Comparative analysis of solar module configuration and tracking systems for enhanced energy generation in South Sakucia Union, Bhola, Bangladesh: A software based analysis

Abstract

Bangladesh is blessed with an extensive range of solar energy generation possibilities; however, the primary impediment to attaining its full potential in the solar energy industry is the inadequate budget in the energy sector. As a result, determining the most economical and efficient solar module configuration for each specific scenario has become a critical necessity. This study offers a comprehensive techno-economic analysis and environmental impact assessment of four distinct solar modules: monofacial, bifacial, dual-axis solar tracker, and seasonal tilt solar module, in an open area of South Sakucia Union, Bhola district, in the southwest part of Bangladesh. By integrating energy-generation capabilities, financial metrics, and environmental benefits, this research provides a holistic evaluation framework to ensure optimal economic performance and minimal adverse environmental effects for sustainable solar solutions in Bangladesh. Utilizing PV*SOL, PVsyst, and System Advisor Model (SAM) software, this study assesses energy-generation capabilities and economic viability. Despite the dual-axis solar tracker exhibiting the highest average energy generation (149,070.3 kWh/year), its higher initial cost renders it less financially viable compared to other configurations. Financial metrics reveal that the seasonal tilt configuration is the most cost-efficient, with the lowest Levelized Cost of Electricity (LCOE) at $0.0452/kWh and the highest Net Present Value (NPV) of $52,887.70. Additionally, it has the shortest Discounted Payback Period (DPBP) at 12.69 years, a favorable Internal Rate of Return (IRR) of 9.460 %, and a Profitability Index (PI) of 1.459, indicating robust returns on investment. These findings emphasize the importance of considering both energy-generation capabilities and financial metrics when evaluating solar module configurations in the southern part of Bangladesh, serving as a valuable reference for policymakers. Moreover, meticulous environmental impact assessments assist in choosing configurations with minimal adverse effects on the environment.

Highlights

• •Comparative analysis of monofacial, bifacial, dual-axis, and seasonal tilt solar modules.
• •Seasonal tilt solar modules offer the lowest LCOE, highest NPV and shortest payback period.
• •Dual-axis solar trackers provide highest energy yield but are less financially viable.
• •Comprehensive economic performance and environmental impact assessment for sustainable solar solutions.

1. Introduction

Over the last few decades, there has been a rapid increase in industrialization, which has negatively impacted the climate and natural ecosystems. The average global temperature has been rising since pre-industrial times, leading to climate change and ecological imbalances that affect sustainable social and economic development [1]. In 2019, global carbon emissions reached a record high of 36.44 billion metric tons, contributing to the increase in the average global temperature by approximately 1.1 °C above pre-industrial levels [2]. Scientists predict that if the current trend continues, the Earth's temperature could rise by 1.5 °C between 2030 and 2052, exacerbating climate-related issues such as extreme weather events, sea-level rise, and biodiversity loss [3]. It is crucial for us to recognize the dangerous consequences of global warming and analyze how to prepare for and address global phenomena that threaten life on our planet [4].

Developing countries are particularly vulnerable to the effects of global warming due to their limited capacity to adapt to climate change. For instance, Bangladesh, with its low-lying geography, faces severe risks from sea-level rise and increased frequency of cyclones, which threaten both agriculture and human settlements. The country experiences economic losses equivalent to 1.5 % of its GDP annually due to climate-related disasters [5]. Similarly, Sub-Saharan Africa is expected to see a reduction in crop yields by up to 30 % by 2050, significantly impacting food security and economic stability in the region [6]. In India, climate change could push 45 million people into poverty by 2030 [7]. Researches show that the primary contributor to climate change and global warming is global carbon emissions, primarily caused by conventional fossil fuel-based power generation [8].

Developing countries extensively reliant on fossil fuels for energy generation are major contributors to significant carbon emissions worldwide. For instance, in 2020, fossil fuels accounted for 85 % of India's energy mix, with coal alone providing 55 % of the total energy supply [9]. Similarly, Nigeria depends heavily on oil and gas, which constitute over 90 % of its total export revenue and significantly contribute to the country's carbon emissions [10]. Indonesia's energy sector is similarly dependent, with fossil fuels making up 88 % of its energy mix in 2020, including 38 % from coal [11]. In South Africa, coal is the predominant energy source, providing 70 % of the country's energy [12]. Bangladesh also largely relies on traditional fossil fuels for electricity generation and transmission, facing an imminent power crisis due to limited reserves [13]. The country's energy sector is predominantly dependent on natural gas, which accounts for around 62 % of the total electricity generation [14]. Additionally, the shortage of land poses a significant challenge for constructing large-scale power generation plants, often resulting in resident displacement or deforestation to make way for power plants [15]. This heavy reliance on fossil fuels not only contributes to global carbon emissions but also exposes these countries to energy insecurity and economic volatility due to fluctuating fossil fuel prices.

