A Novel Decision-Support Framework for Supporting Renewable Energy Technology Siting in the Early Design Stage of Microgrids: Considering Geographical Conditions and Focusing on Resilience and SDGs

Abstract

This research is focused on microgrid design supporting tools and presents an innovative framework for renewable energy (RE) sources’ site selection, integrating multicriteria decision-making (MCDM) methods with resilience considerations and alignment to the Sustainable Development Goals (SDGs). It addresses present climatic challenges, identifies key causes of possible power failures, and develops strategies to mitigate their effects, while providing tools for energy managers and decision-makers to select suitable RE sources/technologies, based on geographical and sustainability criteria. The framework categorizes criteria into quantitative and qualitative types, adopting a cost ($\mathcal{C}$)- and benefit ($\mathcal{B}$)-based approach. The Analytic Hierarchy Process (AHP) calculates criteria weights to ensure accuracy and compatibility in decision-making, integrating SDG objectives into the evaluation process. This study focuses on five major RE options, photovoltaic (PV), wind, wave, tidal, and geothermal, analyzing more than 50 criteria for each energy type. This evaluation incorporates the expertise of over 50 experts and case studies, making it one of the most extensive research efforts in RE site selection. By systematically addressing resilience challenges and linking them with SDG priorities, this study provides a robust framework for evaluating and optimizing RE options. Its methodologies offer significant contributions to advancing sustainable energy development and enhancing energy systems’ resilience to climate and infrastructural challenges.

1. Introduction

Microgrids offer a number of important benefits to the end user, including reliability, resilience, energy management, integration of renewable energy (RE), energy independence, and economic and environmental benefits. Resilience, as one of the characteristics of microgrids, is enabled through a number of design features and operating tactics. However, the variability of RE, inadequate grid infrastructure, and the absence of monitoring and maintenance remain the most common causes of power outages [1].

Motivation: Most studies discuss the post-event phase of resilience. Primary causes generally originate from the pre-event phase, including inadequate planning in the pre-event stage (and site selection criteria can be one); limited maintenance and monitoring; insufficient mechanisms for reactions; and outside factors beyond the control of the RE system, such as weather, disasters, or grid failures. In this case, the resilience of the system would mean the capability of the latter to resist and/or recover from such an external shock. It is, therefore, worth dedicating the pre-event period to those preventative measures that would definitely enhance resilience to improve decision-making related to new sources of energy. In this regard, multicriteria renewable energy source optimization should be conducted in accordance with available resources, resource stability, and system adaptability. The evaluation of the availability and acceptability of renewable energy involves solar potentials, wind patterns, geothermal temperature, and water resources. Such assessments are important in identifying better sources of energy to be selected and integrated into a microgrid.

Literature background: A review of the existing literature reveals a broad spectrum of optimization techniques for integrating renewable energy into microgrids, typically examining factors such as resource selection, cost efficiency, and environmental impacts [1,2,3,4]. Many of these works apply MCDM approaches—like the AHP to systematically evaluate conflicting criteria. These criteria often encompass technical, economic, and environmental dimensions, yet they tend to focus on a limited set of variables or emphasize post-event resilience over detailed pre-event planning [5,6,7].

Furthermore, existing studies frequently underscore the importance of redundancy in renewable energy sources to strengthen microgrid operation, as integrating multiple sources reduces reliance on any single resource and enhances overall resilience. However, a fully comprehensive decision-making framework—one that merges both quantitative (e.g., resource availability, cost metrics, and environmental impacts) and qualitative (e.g., social acceptance, policy support, and risk considerations) criteria for renewable energy selection within microgrids—remains under-explored [8,9,10]. In particular, the absence of a robust pre-event planning perspective leaves a methodological gap in the current literature, suggesting a need for decision-making tools that systematically integrate resilience phases, SDGs, and multi-dimensional criteria to ensure reliable, resilient, and community-supported microgrid solutions.

Contribution: This research paper will try to fill this knowledge gap by developing a new decision-support system (DSS) able to help energy planners of microgrids and policymakers identify the appropriate renewable energy sources for their respective locations. The proposed DSS uses the multicriteria decision-making method (MCDM), namely AHP (Analytical Hierarchical Process), to assign weights to different criteria and ensure consistency through a suitable index. It presents five renewable alternatives—solar, wind, wave, tidal, and geothermal—which are subjected to six sustainability criteria: technical, social, environmental, political, economic, and exclusion criteria. The proposed system will be able to support pre-event resilience planning and consider SDGs in its analysis.

Figure 1 summarizes the various actions to be pursued for the selection of renewable energy sources and resilience in general for microgrid systems during the pre-disaster event phase. Every block will be explained in detail in Section 2.

Organization: The rest of this paper is organized as follows: Section 2 describes the data collection process, including criteria gathered from literature reviews as well as expert opinions and their alignment with the UNO Agenda 2030 and its 17 goals. Section 3 explains how to build the hierarchy structure, including classifying criteria into types and categories. Section 4 explains the process of assigning weights using AHP and its validation using a composed indicator. Section 5 reports the integrated computational tool, made available to the research community by sharing it via Zenodo. Section 6 discusses the ranking assessment and how it opens doors to potential solutions in the search for alternatives. Finally, Section 7 discusses the conclusions and future directions of this research. In Appendix A, the reader can find additional Supplementary Information and details.

2. Data Collection

2.1. Literature Review, Supporting the Experts’ Opinion Paradigm

In this section, the literature review on the criteria for resilience-based suitable site selection of five different renewable energies is presented. In this site selection process, we considered different dimensions to summarize the criteria according to renewable energies.

For site selection, we collected criteria from literature reviews and expert opinions, including evaluation aspects such as technical, environmental, economic, social, and political factors, while additionally establishing exclusion criteria for filtering out unsuitable sites.

