Strategic Selection of Hydrogen Refuelling StationTypes using a Hybrid AHP-TOPSIS Approach

The development of Hydrogen Refueling Station (HRS) infrastructure plays a vital role in advancing Indonesia’s national strategy for energy transition and decarbonization. However, selecting the most appropriate type of HRS presents a significant challenge, particularly in the absence of established national standards for site selection. This study addresses that gap by identifying the most relevant and practical evaluation criteria through a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating the Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). To ensure reliability, expert input was gathered from professionals in the energy and automotive sectors to assess the criteria and alternatives. The analysis revealed that economic considerations are the most influential, followed closely by Health, Safety, Security, and Environmental (HSSE) factors. Based on the TOPSIS results, the Mobile HRS emerged as the most favorable alternative, making it a strategic choice for initiating hydrogen infrastructure deployment in Indonesia. 

Introduction

Hydrogen has emerged as a promising energy carrier in recent decades, initially utilized in industrial sectors such as oil refining and chemical production (Hydrogen Council, McKinsey & Company, & International Energy Agency, 2023). With the growing urgency to transition to cleaner and more sustainable energy sources, hydrogen is gaining momentum as a critical component in global energy transformation strategies. As emphasized by the International Energy Agency (IEA, 2024), many developed countries including Japan, Germany, and the United States have incorporated hydrogen into their national energy roadmaps. These countries aim to phase out fossil fuels, reduce greenhouse gas emissions, and achieve climate targets through the development of hydrogen infrastructure, including Hydrogen Refueling Stations (HRS), which support the expansion of hydrogen-based mobility.

Hydrogen plays at least seven vital roles in global energy transition efforts, ranging from powering fuel cell vehicles to serving as an energy carrier for electricity generation and long-distance energy distribution (Hydrogen Council, McKinsey & Company, & International Energy Agency, 2023). According to the International Energy Agency (IEA, 2024), global demand for hydrogen reached 97 million tonnes in 2023, marking a 2.5% increase compared to the previous year. Despite this growth, low-emission hydrogen continues to represent only a small fraction of overall consumption. However, with the acceleration of large-scale electrolysis projects, the production of low-emission hydrogen is projected to rise significantly up to 49 million tonnes per year by 2030 supported by a planned electrolyzer capacity of nearly 520 GW worldwide (IEA, 2024). While hydrogen is widely recognized as a cornerstone of future low-carbon energy systems, the realization of a full-scale hydrogen economy still faces several institutional, technological, and infrastructural challenges (Ball & Weeda, 2015).

Fuel Cell Vehicles (FCVs), which operate on hydrogen, offer a significant opportunity to decarbonize the transportation sector. These vehicles emit only water vapor as a byproduct, making them an environmentally friendly alternative to conventional fossil-fuel-powered vehicles (IEA, 2024). Countries such as Japan, South Korea, and Germany have already integrated hydrogen into their national energy and transportation policies, thereby accelerating the development of Hydrogen Refueling Station (HRS) networks to support the growing adoption of FCVs (Suzukiet al., 2021; Hydrogen Council, McKinsey & Company, & International Energy Agency, 2023).

Despite its many advantages, hydrogen adoption in the transportation sector continues to face two major barriers: the high production cost of green hydrogen and the limited availability of refueling infrastructure (IEA, 2024). Nevertheless, hydrogen-powered vehicles present a strategic advantage over battery electric vehicles (BEVs), particularly in long-haul freight and commercial transport applications. In these segments, where quick refueling and extended driving ranges are essential, hydrogen is often considered a more practical and scalable solution than conventional battery systems (Hydrogen Council, McKinsey & Company, & International Energy Agency, 2023).

Hydrogen has been identified as a promising component in Indonesia’s long-term decarbonization strategy, particularly in sectors such as transportation and industry (Firdauset al., 2023). Indonesia’s energy transition roadmap has begun to outline the role of hydrogen, though detailed guidance on infrastructure deployment is still evolving (IESR, 2022). Indonesia has only recently begun to explore hydrogen’s potential as a renewable energy source. The national energy policy sets an ambitious target of achieving a 23% renewable energy mix by 2025; however, progress has been relatively modest, with the share reaching only 12.3% as of 2022 (IESR, 2023). In response, Indonesian government through the Ministry of Energy and Mineral Resources has introduced several strategic initiatives to accelerate the development of green hydrogen infrastructure (ESDM, 2023). One of the key enablers outlined in these plans is the establishment of Hydrogen Refueling Stations (HRS), which are expected to support the large-scale distribution of hydrogen for transportation and industrial applications. In Southeast Asia, including Indonesia, regulatory gaps in hydrogen storage and transport pose critical challenges that must be addressed before scaling up HRS networks (Terlouwet al., 2022).

