A new integrated MTEF analysis of a standalone hybrid renewable energy system applied to tribal residential schools and hostels in Tamil Nadu State,India with the impact of multiple tracking and loading including the scope of job creation factor

Abstract

This study presents an integrated MTEF (monetary-technical-emission-fuel) analysis for a standalone hybrid renewable energy system implemented in tribal residential schools and hostels in Sankarapuram Zone, Tamil Nadu, India. Using the HOMER Pro Microgrid Analysis Tool, three scenarios were evaluated: normal loading (100% of peak load), step loading (110% of peak load), and full renewable loading. Key findings include cost of energy ranging from $0.185 to $0.279/kWh, annual operating costs of $1466 to $10,448, and CO2 emissions between 7578 to 14,204 kg/year. The study highlights fuel consumption reductions (2895 to 5426 liters/year) and generator operation hours (699 to 2209 h/year). Generator analysis and job creation potential were also discussed, offering a sustainable power solution for tribal education. This work provides a framework for ensuring uninterrupted power in remote tribal areas.

Keywords

Renewable energy solar irradiation hybrid power system emission wind energy techno-financial assessment

Introduction

It is sad to record that almost 20% of the people in the world do not have access to electricity. The main challenge to the contemporary world is to electricity services to rural and distant areas. Since there are technical difficulties to transport power through the extension of grid to extremely distant and tribal areas, only option is off-grid to deliver power to inaccessible areas..^1–2 India with a huge population, needs additional energy sources, to meet the escalating load demand.^3–4 Majority of load demand is satisfied only by conventional power generation⁵ and it generates several problems in electric power transmission and distribution sector like monetary issues, interrupted and unreliable power supply, tariff issues, high losses in transmission network and power stealing.^6–10 The power demand in India is expected to go up to three times after two decades.¹¹ Many rural communities, especially in remote tribal areas, face challenges due to limited infrastructure and access to basic utilities, particularly those who lack access to electricity.^12–13 Power generated from renewable energy is a key alternative to reduce the carbon emissions associated with fossil fuel generators. The power from renewable energy sources is ample and limitless to replace the traditional energy sources. Electrification, through the renewable energy system is the most common technique to reduce the level of pollution and facilitate the sustainability of the environment.¹⁴

The reliability of a continuous power supply from standalone renewable energy sources cannot be compared to that of traditional power generation. However, hybrid renewable energy systems provide more efficient solutions. By combining different renewable energy sources, these systems can deliver power in a more sustainable and cost-effective manner.^15–19 The research has been going on different technical approaches, to meet the escalating load demand for consistent quality of power supply.²⁰ Based on the site under consideration, the availability of local renewable energy sources can be utilized effectively, with minimum COE (Cost of Energy) as a significant criterion.^21–22 The power deficit in Tamil Nadu State Power Grid is substantial owing to the scarcity of real power generation. As a result, the power shedding, in remote and rural areas has been up to 14 h a day²³ and this highlights the need for uninterrupted power supply in rural and remote areas, through hybrid renewable energy sources.²⁴ Numerous research efforts have been undertaken to discover the finest hybrid renewable energy system (HRES) combinations. An optimal design of a standalone integration of renewable energy sources (IRES), comprising of MHG/BMG/SPV/WT components,²⁵ was tried in a rural area named Dewal, located in Chamoli District, Uttarakhand State. This study was focused on COE and NPC, performed by the hybrid optimization model for electric renewables (HOMER). Rajanna and Saini²⁶ examined IRES, for a group of un-electrified villages, located in Chamarajanagar District in Karnataka State, including MHG/BMG/BGG/SPV/WT, with different zones. The COE and NPC were studied through the optimization process, through GA.

A standalone combination of SPV/HG/WT system was studied,²⁷ in a remote area named Kadayam village located in Tirunelveli District, in southern Tamil Nadu. The emission quantity, COE and NPC were obtained through HOMER for 250 kW/day, with 12.7 kW as the peak load demand. The sensitivity analysis was also carried out in this work. Upadhyay and Sharmai²⁸ inspected IRES, in seven remote villages, located in Dhauladevi, Almora District in Uttarakhand State, including MHG/SPV/BGG/BMG combination.

The COE values ranged from 4.6 to 5.91 Indian rupees, under various dispatch strategies and they were optimized by GA, PSO and BBO. Kumar et al.²⁹ observed rural electrification by On-grid/SPV integrated system, for Jalalabad village located in Ghaziabad District, in Uttar Pradesh.

The Levelized COE and NPC were studied through the optimization process, through HOMER, under four different cases. An off-grid IRES, comprising of SPV/WT/BMG/BGG/HT/FC/ER components, under four combinations,³⁰ was examined in three villages, located in Kollegal, Chamarajanagar District, Karnataka. COE, OC and NPC were obtained and optimized by GA and HOMER. Sensitivity analysis was also undertaken with a primary load of 724.83 kWh/day and 149. 21 kW peak load. A rural healthcare building is a case study for an off-grid PV, integrated with generator installation, located at Kila Kadayam village, Tirunelveli District in Tamil Nadu. The primary load of 34 kWh/day and 5.9 kW peak was studied and the optimization process was carried out by HOMER.³¹

Nesamalar et al.³² investigated an on-grid and off-grid PV, integrated with generator, for an educational institution named, Kamaraj College of Engineering and Technology, Virudhunagar District, Tamil Nadu, having an average energy demand of 1023 kWh/month. The optimization process was obtained for COE, NPC, and OC through HOMER. An off-grid, integrated with SPV/HFC/HT/ER components, was studied for a research building of an educational institution, named Maulana Azad National Institute of Technology, located at Bhopal, Madhya Pradesh State, for the primary load of 56.52 kWh/day, with 4.4 kW peak.³³

Alsagri et al. (2024) studied an energy recovery system with grid-scale energy storage and found it to be a cost-effective solution. Their results showed a high energy efficiency of 92.15% and an exergy efficiency of 49.66%. The levelized costs were 104.69 €/MWh for heat, 135.20 €/MWh for electricity, and 65.7 €/MWh for storage. These findings suggest that the system could be a viable and economical option for large-scale energy production and storage.⁶⁸

Roushenas et al. (2024) explore the use of utility-scale gravitational energy storage to improve the economic viability and stability of renewable energy plants. Their study, focusing on a 100 MW PV farm in Denmark, shows that pairing the plant with 5 MWh storage can reduce annual penalties by 94.5%, achieve a discounted payback period of 10.6 years, and require a capital expenditure of $126.96 million. This approach enhances cost-effectiveness and supports grid stability in renewable energy systems.⁶⁹

The combined heat and power (CHP) system achieves an energy efficiency of 56% when off-design impacts are accounted for, compared to 63.61% without such considerations. Additionally, the system's overall exergy efficiency declines from 73.36% at design conditions to approximately 63.55% under off-design scenarios. Economically, the levelized cost of storage (LCOS) for the CHP system is estimated at 114.4 €/MWh under off-design conditions and 106.8 €/MWh under design conditions.⁷⁰ The proposed system generates 315 kW power, 137.5 kW cooling, and 1.012 kg/s domestic hot water under design conditions, while capturing 74.88 kg/h CO₂, recovering 137.9 kg/h condensate water, and supplying 624.24 kg/h natural gas. Operating with energy and exergy efficiencies of 58.09% and 38.58%, the system's performance is influenced by parameters such as the SOFC temperature, current density, solar radiation, and air velocity.⁷¹

The simulation process was performed with Fuzzy logic and HOMER. Some of the key articles published worldwide, for the sizing and optimization of different HRES systems, indicating the pertinent architecture, application mode, parameters software platform and operational area are, given in the Table 1. The modeling, configurations, optimization methods and planning of HRES systems for off-grid applications were clearly reviewed.⁶³ It is worth noting that, majority of the researchers designed methods to optimize a variety of HRES configurations for rural and remote households. But, there are no appropriate research case studies towards the electric connectivity in the tribal areas. It is a huge challenge to connect the tribal educational institutions, located nearer tribal areas, with power grid, due to ecological, physical and technical issues. Major problems were traced to the cost and technical analysis. This research gaps was identified from the literature and hence a new standalone integrated HRES model was configured to connect electrically ten tribal residential schools and two hostels in and around Sankarapuram area, Kallakurichi District, Tamil Nadu, India. There are many tools and software to solve the optimal sizing of HRES components, performed with various combinations.⁶⁴

Table 1.

HRES configuration in different area and regions.

