1 Introduction

Nowadays, the world’s energy demand increases every day because of the increase in the world population and the standard of people's lives (Rahman et al. 2016) (Ebrahimpour et al., 2022). It has increased about 1.7% per year in the past ten years (Ağbulut & Sarıdemir, 2019). The familiar electrical power sources are primary resources like fossil fuels (e.g., natural gas, oil, coal) -about 85% of global load demand (Ağbulut, 2019). Petroleum derivatives are the essential wellspring of ozone-depleting substances or greenhouse gas (GHG) emissions (Malekli & Aslani, 2022). GHG damagingly affects human and the climate, such as global warming, acid rain, ozone depletion, and an increase in the temperature of the Earth’s surface (Marefati et al., 2019). To diminish GHG production, scientists recommended using Renewable Energy Sources (RES). Today, the RESs are included as a widespread resource for electricity production. So, it can be seen that the usage of renewable utilities such as photovoltaic panels, wind turbines, hydro turbines, and others is increasing worldwide (Ebrahimpour & Shafii, 2022). In developing countries, some people live in rural areas where their population is more than about 2.4 billion (Mandelli et al., 2016). In Iran, most of the entire unconnected rural areas have been electrified in recent years, but still, small villages need energy. There are four types of demand for residential areas, including electricity, fresh water, heating, and cooling, all of which can be supplied by electrical appliances. So, it can be said that in this case, all these four categories can be considered in an electric category.

Meanwhile, there is a good state of solar radiation and wind speed in most regions of Iran. Since in the villages people raise cows and sheep, there is a suitable source of biomass to produce biogas. Moreover, if there is streamflow with a suitable current, the hydro turbine can be utilized (Mousavi et al. 2021). Considering many different regions, Iran has a high potential for the use of renewable resources and also considering the principle that these resources have, in addition to supplying electricity to the region and at the same time emitting microscopic pollutants, caused economic development of the region by creating conditions, e.g., employment, income generation in the region, and social development through access to safe and sustainable energy and increase the quality of life. Renewable systems are systems whose input fuel is from renewable sources, i.e., wind and solar sources (Nguyen et al., 2021). The use of renewable systems has a significant impact on preserving natural resources and preventing water, soil, and air pollution. In other words, RESs significantly affect environmental impacts, and as an example, by utilizing the RESs, GHG emissions are decreasing (Hoang et al., 2021a).

Although RESs have many advantages, they have a big problem as a single supplier: unreliability. To solve this problem, we can integrate the energy systems to create Hybrid Renewable Energy Systems (HRESs) to increase system performance (Mehrpooya et al., 2019). By hybridizing the energy resources, sustainable development can be achieved (Mehrpooya, et al., 2019; Hoang et al. 2021). HRESs are a combination of more than one system that could be grid-connected or stand-alone (Sadeghzadeh et al., 2019). Hybrid renewable systems are used to increase the reliability of the required energy supply and the diversity of the energy production portfolio. Another advantage of these systems is the provision of sudden peaks in energy consumption. To increase the reliability of HRESs, they can be integrated with generators (diesel or biogas) and batteries as backup systems (Ghorbani et al., 2019). Although by connecting HRESs to the electricity transmission lines, the system's reliability grows (Heydari & Askarzadeh, 2016) and causes capital expenses to the government (Rad et al., 2020). Two factors influence the cost of grid extension, including the village's location and the separation from the focal power grid. In Iran, electricity is generated centrally and is transmitted to different parts of the country through a nationwide network. Due to the large size of the country, the amount of waste in transmission and distribution lines is very significant, which leads to attention to distributed generation systems as a way to reduce such losses by moving the production site closer to the place of consumption. The Geographic Information System (GIS) software was used to evaluate the distance of every location from the central network, by using the Power Transmission and Distribution Lines map (Noorollahi et al., 2016) (see Supplementary Fig. 1). To recognize the suitable capacity for each component, three analyses (energy, economic, and environmental) should be applied to the suggested system based on the area’s potential.

In a study, the combination of DG and battery bank as a backup for renewable energy systems illustrated the reduction in cost of energy (COE) (Johannsen et al., 2020). This shows how important backup systems are. Another investigation was done for 20 cases in India (Robert & Gopalan, 2018). They studied the application of integrating the HRESs and grid extension, and the results showed that WT/HT is the most cost-effective proposed system in 17 cases. A PV/WT/BG/Bat design was performed in China using HOMER Pro software (Li et al., 2020). They showed that an installation containing PV/WT/BG/Bat /Converter should be done to make the village economically independent of the grid power and the grid extension. The Net Present Cost (NPC), COE, and Initial Investment of the proposed system are $587.013, $0.201/kWh, and $293.599. Another study studied the integration of PV/WT/DG/Battery in Zeydan village, Iran (Abnavi et al., 2019). The outcomes show that the NPC, COE, and the Renewable Fraction (RF) of the optimized scenario equal $264.91, $0.119/kWh, and 61.4%, respectively. A hybrid system with different dispatch strategies consisting of PV/WT/DG/HT/Bat was simulated to supply electricity to a rural area in India. The proposed systems considered to cover the demand have COE and RF equal to $0.1/kWh–$0.162/kWh and $465,790-$716,658, respectively (Ramesh & Saini, 2020).