State-owned enterprises (BUMN) are expected to play a central role in Indonesia’s hydrogen ecosystem, including the rollout of early-stage HRS (Suhartoet al., 2023). PT Pertamina (Persero) has proactively developed hydrogen infrastructure through its subsidiaries, such as Pertamina Geothermal Energy and Pertamina Patra Niaga has initiated green hydrogen development projects that aim to integrate HRS into existing fuel station networks (Pertamina, 2022). As highlighted in the national hydrogen roadmap (ESDM, 2023), these initiatives aim to produce and distribute hydrogen using geothermal energy, leveraging Pertamina’s extensive national fuel station network. With its significant geothermal potential, Indonesia is well-positioned to produce green hydrogen locally, which supports the decentralization of HRS infrastructure (Saraswatiet al., 2022). However, as noted by IESR (2023), the successful deployment of Hydrogen Refueling Stations (HRS) in Indonesia requires careful consideration in selecting the appropriate site type—whether Stand-Alone, Hybrid, or Mobile—based on multiple criteria including safety, regulatory compliance, infrastructure readiness, and economic viability.

The Indonesian automotive industry is gradually preparing for fuel cell vehicle adoption, which will require parallel investments in HRS infrastructure (Handayaniet al., 2023). Unfortunately, Indonesia currently lacks a national standard for selecting appropriate HRS locations, a condition that has been acknowledged in recent government and independent energy reports (ESDM, 2023). Most studies and planning efforts are heavily focused on technical and economic variables, often overlooking environmental sustainability and social acceptance (IESR, 2023). Furthermore, as noted by Dukićet al. (2022), the application of Multi-Criteria Decision-Making (MCDM) methods—such as AHP and TOPSIS—remains limited in the Indonesian context, particularly for hydrogen infrastructure planning. This analytical gap highlights the need for a more structured and holistic approach that integrates expert judgment with quantitative techniques to identify the most suitable HRS type under Indonesia’s unique infrastructure and regulatory landscape.

Literature Review

Hydrogen Refueling Station (HRS) Location Alternatives

Selecting an appropriate HRS location is a crucial step in building a sustainable hydrogen infrastructure. This decision requires balancing various factors, including technical feasibility, safety considerations, accessibility, and economic viability. Studies from developed countries have identified three common types of HRS: Stand-Alone, Hybrid (integrated with conventional fuel stations), and Mobile units (Kimet al., 2020; Suzukiet al., 2021). Each of these types offers distinct advantages. Stand-Alone HRSs provide operational autonomy and long-term scalability, Hybrid stations benefit from existing infrastructure and cost efficiencies, while Mobile HRSs offer flexibility, making them ideal for pilot programs or areas with uncertain or emerging hydrogen demand.

The suitability of these HRS types varies by country context. In Japan, where land is limited and natural disaster risks are high, Mobile HRS is the preferred solution due to its adaptability and lower infrastructure burden (Suzukiet al., 2021). In contrast, Korea has successfully deployed Hybrid HRS that utilize existing fuel station networks, streamlining investment and implementation (Kimet al., 2020). Learning from hydrogen infrastructure policies in Japan and Korea can provide Indonesia with valuable frameworks for building a scalable and safe HRS network (IEA, 2021). These international examples underline the importance of tailoring HRS deployment strategies to local infrastructure and regulatory environments.

Criteria for HRS Location Selection

The selection of an appropriate HRS location type must take into account a wide range of criteria that encompass both physical and policy related dimensions. Research from diverse geographical and regulatory contexts has consistently highlighted key factors such as access to major transportation routes, infrastructure availability, investment cost, regulatory compliance, safety considerations, environmental impact, and projected hydrogen demand (Dukićet al., 2022). These criteria are typically shaped by each country’s unique transportation behaviors, urban development frameworks, and the maturity of its hydrogen adoption strategy.