Exclusion criteria are also known as restrictive criteria. Some limitations are included in this criteria for being unsuitable for site selection. The limitations of exclusion criteria are based on regions like hazard areas, rural, airports, and agriculture regions. In the distance-based criteria limitations are coastal areas, river bodies, and transmission lines. In pollution-based limitations are air, noise, and land pollution. Finally, emission limitations are greenhouse gases such as (NO_x, SO₂, and CO₂), and electromagnetic emission. According to the above classification, as shown in Figure 2, the commonly used criteria from the published research papers are summarized separately from

Table A1. Criteria for PV.

Criterion	Sub-Criterion	$\mathcal{C}/\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
TEC01	Annual Solar Irradiance	$\mathcal{B}$-QT	Min 1100 [kWh/m2/year]	[6,9,13,14,15,16,17,18]	7, 13
TEC02	Aspect	$\mathcal{B}$-QT	110–200° (SE, partly SW) [°]	[9,13,16,19]	11, 15
TEC03	Efficiency	$\mathcal{B}$-QT	[%]	[5,7,8,10,19,20,21,22,23,24,25,26,27,28,29]	7, 9, 12
TEC04	Flexibility	$\mathcal{B}$-QL	–	[10,19,23,30]	8, 9
TEC05	Lifetime	$\mathcal{B}$-QT	25 [years]	[10,19,23,24,27,28]	9, 12, 13
TEC06	Plant Capacity	$\mathcal{B}$-QT	[kW]	[7,10,19,20,23,31,32,33]	1, 7, 8
TEC07	Reliability	$\mathcal{B}$-QL	–	[5,7,8,10,23,26,29,30,31]	11, 13
TEC08	Slope	$\mathcal{B}$-QT	<5–15 [%]	[9,13,14,16,19]	11, 15
TEC09	Technical Maturity	$\mathcal{B}$-QL	–	[5,7,8,10,19,20,23,24,25,26,27,29,30,31]	9, 17
EVC01	Depth of Frozen Soil	$\mathcal{C}$-QT	[m]	[15]	13, 15
EVC02	Proximity to Multi-Story Houses (>16 Stories)	$\mathcal{C}$-QT	>100 [m]	[13]	3, 11

,

Table A2. Criteria for wind energy.

Criterion	Sub-Criterion	$\mathcal{C}/\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
TEC01	Wind Speed	$\mathcal{B}$-QT	>7 [m/s]	[4,9,17,34,35]	7, 13
TEC02	Aspect	$\mathcal{B}$-QT	[°]	[9,19]	11, 15
TEC03	Efficiency	$\mathcal{B}$-QT	[%]	[5,7,8,10,19,20,21,23,24,25,26,27,28]	7, 9, 12
TEC04	Flexibility	$\mathcal{B}$-QL	–	[10,19,23]	8, 9
TEC05	Lifetime	$\mathcal{B}$-QT	25 [Years]	[10,19,24,27,28]	9, 12, 13
TEC06	Plant Capacity	$\mathcal{B}$-QT	[kW]	[7,10,19,20,23,31,32,33,36]	1, 7, 8
TEC07	Reliability	$\mathcal{B}$-QL	–	[5,7,8,10,23,26,28,31]	11, 13
TEC08	Slope	$\mathcal{B}$-QT	<5 [%]	[9,19,34]	11, 15
TEC09	Technical Maturity	$\mathcal{B}$-QL	–	[5,7,8,10,19,20,23,24,25,26,27,28,31]	9, 17
EVC01	Depth of Frozen Soil	$\mathcal{C}$-QT	[m]	[8,19]	13, 15

,

Table A3. Criteria for wave energy.

Criterion	Sub-Criterion	$\mathcal{C}/\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
TEC01	Mooring System	$\mathcal{B}$-QT	[kWh/m2/year]	[37]	9, 14
TEC02	Inverter Installation Capacity	$\mathcal{B}$-QT	–	[37]	7, 9
TEC03	Efficiency	$\mathcal{B}$-QT	[%]	[8,10]	7, 9, 12
TEC04	Flexibility	$\mathcal{B}$-QL	–	[10]	8, 9
TEC05	Lifetime	$\mathcal{B}$-QT	[Years]	[10]	9, 12, 13
TEC06	Plant Capacity	$\mathcal{B}$-QT	[kW]	[10,37]	1, 7, 8
TEC07	Reliability	$\mathcal{B}$-QL	–	[8,10,28]	11, 13
TEC08	Technical Maturity	$\mathcal{B}$-QL	–	[8,10,28,37]	9, 17
TEC09	Wave Height	$\mathcal{B}$-QT	[m]	[12,38,39]	13, 14
TEC10	Mean Wave Energy Flux	$\mathcal{B}$-QT	[kW/m]	[2,40,41]	7, 14
TEC11	Distance Between Two Waves	$\mathcal{B}$-QT	[m]	[12,38,41]	13, 14
TEC12	Number of Waves	$\mathcal{B}$-QL	–	[38,40]	7, 13
TEC13	Wind Speed	$\mathcal{B}$-QT	[m/s]	[2,4,12,38]	7, 13
TEC14	Wave Duration	$\mathcal{B}$-QT	[s]	[12,39]	7, 14
TEC15	Ocean Salinity Levels	$\mathcal{B}$-QT	[PSU]	[40]	6, 14
TEC16	Anchorage Facilities	$\mathcal{B}$-QL	–	[40]	7, 9
EVC01	Turbulence	$\mathcal{C}$-QT	[m]	[12,38]	14, 15
EVC02	Depth of the Ocean	$\mathcal{C}$-QT	[m]	[2,4,38,39]	14, 15
EVC03	Water Quality	$\mathcal{C}$-QT	–	[12,38]	6, 14
EVC04	Coastal Erosion	$\mathcal{C}$-QT	–	[38,40,42]	14, 15
EVC05	Shipping Density	$\mathcal{C}$-QT	–	[2,38,40]	9, 14

,

Table A4. Criteria for tidal energy.