Given the rapid technological advancements, increasing energy demand, and diminishing fossil fuel reserves, it has become necessary to explore eco-friendly and sustainable renewable energy solutions while considering global energy demands, climate change, and existing technologies [[16], [17], [18]]. Extensive research and studies are being conducted to find innovative methods for clean energy transitions and effective integration of renewables into the conventional energy mix [[19], [20], [21]]. Solar energy is the most promising and environmentally friendly energy source to meet the energy demands of growing economies [22]. The utilization of solar energy helps prevent the emission of carbon dioxide and other harmful gases and waste products. It also reduces the need for transmission lines on grids. Solar energy is abundant, readily available in direct and indirect forms, and serves as a free source of clean energy [[23], [24], [25], [26]]. Therefore, the energy from the sun can be efficiently harnessed during peak load hours in the daytime or stored in batteries for later use. By doing so, solar energy significantly reduces grid consumption and saves a substantial amount of money in the process.

Over the last decade, photovoltaic (PV) investments have gained increased momentum due to falling module costs and rising environmental concerns. PV module prices, which were around 3 US$/W in 2010, have dropped to around 0.27 US$/W in 2020, making PV investments more feasible than ever. The global installed PV capacity has increased from 40.3 GW to 707 GW [27]. Solar energy holds enormous prospects, particularly in irrigation, mini-grids, solar rooftops, and various other fields [28]. Developed countries are actively moving towards solar energy to capitalize on these benefits. China, leading the way, has generated 393,032 MW of solar energy, followed by the United States with 113,015 MW, Japan with 78,833 MW, and Germany with 66,554 MW as of 2023 [29]. These statistics underscore the significant strides made by these nations in adopting solar energy, highlighting its role in reducing carbon emissions, enhancing energy security, and achieving sustainable growth. It is crucial for Bangladesh as well to transition to renewable energy based alternatives. According to the Bangladesh Energy Situation, significant improvements are required in the renewable energy sector, which currently accounts for only 3.3 % of total production [28]. Bangladesh has great potential for solar power, with average daily solar irradiance ranging from 215 W/m² in the northwest to 235 W/m² in the southwest [26]. This makes solar energy exploitable through photovoltaic (PV) technology [13]. Solar PV markets have experienced significant growth in recent times, leading to a substantial increase in demand and use of PV technology worldwide. Despite its vast potential, there are challenges that need to be addressed, including limited land area, lack of appropriate technology, inadequate space, and high installation costs [15]. Therefore, it is crucial to find a feasible solar energy scheme that can overcome these barriers. It is also important to ensure that there is a balance between the budget and energy generation in a solar project.

Various types of solar modules are available, including monofacial, bifacial, dual-axis solar trackers, and seasonal tilt solar modules. All of these solar modules differ in their prices and energy generation. A proper analysis of the relationship between energy production and generation costs is crucial to ensuring profitability. Monofacial solar modules are the most commonly used conventional modules, forming the basis of most solar installations and designs, while bifacial PV panels show great potential for solar energy generation as they can capture sunlight from both the front and rear sides, unlike monofacial modules [30]. This characteristic leads to increased power density, reduced area costs, and higher cell efficiency, with a potential power gain of 50 % compared to monofacial modules [31,32]. However, the yield of bifacial modules depends on factors such as albedo and tilt-angle configurations [33]. Placing bifacial modules 2 m above the ground instead of close to the ground as in traditional installations, can increase the annual energy yield by 30 % [34]. On the other hand, dual-axis solar trackers are advanced systems used to optimize the positioning of solar panels for increased energy generation. Unlike fixed or single-axis trackers, dual-axis trackers can track the sun's movement both horizontally and vertically throughout the day, ensuring that the panels are always perpendicular to the sun's rays. This dynamic tracking improves energy generation and efficiency by maximizing sunlight exposure. Dual-axis trackers offer advantages over monofacial modules as they can optimize sunlight exposure, resulting in higher energy output of 20 %–40 % compared to fixed installations. An alternative to solar trackers is manually adjustable tilt mechanisms, which is familiar as seasonal tilt solar modules. In this method, the tilt angle of arrays is manually and periodically adjusted by manpower, such as semi-annual, seasonal, or monthly. Seasonal tilt solar modules represent a groundbreaking approach to solar panel installations. In contrast to fixed-tilt solar panels that maintain a static angle, this module adjusts its tilt angle to maximize solar energy production by taking into account the varying position of the sun during different seasons. The goal is to dynamically adapt the tilt angle, capturing more sunlight and significantly increasing overall energy yield. While they may not be as effective as solar trackers in terms of energy gain, they can be more efficient in energy generation compared to the other solar modules and can be cost-effective than trackers, as they do not rely on costly electric motors or hydraulic cylinders to change position [27].