Research by Liet al. (2023), Zhouet al. (2023), and Sunet al. (2022) indicates that while technical and economic criteria dominate early considerations, social and environmental concerns are gaining recognition. For instance, fire risk, distance from public facilities, and regulatory compliance are becoming increasingly relevant in densely populated urban settings. This broad criteria requires a decision-making method to accommodate quantitative and qualitative assessments.

In addition to regulatory and demographic considerations, recent studies emphasize the importance of spatial and operational factors in HRS location selection. Accessibility especially proximity to high-traffic roadways remains one of the most influential location criteria in both urban and intercity networks (Parket al., 2018). Forecasted hydrogen demand also plays a critical role, particularly when estimating long-term usage by industrial hubs and projected fuel cell vehicle (FCV) penetration (Zhanget al., 2020). Safety and environmental risk must also be carefully evaluated, including population density, nearby sensitive facilities, and potential hazards from storage and compression systems (Moradi & Groth, 2019). Moreover, integrating HRS with existing energy infrastructure, such as conventional fuel stations, offers cost-saving opportunities and reduces construction complexity (Jeonget al., 2017). Economic considerations such as capital investment, land acquisition costs, and long-term operational efficiency are frequently highlighted in techno-economic analyses (Reußet al., 2019).

Analytic Hierarchy Process (AHP)

The Analytical Hierarchy Process (AHP), developed by Thomas L. Saaty, is one of the most widely used decision-making methods for solving complex, multi-criteria problems. As described in Saaty’s foundational work, AHP enables decision-makers to deconstruct complex issues into a structured, hierarchical format that typically consists of multiple levels namely the overall goal, criteria, sub-criteria, and a set of alternatives (Saaty, 1980). This hierarchical structure facilitates a more transparent and systematic analysis, especially when incorporating expert judgment in infrastructure planning, strategic policy, or project evaluation.

One of the key strengths of the AHP lies in its ability to convert subjective expert judgments into quantifiable priority values. As outlined by Saaty (2008), the method uses pairwise comparisons, where decision-makers evaluate the relative importance of each criteria or alternative against another using a standardized numerical scale, typically ranging from 1 to 9. This scale reflects the intensity of preference or importance. The resulting comparisons are then synthesized into a set of numerical weights that represent the relative priority of each element within the decision hierarchy.

In addition to generating priority weights, the AHP incorporates a built-in consistency check to ensure that the judgments made by decision-makers are logically coherent. As explained by Saaty (2008), the Consistency Ratio (CR) is used to detect inconsistencies in pairwise comparisons, where a CR value of less than 0.1 is typically considered acceptable. This feature enhances AHP’s reliability, particularly in group decision-making contexts where expert input plays a central role.

AHP has been widely applied across diverse domains such as infrastructure development, supplier evaluation, environmental policy, and transportation planning (Odu, 2019). In the context of hydrogen energy infrastructure, AHP offers a structured and transparent framework for evaluating multiple often conflicting criteria, making it particularly useful for prioritizing key factors in selecting appropriate locations for HRS.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a classical multi-criteria decision-making method introduced by Hwang and Yoon in 1981. The central idea behind TOPSIS is intuitive: the best alternative should be the closest to the ideal solution and the farthest from the worst-case scenario (negative ideal solution).

The TOPSIS is a well-established multi-criteria decision-making method that relies on a normalized decision matrix, where each criteria is scaled to enable comparability. Once normalized, the data are weighted according to the relative importance of each criteria often derived from another method such as AHP (Hwang & Yoon, 1981). TOPSIS then computes the Euclidean distance of each alternative from both the positive ideal solution (PIS) and the negative ideal solution (NIS). The final ranking is based on a closeness coefficient, which reflects how close an alternative is to the ideal solution relative to the worst case.

TOPSIS has gained widespread popularity due to its logical simplicity and effectiveness in handling both benefit-type and cost-type criteria. Unlike some other decision-making methods, it does not require the construction of complex utility functions or ranking conversions, making it highly practical and computationally efficient particularly in situations where decision-makers must evaluate a limited number of alternatives across multiple evaluation criteria (Odu, 2019).