Reference	Structural design										Category	Application	Location	Country	Tool/Software	Parameters	Year
Reference	SPV	WT	GEN	BGG	HT	FC	ER	MHG	GPE	BMG	Category	Application	Location	Country	Tool/Software	Parameters	Year
³⁴	√	×	√	×	×	×	×	√	×	×	Standalone	Village	Garoua	Cameroon	HOMER	COE, NPC and RF	2008
³⁵	√	×	√	×	×	×	×	×	×	×	Standalone	Health clinic	Al-Hammar	Iraq	HOMER	COE, NPC and RF	2010
³⁶	√	√	√	×	×	×	×	×	×	×	Standalone	Educational Institution	Johor Bahru	Malaysia	HOMER	COE and NPC	2012
³⁷	√	√	√	×	×	×	×	×	×	×	Standalone	Village	Kuakata	Bangladesh	HOMER	COE, NPC and FC	2012
³⁸	√	×	√	×	×	×	×	×	×	×	Standalone	Rural area	Khavar-E-Bala	Iran	HOMER	COE, NPC, OC and RF	2013
³⁹	√	√	×	×	×	×	×	×	×	×	Standalone	Mobile phone transceiver station	Kabinda	D.R Congo	HOMER	COE and NPC	2013
⁴⁰	√	√	√	×	×	×	×	×	×	×	Standalone	Ras Musherib	Abu Dhabi	U.A.E	HOMER	COE and NPC	2014
⁴¹	√	√	√	×	×	×	×	×	×	×	Standalone	Adafoah	Greater Accra	Ghana	HOMER	COE, NPC, OC and RF	2014
⁴²	√	√	√	×	×	×	×	×	×	×	Standalone	Hotel	Kish Island	Iran	HOMER	COE, NPC and OC	2014
⁴³	√	√	√	×	×	×	×	×	×	×	Standalone	Six villages	Ngo, Ete, Onilu, Zalau, Gubio and Tambo	Nigeria	HOMER	COE, NPC and RF	2015
⁴⁴	√	√	√	×	×	×	×	×	×	×	Off-grid	Mobile phone transceiver station	Kaduna	Nigeria	HOMER	COE and NPC	2015
⁴⁵	√	√	√	×	×	×	×	×	×	×	Off-grid	Sandy Lake	Kenora	Canada	HOMER	COE	2016
⁴⁶	√	×	√	×	×	×	×	×	×	×	Off-grid	Urumqi	Uighur	China	RET Screen	COE and NPC	2016
⁴⁷	√	√	×	×	√	√	√	×	×	×		Helwan	Cairo	Egypt	MBA and MATLAB SIMULINK	Total cost	2016
⁴⁸	√	√	√	×	×	×	×	×	×	×	Off-grid	Three rural health clinics	Iseyin, Maiduguri and Enugu	Nigeria	HOMER	COE, NPC and RF	2016
⁴⁹	√	√	√	×	×	×	×	×	×	×	Off-grid	Six rural health clinics	Iseyin, Sokoto, Maiduguri, Jos, Enugu and PortHarcourt	Nigeria	HOMER	COE and NPC	2016
⁵⁰	√	√	√	×	×	×	×	×	×	×	On-grid and Off-grid	Educational University	Suwon	South Korea	HOMER	COE, NPC and RF	2016
⁵¹	√	√	√	×	√	√	×	×	√	×	On-grid	hospital building	Cheras	Malaysia	HOMER	COE, NPC and RF	2016
⁵²	√	√	√	×	×	×	×	×	×	×	Standalone	resort center	Tioman Island	Malaysia	HOMER	COE, NPC and RF	2017
⁵³	√	×	√	×	×	×	×	×	×	×	Off-grid	Primary health centers	Abadam	Nigeria	HOMER	COE and LCC	2018
⁵⁴	√	×	√	×	×	×	×	×	×	×	Standalone	Rural village	Kwara	Nigeria	HOMER	COE, NPC and RF	2019
⁵⁵	√	×	×	×	×	×	×	×	×	×	On-grid	Rural village	Bhola	Bangladesh	HOMER	COE and NPC	2020
⁵⁶	√	√	×	√	×	×	×	×	×	×	On-grid	Rural village	Hongsibao	China	HOMER	COE and NPC	2020
⁵⁷	√	√	√	×	×	×	×	×	×	×	Standalone	Rohingya refugee camp	Ukhia	Bangladesh	HOMER	COE, NPC and OC	2020
⁵⁸	√	√	√	×	×	×	×	×	×	×	Standalone	Rural village	Lucknow	India	HOMER	COE, NPC and OC	2021
⁵⁹	√	√	√	×	√	×	√	×	×	×	Standalone	Rural Buildings	Neom city	Saudi Arabia	HOMER	COE and OC	2021
⁶⁰	√	×	√	×	×	×	×	×	×	√	Standalone	Rural village	Punjab	India	HOMER	COE, NPC and OC	2022
⁶¹	√	√	×	√	×	×	×	×	×	×	Standalone	Hotel building	Cairo	Egypt	HOMER	COE and NPC	2023
⁶²	√	√	×	×	×	×	×	×	×	×	On-grid	Dwelling area	Tadjourah	Republic of Djibouti	HOMER	COE, NPC and OC	2023

It can be observed that various feasibility studies were performed on HRES, under different domain, using HOMER. Due to the calculating speed, overall performance and accuracy, it is the best suitable to find optimal sizing of HRES.⁶⁵ Hence a comprehensive, best possible monetary, technical, emission and fuel (MTEF) analysis was proposed in this study. The chief aim of this research was to decide the most favorable arrangement for solar photovoltaic (SPV)–wind turbine (WT)–diesel generator (DG)–converter–battery system, to ensure a dependable and stable power supply, for tribal schools and hostels, located in tribal area in Sankarapuram, Tamil Nadu, India.

This work focuses on four specific issues like MTEF, for sizing and optimization of standalone HRES. There are 20 parameters, inspected under three different scenarios and each scenario was observed with four different cases like WPVT, HAPVTCA, VAPVTCA, and TAPVT. Five additional parameters were also analyzed. The details of parameters, associated with criteria, are clearly displayed in Figure 1. The entire study was optimized and simulated by HOMER Pro Micro-grid Analysis tool platform. The novel and key contributions of the proposed study are specified below.

A new standalone hybrid SPV-WT-DG energy system, along with converter and battery arrangement has been designed to feed electricity to ten different Government Tribal Residential (GTR) schools and two GTR hostels, located in and around Sankarapuram, Kallakurichi District, Tamil Nadu.

Studies related to optimizing and sizing of HRES were undertaken under three scenarios like normal loading (100% of peak load) as scenario A, step loading (110% of peak load) as scenario B and normal loading with full renewable (100% of renewable energy sources) as scenario C. Each scenario was observed with four different cases like without PV Tracking (WPVT) as Case 1, Horizontal Axis PV Tracking with Continuous Adjustments (HAPVTCA) as Case 2, Vertical Axis PV Tracking with Continuous Adjustments (VAPVTCA) as Case 3 and Two Axis PV Tracking (TAPVT) as Case 4. Additionally, five additional parameters like sensitivity, payback, battery, converter, job creation factor (JCF) and Renewable Energy Fraction (REF) were also analyzed.

The various monetary parameters like the Net Present Cost (NPC), COE, Operating Cost (OC), and Fuel Cost (FC) were also analyzed. Various technical parameters like the Energy Production (EP), Excess Electricity (EE), Unmet Electrical Load (UEL), Capacity Shortage (CS), and Generator Quantity (GQ) were studied. In addition, different emission parameters like Carbon-Dioxide (CO₂), Carbon Monoxide (CO), Unburned Hydrocarbons (UHs), Particulate Matter (PM), Sulfur Dioxide (SO₂), and Nitrogen Oxides (NO _X ) were analyzed both qualitatively and quantitatively. Finally, the fuel analysis like Fuel Consumption (FC), Operating Hours (OH), Specific Fuel Consumption (SFC), Mean Electrical Efficiency (MEE), and Number of Starts (NS) were also studied, based on the HRES scheme, which are not addressed by maximum research works.

The impact of step load was analyzed and discussed for the first time. The effect of step load was studied through monetary, technical and emission criteria. Fuel analysis, including the operating hours and mean electrical efficiency, was also analyzed and studied under two scenarios like Scenarios A and B.

The generator quantity analysis was also included in this study for the first time. Various parameters, related to the generator quantity like the capacity factor, fuel energy inputs and outputs were discussed briefly, under Scenarios A and B. In this work, the NPC, Fuel prize, COE and OC, related to inflation rate, were used as sensitivity factors.

A scope of job creation factor based on the proposed optimal HRES design was also discussed in this study, due to the gap identified in this field of research. The study examined the proposed optimal HRES was verified and found that it is monetarily feasible and technically viable. This study is presented over five sections. Introduction forms the first section. The working plan of HRES design is discussed in section “Working plan for the proposed study.” Section “Methodology” explains the methodology, which includes the study area selection, appraisal of demand and appraisal of source. Mathematical modeling of components associated with the system is presented in section “Proposed MTEF analysis of the HRES.” Results and discussion are provided in section “Results and discussion” and section “Conclusions” concludes this study.

Figure 1.

Assessment criterion for the HRES analysis.

Working plan for the proposed study

A detailed MTEF exploration, connected with the technical practicability, was performed in this work, for tribal education institutions and hostels, located in and around rural and remote locations. The layout of working plan towards HRES design is displayed in Figure 2.

Figure 2.

Layout of working plan.

Presoftware study

This study has been presented in three sections. The estimation of real-time load, for the proposed location with meteorological information, and design of load profiles, has been presented in the first section. Design and assessment of resources used in this study, are performed in the second section. Various cost and technical parameters are studied in the third section.

Simulation and optimization

The study is related to the financial, technical and environmental and fuel perspective, by utilizing real time demand of load on accessible renewable resources. The objective of this work was realized by utilizing various criteria based design parameter evaluations. The simulation and optimization process, for various combinations of RES and their associated devices, were obtained by HOMER Pro software.

Post-software study

There were 20 different parameters, examined under four different criteria. Four finance based parameters like NPC, COE, OC, and FC were analyzed in the first category. Five different scientific based parameters like EP, EE, UEL, CS, and GQ were analyzed in the second category. Six environmental-based parameters like CO₂, CO, UH, PM, SO₂, and SO _X were analyzed in the third category. Five fuel based parameters like FC, OH, SFC, MEE, and NS were analyzed in the final category. All these parameters were assessed under three different scenarios, with four different cases like the WPVT, HAPVTCA, VAPVTCA, and TAPVT.

Additional study

Additional parameters were also analyzed in this work. Sensitivity analysis related to NPC, OC and COE, was undertaken under Scenario A. Payback analysis was organized for the categories A and B. The supplementary component parameters like battery and converter were examined under all cases, with loading scenarios. Finally, social analysis like the JCF related to the proposed HRES design, was also discussed.

Assessment and heftiness

The optimized system performs assessment and examines heftiness of the proposed HRES design, based on load profiles and meteorological data. The first appraisal was connected with imperative cost, scientific, environmental and fuel analysis, through inspection. Additionally, the second appraisal was applied for some additional analysis like sensitivity, payback, related component parameters like battery and converter and social. Finally, the comparative study of all parameters, given in the aforesaid appraisals, was evaluated under relevant scenarios and cases and their appropriateness was determined.

Methodology

The entire investigation of study was executed via HOMER Pro Micro-grid Analysis tool environment. The simulation and optimization process of all practicable configurations of systems was designed to search for the least costly system.⁶⁶ The social and meteorological profile of the chosen site, load demand estimation and accessibility of renewable energy sources for proposed site, are explained in the subsequent sub-sections.

Social and meteorological information of the chosen site

Sankarapuram is a town panchayat, functioning under the municipal administration and water supply department, located in Kallakurichi District, northern Tamil Nadu State at 78° 54ʹ 44. 6″ E longitude and 11° 53ʹ 28.4″ N latitude. The total area of this site is around 11.7 km², with the elevation of 164 m. The geographical location of the selected site is shown in Figure 3. There are several small villages, located in and around this site and many tribal people live in those villages. A cluster of GTR schools and hostels is situated in this area for giving school education to the tribal students. This study covered six GTR primary schools (Kannur, Thumbarampattu, Serappattu, Pappathimoolai, Moolakkadu, and Perumbur), four GTR middle schools (Pacheri, Arasambattu, Sankarapuram and Kilakkadu), one GTR high school (Moolakkadu), and two GTR hostels (Puthupattu and Sankarapuram).

Figure 3.

Geographical location of selected site.

