1 Introduction

Renewable energy (RE) sources have emerged as an undoubtedly effective alternative to address the issues of the energy crisis, environmental risks, and human energy requirements as the world seeks to lessen the harmful effects of fossil fuel consumption on the environment, climate change and human health (Poudyal et al., 2019). According to the International Energy Agency (IEA), the capacity of renewable energy sources will increase by more than 8% globally in 2022 compared to 2021, surpassing 300 GW for the first time (Kumar et al., 2022; Update, 2022). Because of the rapid growth of renewable energy in the global energy mix, most industrialized nations are making significant attractive schemes and large investments to encourage industrial sectors to switch to less carbon-intensive renewable energy technologies (Gielen et al., 2019; Percy & Edwin, 2022a). India is abundantly blessed with RE sources that are widely dispersed across the country, and the entire potential outweighs the total energy demand in the country (Edwin & Joseph, 2018; Edwin et al., 2022; Khare et al., 2022). However, due to a variety of technological, economic, environmental, and societal restrictions, only a small portion of renewable potential is utilized (Al-Shetwi, 2022). The inequitable availability of RE sources across the entire country and the higher investment costs required for RE sources like solar and wind make it difficult to predict the cost and energy security for the best alternatives on a long-term scale. These are some of the main barriers to the adoption of RE technologies by government and corporate entities (Asante et al., 2022; Solangi et al., 2021; Tseng et al., 2021). Considering such contradictory constraints complicates the decision-making process. An energy source that is ideal for one place could be the most undesirable option for another. Therefore, it is critical to conduct an underside study in rural India, where renewable resources are abundant. However, only limited research on the selection of the most appropriate renewable energy sources in India are available in the literature. For proper practical adoption of RE technologies, it is very much essential to carry out a basic study at the bottom level covering the region where the technology is to be implemented. There is no such research available in the literature on a micro-scale done on a district level for prioritizing RE in India. For selecting the best renewables among alternatives, AHP has been used as an effective tool to solve energy problems (Pathak et al., 2022). AHP considers a set of main criteria having an unequal degree of importance. The main criteria include several sub-criteria and are weighted individually to indicate their relative importance. Based on the summation of performance scores given by experts multiplied by the corresponding weights of the criteria are ranked (Abdul et al., 2022; Alghassab, 2022; Coruhlu et al., 2022). An AHP-based model was used to plan the energy demand considering technical, political, and environmental attributes (Asakereh et al., 2022; Oryani et al., 2021). A study based on AHP was done on the selection of the best RE technology among the solar, wind, biomass, and geothermal sources considering their technical, socio-political, economic, and environmental criteria (Al Garni et al., 2016). Another similar study in Jordan on prioritizing the decentralized RE sources in rural communities was done considering technical, economic, environmental, and social points as risk criteria (Malkawi et al., 2017). AHP was used for the evaluation of RE based electricity generation for various countries (Karaaslan & Gezen, 2022; Kul et al., 2020; Solangi et al., 2019; Wang et al., 2020). AHP was used to determine the food vulnerable zones in a small district of India and ranked based on the regions considered (Vignesh et al., 2021). The likelihood of new employment opportunities for private renewable investments using AHP in the USA ranked solar and wind at the top leaving biomass at the bottom (Budak et al., 2019). A study on exploiting solar power in China focussing on the economic aspects found that investment cost is the most influential factor (Tang et al., 2014). In light of the comprehensive outcomes observed in prior studies, which underscore the efficacy of the Analytic Hierarchy Process (AHP) as a robust decision-making tool for complex, multi-criteria challenges, the utilization of AHP is justified. It is noteworthy, however, that existing literature predominantly focuses on renewable energy assessment and selection at a macro scale. While this approach is valuable, there exists an essential gap in our understanding—the imperative need for resource assessment and ranking of renewable energy at the grassroots level, particularly in rural regions. This study serves as a pivotal bridge in addressing this gap. This study is motivated by a resolute goal—to advance the practical adoption of renewable energy technologies on a national scale and the need for the transition towards sustainable energy sources requires a granular examination of available resources, extending to the grassroots. Hence this research work centers on harnessing the potential of the AHP method to navigate the intricate landscape of renewable energy options. By systematically considering technical, economic, environmental, and social aspects, this study aims to pinpoint the most optimal renewable energy source among alternatives.