Several studies have been conducted by researchers focusing on the techno-economic feasibility of different types of solar modules in various regions around the world. For instance, Kazem et al. conducted a case study on a 1-MW grid-connected PV system in Adam, Oman, optimizing the plant configuration using real-time data [35]. Şenol et al. optimized the design of a self-consumption-based large-scale solar plant at Cyprus International University, performing simulations using PV*SOL for various capacities [36]. Other significant works include the analysis of standalone PV systems in the Jordan Valley by Al-Addous et al. [37], software-based modeling of a 10-MW on-grid PV plant in India by Kumar et al. [38], and simulation-based analysis of grid-connected rooftop PV panels in Ujjain, India by Dondariya et al. [23]. Demirdelen et al. examined fixed, single-axis, and dual-axis tracking systems in a Mediterranean climate, highlighting performance improvements and payback periods [39]. Roy et al. focused on dual-axis trackers, emphasizing their hardware design and potential efficiency improvements [40]. Additionally, notable research on bifacial modules has focused on their irradiation and physical characteristics in tropical and desert conditions. Studies by Nussbaumer et al. explored simulation data accuracy for bifacial modules [41], Chudinzow et al. analyzed energy yield considering various irradiance contributions and ground shadows [42], Pisigan et al. investigated the performance of bifacial modules in tropical urban settlements [43], and Baumann et al. examined vertically installed bifacial PV panels with roof greening in Winterthur, Switzerland [44]. Although dual-axis solar trackers are relatively expensive compared to monofacial and bifacial solar modules [22], combining bifacial modules with dual-axis trackers further amplifies the benefits of bifacial technology. For example, Sergio I. Palomino proposed a methodology to improve the control strategy of two-axis solar tracking systems, reducing tracking error and energy consumption [45], while P. Muthukumar proposed an energy-efficient dual-axis solar tracking system using IoT [16], and Du Onyishi et al. designed a dual-axis solar tracker based on the PIC18F2620 microcontroller [17]. Additionally, seasonal tilt solar modules can significantly enhance the efficiency and performance of solar energy systems, particularly in areas with substantial seasonal variations in solar radiation. Research by Aqeel R. Salih calculated the ideal tilt angles for solar panels in various locations worldwide, including 17 cities in Iraq and 83 cities across 83 countries. The study revealed that adjusting the tilt angles four times a year, corresponding to the changing seasons, can optimize the efficiency of solar panel installations [17].

Numerous studies have been incorporated to understand the current scenario of solar energy in Bangladesh and its future prospects. Miskat et al. reported on the significant potential of solar energy among various renewable resources in mitigating energy demand, outlining the overall view of solar energy applications and ongoing project developments in Bangladesh, while also investigating the technical and theoretical potential and available technologies for harvesting solar energy [46]. Masud et al. summarized the current energy situation and examined available renewable resources, discussing current policies and legislations related to renewable energy generation in Bangladesh, and offering suggestions to tackle the ongoing energy crisis [47]. Bhuiyan et al. reviewed the present status, prospects, and updated information on renewable and sustainable energy sources, providing guidelines for the government to successfully implement their plans [48]. Additionally, studies were performed to incorporate different use cases of solar energy in Bangladesh. For instance, Chowdhury et al. conducted a techno-economic analysis of solar-based irrigation for four major divisions of Bangladesh, modeling system survivability against grid outages using REopt Lite [49]. Saifuzzaman et al. developed an intelligent automation system for luminous control based on light intensity, utilizing solar cells for power supply and automatic traffic monitoring through a video system [50]. Mahmud et al. proposed converting roads into solar highways with vertically installed bifacial solar modules [51], and Chowdhury et al. suggested using barren airport lands for a 5 MW grid-connected solar power plant [52], both optimizing limited land in Bangladesh for clean energy generation.

Many researchers have conducted techno-economic analyses of monofacial and bifacial solar modules, yet limited attention has been given to dual-axis solar trackers and seasonal tilt solar modules, particularly in the context of Bangladesh. This study aims to fill this gap. While numerous studies provide comparative analyses to identify the most efficient solar modules, they are rarely seen in the context of Bangladesh. Our study addresses this shortcoming as well. Recently, the Bangladesh Government has established solar parks in districts such as Dinajpur and Mymensingh [53], utilizing monofacial solar modules for energy generation. However, the southern region, despite its significant solar potential, remains overlooked in this regard. Given the budgetary constraints and limited space for large-scale, stand-alone solar parks, further research in this area is essential. Our study serves as a foundational framework in this context and will make a significant contribution to the existing literature.