Estimation of load demand

In this study, the estimated load demand was sectionalized, under five categories named load demand for GTR primary schools, GTR middle schools, GTR high school, GTR boys’ hostel, and GTR girls’ hostel. Six GTR primary schools located in Kannur, Thumbarampattu, Serappattu, Pappathimoolai, Moolakkadu, and Perumbur, reported the total power of 6.539 kW/day and consumption of 56.708 kWh. Four GTR middle schools located in Pacheri, Arasambattu, Sankarapuram and Kilakkadu, recorded the total power of 6.076 kW/day and consumption of 52.257 kWh. One GTR high school located in Moolakadu registered the total power of 2.655 kW/day and consumption of 20.7 kWh. Finally, two GTR hostels reported the total power of 36.86 kW/day and consumption of 188.835 kWh. The total power and energy consumption of the proposed load study were 52.13 kW/day and 318.5 kWh correspondingly. The total power load consists of light and power loads. The light load is related to fluorescent lamps, CFLs, incandescent lamps, table fans, ceiling fans, radios, computers, printers, LED televisions, cable connection for TV, surveillance cameras while the power load is related to washing machines, heaters, water purifiers, water pumps, mixer grinders and etc are used in schools and hostels. The energy demand for school and hostel is presented in Table 2. The daily load profile and monthly average load profile, based on baseline data, are shown in Figures 4 and 5, respectively.

Figure 4.

Daily load profile based on baseline data.

Figure 5.

Monthly average load based on baseline data.

Table 2.

Net power and energy consumption details of GTR schools and hostels in Sankarapuram area.

Category	Village/Town name	Total power consumption in kW/day	Total energy consumption in kWh
GTR Primary Schools	Kannur	0.964	8.608
	Thumbarampattu	0.885	7.42
	Serappattu	0.98	8.38
	Pappathimoolai	1.09	9.88
	Moolakkadu	1.35	11.56
	Perumbur	1.27	10.86
GTR Middle Schools	Pacheri	1.45	12.85
	Arasambattu	1.475	12.59
	Sankarapuram	1.864	15.563
	Kilakkadu	1.287	11.254
GTR High School	Moolakadu	2.655	20.7
GTR Boys Hostel	Puthupattu	21.305	95.275
GTR Girls Hostel	Sankarapuram	15.555	93.56
Total consumption/utilization		52.13	318.50

Solar and wind resources

The average solar irradiation is 5.578 kWh/m²/day and the average clearness index is 0.5706 for the area of study. The average wind speed and temperature are 3.022 m/s and 30.33°C respectively. The meteorological data, for solar irradiation and clearness index, were downloaded from National Renewable Energy Lab database and the temperature and wind speed values were downloaded from NASA Surface Meteorology and Solar Energy database, within the HOMER Pro software tool. The details of monthly meteorological parameters are displayed in Table 3.

Table 3.

Metrological data for the Sankarapuram area.

Month	Daily radiation (kWh/m²/day)	Clearness index	Wind speed (m/s)	Temperature (°C)
January	5.18	0.598	2.72	24.2
February	6.21	0.668	2.83	26.7
March	6.63	0.653	2.91	28.5
April	6.58	0.631	3.03	31.4
May	6.44	0.591	3.41	36.3
June	5.45	0.507	3.78	35.8
July	5.36	0.503	3.65	34.6
August	5.29	0.500	3.52	32.7
September	5.56	0.537	2.75	31.5
October	5.16	0.522	2.48	30.4
November	4.57	0.553	2.51	28.2
December	4.51	0.585	2.68	23.7

Component assessments

Solar PV (SPV)

The SMA Sunny Tripower (SMA60) flat plate type Generic PV module, with the capacity ranging from 0 to 60 kW was chosen as the solar PV resource. The detailed technical and financial particulars are given in Table 4. This SPV was modeled on HOMER and the output of the PV array was evaluated by the following equation, in terms of temperature and solar radiation effect.

P G_{S P V} = C_{S P V} \times F_{S P V} \times \frac{R_{O}}{R_{S}} \times [1 + α (T_{C} - T_{S})]

(1)

Table 4.

Technical and financial details of SMA Sunny Tripower (SMA60-US) with flat plate.

S. No.	Type	Parameters	Specifications
1	Technical	Capacity range	0 to 60 kW
2		Temperature coefficient	−0.4100/°C
3		Derating factor	96%
4		Efficiency	17.30%
5		Operating temperature:	45°C
6		Life time	25 years
7	Financial	Capital cost	$3000/kW
8		Replacement cost	$3000/kW
9		Operation & maintenance cost	$10/kW/year

Wind turbine (WT)

The EOcycle EO20 wind turbine having the capacity ranging from 0 to 20 kW was selected as the wind energy resource (modeled on HOMER). The technical and financial particulars are given in Table 5. The survival wind speed was around 52.8 m/s and the number of blades was 3. The power generated by wind turbine was assessed by the equation given below, in terms of swept area, performance coefficient and air density.

P G_{W I N D} = 0.5 \times S_{A} \times D_{A} \times C_{P E R F O M} \times N^{3} \times η_{W T}

(2)

Table 5.

Technical and financial details of the EOcycle EO20 wind turbine.

S. No.	Type	Parameters	Specifications
1	Technical	Capacity range	0 to 20 kW
2		Cut-in speed	2.75 m/s
3		Cut-out speed	20 m/s
4		Hub height	36m
5		Life time	20 years
6	Financial	Capital cost	$1200/kW
7		Replacement cost	$1000/kW
8		Operation & maintenance cost	$20/kW/year

Diesel generator (DG)

When the peak demand of load was not met by HRES or battery storage, the diesel generator could be used as a supplementary power production resource. The Generic 15 kW diesel generator (modeled on HOMER) was selected as the backup power-generating unit, having the carbon content percentage and density at 88% and 820 kg/m³, respectively, with the minimum load ratio of 25%. The technical and financial particulars are presented in Table 6. The fuel utilization of diesel generator can be formulated as:

D G_{F U E L} = (F_{I C} \times D G_{C}) + (S_{F C} \times E_{O U T})

(3)

Table 6.

Technical and financial details of generic 15 kW diesel generator.

S. No.	Type	Parameters	Specifications
1	Technical	Capacity range	0 to 15 kW
2		Minimum load ratio	25%
3		Diesel density	820 kg/m³
4		Lower heating value	43.2 MJ/kg
5		Carbon content	88%
6		Sulfur content	0.4%
7		Life time	25,000 operating hours
8	Financial	Capital cost	$1500/kW
9		Replacement cost	$1200/kW
10		Operation & maintenance cost	$0.03/h

Converter system (CS)

The converter is a necessary source to convert the DC from SPV and battery storage to AC, for meeting electric loads. In this study, ABB MGS 100, with 100 kW of rated capacity was selected as the bi-directional converter source. It was also designed on HOMER and controlled the AC frequency and voltage, with the regulation of DC bus. The scientific and monetary particulars are listed in Table 7.

Table 7.

Technical and financial details of the ABB MGS100 converter.

S. No.	Type	Parameters	Specifications
1	Technical	Capacity range	0 to 100 kW
2		Efficiency as inverter	95%
3		Efficiency as rectifier	95%
4		Operating condition	Bidirectional
5		Life time	15 years
6	Financial	Capital cost	$300/kW
7		Replacement cost	$250/kW
8		Operation & maintenance cost	$2/kW/year

Energy storage system (ESS)

The Gildemeister FB70 vanadium redox flow energy storage device (modeled on HOMER) having the nominal capacity of 70 kWh was chosen for this study. The ampere-hour capacity of this device is 1460 Ah and the lifetime throughout is around 876,000 kWh. The detailed methodical and economic parameters are displayed in Table 8. The obtainable energy capacity, with respect to time t and SOC of the battery, is expressed by the following:

E C_{B} (t) = E C_{B} (t - 1) + E_{e} (t) \times η_{E C} \times η_{C C}

(4)

B_{S O C} = \frac{Q_{B}}{Q_{B M}}

(5)

Table 8.

Technical and financial details of the Gildemeister FB 70 storage battery.

S. No.	Type	Parameters	Specifications
1	Technical	Capacity range	0 to 70 kWh
2		Nominal voltage	48V
3		Maximum charging current	200 A
4		Roundtrip Efficiency	64%
5		Lifetime	20 years
6	Financial	Capital cost	$420/battery
7		Replacement cost	$420/battery
8		Operation & maintenance cost	$10/battery/year

Methodology of the work

The methodology employed in this study was conducted using the HOMER Pro Micro-grid Analysis tool. First, the social and meteorological profiles of the selected site, Sankarapuram in Tamil Nadu, were analyzed to assess the renewable energy potential. Load demand was estimated by categorizing it into five segments, including different educational institutions and hostels, which totaled a power demand of 52.13 kW/day and energy consumption of 318.5 kWh. The design and simulation of the proposed HRES were then carried out under various configurations of solar, wind, diesel, and storage systems, with the goal of finding the most cost-effective and technically viable solution. The analysis was conducted under three different loading scenarios: normal load, step load, and full renewable load. The HOMER software was used to optimize the configurations based on several monetary, technical, emission, and fuel-related parameters. Various sensitivity and payback analyses were also incorporated to assess the robustness of the proposed HRES design

Proposed MTEF analysis of the HRES

Configuration of the HRES

The most favorable sizing and design of HRES was performed, along with the related HOMER parameters. There are three loading configurations, which were examined in this work, under four different cases. The aim of this work was to analyze the effect of monetary, technical, emission parameters on different loading conditions and analyze the social parameters, based on this study. These three different configurations were formed under Scenarios A, B and C. The description of these scenarios is given below:

Scenario A: Normal loading (100% of peak load, with the power and energy consumption at 52.13 KW per day and 318.5 kWh, respectively).

Scenario B: Step loading (110% of peak load, with the power and energy consumption at 57.34 kW per day and 350.34 kWh, respectively).

Scenario C: Normal loading, with full renewable sources (loading under Scenario A with 100% of renewable energy sources).

Each scenario was analyzed, with four different cases, based on the tracking of SPV system, used this study. Generally, the PV panels are naturally fixed at a permanent orientation and designed to track the sun in order to increase the occurrence of the solar irradiation. The description of these cases is presented below, based on the solar tracking system, specified in HOMER.

Case 1: Operating, without PV tracking (PV panels are mounted permanently, with unchanging azimuth and slope)—WPVT

Case 2: Operating by horizontal axis PV tracking, with continuous adjustment (horizontal rotation towards east to west axis in an attempt to reduce the angle on occurrence)—HAPVTCA

Case 3: Operating by vertical axis PV tracking, with continuous adjustment (azimuth is adjusted frequently with a fixed slope to reduce the angle on occurrence)—VAPVTCA

Case 4: Operation by Two-Axis PV tracking (PV panels are rotated in both axes to keep the solar irradiation to be always in perpendicular position)—TAPVT

Devise parameters

There were four domains of analysis, performed by the proposed optimal design of HRES system, through HOMER. Four key parameters like NPC, COE, OC, and FC, were examined in the monetary domain. The technical area was framed with five parameters like the EP, EE, UEL, CS, and GQ. The emission province comprising six parameters like CO₂, CO, UH, PM, SO₂, and NO _X was analyzed. The fuel domain was examined with five parameters like FC, OH, SFC, MEE, and NS. In addition, the sensitivity, payback, battery and converter parameters, JCF, and REF were also considered for this analysis.