The main objectives of this study are to (i) to identify the diverse array of renewable energy resources abundant within the study region (ii) to discern the primary and subsidiary criteria pivotal for the successful implementation of renewable and sustainable energy technologies (iii) to meticulously rank these criteria through the prism of AHP, thereby pinpointing the paramount alternative within the realm of renewable energy sources in the selected study region. The significance of this research work reverberates beyond academic realms—it extends to the realms of policy and investment. The outcomes gleaned have the potential to empower policymakers and investors. By shedding light on the most suitable renewable energy sources and unveiling innovative strategies for fostering sustainable power generation systems in rural areas, this research paves the way for a seamless transition from conventional fossil energy sources to the realm of renewable energy. This transition, in turn, holds the key to mitigating the environmental toll of energy consumption, steering our trajectory toward a cleaner, greener, and more sustainable future.

2 Materials and methods

2.1 Identification of study area and electricity structure

The study area considered for this research is India’s southernmost district, Kanyakumari. as shown in Fig. 1. The total geographical coordinates of the study region extend from 77° 6′ 1′′ East to 77° 35′ 26′′ East and 8° 4′ 36″ to 8° 34′ 43″ North with a total area of 1684 km2 (Chrisben Sam & Gurugnanam, 2022; Sam & Balasubramanian, 2022; Sam & Gurugnanam, 2022). The study regionhas a widespread distribution of flora and fauna. Being an agricultural district, the agrarian land spans approximately for about 868 km2, whereas forest covers nearly 545km2, and built up and waterbodies cover around 206km2 and 65km2, respectively (Vignesh et al., 2021). As shown in Fig. 1, the study region is divided into nine blocks for administrative reasons as Munchirai, Killiyoor,Kurunthancode, Rajakkamangalam, Agasteeswaram, Thuckalay, Melpuram, Thiruvattar and Thovalai respectively and these blocks are named as B1,B2,B3,B4,B5,B6,B7,B8 and B9 for this study.

Fig. 1
figure 1

Study area map showing nine administrative blocks used for the AHP process

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The total electricity consumption of the study region is about 1132.759 Million Units during 2020-21 with 250 MVA as the peak demand.The sector-wise electricity consumption is depicted in Fig. 2. Hariharadhas et al., (2019). The details of electricity generated from renewable sources are presented in Table 1.

Fig. 2
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Sector wise electricity consumption in the study region (Hariharadhas et al., 2019)

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Table 1 Installed renewable energy potential of study region (Hariharadhas et al., 2019)
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Table 1 depicts the renewable energy potential in the study area. It showed that only a small portion of renewable energy resources have been utilised for power generation with the exception of wind energy. However, there are a lot of widely available renewable resources in the district that need to be investigated thoroughly for energy conversion.