Criterion	Sub-Criterion	$\mathcal{C}\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
TEC01	Tidal Current Velocity	$\mathcal{B}$-QT	>0.5 [m/s]	[3,43,44]	7, 14
TEC02	Water Depth	$\mathcal{B}$-QT	>10 to <50 [m]	[3]	7, 15
TEC03	Tidal Current Power Density	$\mathcal{B}$-QT	[W/m2]	[3]	7, 9
TEC04	Efficiency	$\mathcal{B}$-QT	[%]	[8]	7, 9, 12
TEC05	Flexibility	$\mathcal{B}$-QL	–	[3]	8, 9
TEC06	Lifetime	$\mathcal{B}$-QT	[Years]	[37]	9, 12, 13
TEC07	Plant Capacity	$\mathcal{B}$-QT	[kW]	[8]	1, 7, 8
TEC08	Reliability	$\mathcal{B}$-QL	–	[8]	11, 13
TEC09	Technical Maturity	$\mathcal{B}$-QL	–	[8,43,44]	9, 17
EVC01	Marine Ecology	$\mathcal{C}$-QT	–	[3]	14, 15

,

Table A5. Criteria for geothermal energy.

Criterion	Sub-Criterion	$\mathcal{C}/\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
TEC01	Thermal Flux	$\mathcal{B}$-QT	20 to <100 [°C]	[17,45,46]	7, 13
TEC02	Intrusive Rock	$\mathcal{B}$-QT	–	[47]	13, 15
TEC03	Efficiency	$\mathcal{B}$-QT	[%]	[7,8,10,19,20,21,23,24,28,45]	7, 9, 12
TEC04	Drainage Density	$\mathcal{B}$-QL	–	[47]	13, 15
TEC05	Lifetime	$\mathcal{B}$-QT	[Years]	[10,19,24,28]	9, 12, 13
TEC06	Plant Capacity	$\mathcal{B}$-QT	[kW]	[7,10,19,20,23,31]	1, 7, 8
TEC07	Reliability	$\mathcal{B}$-QL	–	[7,8,10,23,28,30,31]	11, 13
TEC08	Fault Density	$\mathcal{B}$-QT	–	[47]	15, 17
TEC09	Radioactivity	$\mathcal{B}$-QT	[Bq]	[47]	13, 15
TEC10	Technical Maturity	$\mathcal{B}$-QL	–	[7,8,10,19,20,23,24,28,30,31]	9, 17
EVC01	Depth	$\mathcal{C}$-QT	>3000 to <5000 [m]	[45,46]	13, 15
EVC02	Geological Impact	$\mathcal{C}$-QT	–	[45]	15, 17
EVC03	Mineralization	$\mathcal{C}$-QT	1–1000 [g/dm³] at 1000 to 3000 [m]	[45,46]	6, 15
EVC04	Thickness of Pore Space	$\mathcal{C}$-QT	–	[45]	13, 15
EVC05	Lithology of Cap Rocks	$\mathcal{C}$-QT	–	[46]	15, 17
EVC06	Tectonic Activity	$\mathcal{C}$-QT	–	[46]	15, 17
EVC07	Intergranular Porosity	$\mathcal{C}$-QT	–	[46]	13, 15
EVC08	Fracture Porosity	$\mathcal{C}$-QT	–	[46]	13, 15
EVC09	Type of Structure	$\mathcal{C}$-QT	–	[46]	15, 17
EVC10	Stage of Exploration	$\mathcal{C}$-QT	–	[46]	15, 17

,

Table A6. Common criteria for renewable energy.

Criterion	Sub-Criterion	$\mathcal{C}/\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
ECC01	Construction Cost	$\mathcal{C}$-QT	[USD]	[10,19,41,43,47,48,49]	1, 8, 9
ECC02	Economic Risk	$\mathcal{C}$-QT	[USD]	[6,28]	1, 8, 12
ECC03	Investment Cost	$\mathcal{C}$-QT	[USD]	[5,6,7,8,10,20,21,22,23,24,25,26,27,28,29,30,31,33,36,41,47,50,51]	1, 7, 9
ECC04	LCOE	$\mathcal{C}$-QT	[USD]	[5,7,8,19,20,23,24,25,26,28,29,32,33,41,43,49,51,52]	7, 8, 13
ECC05	Maintenance Cost	$\mathcal{C}$-QT	[USD/kWh]	[5,6,7,8,10,19,20,21,23,25,26,27,29,31,37,41,43,47,48,51]	9, 12, 13
ECC06	NPV	$\mathcal{B}$-QT	[USD]	[5,8,10,19,22,24,27,31,33,35,40,41,43,49,50,51]	1, 8, 9
ECC07	Operation Cost	$\mathcal{C}$-QT	[USD]	[5,6,7,8,10,20,21,23,25,26,29,30,31,37,41,43,48,49,51]	8, 12, 13
ECC08	R&D	$\mathcal{C}$-QT	[USD]	[25,28,37,41,50]	9, 17
SOC01	Impact on Humans	$\mathcal{B}$-QL	–	[10,42,48]	1, 3, 16
SOC02	Impact on Natives	$\mathcal{B}$-QL	–	[37,42,43,48]	10, 16
SOC03	Job Opportunity	$\mathcal{B}$-QL	–	[5,7,8,10,12,19,20,21,22,23,24,25,26,27,28,32,37,40,49,50]	1, 8, 10
SOC04	Social Acceptance	$\mathcal{B}$-QL	–	[5,6,7,8,10,19,20,21,23,24,26,27,29,31,47,51]	5, 10, 16
SOC05	Social Benefit	$\mathcal{B}$-QL	–	[5,8,10,26,29,31]	1, 4, 8, 10
POC01	Foreign Dependency	$\mathcal{B}$-QL	–	[10,28,50]	7, 12, 17
POC02	Government Incentives	$\mathcal{B}$-QL	–	[10,24,27,28,30,43,49]	9, 12, 16
POC03	Government Policies	$\mathcal{B}$-QL	–	[10,12,25,28,37,40,43,48,50]	9, 16, 17
POC04	National Energy Policy Target	$\mathcal{B}$-QL	–	[5,10,23,25,26,27,28,52]	7, 9, 13
POC05	Political Acceptance	$\mathcal{B}$-QL	–	[5,6,7,10,25,29,31,48,50]	9, 16, 17

