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  1. 정회원․교신저자․강릉원주대학교 건설환경공학과 교수 (Corresponding Author․Gangneung-Wonju National University․skyeom0401@gwnu.ac.kr)
  2. 강릉원주대학교 건설환경공학과 연구원 (Gangneung-Wonju National University․rsgis.manik@gmail.com)



스마트수소에너지, 공간 분석, GIS, 다기준의사결정분, 강원도석
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
../../Resources/KSCE/Ksce.2023.43.3.0381/fig1.png
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)
../../Resources/KSCE/Ksce.2023.43.3.0381/fig2.png

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/ )
../../Resources/KSCE/Ksce.2023.43.3.0381/fig3.png

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)
../../Resources/KSCE/Ksce.2023.43.3.0381/fig4.png

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)).

(1)

$ISHEP = PVOUTwPVOUTβi + DNIwDNIβi + HIwHIβi$

$+ TEMwTEMβi + WSwWSβi + WPDwWPDβi + PSwPSβi$

$+ SwSβi + EwEβi + TLwTLβi + SSwSSβi + RwRβi + LCwLCβi$

$+ RAwRAβi + WwWβi + FFwFFβi + LPwLPβi + TRwTRβi$

$+ SLwSLβi + PDwPDβi/Sw$

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
../../Resources/KSCE/Ksce.2023.43.3.0381/fig5.png

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.

Acknowledgments

This research was supported by “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2022RIS-005).

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2021R1C1C2003316).

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