2.2 Potential of renewable energy sources in the study area

2.2.1 Solarenergy potential in the study area

Solar energy is the most abundant and inexhaustible source of sustainable energy that can be used to meet energy demands in urban, rural, and remote locations(El, 2021). India is one of the greatest recipients of solar energy receivers due to its optimal location in the solar belts (40°S to 40°N)(Ramachandra et al., 2011). India on average receives solar radiation of 3.5–6.5 kWh/m2day,with around 12 h of sunlight(Jamil et al., 2016).The equatorial regions receive more solar irradiancebecause the Sun is directly overhead in the equatorial and tropical regions throughout the year(Hartmann, 2003). The selected study area, located 800–876 km away from the equator, receives a global horizontal irradiation (GHI)ranging from 3.77 to 5.56 kWh/m2as shown in Fig. 3.The major land area of the study region is agricultural land (51.52%) followed by reserved forest (32.24%) (Vignesh et al., 2021). It is reported that a small portion of the land of about2.4% (4001 Hectares) is left as Barren and unculturable land in the study region of which the major portion is found along the coastline. Installing solar panels along the coastal shore is much more appealing as they receive higher radiation thaninland.Due to the higher solar radiation and land availability, installing solar farms along the seashore of B1, B2, B3, B4 and B5is recommended for setting up a solar farm. Nevertheless, solar panels are susceptible to corrosion, and the salt present in the wind on crystallization forms a thin film over the surface, reducing the panel’s efficiency (Kazem & Chaichan, 2019). The dense forest cover, agricultural cultivations and higher elevations over the north-eastern regions of the study region, make the blocks B7, B8, and B9 not suitable for solar farm installation. Hencerooftop mounted solar photovoltaic (PV) panels are the best option for harnessing solar power in the study region.Around 47.6% of the total solar power generated in Tamil Nadu comes from solar roof top asthe government offers 40% subsidy up to 3 kW and 20% up to 10 kW for rooftop solar PV panel installation in residential and government buildings (Geetha et al., 2022).The roof top solar power potential is dependent on the built-up land available (Goel, 2016). Hence the blocks with high density of residential regions have the highest roof top solar power potential. Solar street lights, solar lamps, solar water pumps for agricultural practices, solar water heaters are also used by the people which serves as other means for utilizing energy, however, solar rooftop panels have gained much interest and being adopted.The details and cost for setting up a 1 kW solar PV panel is as given in Table 2.

Fig. 3
figure 3

Solar irradiation map of the study area (Atlas & n.d., 2023a)

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Table 2 Specifications for a 1 kW solar roof top PV panel (Ahsan et al., 2016; Satpathy & Pamuru, 2021; Shukla et al., 2016)
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2.2.2 Wind energy potential in the study area

Wind energy is a renewable, inexhaustible and eco-friendly source of energy that has got a huge potential across the globe (Lu & McElroy, 2017).Off shore wind energy generation is favourable in India, which is blessed with a 7517 km of coastline with 5423km on the peninsular region along which the maximum wind potential is concentrated (Chaurasiya et al., 2019). India stands fourth in the world’s installed wind power with a total wind installed capacity of 38.62GW generating 64.64 billion units of electricity. However, a study by the National institute of wind energy has stated that India has a huge potential of wind energy of 302 GW considering 0.5% land availability and 100 m hub height.Tamil Nadu ranks top among all Indian states, accounting for around 24.44% of total wind power installed in India (Suresh Kumar et al., 2022). A study on the assessment of wind power potential in the selected study region revealed that the coastal regions had a desired parameter for electricity generation for wind turbine operation with wind power density(WPD) ranging from 208.64 to 684.2 W/m2 (Saxena & Rao, 2016).However, there are no offshore energy projects in the study region. The selected study region is home to India’s biggest onshore wind farm at Muppandal with an installed capacity of 1500 MW (George & Banerjee, 2009).The wind speed map of the study region is given in Fig. 4. It is observed that the major portion of B5 and B9 regions havea higher wind speed of 22.7 m/s is observed at Thovalai (8.297791° N and 77.520218° E)of B9 block of the study region with WPD of 6190 W/m2. Apart from this, the northern most part of the study region that lies within B7 has a wind power potential, with maximum wind speed of 11.46 m/s and WPD of 2305 W/m2.Furthermore, a village named Muttom (8.12925° N and 77.319717° E) on the southwest coast of B4, has a wind energy potential with a maximum wind speed of 7.24 m/s and WPD of 386 w/m2.To promote wind energy projects, the Indian government is also focusing on policy development to attract investors in the wind energy sector (Sharma & Sinha 2019). The detailed specification for setting up a wind turbine in the selected study region is given in Table 3. Also, an European Union-funded study conductedon the offshore wind potential of Tamil Nadu from 2018 to 2032 aiming from zero to 5GW wind power assessed the pre-feasibility study covering all the constraints on installation, infrastructure ensuring sites for offshore wind projects showed the study region holds a sufficient potential for offshore wind energy projects (Hiloidhari et al., 2014).