and

Table A7. Exclusion criteria and limitations.

Criterion	Sub-Criterion	$\mathcal{C}/\mathcal{B}$-QT/QL	Limit [Unit]	Papers	SDG
EXC01	Distance to Transmission Lines	$\mathcal{C}$-QT	<600 [m]	[3,6,13,14,15,16,34,35,36,39,43]	7, 9, 13
EXC02	Distance to Roadways	$\mathcal{C}$-QT	<500 [m]	[9,13,14,15,17,34,35,36,48,53]	9, 11, 13
EXC03	Distance to Water Disposal	$\mathcal{C}$-QT	[m]	[5,9,19,28]	6, 11, 14
EXC04	Ecology	$\mathcal{C}$-QL	–	[2,4,5,6,10,15,19,23,25,26,27,28,29,31,39,42,43,44,48,50,51]	14, 15
EXC05	CO2 Emissions	$\mathcal{C}$-QT	[g/m2]	[7,8,10,19,20,21,22,23,24,25,26,28,29,30,31,32,33,35,37,42,50,51]	3, 13, 14
EXC06	Dust Emissions	$\mathcal{C}$-QT	[g/m2]	[7,30]	3, 13
EXC07	Electromagnetic Field (EMF)	$\mathcal{C}$-QT	[g/m2]	[7,37]	3, 9, 13
EXC08	NOx Emissions	$\mathcal{C}$-QT	[g/m2]	[5,7,8,10,19,20,21,22,24,25,26,28,29,30,31,32,33,37,42,50]	3, 13, 14
EXC09	SO2 Emissions	$\mathcal{C}$-QT	[g/m2]	[5,7,8,10,19,20,24,25,26,28,29,30,31,32,33,37,42,50]	3, 13, 14
EXC10	Natural Disaster	$\mathcal{C}$-QT	–	[9,19,35]	11, 13, 15
EXC11	Noise Pollution	$\mathcal{C}$-QL	–	[5,10,19,23,28,37,42,47]	3, 11, 12
EXC12	Soil Pollution	$\mathcal{C}$-QL	–	[15,28,37,42]	11, 12, 15
EXC13	Visual Pollution	$\mathcal{C}$-QL	–	[37]	11, 12, 15
EXC14	Air Pollution	$\mathcal{C}$-QL	–	[28,42]	3, 11, 12
EXC15	Water Pollution	$\mathcal{C}$-QL	–	[10,28,37,42,47]	6, 14, 15
EXC16	Agricultural Land Restrictions	$\mathcal{C}$-QT	[m]	[17,48]	2, 11, 15
EXC17	Proximity to Airports	$\mathcal{C}$-QT	[m]	[53]	9, 11, 15
EXC18	Cultural Heritage Sites	$\mathcal{C}$-QT	[m]	[42,48]	11, 15, 16
EXC19	Protected Environmental Areas	$\mathcal{C}$-QT	>500 [m]	[4,14,38,48]	11, 14, 15
EXC20	Lakes and Water Bodies	$\mathcal{C}$-QT	>1000 [m]	[13,16,17,53]	6, 14, 15
EXC21	Land Requirement	$\mathcal{C}$-QT	–	[5,6,7,8,9,10,14,19,20,21,22,23,24,25,26,27,28,29,32,42,48,53]	11, 12, 15
EXC22	Proximity to Railways	$\mathcal{C}$-QT	[m]	[9]	9, 11, 13
EXC23	Proximity to Rivers	$\mathcal{C}$-QT	>500 [m]	[9,14,53]	6, 14, 15
EXC24	Unsuitable Land	$\mathcal{C}$-QT	>500 [m]	[4,9,13,14,16,17,34,39,47,48]	11, 12, 15
EXC25	Proximity to Military Areas	$\mathcal{C}$-QT	[m]	[4,39]	11, 16, 17
EXC26	Water Consumption	$\mathcal{C}$-QT	[L/kWh]	[29,32,42,46]	6, 12, 14

.

2.2. Alignment of Research Criteria with SDGs

Including SDGs into site selection criteria for renewable energy projects guarantees alignment with worldwide environmental sustainability targets, encouraging economic growth, environmental conservation, and social diversity. By using the SDGs as a guiding framework, stakeholders may choose projects that support multiple dimensions of sustainable development, such as supporting clean energy access, minimizing environmental impact, and dealing with societal needs, as shown in Figure 3. This approach promotes the integration of renewable energy initiatives into broader sustainability agendas, improving project acceptance, impact, as well as efficiency in dealing with critical global challenges.

2.3. Linking Criteria Categories to the SDGs

Linking criteria categories to the SDGs ensures that renewable energy evaluations align with global sustainability priorities. This alignment highlights the role of technical, social, economic, and environmental criteria in addressing specific SDGs, as illustrated in Figure 4, thereby promoting a balanced and inclusive approach to decision-making.