This paper investigates the energy generation capabilities of monofacial, bifacial, dual-axis solar trackers, and seasonal tilt solar modules, coupled with a comprehensive techno-economic analysis. The results indicate that for southern Bangladesh, seasonal tilt solar modules present the most viable option for solar park implementation, offering superior energy generation at a lower cost and striking an optimal balance between energy output and budget constraints. Additionally, the study identifies the optimal tilt angle for seasonal tilt solar modules, providing essential insights for future solar plant projects in southern Bangladesh. Overall, this analysis aims to guide policymakers towards adopting a diverse array of solar projects that are compatible with budgetary limitations and energy demands, particularly in the southern regions of Bangladesh.

1.1. Contents and contribution

This study provides a detailed comparative techno-economic analysis of four solar module configurations: monofacial, bifacial, dual-axis solar tracker, and seasonal tilt solar module. Unlike previous studies that focus on single solar module types, this research evaluates multiple configurations across various metrics, including energy generation, financial viability, and environmental impact. This multi-faceted approach offers a holistic evaluation framework, filling a significant gap in existing literature. By focusing on the South Sakucia Union in Bhola, Bangladesh, this research offers valuable context-specific insights relevant to regions with similar climatic and economic conditions. The findings emphasize the importance of considering local factors when selecting solar module configurations, providing valuable guidance for policymakers and investors in developing countries. Furthermore, the study identifies the seasonal tilt solar module as the most cost-efficient configuration, offering the lowest Levelized Cost of Electricity (LCOE) and the highest Net Present Value (NPV). This challenges the common perception that more advanced and expensive technologies, such as dual-axis trackers, are always the best option. Additionally, the detailed analysis of the optimal tilt angle for seasonal tilt modules adds practical value for future solar projects in the region. In addition, a thorough environmental impact assessment quantifies the CO₂ mitigation potential of each solar module type, highlighting significant environmental benefits associated with the adoption of seasonal tilt solar modules. This aspect is crucial for understanding the broader implications of solar energy adoption on climate change mitigation and sustainability goals. The insights from this study offer valuable guidance for policymakers and investors. The detailed financial metrics, including LCOE, NPV, Discounted Payback Period (DPBP), Internal Rate of Return (IRR), and Profitability Index (PI), provide a solid foundation for informed decision-making. The research highlights the need for a balanced approach that considers both financial returns and environmental benefits. Finally, the study identifies its limitations and suggests future research directions, such as field validation of simulation results, integration with other renewable energy sources, and exploration of emerging solar technologies. These recommendations pave the way for subsequent studies to build upon its findings, contributing to ongoing academic dialogue and advancements in the solar energy sector.

In summary, the key contributions of this work are:

• •Comprehensive Comparative Analysis: Provides a multi-faceted evaluation of four solar module configurations, filling a significant gap in the literature.
• •Context-Specific Insights: Offers valuable guidance specific to the South Sakucia Union in Bhola, Bangladesh, and similar regions.
• •Optimal Configuration Identification: Identifies the seasonal tilt solar module as the most cost-efficient option, challenging common perceptions.
• •Environmental Impact Assessment: Quantifies the CO₂ mitigation potential, highlighting the environmental benefits of solar energy adoption.
• •Policy and Investment Guidance: Provides detailed financial metrics for informed decision-making, emphasizing a balanced approach.
• •Future Research Directions: Suggests areas for further investigation, paving the way for subsequent studies and advancements in the field.

2. Methodology

2.1. Software details

For this study, we utilized three distinct software programs: PV*SOL, PVsyst, and SAM. Below, we provide a detailed specification of each software along with their usability in our project.

2.1.1. PV*SOL

PV*SOL is a versatile software tool employed for the detailed simulation and analysis of photovoltaic (PV) systems. In this study, PV*SOL was used to conduct 3D visualization and simulation. Monthly meteorological data was extracted from PV*SOL, by providing the location, enabling accurate environmental inputs. The software allowed for manual adjustments to the module configuration, inclination, orientation, and spacing, thereby optimizing the system design. PV*SOL also facilitated the determination of global horizontal irradiation and horizontal diffuse irradiation. It provided flexibility in selecting the appropriate solar panels, inverters, and wiring, with a wide range of component options available. Moreover, PV*SOL enabled the precise placement of solar panels to maximize energy generation. The software also supported the selection of land texture and the area covered by the solar panels, ensuring comprehensive and effective system design. Furthermore, PV*SOL performs financial analysis, enabling the assessment of the economic viability of different PV system configurations.