Fig. 4
figure 4

Wind speed map at 100 m height at the study region (Atlas & n.d., 2023b)

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Table 3 Detailed specifications of a wind turbine (Arias-Rosales & Osorio-Gómez, 2018; Dröes & Koster 2021)
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2.2.3 Bio energy potential in the study area

Biomass refers to any material of biological origin which can be replenished or regenerated naturally (Ling et al., 2021). The growing energy demand, harmful effects caused by the conventional fossil fuels and the surplus potential of biomass to meet the rising energy need has emerged biomass as a sustainable bio-economy (Ciria & Barro, 2016; Percy & Edwin, 2022b). The energy derived from biomass sources are the most strategically feasible solution for reducing greenhouse emissions and at the same time provides upliftment in the lives of small marginal farmers (Deep Singh et al., 2022; Edwin & Joseph, 2016, 2018; Elkhouly et al., 2022). Evaluating the available biomass resources like forestry waste, agricultural waste, animal manure, and municipal trash as bioenergy resources can help researchers and investors to unlock several new renewable energy prospects (Percy & Edwin, 2022c; Percy & Edwin, 2023). The potential of forestry biomass, agricultural biomass, municipal solid waste and animal manure in the study area is described as follows.

2.2.3.1 Forestry biomass

The selected study region is blessed with widespread distribution of forest along the northern and north-eastern parts of the study region. A study on the selected region on the land use pattern is shown in Fig. 5. It shows that about 32.4% (545.09 km2) of the study region is covered by forest area. For the estimation of forest biomass residues such as limbs, tops, and culled tree components left after logging timber, the biomass estimation equations are used by researchers to predict biomass above ground (BAG). A recent study has provided an adequate equation for predicting the biomass of any woody species across a range of conditions in India (Brahma et al., 2021) and are given as

$$ {\text{BAG}}\,{ = }\,{0}{\text{.349}}\,{ + }\,{1}{\text{.36}}\,{\text{ln}}\,\left( {h_{g} } \right) $$
(1)
$$ {\text{BAG}}\, = \,\left( {0.18d^{2.16} } \right) \times 1.32 $$
(2)

where \(h_{g}\) is the girth height of the tree in meters and d is the diameter of the tree at the girth height. Although the availability of forest biomass is huge in B7, B8 and B9, they remain inaccessible because the forest land is protected as reserved forest.

Fig. 5
figure 5

Land utilization map of the selected study region (Vignesh et al., 2021)

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2.2.3.2 Agricultural biomass

Agricultural biomass refers to all organic material produced as a by-product of agricultural crop harvesting and processing. Figure 5 shows that more than half of the study region is utilized for doing agriculture constituting agrarian land for 867.6 km.2. Residue like rice straw, rubber shell, left over dried stems, dried leaves, twigs, sugar cane tops etc. which is obtained in the field at the time of yield are known as primary residue, whereas those obtained during the processing like rice husk, coconut shell, coconut fronds, sugar cane bagasse, saw dust, coir pith etc.are known as the secondary residue. The primary and secondary residues (Rp and Rs) are calculated by Hiloidhari et al. (2014)

$$ R_{p} = Y_{A} \times A_{g} \times \beta $$
(3)
$$ R_{s} = Y_{A} \, \times \,\left( {1 - E_{f} } \right) \times P_{f} \times A_{g} \times \beta $$
(4)

where YA, Ag, \(\beta\) are the Average yield, the total area under cultivation and residue to product ratio of the selected crop respectively. Ef and Pf are the exported and processed fraction obtained when processing the yield. Table 4. Shows the predominant agricultural biomass residues and their estimated quantities in the study region.

Table 4 Predominant agricultural biomass availability in the study region
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2.2.3.3 Municipal solid waste

Municipal solid waste is usually a mix of residential and commercial waste produced by the living community (Abdelzaher & Shehata, 2022; Abdelzaher 2022). It is estimated that around 193TPD of solid waste is generated in the selected study region. House to house waste collection is practised in the study region and 100% of waste is collected in Nagercoilcorporation, Kuzhiturai and Padmanadapuram municipalities whereas in Colachel municipality about 80% of waste is collected. The waste collected are biomined at specified locations and are as given in the Table 5

Table 5 Potential of MSW collected from various blocks
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2.2.3.4 Livestock biomass

The potential amounts of animal dung resources produced are determined using the number of animals, the yearly average manure generation per animal, and the recoverability percentage. The quantity of animal manure biomass that can be derived for energy use is estimated using Eq. (5) (Edwin & Joseph, 2014; Mboumboue & Njomo, 2018)

$$ R_{{{\text{AM}}}} \, = \,n\, \times \,M_{y} \, \times \,R_{f} $$
(5)

where \(R_{{{\text{AM}}}}\) is the Biomass Residue that can be recovered from animal manure, n is the number of animals, \(M_{y}\) is the average quantity of manure obtained from the animal per day and Rf is the recoverability fraction. The number of livestock available and the estimated biomass that can be recovered is presented in Table 6.