2.3.1. Technical Criteria

• SDG 7 (Affordable and Clean Energy): Technical improvements ensure efficient, reliable renewable energy systems.
• SDG 9 (Industry, Innovation, and Infrastructure): Advanced technologies support innovative solutions and resilient energy infrastructure.
• SDG 11 (Sustainable Cities and Communities): Technically robust RE systems enhance urban resilience.
• SDG 12 (Responsible Consumption and Production): Efficiency and reliability in energy systems foster resource conservation.
• SDG 13 (Climate Action): Technological advancements help reduce emissions and improve climate resilience.

2.3.2. Exclusion Criteria

• SDG 2 (Zero Hunger): Protecting agricultural lands preserves food security.
• SDG 3 (Good Health and Well-Being): Excluding sites near sensitive areas reduces health hazards.
• SDG 6 (Clean Water and Sanitation): Avoiding areas that jeopardize water resources maintains water quality.
• SDG 11 (Sustainable Cities and Communities): Land-use restrictions ensure orderly, sustainable urban growth.
• SDG 13 (Climate Action): Excluding climate-vulnerable zones supports long-term climate resilience.
• SDG 14 (Life Below Water) and SDG 15 (Life on Land): Protecting ecologically sensitive regions safeguards biodiversity and ecosystems.

2.3.3. Environmental Criteria

• SDG 6 (Clean Water and Sanitation): Ensuring minimal impact on water resources.
• SDG 12 (Responsible Consumption and Production): Encouraging environmentally sound resource use.
• SDG 13 (Climate Action): Minimizing greenhouse gas emissions and ecological footprints.
• SDG 14 (Life Below Water) and SDG 15 (Life on Land): Preserving marine and terrestrial habitats through responsible environmental practices.

2.3.4. Social Criteria

• SDG 1 (No Poverty): Community-oriented RE projects can improve local livelihoods.
• SDG 3 (Good Health and Well-Being): Better energy access can improve health outcomes.
• SDG 4 (Quality Education): Reliable power can enhance educational facilities and opportunities.
• SDG 5 (Gender Equality): Equitable job opportunities and social inclusion support gender equality.
• SDG 8 (Decent Work and Economic Growth): RE projects create jobs and stimulate local economies.
• SDG 10 (Reduced Inequalities): Fair access to clean energy reduces disparities.
• SDG 11 (Sustainable Cities and Communities): Inclusive social criteria support cohesive, sustainable communities.

2.3.5. Political Criteria

• SDG 9 (Industry, Innovation, and Infrastructure): Supportive policies encourage infrastructure development and innovation.
• SDG 16 (Peace, Justice, and Strong Institutions): Stable governance, transparent policies, and strong institutions facilitate effective energy planning.
• SDG 17 (Partnerships for the Goals): International cooperation, policy frameworks, and stakeholder engagement strengthen global partnerships.

2.3.6. Economic Criteria

• SDG 1 (No Poverty): Affordable energy can help reduce economic hardship.
• SDG 7 (Affordable and Clean Energy): Economic feasibility ensures accessible, cost-effective RE solutions.
• SDG 8 (Decent Work and Economic Growth): Affordable energy supports business growth, investment, and employment.
• SDG 9 (Industry, Innovation, and Infrastructure): Financially viable RE projects foster industrial development and infrastructure expansion.
• SDG 10 (Reduced Inequalities): Economic improvements from RE can help reduce economic disparities.

3. Construction of Hierarchy Structure

In the following subsections, our novel contribution, specifically the explicit explanation and structuring of types and categories of criteria, will be clearly presented, demonstrating how this approach transcends the mere aggregation of the literature and provides a robust framework for decision-making. In Appendix A, the details of each subsection are reported.

3.1. Construction of the Criteria

3.1.1. Types of Criteria

In the framework of site selection for renewable energy projects/microgrids, criteria are often categorized into quantitative (QT) and qualitative (QL) types, and these are essential for assessing site suitability and constraints, as shown in Figure 5. QT criteria require parameters that can be measured, such as resource availability (e.g., solar irradiance, wind speed), closeness to existing infrastructure, capacity factors, area of land, and estimated expenditures. These indicators offer accurate information sourced from expert assessments and literature reviews, permitting a detailed analysis of potential sites based on actual data. QL criteria involve subjective assessments like environmental impact, regulatory environment, public acceptance, technological feasibility, and flexibility to future changes. These considerations, which are based on expert opinions and stakeholder insights, provide an increased awareness of site suitability and project feasibility beyond mere numerical metrics. Collectively, types and categories of criteria provide a comprehensive framework for evaluating and balancing both measurable data and subjective insights, with categories further identifying between cost-related limitations and benefit-oriented advantages.

3.1.2. Categorization of the Criteria

Within the categories of the criteria, $\mathcal{C}$ represents factors that provide limitations and challenges to site suitability. This includes capital costs for initial investment, operational and maintenance costs, infrastructure investments, and external costs related to environmental evaluations and regulatory compliance. These cost-related considerations may affect the overall feasibility and financial sustainability of renewable energy projects. Conversely, benefits highlight factors that positively impact site suitability, such as energy generation potential, economic gains, and environmental and social improvements. By categorizing criteria into $\mathcal{C}$ and $\mathcal{B}$, stakeholders can effectively evaluate options and make informed decisions to optimize renewable energy site selection while aligning with project limitations and sustainability goals, as shown in Figure 5. This categorization helps to separate the financial constraints from the potential advantages, ensuring a balanced approach to site evaluation.

3.2. Technical Criteria for RE (TEC)

Technical criteria labeled with “TEC” are needed to assess the performance and suitability of various renewable energy sources. In the technical criteria table, Column 1 identifies the criterion, which represents both TEC and environmental (EVC) aspects. Column 2 lists the sub-criteria of the criterion. Column 3 indicates whether the criteria are $\mathcal{C}$ or $\mathcal{B}$, as well as quantitative or qualitative. Column 4 outlines the limitations of each criterion and specifies the unit for particular limits. Column 5 provides expert opinions on the criteria.