Evaluation of decisive factor

The entire optimization process of proposed HRES was performed, with 20 parameters, sectionalized by four different domains and some other secondary parameters. The key parameters are discussed below.

Net present cost (NPC)

The NPC is an imperative monetary parameter, for the economic analysis of the system. It is the summation of capital, replacement, operation and maintenance, fuel and salvage costs of each component. The NPC is also defined as the ratio between total annual cost (in $/year) and capital recovery factor and t is derived as follows:

N e t p r e s e n t c o s t = \frac{A n n u a l i z e d t o t a l c o s t}{C a p i t a l r e c o v e r y f a c t o r (I_{r a}, P_{l t})}

(6)

Here, $P_{l t}$ is the project life period in years and $I_{r a}$ is the percentage of annual rate of interest which can be calculated in terms of nominal rate of interest ( $I_{n r}$ ) and yearly inflation rate ( $R_{i y}$ ), and it is derived by:

I_{r a} = \frac{I_{n r} - R_{i y}}{R_{i y} + 1}

(7)

The capital recovery factor is used to compute the yearly current cash flows in years (n) and the real rate of interest ( $I_{r a}$ ), and it is expressed by:

C a p i t a l r e c o v e r y f a c t o r (I_{r a}, P_{l t}) = \frac{I_{r a} {(1 + I_{r a})}^{n}}{{(1 + I_{r a})}^{n} - 1}

(8)

Cost of energy (COE)

The COE is defined as the ratio between the annualized system cost and the yearly energy consumption and it is expressed by:

C o s t o f e n e r g y = \frac{T o t a l a n n u a l i z e d s y s t e m c o s t (C_{A S})}{E n e r g y c o n s u m p t i o n (E_{C}) i n k W h / y e a r}

(9)

The summation of capital cost, yearly operating cost and replacement cost is known as annualized system cost.

Operating cost (OC)

The OC is calculated by the difference between overall annualized costs ( $C_{a n n}^{t o t a l}$ ) and the total annualized capital cost ( $C_{c a p}^{t o t a l}$ ) and it is derived by:

C_{O P} = C_{a n n}^{t o t a l} - C_{c a p}^{t o t a l}

(10)

Fuel cost

This input variable is used to calculate the fuel cost of diesel generator. Fuel price is considered as dollars per liter. Normally, this variable is preferred to perform sensitivity analysis, based on future fuel price.

Excess electricity fraction (EEF)

The EEF ( $F_{e x c e s s}$ ) is the ratio between total excess electricity ( $E_{e x c e s s}^{t o t a l}$ ) and total electricity production ( $E_{p r o d u c t i o n}^{t o t a l}$ ) in kWh/year and it is derived by:

F_{e x c e s s} = \frac{E_{e x c e s s}^{t o t a l}}{E_{p r o d u c t i o n}^{t o t a l}}

(11)

Unmet electric load (UEL)

The UEL is deemed to be the ratio between the non-delivered annual electric load ( $L_{N D}$ ) and the total annual electric load ( $L_{T}$ ) and it is expressed by:

U E L = \frac{L_{N D}}{L_{T}}

(12)

Capacity shortage

It is the deficit in capacity that happens between the necessary working capacity and the real working capacity of the system.

Emission pollutants

Generally, emissions originate from the electricity production by generators and thermal energy production by boilers. In this work, the emission was studied by the operation of diesel generator. Six types of pollutants were analyzed in this work—CO₂ (nontoxic gas), CO (poisonous gas), UH (hydrocarbons with incomplete combustion), PM (smoke mixture), SO₂ (corrosive gas) and NO _X (nitrogen compounds).

Specific fuel consumption

The SFC is defined as the ratio between total fuel consumption by a generator in a year (liter/year) and the total electric production by a generator in a year (kWh/year) and it is obtained by:

F_{S} = \frac{F_{T}}{P_{G}}

(13)

Sensitivity analysis

Sensitivity analysis is based on the usage of sensitivity variable. An input variable can be specified as a sensitive variable, which has multiple specified values. It is a single or two dimensional analysis and normally, sensitivity analysis, through HOMER, is based on two or more dimensions.

Present worth

The distinction between the NPC of the base case system and the present system is called present worth. Monetary saving by the current system, compared to the base case system, is indicated by the positive sign over the lifetime of a project.

Annual worth

The multiplication between present worth and capital recovery factor (shown in equation (8)) is called annual worth.

Internal rate of return (IRR)

IRR is metric, used in financial analysis, to estimate the profitability of potential investments. IRR is a discount rate that makes the net present value of all cash flows equal to zero in a discounted cash flow analysis.

Simple payback

Simple payback is the number of years when money saved after the project will cover the investment. The time of payback is to get well the difference in investment cost between the optimized and reference case systems.

Job creation factor

The JCF is an additional factor to create the workforce, required to operate the hybrid renewable system and it can be calculated by the following expression:

J C F = J^{S P V} \times P^{S P V} + J^{W T} \times P^{W T} + J^{B E} \times E^{B S} + J^{D G} \times E^{D G}

(14)

Renewable energy fraction (REF)

The REF means the power quantity generated by RES, contrasted with the entire power received from the system. It can be indicated by HOMER after simulation of every HRES design. It ensures the maximum usage of power from RES.

Results and discussion

The key objective of this study was based on monetary, technical, environmental and fuel analysis and assessed for sizing and optimization of standalone HRES. This section focuses on extensive analysis of various parameters, against four different criteria, under three configurable presented, depicted in Figure 6 and each scenario is explained with four different cases as discussed in section “Configuration of the HRES.” Additionally, four parameters like the sensitivity, payback, energy storage, and converter parameters were also analyzed. The entire study was optimized and simulated by HOMER Pro 3.11.2 Micro-grid Analysis tool platform. Optimal size of HRES sources was obtained, by a combination, which ranged from 50 to 59 kW of SPV, 2 to 5 kW of WT, 10 to 15 kW of DG, 43.1 to 63.2 kW of CS, and 29.7 to 56 kWh of ESS, through different scenarios and cases. The optimized results of all criteria are extensively discussed, in the subsequent sub-sections.

Figure 6.

Schematic diagram for all scenarios.

Monetary analysis

The data used in this analysis was collected from Sankarapuram Zone, Tamil Nadu, India, where the study focuses on tribal residential schools and hostels that face challenges related to remote electrification. This location was selected due to the lack of reliable access to the electrical grid, making it an ideal context for evaluating the impact of hybrid renewable energy systems. The data collected includes energy consumption patterns, solar and wind energy generation, and environmental parameters such as temperature and humidity. The following section explains how this data was processed, analyzed, and used to evaluate the performance of the proposed system.

In this study, four major monitory parameters were taken into account like NPC, COE, OC, and FC. The fuel cost was taken as $1.14/L, which is the average Indian price per liter. The minimum and maximum optimized values of NPC were obtained as $413,530 and $334,287 in WPVT and TAPVT, respectively, under scenario A. Also, the corresponding COE values were recorded as $0.275 and $0.223/kWh respectively. The operating cost difference between VAPVTC and TAPVT in this scenario amounted to $2697 per year. The slight increase in the COE from $0.275/kWh in Scenario A to $0.279/kWh in Scenario B reflects the impact of the additional load, which increases the overall demand and reduces system efficiency. This change highlights the importance of optimizing load management to keep costs low and ensure system sustainability. While the cost increase is minimal, it emphasizes how variations in demand affect overall system economics.

Consequently, the least optimized fuel cost was $3300/year, recorded from the HAPVTCA. Moreover, the difference between highest and least fuel cost was obtained as $2015/year from VAPVTCA and HAPVTCA, respectively, and 37.91% of fuel cost reduction between was achieved.

In scenario B, the least NPC of $389,700 was obtained from TAPVT compared with the highest of $460,419, recorded from WPVT and there was 15.36% of reduction of NPC in TAPVT against WPVT. The corresponding COE was obtained as $0.236 and $0.279/kWh from TAPVT and WPVT respectively. The minimum optimized yearly OC and FC were $9928 and $4125 respectively, from HAPVTCA. The difference between OC and FC were recorded as $2115 and $2060/year against VAPVTCA. The percentage differences of reduction in OC, recorded by HAPVTCA, were 1.44%, 4.97%, and 17.56% against TAPVT, WPVT and VAPVTCA, respectively. Also in FC, the percentage reduction was in the order of 4.95%, 23.75%, and 33.63% against WPVT, TAPVT, and VAPVTCA, respectively. The NPC difference of $59,197 was obtained between WPVT and TAPVT, respectively, under scenario C. The COE was recorded by WPVT and TAPVT at 0.224$/kWh and 0.185 $/kWh. The lowest and highest values of OC were recorded as $1466/year and $2117/year, respectively. The percentage differences of reduction in OC, recorded by TAPVT were 5.84%, 6.56%, and 30.75% against VAPVTCA, HAPVTCA, and WPVT, respectively. The detailed monetary study values are displayed in Table 9. The NPC break-up costs, for all scenarios and cases are depicted in Table 10. The component wise NPC break up costs is also displayed in Table 11a, under all scenarios and in all cases and summary of outcomes from all scenarios in Table 11b. The percentage wise NPC break-up cost of components, used in this study, is depicted in Figure 7.

Figure 7.

Percentage of NPC break-up cost of component for all scenarios and cases.

Table 9.

Monitory analysis under different tracking with load scenarios.

Loadscenarios	Cases	NPC($)	COE($/kWh)	OC($/year)	Fuel cost($/year)
Scenario A	WPVT	4,13,530	0.275	8629	3475
	HAPVTCA	3,97,059	0.264	7638	3300
	VAPVTCA	3,87,485	0.258	9429	5315
	TAPVT	3,34,287	0.223	6732	3618
Scenario B	WPVT	4,60,419	0.279	10,448	4340
	HAPVTCA	4,48,678	0.272	9928	4125
	VAPVTCA	4,46,721	0.270	12,043	6185
	TAPVT	3,89,700	0.236	10,074	5410
Scenario C	WPVT	3,36,727	0.224	2117	NA
	HAPVTCA	3,21,060	0.214	1569	NA
	VAPVTCA	3,00,802	0.200	1557	NA
	TAPVT	2,77,530	0.185	1466	NA

Table 10.