For a rural area in South Africa, a decision-making study was carried out between the off-grid system and grid extension (Luta & Raji, 2018). The results revealed that the breakeven distance for this case is 4728.02 km—which means that the grid extension for a case closer than this distance is cost-effective. A techno-economic and environmental analysis consisting of Wave Energy Converter /PV/WT/Battery was carried out to supply electricity for 300 households in three different locations (Jahangir et al., 2020). The results indicated that the HRESs containing PV/WT/Battery have the COE rate of $0.213/kWh, $0.233/kWh, and $0.242/kWh for Jask, Genaveh, and Anzali, respectively. In another investigation, a hybrid renewable system was simulated to see the energy consumption of a building with a 5 kW peak electricity demand (Altun & Kilic, 2020). The results show that a stand-alone system consists of PV/DG/Battery; by increasing the battery capacity, the CO2 emission decreases.

Furthermore, increasing the WT has minimum influence on a PV/DG/WT/Battery configuration. In an economic feasibility analysis, seven different configurations were investigated for three small communities in Peru with different climate conditions (Rinaldi et al., 2021). The economically optimal scenario consisted of Solar/Wind/Diesel. The NPC and COE for Campo serio are $227,335 and $0.478/kWh, for El potrero are $183,851 and $0.460/kWh, and for Silicucho are $146,583 and $0.504/kWh, respectively.

There is a lack of considering the energy quality used in renewable systems in feasibility studies. Exergy is the maximum amount of useful work that can be done by an energy system before it dies. (Mehrpooya et al., 2020). Exergy is an effective indicator that is utilized in several investigations. It can provide the proposed configurations more accurate and a good insight into the system's irreversibility sources (Aasadnia & Mehrpooya, 2018). In recent decades, exergy analysis was implemented in several investigations. A feasibility study was carried out on a stand-alone system in the northern region of Iran -Manjil wind power plant (Aghbashlo et al., 2018). The results revealed that the overall exergy efficiency and irreversibility rates are 63.82% and 147.04 TJ. In a study, the energy and exergy of the energy system applied to an island, Bozcaada, were evaluated (Kalinci et al., 2017), producing hydrogen and power. The modeled system has consisted of PV/WT/Electrolyzer/PEMFC/ Hydrogen Tank/ Convertor. The outcomes indicated that energy and exergy efficiencies were 13.31% and 14.26% for PV, 46% and 50.12% for WT, and 59.68% and 60.26% for Electrolyzer. In another study, energy, exergy, and economic (EEE) analyses of an energy system consisting of PV/WT/hydrogen system/ fuel cell were applied in Bushehr, Iran (Khosravi et al., 2018). The results illustrated that PV systems' average energy and exergy efficiencies were 12% and 16%, respectively. Moreover, PV had a maximum exergy destruction rate of 65%, which is a common challenge to mitigate the irreversibility rate in solar panels.

By applying exergy evaluation, it can be recognized which component is the primary source of irreversibility and consider a solution to decrease it. Some technical parameters can be obtained, e.g., exergy destruction, sustainability index, and exergy efficiency. By looking at prior investigations, it can be recognized that PV panels are the main source of exergy destruction. To improve the system performance, utilizing solar tracking systems can be performed. In a techno-economic and environmental study, different solar tracking systems were investigated. The results indicated that by utilizing the solar trackers, the number of PV panels was reduced (Vaziri Rad et al., 2020a, 2020b).

Moreover, a comprehensive feasibility study was applied to five remote villages in different climate zones and weather conditions in Iran (Mousavi et al. 2021). Different sensitivity analyses were carried out, and the results illustrated that the most cost-effective scenario for all villages was stand-alone HRESs. Nevertheless, as it is known, to increase the exergy efficiency of the PV systems, solar trackers should be applied and make costs on the system. So, to assess the best cost-effectiveness scenario of each tracking system, the CEI was introduced (Mousavi, Mehrpooya, et al. 2020). The integration of HOMER Pro and the exergy analysis makes the results more accurate. In addition, utilizing the HRESs has a positive impact on some social indicators (Zeb et al., 2014), both direct (job creation) (Frondel et al., 2010) and indirect (healthcare, education, etc.) (Cheng & Hammond, 2017). A job generation evaluation has been conducted in rural areas where renewable systems have been used. The results show increased job creation in such areas, and the numbers have differed for each system (Rai et al., 2021).