For PV systems, common TEC include annual solar irradiance (kWh/m²), slope of land surface (%), aspect, and sunshine duration, as detailed in Table A1.

Wind energy utilizes TEC such as wind speed (m/s), slope (%), and wind power density (W/m²), as described in Table A2.

Wave energy criteria represent TEC parameters including wave height, wind speed, wind duration, mean wave energy flux, and distance between waves, as shown in Table A3.

Tidal energy systems depend upon TEC like tidal current (m/s), water depth, and tidal current power (W/m²) in Table A4.

Although less discussed, geothermal energy TEC may involve fault density, radioactivity, thermal flux, and intrusive rock characteristics, as shown in Table A5.

3.3. Environment Criteria for RE (EVC)

In the environmental criteria for renewable energies, all the experts followed common criteria like water consumption, land degradation, water disposal need, and climate change. Additionally, PV includes proximity to multi-story houses to avoid shadow loss, and tidal and wave energy criteria include marine ecological, coastal erosion, and water quality. Geothermal environmental criteria are geological impact, depth, mineralization, stage of exploration, and tectonic activity.

3.4. Common Criteria for RE

According to the experts’ opinion from the literature review, the economic (ECC), social (SOC), and political criteria (POC) are common criteria for all renewable energies shown in Table A6. The most commonly used economic criteria are cost-based criteria like construction cost (USD), investment cost, operation cost, LCOE, net present cost, and R&D cost. As for social criteria, these are beneficial criteria for RE like the impacts made by humans, impacts of native individuals, job opportunity, social acceptance, and social benefit. The most commonly used political criteria are foreign dependency, government incentives, government policies, national energy policy targets, and political acceptance.

3.5. Exclusion Criteria for RE (EXC)

Exclusion criteria (EXC) encompass multiple subcategories, including, but not limited to, emissions ($\mathcal{E}$), pollution ($\mathcal{P}$), distance ($\mathcal{D}$), and region ($\mathcal{R}$), as detailed in Table A7. Although most renewable energy technologies produce relatively minimal greenhouse gas emissions, these can still vary by source. For instance, photovoltaic (PV) and wind power primarily contribute indirect emissions; wave and tidal systems generate very low direct emissions; and geothermal energy may release small amounts of naturally occurring CO₂ and CH₄, reflecting the specific reservoir conditions in geothermal fields.

3.6. Novelty of the Proposed Framework

In conclusion, the proposed framework provides a new methodology in the construction and categorization of criteria related to renewable energy site selection. Instead of giving major importance to only a few factors of technical or environmental nature, this framework involves all criteria—technical, environmental, economic, social, political, and exclusion ones. This also ensures that these criteria align with the SDGs, thus offering a relevant and holistic evaluation framework from a global relevance. The research thereby offers a robust decision-making tool for optimizing site selection and project planning by dividing the criteria into cost- and benefit-oriented groups, and it also embodies quantitative and qualitative assessments.

4. Weight Assignment

4.1. MCDM Technique

MCDM stands as an important section in decision analysis, providing a structured approach to selecting among various alternatives characterized by their attributes. The scope of MCDM techniques encompasses a multitude of techniques, each with unique mathematical foundations, challenging approaches, and result types. Customized to specific problem domains, these techniques often challenge generic applications, leading to careful consideration of keywords and criteria for technique selection. In the realm of sustainable and renewable energy systems, MCDM serves as a vital tool for dealing with complex decision-making areas. The process typically involves determining attributes, collecting relevant data, prioritizing decision-maker preferences, assessing feasible options, and ultimately selecting an appropriate technique for evaluation and outranking. Experts play a crucial role in evaluating projects, helping decision-makers in clarifying objectives, assessing solution feasibility, and ultimately implementing chosen solutions.

4.2. AHP Method

The AHP, developed by Saaty in 1997, is a widely utilized decision-making technique used to assign weights to evaluation criteria, thus figuring out their relative importance within decision-making models. AHP offers an organized methodology for decision-making by setting up factors hierarchically and utilizing pairwise comparisons to establish criteria weights.

The Saaty scale of

Table 1. Saaty scale.

Value	Description
1	Equal importance
2	Weak importance
3	Moderate importance
4	Strong importance
5	Very strong importance
6	Extreme importance
7	Absolute importance

is made up of seven points ranging from 1 to 7, each representing a different level of importance. This scale is applied in pairwise comparisons to evaluate the relative importance of criteria or alternatives. Participants in the decision-making process allocate values to pairs of criteria or alternatives based on their perceived importance, using the scale as a reference.

4.3. Mathematical Foundations of AHP

The Analytical Hierarchy Process (AHP) is a structured decision-making methodology that simplifies complex problems by organizing them into a hierarchy and applying mathematical computations. The AHP is particularly effective in addressing multicriteria decision-making problems, where several conflicting criteria influence the outcome.

The process begins by decomposing the decision problem into a hierarchical model, consisting of goals, criteria, and alternatives. The foundation of the AHP lies in pairwise comparisons, where two criteria or alternatives are compared under a specific criterion. Decision-makers assign preferences such as weak, strong, or very strong for these comparisons, leading to the creation of a pairwise comparison matrix.

For n criteria, the matrix requires $n(n-1)/2$ comparisons. The matrix is filled above the diagonal with these comparisons, and the reciprocal values populate the lower triangular part. The diagonal of the matrix is always 1. This structure ensures a positive reciprocal decision matrix, also known as a judgment matrix.