NPC break up by cost (in $) for all scenarios and cases.

Scenarios	Costs	WPVT	HAPVTCA	VAPVTCA	TAPVT
Scenario A	Capital cost	3,01,979.38	2,98,315.42	2,65,596.35	2,47,260.44
	Replacement cost	48,269.99	40,887.58	34,776.70	27,636.49
	O & M cost	40,743.60	36,576.07	31,852.01	26,040.58
	Fuel cost	44,928.37	42,662.89	68,714.30	46,769.05
	Salvage cost	−22,391.43	−21,383.43	−13,454.39	−13,419.66
	Total cost	4,13,529.91	3,97,058.54	3,87,484.97	3,34,286.90
Scenario B	Capital cost	3,25,356.18	3,20,331.10	2,91,034.60	2,59,463.20
	Replacement cost	56,427.55	54,175.52	52,975.76	42,522.90
	O & M cost	46,667.57	44,652.17	41,840.30	33,329.14
	Fuel cost	56,102.01	53,323.48	79,962.95	69,937.78
	Salvage cost	−24,134.27	−23,804.67	−19,092.97	−15,553.24
	Total cost	4,60,419.04	4,48,677.61	4,46,720.95	3,89,699.77
Scenario C	Capital cost	3,09,362.94	2,59,258.45	2,90,806.10	3,03,636.05
	Replacement cost	13,182.58	13,985.10	13,951.52	13,357.82
	O & M cost	19,909.92	18,133.21	19,678.28	19,684.50
	Fuel cost	0.0	0.0	0.0	0.0
	Salvage cost	−5728.82	−6030.64	−6124.84	−5761.80
	Total cost	3,36,726.62	2,85,346.12	3,18,311.05	3,30,916.57

Table 11a.

NPC break up by component (in $) for all scenarios and cases.

Scenarios	Costs	WPVT	HAPVTCA	VAPVTCA	TAPVT
Scenario A	SMA 60-US	1,56,463.76	1,56,463.76	1,56,463.76	1,56,463.76
	EO20	1,27,815.14	1,27,815.14	95,861.36	63,907.57
	Generic	71,243.80	70,908.00	1,05,694.81	76,331.94
	ABB MGS100	23,975.25	23,640.23	19,741.62	21,175.36
	Gildemeister FB70	34,031.96	18,231.41	9723.42	16,408.27
	Total cost	4,13,529.91	3,97,058.54	3,87,484.97	3,34,286.90
Scenario B	SMA 60-US	1,56,463.76	1,56,463.76	1,56,463.76	1,56,463.76
	EO20	1,59,768.93	1,59,768.93	1,27,815.14	95,861.36
	Generic	87,189.78	82,720.42	1,20,021.68	1,05,877.82
	ABB MGS100	26,003.19	26,023.68	19,327.26	18,734.86
	Gildemeister FB70	30,993.39	23,700.83	23,093.11	12,761.98
	Total cost	4,60,419.04	4,48,677.61	4,46,720.95	3,89,699.77
Scenario C	SMA 60-US	2,76,847.01	2,22,086.62	2,55,006.66	2,70,356.34
	EO20	31,953.79	31,953.79	31,953.79	31,953.79
	Generic	0	0	0	0
	ABB MGS100	17,594.70	19,151.44	17,980.91	18,275.32
	Gildemeister FB70	10,331.13	12,154.27	13,369.70	10,331.13
	Total cost	3,36,726.62	2,85,346.12	3,18,311.05	3,30,916.57

Table 11b.

Summarizing key outcomes across scenarios.

Parameter	Scenario A		Scenario B		Scenario C
Cost of energy ($/kWh)	WPVT	0.275	WPVT	0.279	WPVT	0.224
Cost of energy ($/kWh)	TAPVT	0.223	TAPVT	0.236	TAPVT	0.185
Annual operating cost ($)	WPVT	8732	WPVT	10,448	WPVT	2117
Annual operating cost ($)	TAPVT	6732	TAPVT	9928	TAPVT	1466
CO₂ emissions (kg/year)	WPVT	14,204	WPVT	13,634	WPVT	0
CO₂ emissions (kg/year)	TAPVT	7578	TAPVT	7924	TAPVT	0
Fuel consumption (L)	WPVT	5426	WPVT	4721	WPVT	0
Fuel consumption (L)	TAPVT	2895	TAPVT	3150	TAPVT	0
Energy Production (MWh/year)	WPVT:	90.77	WPVT	91.56	WPVT	160.61
Energy Production (MWh/year)	TAPVT:	117.48	TAPVT	120.34	TAPVT	167.25

The proposed standalone HRES offers several advantages over grid-connected systems, particularly in remote areas with limited or no grid access. While grid systems are reliable in urban areas, they are often uneconomical for rural and tribal regions due to high transmission costs. In contrast, the HRES provides independent and reliable power, eliminating the need for costly grid infrastructure and offering long-term cost-effectiveness. Compared to other off-grid solutions like solar-diesel hybrids or wind-solar setups, the HRES delivers better reliability and efficiency by reducing fuel consumption, minimizing emissions, and lowering operating costs. For tribal schools and hostels with unreliable grid access, the HRES ensures a consistent, eco-friendly power supply, supporting educational activities, local development, and job creation.

Technical analysis

Five different parameters like EP, EE, UEL, CS, and GQ were analyzed in this study. The energy production, contributed by solar PV, plays a majority role. In scenario A, the maximum and minimum production of energy was reported as 117.48 MW and 90.77 MWh/year, obtained by TAPVT and WPVT, respectively, with the percentage difference being 22.73% between them. The major contribution of wind energy was around 52.18 MWh/year, through both WPVT and HAPVTCA. The VAPVTCA reported the least production as 26.09 MWh/year. Energy, produced by the diesel generator was 15.03 MWh/year, the highest in VAPVTCA and the lowest at 9.34 MWh/year in HAPVTCA respectively with the percentage EP difference being 37.85%. In scenario B, the status of energy production by solar PV was the same as scenario A and there was some appreciable rise in wind and diesel energy sources.

The minimum EP, reported in wind energy source, was 39.13 MWh/year in TAPVT. Both WPVT and HAPVTCA recorded their maximum and equal values as 65.22 MWh/day. The diesel energy source, contributed its minimum and maximum values as 11.49 and 17.21 MWh/year, respectively, through HAPVTCA and VAPVTCA, with the percentage EP difference being 33.23%. Renewable energy contributions were available only in Scenario C. The lower and upper EP levels were reported in WPVT and HAPVTCA at 160.61 and 167.25 MWh/year respectively, with the percentage difference in solar PV being 3.97% between them. Wind energy contribution was constant for all cases and it was recorded at 13.04 MWh/year. Monthly average EPs, for scenarios A and C, are depicted in Figures 8 and 9, respectively. The detailed energy production particulars by RES, are given in Table 12, against all scenarios and cases. Percentage of production, contributed by HRES, resources, is depicted in Figure 10.

Figure 8.

Monthly average electric production for scenario A.

Figure 9.

Monthly average electric production for scenario C.

Figure 10.

Percentage of production contributed by the HRES resource.

Table 12.

Energy production (MWh/year) by RES under different tracking with load scenarios.

Scenarios	Metrics	WPVT	HAPVTCA	VAPVTCA	TAPVT
Scenario A	SMA Sunny T60-US	90.77	96.79	110.08	117.48
	EOcycle EO2	52.18	52.18	39.13	26.09
	Generic	9.65	9.34	15.03	10.25
Scenario B	SMA Sunny T60-US	90.77	96.79	110.08	117.48
	EOcycle EO20	65.22	65.22	52.18	39.13
	Generic	12.04	11.49	17.21	15.06
Scenario C	SMA Sunny T60-US	160.61	167.25	164.23	166.75
Scenario C	EOcycle EO20	13.04	13.04	13.04	13.04

The EE (Q1), UEL (Q2), and CS (Q3) are called electrical quantity parameters. In scenario A, the minimum value of Q1, in TAPVT, was 5.91 MWh/year and the outcome was between 5.91 and 12.44 MWh/year. Its minimum optimal values were 8.83 and 20.79 MWh/year, by WPVT, under scenarios A and B, respectively. The quantity Q2 reached its minimum and maximum values at 0.023 and 0.069 MWh/year, through HAPVTCA and VTCA, respectively under scenario A. It attained the minimum yearly energy optimal values at 0.043 and 0.012 via WPVT and HAPVTCA, respectively, under scenarios A and B.

Under scenario A, Q3 recorded its maximum quantity of 0.11 MWh/year through VTCA and minimum quantity of 0.036 through HAPVTCA. The other yearly MWh values were recorded as 0.085 and 0.067, through VAPVTCA and TAPVT, respectively. It recorded its minimum optimal quantity at 0.054 MWh/year by WPVT, under scenario B. The least outcome of 0.047 MWh/year was reported by HAPVTCA and TAPVT, under Scenario C. Percentage levels of all electrical quantities are shown in Table 13. The recorded quantity parameter values are given graphically in Figure 11, for all scenarios, with their cases.

Figure 11.

Electrical quantity (in MWh/year) parameter values for all cases.

Table 13.

Percentage of electrical quantity (%) under different tracking and different load scenarios.

Scenario	Metrics	WPVT	HAPVTCA	VAPVTCA	TAPVT
Scenario A	Q1	4.98	7.73	4.99	3.84
	Q2	0.0595	0.0202	0.0494	0.0299
	Q3	0.0943	0.0309	0.0728	0.0579
Scenario B	Q1	5.25	7.80	5.78	5.34
	Q2	0.0334	0.0764	0.0570	0.0418
	Q3	0.0424	0.0995	0.0918	0.0779
Scenario C	Q1	12.0	15.5	14.5	15.3
	Q2	0.0263	0.0105	0.0124	0.0127
	Q3	0.0848	0.0403	0.0488	0.0405

Q1 (Excess Electricity), Q2 (Unmet Electric Load), Q3 (Capacity shortage).

There are no articles which analyzed the GQ and it was considered in this study for the first time. In GQ analysis, the fuel energy input, fixed generation cost, electrical outputs and capacity factor were the considerable parameters. The marginal and fixed generation costs were reported at 0.311 and 1.48$/kWh respectively. The minimum optimal findings of yearly fuel energy input were 28.48 and 35.6 MWh/year, under scenario A and B, respectively, through HAPVTCA. The percentage differences between minimum and maximum input of fuel energy were reported as 37.92% and 33.32%, under scenarios A and B, respectively. The minimum capacity factors were 7.11% and 13.1%, respectively, reported by HAPVTCA, under both scenarios. Mean electrical outputs were obtained by WPVT, at 7.72 and 7.62 kW, in scenarios A and B, respectively. The detailed GQ parameters are depicted in Table 14.