Multi-Criteria Decision-Making (MCDM) methods such as AHP and TOPSIS have been widely adopted in sustainable decision-making frameworks, particularly for evaluating complex alternatives involving environmental, economic, and operational trade-offs (Govindanet al., 2015).

The application of TOPSIS in HRS planning has been explored in various geographical contexts. Kumaret al. (2017), for instance, applied TOPSIS to identify the optimal location for a hydrogen refueling station in India by assessing technical, environmental, and economic factors. Their findings highlight the method’s practicality and its capacity to incorporate multiple conflicting criteria in infrastructure decision-making (Kumaret al., 2017).

In the context of HRS planning, TOPSIS is frequently used in combination with the AHP a hybrid decision-making framework in which AHP determines the weight of each criteria, and TOPSIS ranks the alternatives accordingly. This integrated approach strengthens the objectivity of the evaluation process by combining expert judgment with mathematical rigor. As demonstrated in studies such as those by Xuet al. (2023), the AHP-TOPSIS hybrid model effectively balances both qualitative and quantitative inputs, making it especially valuable for infrastructure-related decisions under conditions of uncertainty and complexity.

In HRS planning, TOPSIS is often combined with AHP, where AHP supplies the weights and TOPSIS performs the ranking. This hybrid approach enhances objectivity, balances qualitative and quantitative considerations, and provides a robust framework for making infrastructure-related decisions in uncertain and dynamic environments.

Methodology

This research adopts a structured and quantitative approach by employing a hybrid AHP-TOPSIS method, which is recognized as a robust MCDM technique for addressing complex infrastructure planning challenges (Asadabadiet al., 2019). The methodology is carried out in two main stages: (1) the AHP is used to derive the weights of the evaluation criteria based on expert judgment, and (2) the TOPSIS is applied to rank the available HRS alternatives. This integrated framework is specifically designed to fill the methodological and practical gap in HRS planning in Indonesia by bridging expert knowledge with analytical precision (Ponhan & Sureeyatanapas, 2022).

Research Framework

The first stage of this study begins by establishing the research objective: to determine the most appropriate type of HRS based on a set of predefined evaluation criteria. These criteria were initially identified through an extensive literature review (Xu et al., 2023; Kumaret al., 2017) and subsequently validated through structured consultations with three domain experts representing the energy and automotive sectors. The finalized criteria were organized into a hierarchical structure suitable for analysis using the AHP. After deriving the weight of each criterion through expert-based pairwise comparisons, the study proceeded to the second stage by applying the TOPSIS. This phase focused on ranking the three HRS alternatives Stand-Alone, Hybrid, and Mobile based on their relative distance from the ideal solution.

Data Collection and Expert Involvement

This study employed a primary data collection approach based on expert judgment. Three experts were purposively selected for their extensive experience in energy infrastructure planning and renewable energy development. Each expert was asked to conduct pairwise comparisons among the established criteria using Saaty’s 1–9 fundamental scale, which is designed to reflect the relative importance between elements in a structured decision-making framework (Saaty, 1980). The individual judgments were then synthesized using the geometric mean method, a widely accepted aggregation technique in AHP that helps mitigate bias and ensure consistency across multiple evaluators (Dožić & Kalić, 2014). To ensure the reliability of the input data, the Consistency Ratio (CR) was calculated for each comparison matrix, and all CR values were found to be below the acceptable threshold of 0.1—indicating a logically sound and consistent decision-making process.

Criteria and Sub-Criteria Formulation

Based on the literature review and expert validation, the evaluation framework consisted of four main criteria and nineteen sub-criteria, structured hierarchically as in Table I.