Here is a table that summarizes the prior literature evaluating this area reviewed before. They were categorized by publication year, methodology, optimum scenario, and presented indicators (see Table 1) (Mousavi et al. 2021). As can be seen, it can be concluded that most of the previous studies had performed just a specific resource and did not comprise all the potential of the areas. Also, there is a lack of considering the integration of exergy with each energy, economic, and environmental indicators (specifically environmental). Another analysis that has been neglected in most previous researches is the social analysis of renewable energy systems. Social indicators are one of the three parts of sustainable development.

Table 1 A review of techno-economic, environmental, and exergy investigations for RESs potential analyses literature
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In this study, for the first time, a comprehensive checklist is obtained to analyze the feasibility of the HRESs using exergy, economic, environmental, and social evaluations to supply electrical energy to the remote areas of Iran. Also, the integration of three methodologies, e.g., HOMER Pro, exergy analysis, and GIS, is performed for the first time. By utilizing the integration of these methodologies, an integration of HRESs with the most reliable, sustainable, and environmentally friendly has been performed. The reason for selecting Iran as a case study is that it has various weather conditions that make this investigation more Universal. Five locations were chosen with various climate zones (moderate & rainy-cold & dry-hot & arid-cold & arid-semi-arid), low population, and no grid access. By considering different climate zone, all regions of Iran are covered. The breakeven distance is obtained for each village, and the effect of utilizing HRESs on the social indicators was investigated for the first time. The most important technical (e.g., utility production, renewable fraction), economic (e.g., NPC, COE, etc.), exergy (e.g., exergy efficiency, irreversibility, etc.), and environmental (GHG emissions) parameters are reported. At last, by applying sensitivity analysis, the proposed checklist can be applied to a variety of communities around the world with similar climates and economic, environmental, and social situations.

2 Methodology

In this section, the specifications of the selected locations and their load profiles are introduced. Then, by including the potential of each village, the available utilities are selected. The simulator’s input data such as fuel price, nominal discount rate, inflation rate, and project lifetime are identified. The economic, energy, and exergy equations are presented in Supplementary data. After that, the energy, exergy, environmental, and social analyses are applied to the selected areas. The sensitivity analyses, including areas’ load, economic indexes, renewable potentials, etc., have been done. Eventually, by considering the outputs of these analyses, a comprehensive checklist is performed. The equations for the methodology calculations are given in the Supplementary data.

2.1 Location description and load profile

The locations of selected areas are illustrated in the Iran map in Fig. 1. The most important reasons for selecting these areas are that they have no access to the grid and are close to the borderline. Qarmaseh (36o59′21"N, 54° 03′01"E) is a village located in the north of Iran, Golestan province, with no access to the grid and a population of about 325 people (89 households), based on the latest census. In addition, Gavij and Havian have the most and the least population, respectively. Utilizing a hybrid renewable system can be a valuable answer to electrifying the village. The annual electrical consumption and peak load demands are 502.85kWh/day and 69.58 kW, respectively. Figure 2 illustrates the monthly load demand for the villages. The descriptions of other villages are summarized in (Supplementary A, Table A1). It can be noticed that the selected areas cover all different border areas of Iran, and the excess production can be supplied to the nearby countries. Gavij village has higher load demand due to the population and hot weather; in contrast, Havian village has the lowest load demand among the selected areas.

Fig. 1
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Location of the remote villages on Iran’s map

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Fig. 2
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Load demand profile of villages: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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2.2 Software

In order to design and analyze maps and different transmission lines in the geographical coordinates, the GIS software is used (Moradi et al., 2020). With the help of GIS, the distance between different points and the shortest distance between a specific line and a coordinate point can be determined. In this research, GIS has been used to create a map of Iran's main electricity transmission lines (Noorollahi et al., 2022). Also, the shortest distance between the selected villages and the transmission lines has been used. Then, after obtaining the distances, sensitivity analysis was performed in order to obtain the Economical Distance Limit (EDL) -a breakeven distance, and the points of cost-effectiveness of expansion or non-expansion of power transmission lines were obtained.

This paper uses powerful software to analyze hybrid renewable systems to simulate technical–economic and environmental analyses. This software considers the load demand, economic factors, and available renewable resources. Then by implementing optimizing technics, propose available energy configuration considering the cost and report some economic and environmental indicators like NPC, COE, Operation Cost (OC), Renewable Fraction (RF), and Initial Capital (IC) (Mousavi et al. 2021). A comparison between several investigations that utilized different software concluded that HOMER Pro software is the most well-known among the researchers. It is suitable and has accurate results (Sinha & Chandel, 2014). HOMER Pro software sorts output scenarios based on the best NPC value and needs to be improved technically. This study was done by applying exergy analysis to the output data.