Table 6 Statistics of Livestock and the estimated biomass recovered from them(Ludlow et al., 2021; Sasikumar et al., 2021; Tolessa et al., 2020)
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2.2.4 Ocean energy potential in the study area

Ocean is one among the renewable energy sources that has a potential to meet part of the world’s energy demand (Alamian et al., 2014). Tidal and wave energy are the two forms of ocean energy. In the selected study region, there is no much variation in tides. But the study region has got a considerable potential for wave energy. A research conducted for identifying the wave energy potential of India has identified a site along the coast of Kanyakumari (8° N, 77.5° E) with an average wind power of 23 kW/m as one among the best sites for generating wave poweras shown in Fig. 6. Sannasiraj and Sundar (2016). Wave energy potential in the study region is confined to blocks B1,B2,B3,B4 and B5 as they are situated along the coastline.

Fig. 6
figure 6

Wave energy potential along the coast line of the study area

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2.3 Multi criteria decision making analysis (MCDMA)

Prioritization of renewable energy sources includes multiple conflicting sub criteria and different perspectives in selecting the suitable resources. For such problems, MCDMA technique provides valuable measures in identifying the most influential criteria, the weightage of each criteria and various preference criteria that can be appropriately studied before making decisions for complicated and un organized problems. In this research, MCDMA analysis is performed utilising the Analytical Hierarchy Process (AHP) to simplify all of the criteria in a hierarchical order and select the best decision. AHP has the following steps (Ahmad & Tahar, 2014; Luthra et al., 2015; Wang et al., 2019).

  1. i.

    Framing the hierarchical structure:Frame a level hierarchy for the decision issue, with the aim at the top followed by criteria, sub-criteria, and alternatives at the bottom.

  2. ii.

    Construction of pair wise comparison matrix: A survey conducted among 50 experts consisting of professionals, industrialists, Academicians, Researchers and common people using nine point scale as given in Table 7 is done and the values are consolidated using geometric mean and formed as a (n × n) pairwise matrix.

    Table 7 Measurement scale used in AHP (Chatzimouratidis & Pilavachi, 2009; Sun et al., 2021)
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  1. iii.

    Calculating the consistency: The relative weights or maximum Eigen value (\(\lambda_{\max }\)) are determined to verify that the consistency of the pairwise comparison. Further, the consistency of the comparisons made is checked using consistency index (CI) and consistency ratio(CR) using Eqs. (6) and (7).

    $$ {\text{CI = }}\frac{{\lambda_{{{\text{max}}}} - n}}{{n - {1}}} $$
    (6)
    $$ {\text{CR}}\,{ = }\,\frac{{{\text{CI}}}}{{{\text{RI}}}} $$
    (7)

    where RI is the Random Index and has the values of 0,0.58,0.9,1.12 for matrix with dimensions 2,3,4 and 5 respectively.The value of CR must be less than 0.1, only then the consistency between expert and academic answers is acceptable.

  2. iv

    Formation of decision matrix: After calculating relative weights, the decision matrix is formed and the final weights of alternatives are obtained and ranked to arrive at ranking for alternatives. The Hierarchical tree structure for selecting the best renewable energy source is shown in Fig. 7.

    Fig. 7
    figure 7

    The hierarchical tree structure for selection of the best renewable energy source among the other potential renewables

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2.4 Ranking of sub-criteria considered based on AHP

In multiple attribute problems, every criterion includes several sub-criteria that govern the feasibility of the criteria. This study investigates the effects of five different sub-criteria on the criteria to find the individual influence of each sub-criteria and the most influential criteria and rank them so that it will assist to pick appropriate results.