Table I. Criteria and Subcriteria Selecting the Optimal HRS

No	Criteria	Code	Subcriteria
1	Accessibility	C1	Ease of vehicle access
		C2	Availability of transportation network
		C3	Strategic location
2	Infrastructure	C4	Supporting facilities
		C5	Business licensing
		C6	Road conditions to HRS site
		C7	Electricity supply availability
		C8	Implementation stage
3	HSSE	C9	International safety standards
		C10	Location security and monitoring
		C11	Infrastructure safety distance
		C12	Fire risk
		C13	Government regulation
4	Economics	C14	Investment value
		C15	Operating costs
		C16	Hydrogen sales volume
		C17	Government subsidy support
		C18	Return on Investment (ROI)
		C19	Maintenance cost

AHP Implementation for Weight Derivation

The AHP is a structured technique for organizing and analyzing complex decisions. It relies on pairwise comparisons to derive priority scales based on expert judgments using qualitative and quantitative criteria:

• Step 1: Constructing the Pairwise Comparison Matrix. Each expert is asked to compare every pair of criteria using a 1–9 scale as proposed by Saaty (1980). The result is a square. The pairwise comparison matrix is denoted as follows:

(1) $$\begin{array}{r}A=\left[\begin{array}{cc}\begin{array}{cc}1& a\\ \frac{1}{a_{12}}& 1\end{array}& \begin{array}{cc}\cdots & a_{1n}\\ \cdots & a_{2n}\end{array}\\ \begin{array}{cc}⋮& ⋮\\ \frac{1}{a_{1n}}& \frac{1}{a_{2n}}\end{array}& \begin{array}{cc}\ddots & ⋮\\ \cdots & 1\end{array}\end{array}\right]\end{array}$$

where a_ij represents the relative importance of criteria i compared to criteria j, and follows the reciprocal property:

(2) $$\begin{array}{r}{a}_{ji=\frac{1}{a_{ij}}}\end{array}$$

• Step 2: Normalization and Priority Vector (Weight Calculation). To calculate the priority vector w, each element of the matrix is normalized by column, and the average of each row is taken:

(3) $$\begin{array}{r}Aw={\lambda }_{max}w\end{array}$$

• Step 3: Consistency Check. To ensure logical consistency in the expert’s responses, the Consistency Index (CI) and Consistency Ratio (CR) are calculated:

(4) $$\begin{array}{r}CI=\frac{{\lambda }_{max}-n}{n-1}\end{array}$$

where ???? max is the maximum eigenvalue of matrix A and n is the number of criteria.

TOPSIS for Alternative Ranking

Once weights were obtained from AHP, the study proceeded with the TOPSIS method to evaluate and rank the three HRS alternatives:

• Step 1: Construct the Decision Matrix: Let xij be the score of alternativei under criteria j:

(5) $$\begin{array}{r}X=\left[\begin{array}{ccc}{x}_{11}& \cdots & x_{1n}\\ ⋮& \ddots & ⋮\\ x_{m1}& \cdots & x_{mn}\end{array}\right]\end{array}$$

where m is the number of alternatives, and n is the number of criteria.

• Step 2: Normalize the Matrix: Each value is normalized using vector normalization:

(6) $$\begin{array}{r}{r}_{ij}=\frac{x_{ij}}{\sqrt{\sum _{i=1}^m{x}_{ij}^2}}\end{array}$$

• Step 3: Weighted Normalized Decision Matrix: Each normalized value is multiplied by its corresponding AHP-derived weight wj:

(7) $$\begin{array}{r}{v}_{ij}=w_i\times r_{ij}\end{array}$$

• Step 4: Determine Positive and Negative Ideal Solutions: The positive ideal solution (A⁺) is obtained by selecting the best value for each criteria:

(8) $$\begin{array}{r}{A}^{+}=\{v_1^{+},v_2^{+},\dots ,v_n^{+}\}\end{array}$$

The negative ideal solution (A⁻) is obtained by selecting the worst value for each criteria:

(9) $$\begin{array}{r}{A}^{-}=\{v_1^{-},v_2^{-},\dots ,v_n^{-}\}\end{array}$$

• Step 5: Calculate Distance to Ideal Solutions: The distance of each alternative from the positive ideal solution is calculated by

(10) $$\begin{array}{r}{D}_i^{+}=\sqrt{\sum _{j=1}^n(v_{ij}-v_j^{+}{)}^2}\end{array}$$

The distance of each alternative from the negative ideal solution is calculated by:

(11) $$\begin{array}{r}{D}_i^{-}=\sqrt{\sum _{j=1}^n(v_{ij}-v_j^{-}{)}^2}\end{array}$$

• Step 6: Calculate the Closeness Coefficient: The relative closeness of each alternative to the ideal solution is:

(12) $$\begin{array}{r}{C}_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}}\end{array}$$

where Ci ∈ [0, 1], the higher the value of Ci, the closer the alternative is to the ideal solution.