2.3 Financial indexes

In this investigation, the inflation rate, nominal discount, and project lifetime are taken to be 18%, 15%, and 20 years, respectively (Kasaeian et al., 2019). The annual capacity shortage was taken as zero. The Diesel fuel price is $0.10 per Liter (Jahangir et al., 2019). The financial parameters of the simulations, including biomass price and grid extension cost, are presented in Supplementary C, Table C1.

2.4 Components’ specification

The utilities such as PV panel, wind turbine, hydro turbine, diesel generator, biogas generator, convertor, and battery are selected among the available ones in Iran and vary in different countries. Moreover, to generalize the outputs, sensitivity analyses have been applied. This section describes the technical and economic characteristics of the components used in this study. In this part, the integration of the proposed system is illustrated in Fig. 3. The specification of each subsystem of the proposed integration is presented in (Supplementary D, Table D1-5). The utilities' economic parameters and solar trackers' economic parameters are presented in Table 2 and Table 3, respectively.

Fig. 3
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Proposed hybrid system integration for the selected rural areas

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Table 2 Economic specifications of devices used in the simulations
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Table 3 Financial parameters of solar trackers (Vaziri )
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2.5 Emission penalties

Although utilizing generators (diesel/biogas) and grid have their effect on the environment, generators should be utilized as a backup system, or grid power should be entered into the circuit when the renewable resources are not reachable at all hours a day. Further, to include their environmental impact on the proposed integration, the policies for the released ingredients such as CO2, SO2, NOx, CO, and UHC must be applied. The emission penalties for each released component for Iran power plants are presented in (Supplementary D, Table D1). CO2 has the lowest tax amount ($2.86/ton) and the highest contribution (660 g/kWh). The most outstanding amount of tax is for a Particular Matter (PM) ($1228.60/ton) because it is dangerous for humans and the environment (Kasaeian et al., 2019).

2.6 Renewable potential assessment

First of all, by reviewing the utility’s specifications and obtaining the average minimum operating solar radiation, the operating temperature of solar panels, cut-in and cut-off wind speed, load peak, minimum operating flow rate, and head of nearby rivers were obtained. Then by considering these indicators, the utilities were chosen for each location. Table 4 illustrates the annual average of solar GHI, Clearness Index, wind speed, ambient temperature, and streamflow. Moreover, the highest rate of ambient temperature is in July for all the five selected villages, and Havian and Qarmaseh have the highest and lowest average rates, respectively. Solar radiation, ambient temperature, wind speed, and monthly flow rate of the Gorgan river (Qarmaseh) are presented in Fig. 4. Also, the villages have the potential for biomass resources because of having cows and sheep. The number of cows and sheep and the available biomass resources for each village are shown in Supplementary A, Table A2.

Table 4 Renewable specifications of selected villages
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Fig. 4
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Monthly average of daily renewable resources for selected villages: a solar GHI & Clearness Index, b Temperature, c Wind speed, d Gorgan River flowrate

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2.7 Grid extension and annual electricity tariffs

As the selected locations do not have any access to the grid as an answer to electrifying them, the grid extension could be applied. By implementing Iran Power Transmission and Distribution Lines map (see Supplementary Fig. 1), the distance of each area from the central grid can be obtained (Rajbongshi et al., 2017). Also, the increase in population causes the load demand elevation. To study the grid extension feasibility, several parameters are involved, such as the distance from a nearby network (km), extension capital cost ($/km), the grid power purchase rate ($/kWh), and annual load demand (kWh/day) (Mousavi, Zarchi, et al. 2021). Figure 5 illustrates the annual electricity tariff for the grid in Iran.

Fig. 5
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Annual tariff of Iran's electricity network (Mousavi et al., 2021)

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2.8 Generalization

As the years pass, the load demand usually increases or decreases due to the increase or decrease of the population and the technology development. To determine the best scenario because of an increase or decrease in electricity consumption, sensitivity analyses must be implemented. The load consumption and the grid distance from the village are considered variables. A parameter called breakeven distance or economic distance limit (EDL) will be introduced for each location using this analysis's output data. EDL is a parameter that illustrates the maximum distance from the grid that the cost of utilizing the distributed system and grid extension is equal. Also, other analyses are implemented in this study to investigate the effect of the variation of other input indexes, such as fuel price, solar radiation, wind speed, River’s flowrate, biomass cost, CO2 penalty, capital costs of PV panel, wind turbine, battery, hydro turbine, grid extension, etc. some these sensitivity analyses are applied for the first time.