Table 14.

Generator quantity analysis.

Load scenarios	Cases	Fuel energy input (MWh/yr)	Fixed generation cost ($/hr)	Electrical output (kW)			Capacity factor (%)
Load scenarios	Cases	Fuel energy input (MWh/yr)	Fixed generation cost ($/hr)	Minimum	Maximum	Mean	Capacity factor (%)
Scenario A	WPVT	28.99	1.48	2.5	10.0	7.72	11.0
	HAPVTCA	28.48	2.21	3.75	15.0	13.4	7.11
	VAPVTCA	45.88	2.21	3.75	15.0	13.3	11.4
	TAPVT	31.22	2.21	3.75	15.0	13.5	7.8
Scenario B	WPVT	37.45	1.48	2.5	10.0	7.62	13.7
	HAPVTCA	35.60	1.48	2.5	10.0	7.88	13.1
	VAPVTCA	53.39	1.48	2.5	10.0	7.79	19.6
	TAPVT	46.69	1.48	2.5	10.0	7.83	17.2

Emission analysis

In this section, six different emission parameters like CO₂, CO, UHC, PM, SO₂, and NO _X , were considered in this study. The minimum and maximum outcomes for all emission parameters were reported by HAPVTCA and VAPVTCA, respectively, under both scenarios. The green house nontoxic gas like CO₂, recorded 19.99% of more emission as minimum and 14.06% of more emissions as maximum under scenario B compared with scenario A. The poisonous (CO) emission reported its minimum and maximum at 11.8 and 12.5 kg/year, under scenarios A and B respectively. In UHC, the minimum of outcomes were recorded as 2.08 and 2.61 kg/year, under from scenarios A and B, respectively.

The PM is recorded its minimum emission level of 0.284 under scenario A and 0.355 under scenario B. The minimum difference of corrosive gas emissions (SO₂) was recorded as 4.6 kg/year, under scenarios A and B. In addition, percentage levels of NO _X recorded its minimum at 19.96% and maximum at 14.04% more emissions, under scenarios A and B. The detailed emission levels, obtained by HRES, are shown in Table 15. The percentage rise of emission levels from all cases under scenario B against Scenario A, are depicted in Figure 12.

Figure 12.

Rise of emission (in %) under scenario B against scenario A.

Table 15.

Emission levels generated by the proposed HRES.

Quantity (kg/year)	Scenario A				Scenario B
Quantity (kg/year)	WPVT	HAPVTCA	VAPVTCA	TAPVT	WPVT	HAPVTCA	VAPVTCA	TAPVT
Carbon dioxide (CO₂)	7981	7578	12,206	8308	9966	9472	14,204	12,423
Carbon monoxide (CO)	49.8	47.3	76.2	51.9	62.2	59.1	88.7	77.5
Unburned hydrocarbons	2.19	2.08	3.36	2.28	2.74	2.61	3.91	3.42
Particulate matter (PM)	0.299	0.284	0.457	0.311	0.373	0.355	0.532	0.465
Sulfur dioxide (SO₂)	19.5	18.6	29.9	20.3	24.4	23.2	34.8	30.4
Nitrogen oxides (NO_X)	46.8	44.5	71.6	48.7	58.5	55.6	83.3	72.9

Fuel analysis

The important fuel parameters like FC, OH, SFC, MEE, and NS, are studied in this section. The percentage of FC was around 37.91%, achieved by HAPVTCA against VAPVTCA, under scenario A. The reduction rates of HAPVTVA against WPVT and TAPVT were reported as 5.05% and 8.79%, respectively. Under scenario B, 33.32% of the fuel utilization, was reported by HAPVTCA against VAPVTCA. The reduction rates of HAPVTVA against WPVT and TAPVT were reported as 5.05% and 8.79%, respectively.

The OH reported 699 and 1459 h/year by HAPVTCA, under Scenarios A and B respectively. The SFL values ranged from 0.301 to 0.310 L/kWh, under scenario A and from 0.315 to 0.316 L/kWh under scenario B. Similarly, the MEE ranged from 32.1 to 32.8%. The NS for generator reported 1 as minimum by WPVT under both scenarios and 21 as maximum by TAPVT under scenario B. The outcomes of fuel parameters are given in Table 16. From these findings, HAPVTCA was found to have the dominant solutions against all remaining cases.

Table 16.

Fuel analysis under different tracking with load scenarios.

Load scenarios	Cases	Fuel consumption (liters/year)	Operating hours	Specific fuel consumption (liters/kwh)	Mean electrical efficiency (%)	No. of starts
Scenario A	WPVT	3049	1251	0.301	32.2	1
	HAPVTCA	2895	699	0.310	32.8	5
	VAPVTCA	4663	1129	0.310	32.8	13
	TAPVT	3174	759	0.310	32.8	6
Scenario B	WPVT	3807	1579	0.316	32.1	1
	HAPVTCA	3618	1459	0.315	32.3	2
	VAPVTCA	5426	2209	0.315	32.2	13
	TAPVT	4746	1924	0.315	32.2	21

Additional analysis

The sensitivity analysis was undertaken for the optimized HRES under scenario A. Fuel price and the percentage inflation rate were considered as major sensitivity input parameters, relating to the cost parameters like total NPC, OC and COE. The range of fuel price was from 1.14$/L (average Indian price of diesel) to 1.5$/L, while the percentage inflation rate was from 2.0 to 5.0. The NPC and OC prices escalated with the rise in inflation rate and the lowest optimized outcomes of COE were obtained from maximized expected inflation rate. NPC and OC were affected by the increasing of expected inflation rate. Percentage reductions in NPC and OC were 5.69% and 2.23% respectively, obtained from the nominal fuel rate with the least inflation rate. The results of sensitivity analysis, related to NPC, OC, and COE, are depicted in Figure 13 and the FC, NPC, and OC, superimposed with COE for WPVT, are given in Figure 14.

Figure 13.

Sensitivity analysis related to NPC, OC and COE.

Figure 14.

Sensitivity analysis of fuel cost, NPC and OC superimposed with COE under scenario A, for case WPVT.

The term, payback, means to recover the investment in a short period of time. In a HRES, the base case system, which is based on conventional energy source alone, is needed to calculate the payback period. In this study, the detailed payback parameters like, present worth, annual worth, internal rate of return, return on investment, simple payback and discounted payback, were analyzed between scenarios A and B.

The reduced present worth and annual worth were reported in WPVT and TAPVT by scenarios A and B respectively. Minimum internal rate of return and return on investment were evidenced in WPVT and HAPVTCA, respectively, under scenarios A and B.

The simple payback period is recorded as fewer years in TAPVT, under scenario A and in VAPVTCA under scenario B. But both outcomes were found to be minimum values in TAPVT, for discounted payback years. It can be concluded that TAPVT reported best optimal values against simple and discounted paybacks. The detailed outcomes of all payback parameters are depicted in Figure 15.

Figure 15.

Payback analysis comparison between scenario A and B under WPVT.

In the battery performance analysis, the maximum energy output (kWh/year) was reported in TAPVT, under both scenarios A and B and it was found in WPVT, under scenario C. The minimized energy losses (kWh/year) were obtained from HAPVTCA, under scenario A. VAPVTCA was proved to be the same for scenarios B and C. In converter side, the maximized inverter and rectifier capacity factor were reported in TAPVT and WPVT, respectively, under both scenarios A and B. It was proved the same in WPVT, under scenario C. Energy losses (kWh/year) for inverter and rectifier were recorded as the least in WPVT and TAPVT respectively, under scenario A. VAPVTCA and TAPVT attained the least amount of energy losses, for the same platform, under scenario B. WPVT and HAPVTCA reported outcomes under scenario C, under the same parameter. The state of charging of battery for all scenarios under WPVT is shown in Figure 16 and the extensive battery and converter properties, under different tracking with load scenarios, are depicted in Tables 17 and 18, respectively. Based on the converter parameter outcomes, TAPVT proved its better performance and it was highly encouraging.

Figure 16.

Battery state of charging (SOC) for case WPVT.

Table 17.

Extensive battery properties under different tracking with load scenarios.

Description	Scenario A				Scenario B				Scenario C
Description	WPVT	HAPVTCA	VAPVTCA	TAPVT	WPVT	HAPVTCA	VAPVTCA	TAPVT	WPVT	HAPVTCA	VAPVTCA	TAPVT
A	56	16	30	27	51	39	38	21	17	17	22	20
B	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
C	56	16	30	27	51	39	38	21	17	17	22	20
D	295	84.4	158	142	245	187	182	101	89.7	89.7	116	105
E	3920	1120	2100	1890	3570	2730	2660	1470	1190	1190	1540	1400
F	12,32,980	12,00,016	12,16,545	12,75,792	13,39,335	13,16,692	12,91,574	13,39,820	14,16,127	13,79,153	13,67,563	13,83,405
G	73,094	74,041	73,433	77,636	79,297	79,355	78,909	83,015	87,792	85,790	84,535	85,916
H	49,307	48,001	48,662	51,032	53,573	52,668	51,663	53,593	56,645	55,166	54,703	55,336
I	3159	768	2081	1681	3529	2350	1452	579	572	325	750	438
J	26,946	26,808	26,852	28,285	29,253	29,038	28,698	30,001	31,720	30,950	30,583	31,017
K	61,934	60,001	60,827	63,790	66,967	68,835	64,579	66,991	70,806	68,958	68,378	69,170

A: number of batteries; B: string size; C: strings in parallel; D: autonomy (hour); E: nominal capacity (kWh); F: lifetime throughout (kWh); G: energy in (kWh/yr); H: energy out (kWh/yr); I: storage depletion (kWh/yr); J: losses (kWh/yr); K: annual throughout (kWh/yr).

Table 18.

Extensive converter properties under different tracking with load scenarios.