Software and Tools

All calculations were performed using Microsoft Excel, incorporating built-in matrix operations, consistency checks for AHP, and normalized distance and ranking calculations for TOPSIS. This ensured transparency and reproducibility of the methodology.

Results and Discussion

This section outlines the results derived from the application of the hybrid AHP-TOPSIS method, which was utilized to evaluate and rank three types of HRS suitable for implementation in Indonesia: Stand-Alone, Hybrid, and Mobile. The analysis was conducted in two sequential stages. In the first stage, the AHP was employed to assign relative weights to the main criteria and subcriteria based on expert judgments (Saaty, 1980). In the second stage, these weights were integrated into the TOPSIS framework to assess the performance of each HRS alternative, ultimately ranking them based on their closeness coefficient to the ideal solution (Hwang & Yoon, 1981).

AHP Weighting Results

The AHP process engaged three experts with extensive backgrounds in hydrogen technology, energy infrastructure development, and renewable energy policy. Each expert was tasked with conducting pairwise comparisons among the established criteria and sub-criteria using Saaty’s fundamental scale, which ranges from 1 (equal importance) to 9 (extreme importance) (Saaty, 1980). To ensure consistency and reduce individual bias, their judgments were aggregated using the geometric mean method, a standard approach in group decision-making within AHP (Dožić & Kalić, 2014). The resulting Consistency Ratios (CR) for all matrices were found to be below 0.1, confirming an acceptable level of consistency in the expert inputs and thus validating the reliability of the data used in the weighting process.

At the main criteria level, the AHP results revealed that the Economic dimension was considered the most critical factor in determining the appropriate type of HRS, with a global weight of 0.3540. This was followed by the HSSE (Health, Safety, Security, and Environment) criteria, which received a weight of 0.3466. Accessibility and Infrastructure were also deemed necessary, though with relatively lower weights of 0.1585 and 0.1409, respectively. Economic feasibility is a critical factor in hydrogen infrastructure planning, as delivery and distribution costs can significantly influence the optimal station type and location, particularly in early market deployment phases (Yang & Ogden, 2007).

At the sub-criteria level, the top-ranking factors were:

• C14: Investment Value (0.1240)

• C13: Government Regulation (0.0800)

• C16: Hydrogen Sales Volume (0.0610)

• C10: Location Security and Monitoring (0.0530)

These results reflect a strong concern among decision-makers regarding financial feasibility and regulatory clarity, particularly relevant in Indonesia’s evolving hydrogen policy landscape. Safety and sales potential emerged as decisive factors, especially for early-phase infrastructure deployment.

TOPSIS Ranking Results

After determining the criteria weights using AHP, the study applied TOPSIS. The decision matrix was constructed using qualitative assessments of each HRS alternative against the 19 sub-criteria. The matrix was then normalized, and each criterion was weighted using the values derived from AHP (see Table II).

Table II. The Weight Calculation Value

Solution	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13	C14	C15	C16	C17	C18	C19
Weight	0,025	0,053	0,035	0,010	0,015	0,017	0,036	0,023	0,071	0,053	0,080	0,124	0,053	0,028	0,021	0,061	0,055	0,034	0,030
Stand alone	0,005	0,023	0,015	0,002	0,005	0,006	0,012	0,010	0,027	0,021	0,027	0,033	0,018	0,005	0,004	0,025	0,022	0,006	0,004
Hybrid	0,006	0,015	0,011	0,007	0,005	0,006	0,012	0,008	0,026	0,010	0,026	0,040	0,018	0,007	0,005	0,025	0,016	0,009	0,010
Mobile	0,014	0,010	0,007	0,001	0,005	0,006	0,012	0,004	0,014	0,014	0,018	0,049	0,018	0,011	0,008	0,008	0,022	0,017	0,010
Root square	0,016	0,029	0,020	0,007	0,009	0,010	0,021	0,013	0,040	0,027	0,041	0,071	0,031	0,014	0,010	0,036	0,035	0,021	0,015

Subsequently, the positive ideal solution (A⁺) and negative ideal solution (A⁻) were identified. The Euclidean distances of each alternative from both A⁺ and A⁻ were calculated, followed by the computation of the closeness coefficient (Ci) for each HRS alternative.