2.9 Exergy assessment

Another essential technical parameter that is often overlooked while having a significant impact is the exergy of subsystems and the system. In order to analyze the value of exergy and, in fact, the quality of energy entering and leaving the devices, exergy analysis is applied to them. The exergy degradation in systems by considering the quality of input and output energy is calculated. As mentioned, the highest rate of exergy degradation is related to solar systems. Tracking systems are used to eliminate or reduce the rate of destruction. Since using a new system entails a financial burden for the project, it is necessary to examine the extent of the increase in system production compared to the financial burden. Therefore, a parameter is defined as the CEI (Mousavi, Mehrpooya, et al. 2020), which is the rate of changes in the exergy efficiency of the whole system compared to changes in COE (See Supplementary E.8). Exergy has a significant role in choosing the best tracking system because they increase and decrease exergy efficiency and destruction, respectively.

2.10 Social assessment

Today, the effects of using renewable systems in various fields are examined. One of the essential effects of utilizing HRESs is their impact on sustainable development parameters. Various analyses of economic and environmental issues have been performed in this research. Also, in this section, the changes made after applying these systems to the employment index (Basir Khan et al., 2015; Khosravi et al., 2018), which is one of the essential social indicators and has a familiar form with the economic dimension, have been studied. The number of jobs created after using each renewable system based on employment capacity is presented in Table 5 (Khan, Pal, and Saeed 2021).

Table 5 Job generation for each type of plant per MW (Rai et al., 2021)
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2.11 The proposed checklist

As can be seen, after designing the map of Iran's power transmission lines in GIS software and its outputs, which is the distance between the selected villages and the power transmission lines, these data as the input of HOMER Pro software in the analysis of power network expansion and determining the EDL point is used. After performing all the mentioned analyses in the methodology section, the outputs of each stage are used as inputs for the next stage. Also, these outputs are practical as effective parameters in selecting devices. In other words, the selection of equipment is made in a cycle until they meet the limitations of the problem. At the end of the result section, a table will be presented as a comprehensive checklist that has been prepared and compiled for the first time. It can be used to select the most optimal combination of renewable and non-renewable systems. Another advantage of this checklist is the ability to be flexible and adaptable based on the different inputs and conditions of each region, so that by comparing each of the parameters determining the choice of systems with the acceptable range of that parameter specified in this checklist, a selected scenario appropriate to the environmental conditions of that area can be selected. Finally, a study has been conducted on their use of social parameters. Figure 6 indicates a schematic diagram of the general process of the checklist is presented. As shown in the figure, there is a cycle in determining and selecting systems to achieve a combination of systems and meet the desired needs.

Fig. 6
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A schematic form of the comprehensive checklist of selecting the most cost-effective scenario by considering exergy, financial, environmental, and social analyses

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3 Results and discussions

In this part, firstly, the results of HOMER Pro, including economic, environmental, and technical aspects, are discussed in detail; furthermore, sensitivity analyses results are presented consisting of the economic parameters, renewable potentials, grid extension, and EDL point. Also, exergy indicators and cost-effectiveness index resulting from HOMER Pro outputs are explained. Eventually, the social effects of utilizing HRESs are computed and discussed.

3.1 HOMER Pro outputs

In order to analyze the financial and environmental part of the plant, the HOMER Pro outputs were utilized. Due to their economic parameters (e.g., NPC or COE), the optimum configurations based on three scenarios have been chosen and are listed below (see Table 6). The first scenario for each village is the best configuration with the lowest NPC value. The second one is the best renewable-independent scenario (except BG) based on the local potential of villages (biomass recourses). The third one is based on the 100% renewable fraction, and the non-renewable-independent has zero emission and is environmentally friendly. Considering different needs and goals, one can choose each suggested scenario.

Table 6 Technical, financial, and environmental outputs of HOMER Pro
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The best optimum configuration for Gavij by considering the first, second, and third scenarios is PV/WT/DG/BG/Bat, DG/BG/Bat, and PV/WT/Bat, and the NPC of these configurations is computed to be $404,034, $435,793, and $946,232, respectively. The most considerable rate of CO2 emission is for the second scenario in Qarmaseh and Gavij due to their population and low renewable potential, which is led to use the of generators to satisfy their load demand.

Table 7 illustrates the grid's electricity loss between the selected locations and the Iran power line transmission that are calculated by considering the Supplementary B equations. The results indicated that Gavij and Havian have the highest and lowest rate of grid electricity loss per year due to their load demand and distance from the power line. Furthermore, by considering the condition of the areas, Havian has the most significant loss percent among the villages. Several reasons result in a high percentage of grid loss, but the most important are grid extension distance and ambient temperature.

Table 7 Electricity loss of grid for selected villages
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The monthly electricity product divided by each system for each selected area is presented in Fig. 7. Moreover, it can be found that in the situation where the renewable resources are inaccessible or not enough, the non-renewable systems are back to business to satisfy the load demand. Regarding the high potential of the hydro and low potential of solar and wind in Qarmaseh, the highest share of output power is for hydro, and the generators are just a backup system to compensate for the decline in water turbine production.