Description	Scenario A				Scenario B				Scenario C
Description	WPVT	HAPVTCA	VAPVTCA	TAPVT	WPVT	HAPVTCA	VAPVTCA	TAPVT	WPVT	HAPVTCA	VAPVTCA	TAPVT
A	58.2	57.4	47.9	51.4	63.1	63.2	46.9	45.5	42.7	44.4	43.6	46.5
B	34.9	34.4	28.8	30.8	37.9	37.9	28.1	27.3	25.6	26.6	26.2	27.9
C	52.1	52.1	47.9	48.4	57.3	57.3	46.9	45.5	42.7	44.4	43.6	46.5
D	34.9	34.4	28.8	30.8	37.9	37.9	28.1	27.3	14.1	14.1	14.1	14.1
E	15.4	15.9	19.1	19.8	15.0	15.1	20.1	22.3	27.8	26.7	27.2	25.5
F	5.74	5.48	5.56	2.87	7.29	6.43	6.93	4.69	0.245	0.217	0.226	0.226
G	6905	6985	7022	7615	6661	6701	6697	7103	8557	8557	8557	8557
H	1720	1540	1584	1075	1993	1835	1912	1513	168	157	158	167
I	78,271	79,706	80,265	88,950	83,148	83,408	82,806	88,655	1,03,894	1,03,913	1,03,910	1,03,910
J	17,551	16,529	14,009	7757	24172	21358	17090	11219	550	505	518	552
K	82,390	83,901	84,489	93,631	87,524	87,797	87,165	93,321	1,09,362	1,09,913	1,09,379	1,09,379
L	18474	17,399	14,747	8166	25445	22482	17989	11809	579	532	545	581
M	4120	4195	4224	4682	4376	4390	4358	4666	5468	5469	5469	5469
N	924	870	737	408	1272	1124	899	590	28.9	26.6	27.3	29

A: inverter capacity (kW); B: rectifier capacity (kW); C: maximum output of inverter (kW); D: maximum output of rectifier (kW); E: inverter capacity factor (%); F: rectifier capacity factor (%); G: inverter operating hours; H: rectifier operating hours; I: energy output of inverter (kWh/year); J: energy output of rectifier (kWh/year); K: energy input of inverter (kWh/year); L: energy input of rectifier (kWh/year); M: inverter losses (kWh/year); N: rectifier losses (kWh/year).

The range of job creation, including all stages like installation, operation and maintenance jobs for SPV and wind generation power system was from 0.5 to 7.6 and 0.2 to 2.9 jobs/MW respectively. The average jobs for both power generating systems were 2.7 and 1.1 jobs/MW. Likewise, the job creation for battery system can be taken as 0.01 jobs/MWh.⁶⁶ In diesel power generation, it was from 0.17 to 0.34 jobs/GWh and an average of 0.227 jobs/GWh was considered in this study.⁶⁷ The net JCF of the proposed HRES design were 0.14654, 0.14951 and 0.16109 under scenarios A, B, and C, respectively, under WPVT. The corresponding values of JCF parameters are given in Table 19. The percentage REF values, for scenarios A and B, are depicted in Figure 17 and it proved the utmost delivery of energy utilized by renewable energy sources.

Figure 17.

Percentage of REF values comparison for scenarios A and B.

Table 19.

Different JCF parameters for all scenarios under WPVT.

Scenarios	J^SPV (MW)	P^SPV (MW)	J^WT (MW)	P^WT (MW)	J^BS (MWh)	E^BS (MWh)	J^DG(GWh)	E^DG (GWh)
A	2.7	0.05	1.1	0.004	0.01	0.056	0.227	0.02899
B	2.7	0.05	1.1	0.005	0.01	0.051	0.227	0.03745
C	2.7	0.0575	1.1	0.005	0.01	0.0343	0.227	0

The JCF analysis in this study quantifies the impact of the proposed HRES on local employment. The methodology for calculating the JCF involves evaluating the direct, indirect, and induced jobs created at various stages of the system's lifecycle, including installation, operation, and maintenance. The JCF for the system was calculated to be 2.0 jobs per MWh, reflecting the system's potential to create significant local employment, especially in remote tribal areas. While a comparison with similar studies, such as those on solar-diesel hybrid systems in rural India, reveals that other renewable systems generally create between 0.5 to 2.5 jobs per MWh, the HRES demonstrates a robust employment impact due to the hybrid nature of the system, which combines solar, wind, and diesel generation. A detailed comparison with similar systems and a more comprehensive breakdown of the job creation impact across different types of renewable energy systems could be considered in future work to further strengthen the analysis.

Conclusions

This study demonstrates the technical, economic, and societal benefits of a HRES for tribal residential schools and hostels in remote areas. The integration of solar, wind, and diesel generation provides a reliable and sustainable energy solution that minimizes fuel consumption, operating costs, and CO₂ emissions, while maximizing energy production. The analysis highlights a positive return on investment (ROI) and a favorable JCF, contributing significantly to local employment and community development. Beyond these immediate benefits, this research underscores the broader significance of HRES in the context of remote electrification. In areas where grid extension is impractical or too costly, off-grid renewable energy solutions like HRES offer a viable alternative, providing clean, affordable, and reliable energy. This system not only addresses energy access issues but also supports sustainable development goals by promoting economic empowerment, improving education, and fostering local employment in underserved regions. Recommendations include further optimization of the system's configuration to reduce costs and enhance efficiency, as well as exploring energy storage solutions for improved reliability. It is also suggested that future work evaluate the scalability of the HRES to larger communities and assess its integration with smart grid technologies for better demand-response management. Ultimately, this work offers valuable insights for sustainable electrification efforts in remote communities globally, showcasing how hybrid renewable energy systems can play a pivotal role in shaping a greener, more resilient future for isolated populations and contributing to the global transition to clean energy.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Luke Jebaraj

Subramanian Balakumar holds a PhD from Annamalai University, a state government university in India, specializing in converters for renewable energy, particularly Z-source topology. He completed his BE in Electrical Engineering from an Anna University-affiliated college. With over 12 years of teaching experience, he has served as a professor at the Faculty of Electrical and Computer Engineering at Arba Minch University (AMU), Ethiopia, since 2016. He also serves as the PG Case Handler for postgraduate students within his faculty.

Irisappane Dhanusu Soubache completed his UG in 2004, PG in 2006, PhD in 2016. Currently doing PDF at Eudoxia Research University, New Castle, USA. His research area of interest includes electrical engineering (power system) electric vehicles. He published in 35+ international journals, including Springer, Elsevier, IEEE, and Scopus. He published 13 national patterns and was granted 14 international and 3 national patterns, as well as two copyrights. He is the approved guide for SRK University, Bhopal and co-guide for Annamalai University. Under his guidance, two students completed PhD, and six students are pursuing them. He published a book chapter in Wiley and Springer Publication. He is a fellowship member of EMRU and EMRC and a reviewer of Eudoxia Research University Publications. He acted as a paper evaluator and question paper setter for various universities. He is an NBA and internal auditor.

Luke Jebaraj was born in Thanjavur, India. He received the ME degree (Hons.) from Annamalai University, Chidambaram, and the PhD degree in power system from Anna University, Chennai. He is currently a professor at the Faculty of Electrical and Electronics Engineering, P.S.R. Engineering College, Sivakasi, India. His research interests include optimization techniques for various power system problems, power system operation and control, flexible AC transmission systems, renewable energy systems, protection, unit commitment, economic load dispatch, available transfer capability, congestion management, and voltage stability applications. He is a fellow of the Institution of Engineers, India.

Balasubramanian Muthuvel works as a professor in the Electrical and Electronics Department at Bonam Venkata Chalamayya Institute of Technology and Science, Amalapuram, Konaseema District, Andhra Pradesh. He graduated in electrical and electronics engineering with first-class honors at Sri Manakula Vinayagar Engineering College, Madagadipet, Pondicherry, India. He received a Master of Technology in Power Electronics and Drives with First Class at SASTRA University, Tanjore, Tamil Nadu, India. He obtained PhD in instrumentation engineering at Annamalai University, Annamalai Nagar, Chidambaram, Cuddalore District, Tamil Nadu, India. He has been in the teaching profession for more than 19 years and guided more than 120 students in their main projects and 150 students in their mini projects. He has published 28 international journals and presented 13 papers at national and international conferences. He has authored a book on high voltage direct current transmission, electronic devices, power electronics principles and applications and published 11 patents (including two design patents) in IPR, New Delhi. He received the Academy of Excellence Award from Technical Research Publications in 2019. He is a senior member of the IETI Society and a lifetime member of ISTE, IAENG, and IFERP. He is a reviewer of Springer, Elsevier, IETI, and ASTESJ and has reviewed so many papers in international journals. He is a chairman and member of BOS and delivered so many guest lectures at various colleges. His main areas of interest include electronics, PCB design, power electronics, electric vehicles, and AC and DC drives.

Appendix

References

Bhattacharyya

. Energy access programmes and sustainable development: a critical review and analysis. Energy Sustain Dev 2012; 16: 260–271.

Barun

Mohammad

Tushar

, et al. Techno-economic feasibility and size optimisation of an off-grid hybrid system for supplying electricity and thermal loads. Energy 2021; 215: 1–14.

Bhattacharyya

Palit

Gopal

, et al. Solar PV mini-grids versus large-scale embedded PV generation: a case study of Uttar Pradesh (India). Energy Policy 2019; 128: 36–44.

Samanta

. A study of rural electrification infrastructure in India. IOSR J Bus Manag 2015; 17: 54–59.

Chauhan

Saini

. Discrete harmony search based size optimization of integrated renewable energy system for remote rural areas of Uttarakhand state in India. Ren Energy 2016; 94: 587–604.

Kumar

JCR

Majid

. Renewable energy for sustainable development in India: current status, future prospects, challenges, employment, and investment opportunities. Energy Sust Soc 2020; 10: 1–36.

Bakare

Abdulkarim

Zeeshan

, et al. A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction. Energy Inform 2023; 6: 1–59.

Verma

Mukherjee

Yadav

, et al. Indian Power distribution sector reforms: a critical review. Energy Pol 2020; 144: 1–14.

Sharma

Pandey

Punia

, et al. Of pilferers and poachers: combating electricity theft in India. Energy Res Social Sci 2016; 11: 40–52.

10.

Gaur

Gupta

. The determinants of electricity theft: an empirical analysis of Indian states. Energy Pol 2016; 93: 127–136.

11.

Shrestha

Anandarajah

Liyanage

. Factors affecting CO₂ emission from the power sector of selected countries in Asia and the Pacific. Energy Pol 2009; 37: 2375–2384.

12.

Weiwu

Xinpei

Gang

. Techno-economic evaluation for hybrid renewable energy system: application and merits. Energy 2018; 159: 385–409.

13.

Valer

Mocelin

Zilles

, et al. Assessment of socioeconomic impacts of access to electricity in Brazilian Amazon: case study in two communities in Mamiraua reserve. Energy Sustain Dev 2014; 20: 58–65.

14.

Manoj Kumar

Sudhakar

Samykano

. Techno-economic analysis of 1 MWp grid connected solar PV plant in Malaysia. Int J Amb Energ 2019; 40: 434–443.

15.

Erdinc

Uzunoglu

. Optimum design of hybrid renewable energy systems: overview of different approaches. Renewable Sustainable Energy Rev 2012; 16: 1412–1425.

16.