2.4.1 Ranking of technical sub-criteria

The technical criteria include five sub criteria namely efficiency, capacity, availability, life expectancy, and lead time. The pair wise comparison matrix derived from expert decisions for technical sub criteria is shown in Table 8.

Table 8 Pair wise comparison matrix for technical, economic, environmental and social sub criteria
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The relative weights for the five technical sub criteria calculated using from the pairwise comparison matrix is represented in Fig. 8. It shows that the efficiency (38.86%) of the system is the criteria that has major effect over the technical parameters. This is because any renewable energy power generation system should have good efficiency for its practical application. The second influential criteria is capacity of the system with relative weight of 17.52% followed by life expectancy (17.23%) of the project, Availability of renewable energy source (13.54%) and lead time (12.04%) required for construction of the project. Improving the renewable energy system with new innovations for improving the efficiency and user friendly simple technologies will attracts more new uses. Similar trends have been reported in Abdul et al. (2022)

Fig. 8
figure 8

Relative weights of the technical sub criteria

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2.4.2 Ranking of economic sub-criteria

To find the influence of the sub criteria over the economic criteria, a pairwise comparison matrix and the inconsistency index is constructed as shown in Table 8. The results of the priority weights as given in Fig. 9, shows that the Investment cost (57.94%) is the most important parameter. Commercial power generation requires installation of large capacity renewable systems that requires a high investment cost.The second most important criteria is the Maintenance cost (19.6%) followed by Subsidy (10.61%) and Rate of Interest (6.83%).The commercialization potential (5.56%) is observed as the least important subcriteria. Large scale renewable systems like wind farms, solar parks are installed by companies and they have appropriate knowledge on the tariff rates regarding commercialization. However, for small-scale installations like PV panels and biomass power production by households, many are unfamiliar with the advantages of selling excess electricity back to the grid at higher prices, that is nowimproving due to the advent of the net metering approach.

Fig. 9
figure 9

Relative weights of the economic sub criteria

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2.4.3 Ranking of environmental sub-criteria

Environmental concern is the important criteria for shifting from conventional sources to renewable energy. Table 8 shows the pairwise comparison matrix for the sub criteria against environmental criteria. The table results reveal that, despite the fact that there are various sub criteria regarding pollution concerns, land requirement (43.38%) ranks top among the environmental sub criterion against the aim.The selected study region is mainly depend on agriculture with dense population and the availability of barren or uncultivable land is very low. Hence due to the unavailability of land, and unwillingness of the people to sell their lands or set a Renewable energy system near their residence makes land requirement sub criteria ranks top among the environmental attributes. As visualized in Fig. 10, air pollution (22.5%) is the second important criteria followed by water pollution (14.16%), waste disposal (12.62%) and soil pollution (7.34%).

Fig. 10
figure 10

Relative weights of the environmental sub criteria

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2.4.4 Ranking of social sub-criteria

The pair wise comparison matrix and relative weights derived from expert decisions for the social sub criteria is shown in Table 8 and Fig. 11 respectively. The results from Table 8 and Fig. 11, disclose that government policies and regulations (38.35%) is the first priority criteria. The reason is that for commercial renewable installations require approval from the government bodies like pollution board, agricultural board for land acquisition. Public acceptance (27.40%) is the second most important criteria as the local communities are not much aware of the benefits for renewable power installations on large scale. Furthermore, due to the study areas high population, land acquisition is a serious challenge for the development of renewable power plants, since many are unwilling to sell their properties and resist construction near their residences. Creation of new employment opportunities (20.98%) stands next followed bygeographic subcriterion (7.21%). The cultural and behavioural aspects of the people (6.07%) is the least important among the social sub criteria.

Fig. 11
figure 11

Relative weights of the social sub criteria

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The priority weights of each sub criteria namely technical, economic, environmental and social have been consolidated as a single matrix and ranked based on the priority weights as shown in Table 8. Figure 12. depicts that among the main criteria considered for AHP ranking for the selection of best renewable energy resource in the study region, the economic criteria has the highest priority with a relative weight of 43.38% followed by Technical 22.50%, Environmental 14.16% and social 12.62%. Hence it is recommended to choose the renewable energy source having a highest value over the economic criteria. As observed in Table 8, among the economic criteria, Renewable energy system requires high investment cost which serves as a barrier for attracting investors. If proper regulations are framed by the government such as subsidy schemes, providing low interest rate for loans, and guaranteed purchase of electricity, the renewable energy sources will become more attractive to investors.The results of consistency index that were obtained in this study are in close relevance with the results obtained from previous literature as presented in Table 9.