Real-world decision-making often involves inconsistencies, as perfect judgments are rare. For example, if criterion $x_1$ is slightly more important than $x_2$, and $x_2$ is slightly more important than $x_3$, concluding that $x_3$ is equally or more important than $x_1$ introduces inconsistency. The AHP allows decision-makers to assess and minimize these inconsistencies effectively.

AHP Process: Step-by-Step Guide

• Structuring the Decision Problem Hierarchy: Identify the goal, criteria, and alternatives, arranging them hierarchically.
• Completion of Pairwise Comparison Matrices: Construct comparison matrices for each level of the hierarchy using the Saaty scale. This involves comparing the importance of criteria and alternatives pairwise, with values ranging from 1 to 7 based on the Saaty scale. The pairwise comparison matrix (C) is represented as

  $$C=\left[\begin{array}{cccc}{c}_{11}& c_{12}& \cdots & c_{1n}\\ c_{21}& c_{22}& \cdots & c_{2n}\\ \cdots & \cdots & \ddots & \cdots \\ c_{n1}& c_{n2}& \cdots & c_{nn}\end{array}\right]$$
  Each $c_{ij}$ represents the pairwise comparison rating between the ith and jth criteria or alternatives.
• Calculation of Priority Vectors: Compute priority vectors for each criterion by normalizing the pairwise comparison matrices and determining the weighted sum vector. To calculate the priority vector, the following formula is used:

  $$\mathrm{Priority}\mathrm{Vector}={\displaystyle \frac{\mathrm{Weighted}\mathrm{Sum}\mathrm{Vector}}{\mathrm{Maximum}\mathrm{Eigen}\mathrm{Value}}}$$
  This formula calculates the priority vector, indicating the relative importance of each criterion or alternative in the decision-making process. The weighted sum vector is divided by the maximum eigenvalue obtained from the pairwise comparison matrices.

4.4. Criteria Weights

Weights are calculated using the AHP method and the Saaty scale. Once the priority vector is computed, the matrix is normalized. Ultimately, the sum of all criteria weights for each renewable energy source should equal one, ensuring accurate results. Different numbers of criteria are used for each renewable energy type: 52 for PV, 50 for wind, 55 for wave, 40 for tidal, and 57 for geothermal. The criteria weights for each renewable energy type are illustrated in Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10. In these figures, the x-axis represents the number of criteria, the left y-axis shows the experts’ opinions on each criterion, and the right y-axis displays the criteria weights.

4.5. Consistency Ratio Analysis

In the AHP, guaranteeing the consistency of pairwise comparisons is essential for the reliability of decision-making. The CR is a metric used for evaluating the consistency of judgments made during the pairwise comparison process. It evaluates how consistent the decision-maker’s preferences are over all pairwise comparisons.

4.5.1. Formula for Calculating CR

The CR is computed using the CI and the RI. The CI is determined by the following formula:

$$CI={\displaystyle \frac{{\lambda }_{\max }-n}{n-1}}$$

(1)

where ${\lambda }_{\max }$ represents the maximum eigenvalue obtained from the pairwise comparison matrix and n denotes the number of criteria used in each alternative.

The Random Index (RI) serves as a reference value based on n criteria:

$$RI={\displaystyle \frac{0.9\times n}{n-1}}$$

(2)

After calculating the CI, the CR is obtained by dividing the CI by the RI:

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

(3)

4.5.2. Interpretation of CR

A CR value below 0.1 (10%) is generally considered acceptable, indicating that the decisions are reasonably consistent. In contrast, a CR above 0.1 suggests inconsistency in the pairwise comparisons, making the judgments less reliable. Our novel contribution is highlighted in

Table 2. Consistency Ratio result.

RE	n	CI	RI	CR	Weights
PV	52	0.06264	0.91764	0.06826	Figure 6
Wind	50	0.06591	0.91836	0.07177	Figure 7
Wave	55	0.04116	0.91666	0.04491	Figure 8
Tidal	40	0.03682	0.92307	0.03989	Figure 9
Geo	57	0.06796	0.91607	0.07418	Figure 10

, where we demonstrate that each renewable energy alternative’s criteria remain within the acceptable CR threshold. Here, n represents the number of criteria considered for each alternative, emphasizing the breadth of our evaluation.

This contribution is unique because it goes beyond simply compiling criteria from existing studies. Instead, we rigorously validate the consistency of the decision-making process, ensuring that the resulting weights and rankings are both credible and data-driven. By confirming that the CR stays below 0.1 for each alternative, we provide tangible evidence that our methodology produces reliable, reproducible, and meaningful insights, distinguishing our work from the literature that merely aggregates criteria without such verification.

The AHP was utilized to calculate the proportional importance of the selected criteria, and the resulting weights were validated using the CR. CR values, as reported in Table 2, confirm the reliability and accuracy of the results, with values for PV (0.0682), wind (0.0717), wave (0.0494), tidal (0.03989), and geothermal (0.0741), all within acceptable limits. These results emphasize the tool’s effectiveness in establishing precise weights for renewable energy evaluation, for instance, when comparing solutions through costs and/or productions, and environmental benefits.

5. Pseudocode for Calculating Weights Using the AHP

In contrast to the previous sections, which integrated knowledge from the literature, this section goes beyond simple aggregation by presenting a new decision-support framework realized through an integrated computational tool. Our contribution is not only in systematically applying AHP-based methodologies to an extensive set of more than 50 criteria for each renewable energy source, but also in providing the underlying code for replication and further customization.

To ensure transparency and encourage community engagement, we have uploaded the complete code base and Supplementary Materials associated with this framework to Zenodo [11]. By making the code publicly accessible, we offer a tangible resource that enables other researchers, practitioners, and policymakers to apply, test, and refine the decision-making procedures presented here. This direct contribution differentiates our work from those that merely rest on literature synthesis, as we furnish both the methodological innovation and the operational means for practical implementation and future improvements.

1.