Fig. 7
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Monthly product of systems of villages: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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3.2 Generalization

3.2.1 Economical distance limit (EDL)

In order to obtain the economic distance limit in determining the expansion or non-expansion of power transmission lines, a sensitivity analysis called EDL has been performed. This sensitivity analysis is the changes in energy demand in the upcoming years or some other regions around the world with relatively similar conditions to selected villages compared to the distance of villages to power transmission lines. Eventually, a breakeven distance point will be obtained for each village (see Fig. 8). The EDL, in this paper, is the distance with the equal NPC between the hybrid grid extension and the renewable systems and the stand-alone system (Mousavi, Zarchi, et al. 2021). It also shows that the off-grid configuration is optimal for the higher distance from the transmission line.

Fig. 8
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Economic distance limit (EDL) of villages: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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As shown in Fig. 8, Qarmaseh and Kolineh villages have the EDL point. Other villages have no EDL point due to the extended power transmission and distribution line distance. The EDL point on the output form of HOMER Pro software is the point between grid and non-grid scenarios for a specific load demand. Thus, this point is 7.40 km and 6.21 km for Qarmaseh (Mousavi, Zarchi, et al. 2021) and Kolineh villages. This shows that in order to extend the grid into the unelectrified areas with the same condition, the distance to the central grid should be lower than the mentioned numbers to be cost-effective. For the higher distance, stand-alone systems are recommended. For other villages, the distance from the power transmission and distribution line, emission penalty, annual electricity tariff for the grid in Iran, low rate of renewable resources, and grid capital cost are why the EDL point cannot be found. So, grid extension is not affordable, and with some changes in these terms, it can be extended. Also, it is illustrated that the optimal configuration will be changed by increasing or decreasing load demand in those areas. So, because of the long distance between the selected area and the grid power line of Iran, which is more than the EDL point for all of them, the stand-alone scenario is more cost-effective for all of the villages. In this regard, the analyses applied for the areas are based on off-grid scenarios.

3.2.2 Grid extension analysis

In order to evaluate the effect of changes in some of the above indicators, a sensitivity analysis has been applied to two indexes: (1) grid capital cost and (2) grid extension distance. The variation range for the grid capital cost is between $3000 and $13,000. Figure 9 illustrates that lowering the costs and being closer to the power lines for Qarmaseh, Kolineh, and Gavij villages are the parameters that increase the possibility of employing the grid extension. On the other hand, for Potkak and Havian, the parameters mentioned above are not totally effective because of their significant renewable resources or high electricity tariffs.

Fig. 9
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Grid extension analysis of villages for grid capital-grid distance: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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3.2.3 Inflation-discount sensitivity

Regarding the effect of inflation and discount rate on the total NPC and the importance of their variation, sensitivity analyses of inflation and discount rate have been performed. As shown in Fig. 10, the changes in NPC for all selected areas are in the same way, and in constant inflation, by increasing the discount rate, NPC is devaluating and vice versa. In contrast to the discount rate by reducing inflation, the NPC is decreasing too. Though, it can be proved that in countries with a high and low rate of discount and inflation, it is more suitable to use HRESs (Kasaeian et al., 2019).

Fig.10
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Sensitivity analysis for an inflation-discount rate of selected areas: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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3.2.4 Diesel fuel price-CO2 penalty

The analysis consists of diesel price changes, and the tax for CO2 emission has been applied to selected villages. It is demonstrated that in areas with a higher renewable fraction rate, the change in CO2 tax rates does not significantly impact CO2 emissions, e.g., in Qarmaseh and Potkak villages. Further, change in diesel price is more sensitive in those villages, and a constant rate of CO2 penalty, with the changes in diesel prices, it can be seen that many fluctuations in emissions. Moreover, in some areas like Gavij and Havian, it is more sensible by increasing the rate of tax the CO2 emissions decrease more than the rate of 2.8 $/kg, in other words, in the range of 1 to 2.8 $/kg, the carbon tax has no meaning and no effect on emissions (see Fig. 11).

Fig.11
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CO2 tax and diesel price sensitivity analysis: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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3.2.5 Renewable potential analysis

Figure 12 demonstrates the impact of alternation in solar radiation and wind speed on renewable fraction for the villages of Kolineh, Gavij, Potkak, and Havian, as well as changes in hydro turbines price and water flow rates for the village of Qarmaseh. It can be found that the low rate of solar GHI and wind speed renewable systems are not cost-effective. High wind speed renewable fraction does not depend much on radiation changes because wind turbines account for most production due to their higher efficiency than solar. The difference in heatmap charts is due to the variation of WT utilized in villages. In addition, for the Qarmaseh village, the renewable assessment has been done for the hydro turbine due to the low solar and wind potential rate. The results show that because of the prohibitive cost of the hydro turbine, the variation of HT capital cost is not impressive on RF. By increasing the solar radiation and wind speed by about 35%, the renewable fraction increases 16%, 25%, 18%, and 31% for Kolineh, Gavij, Potkak, and Havian, respectively.

