
5.1 - 10.0
|
2
|
0.250
|
|
10.1 - 15.0
|
3
|
0.500
|
|
15.1 - 20.0
|
4
|
0.750
|
|
>20.0
|
5
|
1.000
|
|
Distance from Shorelines (SL)
[km]
|
0 - 3.0
|
1
|
0.000
|
0.010
|
|
3.1 - 5.0
|
2
|
0.250
|
|
5.1 - 10.0
|
3
|
0.500
|
|
10.1 - 15.0
|
4
|
0.750
|
|
>15.0
|
5
|
1.000
|
|
Distance from typhoon tracks (TR)
[km]
|
0 - 5.0
|
1
|
0.000
|
0.014
|
|
5.1 - 10.0
|
2
|
0.250
|
|
10.1 - 15.0
|
3
|
0.500
|
|
15.1 - 20.0
|
4
|
0.750
|
|
>20.0
|
5
|
1.000
|
3. Results and Discussion
The GIS-based site suitability analysis provided a valuable tool for identifying the
optimal location for the smart hydrogen energy plant in the Gangwon-Do region. To
analyze the various spatial factors, including horizontal irradiation, direct normal
irradiation, potential photovoltaic electricity production, mean wind speed, mean
wind power density, average air temperature, altitude, slope, distance from public
service/facilities, distance from sub-stations, distance from transmission lines,
distance from road networks, distance from the residential area, distance from water
bodies, population density, landuse/land cover, distance from the forest fire, distance
from landslide potential, distance from shorelines, and distance from typhoon tracks,
an spatial analysis tool was used. These spatial factors are critical in determining
the suitability of a location for the plant. The maps of various influencing factors
were combined, with their weights calculated using AHP (Table 2). The Jenks natural breaks were used to categorize the site suitability index into
five classes: unsuitable, low, moderate, high, and extremely suitable, as shown in
Fig. 5. Jenk's natural break method was chosen because it ensures that the difference between
data values within a class is minimized while the difference between classes is maximized
(Huang and Zhao, 2018). The site suitability map (Fig. 5) shows that 4.26 % of the total areas are classified as extremely suitable. The areas
classified as highly, moderately, low, and unlikely/unsuitable zones were 24.85%,
40.52%, 24.53%, and 5.83%, respectively. The results identified some areas in the
Cheorwon-gun, Chuncheon-si, Wonju-si, Yanggu-gun, Gangneung-si, Hoengseong-gun, and
near the coastal region along the east coast were suitable for solar and wind energy
utilization. The suitability map showed that these locations had high scores for proximity
to infrastructure, renewable energy potential, and favorable topography.
The GIS-based site suitability analysis comprehensively assessed potential locations
for the smart hydrogen energy plant in the Gangwon-Do region, highlighting the importance
of considering various spatial factors in location planning for sustainable energy
projects. The results of the analysis align with the South Korean government's efforts
to promote the use of hydrogen energy in the country. The plant's location in the
eastern part of the region provides access to the sea, which is crucial in hydrogen
production and reduces potential environmental impacts on the region's population
and industries. This area had a high level of solar irradiation and high accessibility
to the existing hydrogen infrastructure, as well as access to water resources. In
addition, the area had low population density and minimal land use conflicts, making
it an ideal location for the hydrogen energy plant. The analysis also revealed that
the coastal area had a lower risk of natural disasters, such as landslides, coastal
hazards and forest fires, compared to other parts of the region. This was important
for the safety of the hydrogen energy plant and its surrounding environment. The analysis
also showed that the coastal area had a more favorable economic environment than other
parts of the region. This would benefit the local economy and the hydrogen energy
plant. Policymakers may use the outcome of this study, energy companies, and other
stakeholders in determining the optimal location for future smart hydrogen energy
plants in the region and beyond.
Fig. 5. GIS-MCDA-Based Integrated Map Exhibited a Suitable Site for a Smart Hydrogen Energy Production Plant in the Gangwon Region
4. Conclusion
GIS-based site suitability analysis for smart hydrogen energy plant location planning
in the Gangwon-Do region, South Korea, was useful for identifying the most suitable
sites for developing hydrogen energy plants. This analysis provided a comprehensive
evaluation of the suitability of the various sites based on the available spatial
data and the criteria used for the selection. The use of various spatial data layers,
coupled with MCDA, enabled the evaluation of potential sites based on multiple criteria,
including hydrogen generation potential and climatic conditions, environmental and
topographic conditions, and natural catastrophic conditions. The MCDA- based suitability
index was classified into five classes, i.e., unlikely/unsuitable, low, moderately,
highly and extremely high suitable for solar and wind-power hydrogen production installation
systems. The analysis revealed that 5.83% (974.65 km2) of the study region has unsuitable
for SHEP, 24.53% (4098.43 km2) has low suitability, 40.52% (6770.84 km2) has moderately
suitability, 24.85% (4152.67 km2) has highly suitable, and 4.26% (712.14 km2) has
extremely suitable for installation of SHEP. It was observed that several locations
in the Cheorwon-gun, Chuncheon-si, Wonju-si, Yanggu-gun, Gangneung-si, and Hoengseong-gun,
as well as those close to the coastal region along the east coast, were appropriate
for the use of solar and wind energy. Therefore, the implementation of a smart hydrogen
energy plant in extremely suitable regions will have significant environmental, economic,
and social benefits. Further, the results of the analysis suggest that the optimal
location for the hydrogen energy plant should consider accessibility to proximity
to renewable energy sources and the impact of natural disasters. GIS-based site suitability
analysis can support sustainable and efficient regional energy production and distribution
while minimizing potential environmental negative effects. Finally, this study highlights
the importance of utilizing advanced spatial analysis techniques in decision-making
processes for renewable energy infrastructure development and the potential benefits
of hydrogen energy production for a more sustainable future.