Input:

• An Excel file with two columns:

  –

  Criteria Name: Names of the criteria.

  –

  Expert Suggestions: Number of experts prioritizing each criterion.

2.

Steps:

(a)

Read the expert suggestions for each criterion from the Excel file.

(b)

Convert the suggestions to Saaty’s scale (1 to 7) using the following formula:

$${\mathrm{SaatyScale}}_i=\lceil \left({\displaystyle \frac{{\mathrm{ExpertSuggestion}}_i-min\left(\mathrm{ExpertSuggestions}\right)}{max\left(\mathrm{ExpertSuggestions}\right)-min\left(\mathrm{ExpertSuggestions}\right)}}·(7-1)+1\right)\rceil$$

(c)

Construct the pairwise comparison matrix A of size $n\times n$ (where n is the number of criteria):

$$A[i,j]={\displaystyle \frac{{\mathrm{SaatyScale}}_i}{{\mathrm{SaatyScale}}_j}},\forall i,j=1,2,\dots ,n$$

(d)

Normalize the pairwise comparison matrix:

$$\mathrm{NormMatrix}[i,j]={\displaystyle \frac{A[i,j]}{{\sum }_{k=1}^{n}A[k,j]}}$$

(e)

Calculate the weights of the criteria as the average of each row in the normalized matrix:

$$\mathrm{Weight}\left[i\right]={\displaystyle \frac{{\sum }_{j=1}^n\mathrm{NormMatrix}[i,j]}{n}}$$

(f)

Verify that the total weight satisfies the following:

$$\sum _{i=1}^n\mathrm{Weight}\left[i\right]\approx 1$$

(g)

Check consistency:

• Compute the consistency vector:

  $$\mathrm{ConsistencyVector}\left[i\right]=\sum _{j=1}^{n}A[i,j]·\mathrm{Weight}\left[j\right]$$
• Calculate the largest eigenvalue (${\lambda }_{max}$):

  $${\lambda }_{max}={\displaystyle \frac{{\sum }_{i=1}^n\frac{\mathrm{ConsistencyVector}\left[i\right]}{\mathrm{Weight}\left[i\right]}}{n}}$$
• Compute the Consistency Index (CI):

  $$\mathrm{CI}={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$
• Compute the Random Index (RI):

  $$\mathrm{RI}={\displaystyle \frac{0.9·n}{n-1}}$$
• Compute the Consistency Ratio (CR):

  $$\mathrm{CR}={\displaystyle \frac{\mathrm{CI}}{\mathrm{RI}}}$$
• Verify consistency:

  $$\mathrm{If}\mathrm{CR}<0.1,\mathrm{the}\mathrm{matrix}\mathrm{is}\mathrm{consistent}.$$

3.

Output:

• Weights: Priority of each criterion.
• Consistency Ratio (CR): Should be less than 0.1 for consistency.

6. Ranking Assignment

6.1. Types and Categories of Criteria

The literature provides several answers to these sorts of problems, one of which is MCDM for dealing with various data types such as qualitative and quantitative variables for decisions. Technical criteria, economic criteria, and environmental criteria such as slope, annual irradiance, wave height, and cost-based criteria are examples of qualitative data. Non-numerical data types that describe traits, features, and properties such as impacts on humans, job opportunities, and government incentives are examples of qualitative data. Some data types were combined with other sorts of criteria, such as technical maturity and pollution. The main reason the criteria are divided into benefit and cost is that some of the criteria like irradiance, reliability, and efficiency are more profitable when the value is higher, but cost-related criteria or emission criteria are of higher value and not considered profitable. Criteria are collected and constructed into types and categories to build a hierarchy structure.

6.2. Open Method for Ranking Assessment

In renewable energy site selection, ranking potential sites is critical for identifying the most suitable locations. To support this, we suggest the use of the TOPSIS method as an effective approach for ranking site alternatives.

The TOPSIS method, introduced by Hwang and Yoon, is a widely used decision-making approach for sustainable energy problems [12]. It consists of key steps such as normalizing the decision matrix based on assigned criterion weights, determining the ideal (best) and anti-ideal (worst) solutions, calculating the Euclidean distances of each alternative to these solutions, and computing the relative closeness to the ideal solution. Alternatives are then ranked based on their relative closeness.

Although the current study focuses on criteria weighting using the AHP, our planned future work involves applying TOPSIS to rank renewable energy alternatives systematically. This approach is expected to enhance the decision-making framework by providing clear, actionable rankings that consider both quantitative and qualitative criteria, thereby supporting more informed and resilient energy planning decisions.

7. Conclusions

This study successfully developed a new supporting tool to assist energy planners, decision-makers, and researchers in choosing appropriate renewable energy sources, considering resilience phases, and in line with achieving several SDGs. This proposed tool assesses five different renewable energy alternatives—solar, wind, wave, tidal, and geothermal—by applying a multicriteria evaluation framework based on six various sustainability criteria, namely technical, social, environmental, political, economic, and exclusion criteria. For each energy source, nearly 50 sub-criteria were painstakingly considered to ensure a robust, objective analysis.

The AHP was utilized to calculate the proportional importance of the selected criteria, validating the reliability and accuracy of the weights. These findings demonstrate the tool’s effectiveness in establishing precise weights for renewable energy evaluation, particularly in comparing solutions through costs, production efficiencies, and environmental benefits.

The results of this study expose the versatility of the supporting tool in capturing various critical factors of sustainability across more renewable energy sources. While dividing the criteria into a cost- and benefit-oriented group, the tool supplies a structured path to both evaluate and prioritize renewable energy alternatives effectively. Further work will focus on the application of the TOPSIS method to provide the ranking of alternatives appropriately and offer actionable insights toward practical implementation for resilient and sustainable energy systems to support global SDG targets.