2.1.2. PVsyst

PVsyst is a comprehensive simulation software extensively utilized for the design, sizing, and analysis of photovoltaic (PV) systems. In this study, PVsyst was employed to calculate the energy generation of the solar modules. The software offered the flexibility to manually adjust the tilt and azimuthal angles of the solar panels and supported the use of seasonal tilt solar modules. PVsyst provided options for selecting either standalone or grid-connected solar modules. In this study, a standalone solar module was utilized. Similar to PV*SOL, PVsyst allowed for the manual selection of PV modules and inverters. Additionally, it offered capabilities for inverter voltage and power sizing, ensuring precise and optimized system configurations. Furthermore, PVsyst can perform financial and environmental analyses, providing a comprehensive assessment of the economic viability and environmental impact of different PV system configurations.

2.1.3. SAM

System Advisor Model (SAM) is a robust performance and financial model designed to facilitate decision-making for renewable energy projects. SAM offers detailed simulations of PV system performance, incorporating critical factors such as weather data and system configuration. In this study, SAM was utilized to calculate the energy generation of four solar modules. Like PV*SOL and PVsyst, SAM allowed for location-specific input to provide meteorological data for simulations. It enabled the selection of appropriate solar module and inverter configurations, providing comprehensive details such as maximum power, open-circuit voltage, short-circuit current, and other parameters. SAM also allowed for the modification of the electrical configuration and sizing of inverters and other components. Additionally, SAM offered the flexibility to adjust the tilt of the solar modules for different months in the case of seasonal tilt solar modules. It supported backtracking when using solar trackers. Moreover, SAM permitted the manual input of component lifetime data and degradation levels, ensuring precise and customized system simulations.

2.2. Site details

The solar park implementation in Bhola took place in the South Sakucia Union, with the designated site having latitude 22.17850 N and longitude 90.7101 E. To accommodate the project's requirements, a square land area measuring 650 m² was selected for the installation of photovoltaic (PV) panels. The chosen area is an open space, ensuring there are no external shading factors from trees or any other objects that could affect the efficiency of the solar panels. This strategic choice of location maximizes the potential for harnessing solar energy and facilitates the successful operation of the solar park. The location of Bhola district is depicted on Fig. 1 and the location of South Sakucia is depicted on Fig. 2. A 3D model mentioning physical specifications of the proposed solar park is also illustrated in Fig. 3, Fig. 4.

Fig. 1.

Bhola District, the southernmost part of Bangladesh.

Fig. 2.

Char Sakucia, Bhola.

Fig. 3.

3D model of the solar park at Bhola District.

Fig. 4.

Specification of the PV module arrangement.

2.3. Meteorological data

Meteorological data from various weather databases, such as NREL, Meteonorm, Meteosyn, and others, have been meticulously collected [36,54]. The data considered for analysis encompass crucial factors like average temperature, wind velocity, humidity, and annual global irradiance. To facilitate simulation, these essential data have been tabulated in Table 1.

Table 1.

Meteorological data of the selected site.

Location	South Sakucia Union, Monpura Upazilla, Bhola, Bangladesh
Latitude & Longitude	22.17850 N, 90.71010E
Annual global irradiance	$1584.0\text{kWh}/{\mathrm{m}}^2$
Average temperature	$26.42°C$
Wind Velocity	$2.8\mathrm{m}/\mathrm{s}$
Humidity	81 %

Among all the meteorological parameters, irradiance and temperature hold the utmost significance in their impact on solar-cell performance. Temperature increases can have both positive and negative effects on solar panel performance. Elevated temperatures can reduce efficiency by increasing resistance, leading to energy loss and decreased output voltage. However, some solar panel technologies are designed to handle higher temperatures more effectively. To counteract the negative impact, cooling mechanisms and proper maintenance are often employed to maintain optimal operating temperatures, ensuring the long-term effectiveness of solar energy systems. The monthly ambient temperature of data is plotted in Fig. 5.

Fig. 5.

Monthly Temperature Data at Bhola District. Source: Meteonorm 7.2 data from PVsyst 7.0.

When irradiance levels increase, both the open-circuit voltage and short-circuit current experience a rise, subsequently leading to variations in the maximum power point (MPP). Global Horizontal Irradiance (GHI) and Horizontal Diffuse Irradiance significantly impact solar panel performance. GHI represents the total solar radiation received on a horizontal surface, accounting for both direct sunlight and scattered light from the atmosphere. It determines the overall available solar energy at a location. On the other hand, Horizontal Diffuse Irradiance refers to the scattered component of solar radiation reaching a horizontal surface. While the direct component from GHI contributes to the solar panel's maximum power point with higher voltages and currents, the diffuse component provides more consistent illumination, reducing rapid output changes due to cloud cover or shading. To optimize solar panel efficiency, a balance between direct and diffuse irradiance is essential, which can be achieved through proper panel positioning, orientation, and tracking systems. Accurate knowledge of GHI and Horizontal Diffuse Irradiance is vital for designing efficient solar energy systems and maximizing electricity generation. The Global Horizontal Irradiance and Horizontal Diffuse Irradiance data of Bhola district is plotted and compared in Fig. 6.