Fig.12
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Sensitivity analyses of wind speed and solar radiation: a Kolineh, b Gavij, c Potkak, d Havian and hydro turbine capital and hydro flow rate, e Qarmaseh

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3.2.6 PV and WT cost analysis

Due to the importance of economic indicators for utilizing the RESs, an investigation was applied for wind turbines and photovoltaic panels' capital costs and their effect on total fuel cost, that is, the sum of generators' cost per annual. The results indicated that all villages—except Qarmaseh-because of the low solar and wind potential—are sensitive to these parameters (see Fig. 13). Furthermore, it can be found that in Kolineh and Potkak, both of the parameters are directly related to fuel cost. By increasing their capital costs, the total fuel cost is increasing as well for Gavij and Havian due to their low potential of the solar resource; changes in PV price do not have much effect on fuel demand, but due to a good wind source, total fuel consumption is strongly dependent on changes in wind turbine prices. Moreover, with a change in capital cost in both of them, about 25%, the total fuel consumption rate is changing about $1800/year, $3250/year, $410/year, $215/year, $185/year for Kolineh, Gavij, Potkak, and Havian, respectively.

Fig.13
figure 13

Wind-PV capital sensitivity analyses for the selected areas: a Qarmaseh, b Kolineh, c Gavij, d Potkak, e Havian

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3.3 Exergy outputs

In this investigation, the exergy analysis was not applied for the Qarmaseh due to the low solar and wind potential. In Fig. 14, the diagrams demonstrate the changes in exergy degradation and exergy efficiency in different months of the year for the villages of Koline, Gavij, Potek, and Havian for solar panels. The diagrams show that the exergy efficiency decreases with the increasing exergy degradation rate. These changes are due to using one type of photovoltaic panel in the same way. The irreversibility rate for the photovoltaic panels is due to their operating conditions. As a matter of fact, silicon modules convert only a small portion of solar radiation's high exergy content into electrical power. Tracking systems can be an answer to boost PV generation while also improving energy efficiency. Improving them can positively affect performance and technical parameters, especially exergy variables.

Fig.14
figure 14

Exergy destruction and exergy efficiency of photovoltaic panels: a Kolineh, b Gavij, c Potkak, d Havian

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As shown in Fig. 15, each component's exergy destruction fraction rate has been performed. In previous studies, as well as in this paper, it has been shown that the most significant rate of exergy losses occurs in solar panels, more than 50%; in other words, the highest rate of exergy destruction is for the PV panels, and it depends on several parameters mainly the heat loss. This low exergy efficiency in solar systems has caused the excess level of input exergies to be destroyed, e.g., 26,269.36kWh/year for Potkak. The destructed exergy fraction is 92%, 75%, 71%, and 51% for Kolineh, Gavij, Potkak, and Havian. Also, by improving the operating condition of PV panels, the solar panels and the whole system’s exergy efficiency can be increased. In this regard, three types of trackers consisting of (1) vertical, (2) horizontal, and (3) dual axis have been utilized. The specifications of trackers are represented in Table 3.

Fig.15
figure 15

Exergy destruction rate of each component: a Kolineh, b Gavij, c Potkak, d Havian

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3.4 Cost-effectiveness index (CEI)

Solar tracking systems are used to increase efficiency and reduce the exergy destruction of solar panels. These systems increase the output power by reducing the radiation angle relative to the line perpendicular to the plates. On the other hand, each system imposes a financial burden on the whole system, depending on the tracker type. In order to achieve the best type of tracking system, in terms of the minimum cost to maximum power production, the CEI has been introduced by Mousavi et al. (Mousavi, Mehrpooya, et al. 2021) (see Supplementary F, Table F. 12). This parameter is the ratio of changes in the total system exergy efficiency to changes in the total energy cost of the system. The more value of CEI, the higher the system's cost-effectiveness with the tracking system (see Fig. 16).

Fig.16
figure 16

CEI rate of each tracking system: a Kolineh, b Gavij, c Potkak, d Havian

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In order to compare the systems, based on each of the desired exergy parameters, the exergy results are listed in Table 7, and at last, the best tracker can be selected. Also, it presents two types of total system efficiencies, one of which considers the last and the other is without considering the last. In fact, it can be seen that the efficiency changes of the whole system considering the loss for different solar tracking systems will be different from the performance data without considering the loss. For this reason, it is recommended to use the total efficiency by considering the loss in order to achieve more accurate output data. The SI parameter is considered the Sustainability Index, which measures the responsiveness of a specific company in social and environmental areas. However, due to the low potential of solar resources in the north of Iran, the evaluation of trackers is not considered for Qarmaseh village.