These results (see Table III) indicate that the Mobile HRS alternative is the most preferred option, followed by the Hybrid and Stand-Alone types. The high closeness coefficient of the Mobile HRS (0.7912) reflects its favorable position across several key criteria, particularly investment flexibility, regulatory compliance, and implementation speed. This finding is aligned with international practices, such as in Japan, where mobile stations have been deployed successfully in urban and high-risk zones due to their low infrastructure footprint and mobility.

Table III. The Final Results

Alternative	D+	D−	Ci	Rank
Mobile	0.0177	0.0669	0.7912	1st
Hybrid	0.0294	0.0802	0.7319	2nd
Stand-Alone	0.0443	0.0959	0.6841	3rd

The Hybrid HRS, which integrates hydrogen with conventional fuel stations, ranks second. It benefits from infrastructure synergy and ease of integration, but may face challenges related to safety zoning and government permits. While offering autonomy and scalability, the Stand-Alone HRS is ranked lowest due to its relatively higher costs, longer development timelines, and greater land requirements.

Discussion of Implications

The findings of this study have several important implications. Firstly, they provide a structured, data-driven rationale for prioritizing Mobile HRS deployment in Indonesia’s early hydrogen transition phase. Given Indonesia’s limited hydrogen infrastructure and evolving regulatory framework, Mobile HRS is a flexible and low-risk solution that can be implemented quickly while minimizing investment exposure.

Secondly, the study highlights the significance of aligning infrastructure decisions with financial viability and regulatory readiness. The high weight given to investment cost and government regulation suggests that policy incentives, subsidies, and clear guidelines will be critical in accelerating hydrogen adoption.

Lastly, the methodology applied in this study offers a transparent decision-making framework that can be replicated for other energy infrastructure planning efforts. Integrating AHP and TOPSIS allows stakeholders to combine expert knowledge with quantitative evaluation, reducing bias and increasing decision reliability.

Conclusion

This study aimed to determine the most appropriate type of HRS to be implemented in Indonesia by applying a hybrid decision-making approach that integrates the AHP and the TOPSIS. The research addressed a significant gap in Indonesia’s hydrogen infrastructure planning by providing a structured, data-driven approach to evaluate alternative HRS types Stand-Alone, Hybrid, and Mobile based on multiple criteria involving technical, economic, safety, and environmental considerations.

The AHP method was used to derive weights for a hierarchical structure of criteria and sub-criteria involving four main dimensions: Accessibility, Infrastructure, HSSE (Health, Safety, Security, and Environment), and Economics. Economics emerged as the most critical factor (weight = 0.3540), followed closely by HSSE (weight = 0.3466). The most influential sub-criteria were Investment Value, Government Regulation, and Hydrogen Sales Volume, which reflected expert concerns regarding cost efficiency, legal certainty, and market readiness.

Using these weights, the TOPSIS method was applied to rank the HRS alternatives. The results showed that the Mobile HRS type achieved the highest closeness coefficient (Ci = 0.7912), indicating its strong alignment with the ideal solution across all evaluation criteria. This was followed by the Hybrid HRS (Ci = 0.7319) and the Stand-Alone HRS (Ci = 0.6841). These findings suggest that the Mobile HRS is the most suitable option for early-stage implementation in Indonesia, particularly considering the current regulatory ambiguity and limited infrastructure.

This research offers practical insights for policymakers, state-owned enterprises, and investors seeking to develop hydrogen infrastructure in Indonesia. The AHP-TOPSIS framework used in this study provides a replicable and transparent approach to support multi-criteria decision-making in complex infrastructure planning scenarios. In light of Indonesia’s national energy transformation agenda, the strategic deployment of Mobile HRS could serve as a stepping stone toward broader hydrogen adoption.

Future studies are encouraged to expand the scope of analysis by incorporating spatial modeling tools (such as GIS), supply chain dynamics, or cost-benefit simulation to enhance the robustness of HRS planning further. Moreover, as Indonesia’s hydrogen market evolves, periodic reassessment of criteria weights and policy variables will be essential to ensure continued alignment with national goals and market realities.

Conflict of Interest

Conflict of Interest: The authors declare that they do not have any conflict of interest.