Fig. 12
figure 12

Relative weights of the criteria considered for AHP ranking for the selection of best renewable energy resource

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Table 9 Comparison of Consistency index of the present work with previous literature
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2.5 Block wise ranking of renewable energy resources

Once the relative weights for different criteria and sub criteria are determined, the convergence step is done. In this, the combined weights of alternatives are determined by aggregating relative weights throughout the hierarchy process. To calculate the combined relative weights (normalized weight) of all the alternatives, the relative weights of alternatives are multiplied by the relative weight of criteria with respect to the goal and the results for individual block are presented in Table 10.

Table 10 Normalized weight of alternatives with respect to block
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From Fig. 13, it is seen that B1 and B8block has a significant potential of only solar and bio energy. Howeverblocks B4 andB5 holds potential for all the four renewables. The Potential of solar energy is higher in B1, B2, B3, B4, B5blocks as they are along the seashore. Furthermore, B6, also holds higher solar potential due to the greater number of built-up available in that block. However,blockB7, B8 and B9 have very less potential because of the hilly terrain and dense forest cover. Wind energy has a highest potential in B9block followed by B5,B7, B4 and B6 respectively. Bioenergy has an average potential throughout all blocks. The maximum bio energy potential is found in B7 and the least potential is observed in B1block. Ocean energy though is not implemented holds a high potential in the form of wave energy in B1, B2, B3, B4 and B5blocks respectively.

Fig. 13
figure 13

Normalized weight of alternatives with respect to block

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3 Conclusion

In order to efficient utilisation of the available renewable energy resources in the selected study area, a complete evaluation of the available renewable energy resources and the factors influencing the selection of energy resources prior to implementation is conducted in this study.Hence the entire study region is divided in to nine blocks based on the administrative block division. Prioritization of renewables in each block identified in the study area is done by AHP process taking four criteria namely technical, economic, environmental and social.

The AHP analysis revealed insightful findings. The AHP model showed that the efficiency of the system emerged as the foremost technical consideration having the relative weight of 38.86% among the five technical sub criteria. The investment cost of the system having the relative weight of 57.94% is the top-prioritized criteria among the five economic sub criteria.It also revealed that theland requirement for the implementation of renewable energy system is the highly prioritized criteria (43.38%) among the five environmental sub criteria. Government policies and regulations is the highest priority criteriaas 38.34% among the five social sub criteria.. Notably, economic factors emerged as the most influential criterion for selecting optimal renewable energy sources, surpassing technical, environmental, and social considerations.

Prioritization of renewables using AHP results showed that solar energy is of highest priority in block 2 whereas wind energy potential is higher in block 5 and 9in the study area. Due to the dense population and wide spread agricultural plantations; Bio energy has average priority over the entire study region. However, the potential of wave energy is prioritized only in blocks 2, 3, 4 and 5.

The study’s limited focus on a specific region may hinder the applicability of results to different areas, as unique climatic, resource, and socio-economic factors could differ from one region to other. Additionally, the study mainly considers current conditions and doesn’t thoroughly address potential future changes in technology, policies, or environmental aspects. Relying on available data for prioritization might introduce uncertainties due to data gaps or inaccuracies. Although the study uses a comprehensive analysis, simplifying complex criteria into distinct categories might overlook intricate interactions. The subjective weighting of criteria in the AHP framework introduces variability and potential bias in prioritization outcomes.

This study offers valuable insights for potential investors seeking to make informed decisions regarding investments in renewable energy. Moreover, it provides a foundation for researchers to delve into the intricacies of challenges related to the installation of renewable energy systems and to pioneer innovative technologies for optimal resource utilization. Furthermore, this study not only aids researchers in prioritizing renewable energy sources within various study regions but also prompts future investigations that leverage the awareness of limitations to explore new avenues for advancements in the field.