3.5 Comprehensive checklist

At the final state of this paper, a comprehensive checklist is performed to list all the influential parameters to decide on utilizing the renewable and non-renewable systems. However, many indexes are related to these systems, and the most important and influential ones are listed below. The range of parameters is from the sensitivity analyses, local potentials, and the systems technical information taken from previous articles. The decision-making process can be done by considering these ranges, previous literature, and the local information, and the decision-making process can be done. In addition, this checklist has the ability to be flexible and changeable for different purposes. Thus, according to the environmental conditions of each area and changing the type of electricity consumption, for example, the electricity supply of a building, hospital, city, or any other location, this checklist can be generalized to any of them. Table 8 demonstrates a checklist for selecting the components based on the local potential and considering analyses applied in this paper for Qarmaseh village. There is a checkbox for each parameter, and to sum up these checkboxes, there is a final check. In other words, if all those checkboxes are positive, the final checkbox will also be positive, and the equipment can be used Table 9.

Table 8 Exergy outputs for solar trackers’ scenario of each village
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Table 9 A comprehensive checklist for component selection by considering exergy, financial, and environmental investigations for Qarmaseh village
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3.6 Social analysis

In order to calculate the impact of using renewable and non-renewable systems on some crucial social parameters, such as job formation and land use, various articles were reviewed. The order of these values in previous articles in this field has been in the range of Megawatts, and since the plants in this article were in the order of kilowatts, they were practically unusable. However, by the increase in the load demand and the produced power by the utilities, the social analysis can be applied; for this reason, as a suggestion for future work, it can be said that researchers can conduct studies on job creation and land use per kilowatt.

4 Concluding remarks and future prospects

In this investigation, an innovative and powerful checklist to assess the feasibility of hybrid renewable configurations and grid extension in remote areas is presented. By applying the GIS, exergy analysis, and HOMER Pro software, the economic, technical, sociological, and environmental viewpoints for each scenario are investigated. To evaluate the introduced checklist, a country with various climatic zones must be selected. Therefore, the checklist is applied to five remote villages with different climates in Iran. First, the effective technical, financial, and environmental indexes of renewable components and the grid extension option are investigated. If all parameters are passed the applied limitations, the device is chosen. The EDL analysis results indicated that the use of local renewable resources as stand-alone for all villages is the affordable option. The values of EDL for the Qarmaseh and Kolineh villages are computed as 7.40 and 6.21 km, respectively. For the rest of the villages, the on-grid option within the range of distance and load demand of sensitivity analyses is not found as a cost-effective scenario. In fact, due to the high price of grid extension, long distance between selected villages and electricity network, and high emission penalty, the pure extension, and grid-connected options are not affordable. According to the exergetic outputs, the contribution of PV modules to the overall irreversibility for all villages is more than 50%. Thereafter, to improve the technical performance of the hybrid configuration, the utilization of solar tracker systems is recommended as well as the techno-economic assessment is applied to each tracker mode.

Based on the CEI values, the vertical tracker mode is found as the most cost-efficient option for all chosen villages. It was also found that by employing the vertical tracking unit, the values of overall exergetic efficiency are improved to the values of more than 35% as well as the amount of entropy generation is outstandingly diminished. Eventually, the parametric analyses are applied to the techno-economic indexes to specify the effectual factors on system performance. Because of the prohibitive cost of the hydro turbine, the variation of HT capital cost is not really impressive on RF. By increasing the solar radiation and wind speed by about 35%, the renewable fraction increases 16%, 25%, 18%, and 31% for Kolineh, Gavij, Potkak, and Havian, respectively. For Gavij and Havian villages, due to their low solar resource potential, changes in PV price do not have much effect on fuel demand. However, versus due to a good wind source, total fuel consumption is strongly dependent on changes in wind turbine prices. Also, it was deduced that in higher load demands (more than 1 MWh/year), job formation could be considered.

The results indicated that the proposed checklist has admissible accuracy and reliability, as well as it can be developed for other renewable applications. By employing this checklist, a reliable decision-making process is conducted, which leads to the proposal of the most suitable and affordable hybrid renewable configurations.

To improve the quality and accuracy of the proposed checklist, the following recommendations could be implemented in future projects:

  • In addition to a single-year, multi-year parametric analyses on economic and technical factors could be conducted.

  • Evaluation of the results in terms of policy implications.

  • The proposed checklist could be developed by considering the cooling, heating, and freshwater demands.

  • Investigation of the effect of using various dispatch strategies on exergetic performance and financial parameters of optimal scenarios.

  • Implementing the Life Cycle Assessment (LCA) and energy methods, including construction, operation, and decommissioning phases for each scenario.

  • Integration of other optimization tools to the GIS software and exergetic assessment.