Fig. 6.

Comparison between global horizontal irradiation and horizontal diffused irradiation.

2.4. Solar system components

The LG450N2W-E6, manufactured by LG Electronics, is a monofacial solar module renowned for its exceptional performance. The utilization of high-efficiency monocrystalline cells enables it to efficiently convert sunlight into electricity, surpassing the performance of other solar cell types. Recent research has underscored the benefits of monocrystalline silicon cells, providing further support for their extensive adoption. Recent progress in monocrystalline Si passivated emitter and rear cells (PERCs) has achieved average efficiencies of 22.2 % and maximum efficiencies of up to 22.5 %, comparable to those of Czochralski-grown silicon counterparts [55]. In addition, monocrystalline silicon technology combines low cost with high efficiency, providing a competitive option that can achieve performance similar to current silicon counterparts for our proposed region [[56], [57], [58]]. This makes it a popular choice among solar project developers, homeowners, and businesses seeking to harness solar energy effectively. The module's reliability and effectiveness have earned it a strong reputation in the industry, making it a sought-after option for various solar energy projects and applications [59]. Fig. 7 illustrates the I–V curve of an LG monofacial solar module, while Fig. 8 showcases the LG450N2W-E6 monofacial solar module.

Fig. 7.

I–V curve of LG monofacial solar module (adopted from Ref. [59]).

Fig. 8.

LG450N2W-E6 monofacial solar module (adopted from Ref. [59]).

The LG455N2W-E6, manufactured by LG Electronics, is a bifacial solar module capable of generating electricity from both the front and rear sides, capturing reflected sunlight and diffuse light. This innovative technology enhances energy output and overall efficiency, making it a popular choice for solar energy installations. With LG's reputable standing in the solar industry, the LG455N2W-E6 module represents a reliable and cutting-edge solution, offering higher energy yields and optimized solar power generation for a variety of environmental conditions [60]. Fig. 9 exhibits the I–V curve of an LG bifacial solar module, while Fig. 10 displays the LG455N2W-E6 bifacial solar module.

Fig. 9.

I–V curve of LG bifacial solar module (adopted from Ref. [60]).

Fig. 10.

LG455N2W-E6 Bifacial solar module (adopted from Ref. [60]).

Canadian Solar Inverter was used for this project. We have gone through the data sheet of our selected inverter, and matched its electrical characteristics, especially the power, voltage and efficiency temperature coefficients with the ones that are been used in different literature for similar climatic regions. Canadian Solar Inc. is a leading player in the renewable energy sector, manufacturing high-quality solar inverters that convert DC electricity generated by solar panels into AC electricity for homes, businesses, and the grid. With a commitment to technological advancements, their inverters incorporate MPPT algorithms to optimize energy production, ensuring efficiency and reliability. Catering to various solar system sizes and applications, Canadian Solar offers a diverse range of string inverters, microinverters, and central inverters, contributing to the widespread adoption of solar power worldwide. Their dedication to sustainability and global presence further solidifies their position as a trusted choice for clean energy solutions. Table 2 presents a comprehensive specification of the inverter employed for this project in the solar park [61].

Table 2.

Specification of Inverter used for Solar Park.

Inverter Name	CSI-60KTL-GS
Maximum DC voltage	1000 VDC
Operating DC Input Voltage Range	200-850 VDC
Operating Current (Imp)	114 A
Maximum Input Current (Isc)	220 A
Maximum AC Output Power	60 kW
Output Voltage Range	422.4–528 VAC
Output Frequency Range	59.5–60.5 Hz
Efficiency	98.401 %
Operating Temperature Range	$-25°C\sim 60°C$
Power use during operation	92.9937 Wdc
Power Factor	1 ($\pm 0.8$ adjustable)
Current THD	<3 %
Power use at night	<1 Wac

For this particular project, we have considered four types of experimental setups. Monofacial solar module, bifacial solar module, dual-axis solar tracker, and seasonal tilt solar module. For monofacial solar tracker and seasonal tilt solar modules, we have used the LG450N2W-E6 produced by LG Electronics [59]. For the bifacial module, we have used the LG455N2W-E6 [60]. Both of the modules are mounted at a 22° angle. Detailed specification of those modules is provided in Table 3. We have used 200 modules. For all the modules, we have used an inverter from Canadian Solar Inc.

Table 3.

Specification of the Monofacial and Bifacial module.