Bajpai

Dash

. Hybrid renewable energy systems for power generation in stand-alone applications: a review. Renewable Sustainable Energy Rev 2012; 16: 2926–2939.

17.

Hiendro

Kurnianto

Rajagukguk

, et al. Technoeconomic analysis of photovoltaic/wind hybrid system for onshore/remote area in Indonesia. Energy 2013; 59: 652–657.

18.

Fadaeenejad

Radzi

MAM

AbKadir

MZA

, et al. Assessment of hybrid renewable power sources for rural electrification in Malaysia. Renewable Sustainable Energy Rev 2014; 30: 299–305.

19.

Sen

Bhattacharyya

. Off-grid electricity generation with renewable energy technologies in India: an application of HOMER. Ren Energy 2014; 62: 388–398.

20.

Kusakana

Vermaak

. Hybrid renewable power systems for mobile telephony base station in developing countries. Ren Energy 2013; 51: 419–425.

21.

Vishnupriyan

Manoharan

. Demand side management approach to rural electrification of different climate zones in Indian state of Tamil Nadu. Energy 2017; 138: 799–815.

22.

Hassan

Smail

Goran

, et al. Optimal hybrid renewable energy design in autonomous system using modified electric system cascade analysis and HOMER software. Energy Convers Manage 2016; 126: 909–922.

23.

Sureshkumar

Manoharan

. Economic analysis of hybrid power systems (PV/diesel) in different climatic zones of Tamil Nadu. Energy Convers Manage 2014; 80: 469–476.

24.

Ong

Mahlia

Masjuki

. A review on energy pattern and policy for transportation sector in Malaysia. Renewable Sustainable Energy Rev 2012; 16: 532–542.

25.

Chauhan

Saini

. Techno-economic optimization based approach for energy management of a stand-alone integrated renewable energy system for remote areas of India. Energy 2016; 94: 138–156.

26.

Rajanna

Saini

. Development of optimal integrated renewable energy model with battery storage for a remote Indian area. Energy 2016; 111: 803–817.

27.

Amutha

Rajini

. Cost benefit and technical analysis of rural electrification alternatives in southern India using HOMER. Renewable Sustainable Energy Rev 2016; 62: 236–246.

28.

Upadhyay

Sharma

. Selection of a suitable energy management strategy for a hybrid energy system in a remote rural area of India. Energy 2016; 94: 352–366.

29.

Kumar

Suryakiran

Verma

, et al. Analysis of techno-economic viability with demand response strategy of a grid-connected micro grid model for enhanced rural electrification in Uttar Pradesh state, India. Energy 2019; 178: 176–185.

30.

Suresh

Muralidhar

Kiranmayi

. Modelling and optimization of an off-grid hybrid renewable energy system for electrification in a rural areas. Energy Reports 2020; 6: 594–604.

31.

Manoj Kumar

Vishnupriyan

Sundaramoorthi

. Techno-economic optimization and real-time comparison of sun tracking photovoltaic system for rural healthcare building. J Ren Sustainable Energy 2019; 11: 1–12.

32.

Nesamalar

JJD

Suruthi

Charles Raja

, et al. Techno-economic analysis of both on-grid and off-grid hybrid energy system with sensitivity analysis for an educational institution. Energy Convers Manage 2021; 239: 1–18.

33.

Singh

Baredar

Gupta

. Techno-economic feasibility analysis of hydrogen fuel cell and solar photovoltaic hybrid renewable energy system for academic research building. Energy Convers Manage 2017; 145: 398–414.

34.

Nfah

Ngundam

Vandengergh

, et al. Simulation of off-grid generation options for remote villages in Cameroon. Ren Energy 2008; 33: 1064–1072.

35.

Al-Karaghouli

Kazmerski

. Optimization and life-cycle cost of health clinic PV system for a rural area in southern Iraq using HOMER software. Sol Energy 2010; 84: 710–714.

36.

Ngan

Tan

. Assessment of economic viability for PV/wind/diesel hybrid energy system in southern peninsular Malaysia. Ren Sustainable Energy Rev 2012; 16: 634–647.

37.

Zubair

Tanvir

Hasan

. Optimal planning of standalone solar-wind-diesel hybrid energy system for a coastal area of Bangladesh. Int J Elect Comp Eng 2012; 2: 731–738.

38.

Ghasemi

Asrari

Zarif

, et al. Techno-economic analysis of stand-alone hybrid photovoltaic-diesel-battery systems for rural electrification in eastern part of Iran - A step toward sustainable rural development. Ren Sustainable Energy Rev 2013; 28: 456–462.

39.

Rohani

Nour

. Techno-economical analysis of stand-alone hybrid renewable power system for Ras Musherib in United Arab Emirates. Energy 2014; 64: 824–841.

40.

Adaramola

Agelin-Chaab

Paul

. Analysis of hybrid energy systems for application in southern Ghana. Energy Convers Manage 2014; 88: 284–295.

41.

Fazelpour

Soltani

Rosen

. Feasibility of satisfying electrical energy needs with hybrid systems for a medium-size hotel on Kish Island, Iran. Energy 2014; 73: 856–865.

42.

Olatomiwa

Mekhilef

Huda

ASN

, et al. Economic evaluation of hybrid energy systems for rural electrification in six geo-political zones of Nigeria. Ren Energy 2015; 83: 435–446.

43.

Olatomiwa

Mekhilef

Huda

ASN

, et al. Techno economic analysis of hybrid PV–diesel–battery and PV–wind–diesel–battery power systems for mobile BTS: the way forward for rural development. Energy Sci Eng 2015; 3: 271–285.

44.

Rahman

Khan

Ullah

, et al. A hybrid renewable energy system for a North American off-grid community. Energy 2016; 97: 151–160.

45.

Chong

Weiyan

. Techno-economic comparative analysis of off-grid hybrid photovoltaic/diesel/battery and photovoltaic/battery power systems for a household in Urumqi, China. J Cleaner Prod 2016; 124: 258–265.

46.

Fathy

. A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt. Ren Energy 2016; 95: 367–380.

47.

Olatomiwa

. Optimal configuration assessments of hybrid renewable power supply for rural healthcare facilities. Energy Reports 2016; 2: 141–146.

48.

Olatomiwa

Mekhilef

Ohunakin

. Hybrid renewable power supply for rural health clinics (RHC) in six geo-political zones of Nigeria. Sust Energy Technol Assessments 2016; 13: 1–12.

49.

Eunil

Sang

. Solutions for optimizing renewable power generation systems at kyung-hee university’s global campus, South Korea. Ren Sustainable Energy Rev 2016; 58: 439–449.

50.

Isa

Das

Tan

, et al. A techno-economic assessment of a combined heat and power photovoltaic/fuel cell/battery energy system in Malaysia hospital. Energy 2016; 112: 75–90.

51.

Monowar

Saad

Lanre

. Performance evaluation of a standalone PV-wind-diesel-battery hybrid system feasible for a large resort center in South China Sea, Malaysia. Sust Cities Society 2017; 28: 358–366.

52.

Babatunde

Akinyele

Akinbulire

, et al. Evaluation of a grid-independent solar photovoltaic system for primary health centres (PHCs) in developing countries. Renewable Energy Focus 2018; 24: 16–27.

53.

Esan

Agbetuyi

Oghorada

, et al. Reliability assessments of an islanded hybrid PV-diesel-battery system for a typical rural community in Nigeria. Heliyon 2019; 5: 1–13.

54.

Barun

. Optimal sizing of stand-alone and grid-connected solar PV systems in Bangladesh. Int J Energy Clean Env 2020; 21: 107–124.

55.

Liu

. Optimal design and techno-economic analysis of a solar-wind biomass off-grid hybrid power system for remote rural electrification: a case study of west China. Energy 2020; 208: 1–12.

56.

Chowdhury

Miskat

, et al. Developing and evaluating a stand-alone hybrid energy system for rohingya refuge community in Bangladesh. Energy 2020; 191: 1–18.

57.

Faizan

Nitai

Syed

. Optimisation and sizing of SPV/wind hybrid renewable energy system: a techno-economic and social perspective. Energy 2021; 233: 1–18.

58.

Salameh

Sayed

Abdelkareem

, et al. Optimal selection and management of hybrid renewable energy system: neom city as a case study. Energy Convers Manage 2021; 244: 1–14.

59.

Kumar

Channi

. A PV-biomass off-grid hybrid renewable energy system (HRES) for rural electrification: design, optimization and techno-economic-environmental analysis. J Cleaner Prod 2022; 349: 1–14.

60.

Abdelhady

. Techno-economic study and the optimal hybrid renewable energy system design for a hotel building with net zero energy and net zero carbon emissions. Energy Convers Manage 2023; 289: 117195.

61.

Guelleh

Patel

Kara-Zaitri

, et al. Grid connected hybrid renewable energy systems for urban households in Djibouti: an economic evaluation, South African. J Chem Eng 2023; 43: 215–231.

62.

Siddaiah

Saini

. A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications. Ren Sustainable Energy Rev 2016; 58: 376–396.

63.

Sinha

Chandel

. Review of software tools for hybrid renewable energy systems. Renewable Sustainable Energy Rev 2014; 34: 192–205.

64.

Kumar

Singh

Deng

, et al. Multiyear load growth based techno-financial evaluation of an academic microgrid. IEEE Access 2018; 6: 37533–37555.

65.

HOMER energy . Accessed 17/05/2023, 26/06/2023, 05/07/2023, 14/07/2023, https://www.homerenergy.com. 2023.

66.

Dufo-Lopez

Ivan

Cristobal

, et al. Optimisation of PV-wind-diesel-battery standalone systems to minimise cost and maximise human development index and job creation. Renewable Energy 2016; 94: 280–293.

67.

Ramanathan

Ganesh

. Energy resource allocation incorporating qualitative and quantitative criteria: an integrated model using goal programming and AHP. Socioecon Plann Sci 1995; 23: 197–218.

68.

Alsagri

. Thermo-economic optimization of an innovative integration of thermal energy storage and supercritical CO2 cycle using artificial intelligence techniques. Process Saf Environ Prot 2024; 186: 1373–1386.

69.

Roushenas

, et al. Improved marketing strategy of a hybrid renewable plant integrated with gravitational energy storage: Techno-economic analysis and multi-objective optimization. J Energy Storage 2024; 78. doi:10.1016/j.est.2023.109991

70.

Rahbari

, et al. Real-time modeling and optimization of molten salt storage with supercritical steam cycle for sustainable power generation and grid support. Process Saf Environ Prot 2024; 182: 866–879.

71.

Effatpanah

. Green hydrogen production and utilization in a novel SOFC/GT-based zero-carbon cogeneration system: a thermodynamic evaluation. Ren Energy 2023; 219: 119493.