Manufacturer	Monofacial Module	Bifacial Module
	LG Electronics	LG Electronics
Model	LG450N2W-E6	LG455N2W-E6
Cell Properties (Material)	Monocrystalline	Monocrystalline
Number of Cells	144	144
Module Power [W]	450	455
Module Efficiency [%]	20.5	20.72
Rated Voltage (Vmpp) [V]	41.8	42.1
Rated Current (Impp) [A]	10.8	10.83
Open Circuit Voltage (Voc) [V]	49.7	49.9
Short Circuit Current (Isc) [A]	11.3	11.4
Module Dimensions (L*W*H) [mm]	2110*1042*40	2110*1042*40
Nominal Module Operating Temperature [$°C$]	$44\pm 3$	$42\pm 3$
Temperature Coefficient of Pmax [$\%/°C$]	−0.33	−0.33
Temperature Coefficient of Voc [$\%/°C$]	−0.26	−0.26
Temperature Coefficient of Isc [$\%/°C$]	0.04	0.04

2.5. Estimation of electricity generation from PV module

The calculation of power output from a PV panel is intrinsically linked to two main variables which is the amount of solar radiation received and the cell temperature of the PV module. Equation (1) shows the formula to quantify this output which is as follows [62]:

$$P_{PV,out}=P_{STC}{d}^{PV}\left[1+{\alpha }_p\left(T_C-T_{C,STC}\right)\right]\left(\frac{H_T}{H_{STC}}\right)$$ (1)

where $P_{PV,out}$ is the power output under current conditions. $P_{STC}$ refers to the rated power output under standard test conditions (STC). $d^{PV}$ is the derating factor that compensates for losses due to wiring, shading, dirt accumulation (soiling), and degradation due to aging. $H_T$ represents the actual solar radiation the PV system receives. $H_{STC}$ is the solar radiation under standard test conditions, typically 1 kW/m². ${\alpha }_p$ is the temperature coefficient of power. $T_C$ is the cell surface temperature under current operational conditions. $T_{C,STC}$ is the cell surface temperature under standard test conditions. Usually, other than $T_C$, all other values can be found in the datasheet of the PV module [63].

To accurately estimate the power output of a PV panel, one must consider the actual conditions the panel operates under and adjust the ideal output (as rated under STC) accordingly. The derating factor $d^{PV}$ is crucial as it reflects real-world inefficiencies that are not present in standard test conditions. Additionally, the equation takes into account the temperature's effect on power output; as cell temperature increases, the efficiency of the panel typically decreases, which is accounted for by the temperature coefficient ${\alpha }_p$. This detailed formula enables more precise predictions of a PV panel's performance in situ, which is vital for planning and managing solar energy systems.

The temperature of the PV cell is a significant factor that influences the efficiency and performance of a solar panel. As environmental conditions fluctuate, so does the cell temperature, affecting how much electricity the PV panel generates. Particularly, an increase in cell temperature beyond the standard test conditions (STC) temperature, $T_{C,STC}$ usually leads to a decrease in the efficiency of the PV panel.

To accurately determine the PV panel's output, it is essential to estimate the cell temperature, $T_C$, which is calculated using an equation derived from the balance of absorbed energy by the PV panel and the electrical output and heat transfer to the environment. This is shown in Equation (2) which is as follows:

$$T_C=\frac{T_{\alpha }+\left(T_{C,NOCT}–T_{\alpha ,NOCT}\right)\left(\frac{H_T}{H_{STC}}\right)\left[1-\frac{{\eta }_{m,STC}\left(1-{\alpha }_P{T}_{C,STC}\right)}{\tau \alpha }\right]}{1+\left(T_{C,NOCT}–T_{\alpha ,NOCT}\right)\left(\frac{H_T}{H_{STC}}\right)\left(\frac{{\alpha }_P{\eta }_{m,STC}}{\tau \alpha }\right)}$$ (2)

where $T_C$ is the PV cell temperature under current conditions. $T_{\alpha }$ represents the ambient temperature. $T_{C,NOCT}\text{and}{T}_{\alpha ,NOCT}$ denote the cell and ambient temperatures, respectively, under nominal operating cell temperature (NOCT) conditions. $H_T$ is the actual solar irradiance. $H_{STC}$ refers to the solar irradiance under standard test conditions (1 kW/m²). ${\eta }_{m,STC}$ stands for the maximum power point efficiency under STC. $\tau$ and $\alpha$ are the solar transmittance and absorptance of the PV panel, respectively. ${\alpha }_p$ is the temperature coefficient of power [64].

This equation accounts for the complex interplay between the PV panel's absorption of solar energy, its efficiency at converting this energy, and the environmental conditions it faces. By incorporating these variables, the equation provides a robust model for predicting the PV cell temperature, which in turn is instrumental in calculating the expected power output of the panel under varying conditions. Understanding the cell temperature's influence on performance is vital for designing efficient PV systems and for conducting accurate energy yield assessments.

2.6. Economic determinants

Assessing the economic viability of renewable energy projects is indispensable alongside evaluating their energy generation performance. This dual evaluation is vital for making informed decisions and ensuring the successful implementation of RE initiatives. In current literature, economic performance is typically evaluated based on two key aspects: cost-competitiveness and profitability.

Cost-competitiveness is primarily measured using the levelized cost of energy (LCOE), providing insights into how economically feasible a project is compared to conventional energy sources. On the other hand, economic profitability is assessed through metrics such as net present value (NPV), discount payback period (DPBP), profitability index (PI) and Internal Rate of Return (IRR). NPV calculates the present value of future cash flows, considering the time value of money, while DPBP determines the time required to recoup the initial investment. Additionally, PI compares the present value of future cash flows to the initial investment, indicating the efficiency of capital utilization whereas IRR is the rate which makes the NPV equal to zero These economic evaluations serve as crucial decision-making tools, guiding stakeholders towards financially sound and sustainable RE projects.

2.6.1. Levelized cost of energy (LCOE)

The LCOE serves as a critical metric for assessing the economic feasibility and comparative cost-effectiveness of various energy generation projects. Defined in terms of dollars per kilowatt-hour ($\$/\text{kWh}$), the LCOE encapsulates the average cost per unit of electricity produced, considering the net present value of all costs over the lifecycle of the project [65]. This metric is instrumental in providing a standardized method for comparing the cost efficiency of different energy sources, including renewable and non-renewable technologies. The calculation of LCOE in Equation (3) integrates several key financial and operational parameters:

$$\text{LCOE}=\frac{{\sum }_{t=0}^n\frac{C_t}{{\left(1+r\right)}^t}}{{\sum }_{t=0}^n\frac{E_t}{{\left(1+d\right)}^t}}$$ (3)

where ${\mathrm{C}}_{\mathrm{t}}$ represents the total costs in year t, which include initial capital expenditures (${\mathrm{C}}_0$), as well as ongoing operation and maintenance (O&M) costs. ${\mathrm{E}}_{\mathrm{t}}$ is the amount of electricity generated by the system in year t, accounting for factors such as system degradation over time. N denotes the project's operational lifetime. R is the discount rate, reflecting the time value of money and investment risk. D stands for the degradation rate, indicating the decline in system performance over time.

This formula underscores the importance of considering both upfront and recurring costs, as well as the expected energy production capacity, in evaluating the economic viability of energy projects. By doing so, the LCOE offers a comprehensive view of the cost dynamics involved in energy production, facilitating informed decision-making among investors, policymakers, and energy planners [66]. The versatility of the LCOE model allows for its application across a wide spectrum of energy technologies, enabling a more nuanced understanding of the economic impacts of energy investments and the pursuit of cost-effective, sustainable energy solutions.

2.6.2. Net present value (NPV)

The NPV is a financial metric used to assess the profitability of an investment project, particularly within the energy sector. It represents the difference between the present value of cash inflows and outflows over the lifespan of the project. The NPV calculation takes into account the time value of money, a fundamental principle that posits a dollar today is worth more than a dollar in the future due to its potential earning capacity [67]. The NPV can be calculated by Equation (4):

$$\text{NPV}=-C_0+{\sum }_{t=1}^n\frac{C_{in,t}-C_{out,t}}{{\left(1+r\right)}^t}$$ (4)

where $C_0$ is the initial investment cost at time zero. $C_{in,t}\text{and}{C}_{out,t}$ are the net cash inflows and outflows at time t, respectively. R is the discount rate, which adjusts future cash flows to their present value. N is the number of time periods the project spans.

A positive NPV indicates that the projected earnings (adjusted for the time value of money) exceed the anticipated costs, suggesting that the investment is likely to be profitable. Conversely, a negative NPV suggests that the costs outweigh the benefits, rendering the investment unprofitable. Decision-makers in the energy sector employ the NPV to compare different projects and decide where to allocate capital to maximize value for shareholders [68]. As with any financial metric, the reliability of the NPV outcome is contingent on the accuracy of the input variables, such as the discount rate and projected cash flows.

2.6.3. Discounted Payback Period (DPBP)

The DPBP of a project calculates the amount of time (usually in years) it takes for an investment to reach a break-even point in terms of present value of cash flows. Unlike the simple payback period, which does not take into account the time value of money, the DPBP discounts future cash flows to present values before assessing the payback period. DPBP modifies the conventional payback period by considering the discount rate, which acknowledges that money received in the future is not worth as much as money received today due to inflation and the opportunity cost of capital [69,70]. The DPBP is reached when the net present value (NPV) of the cumulative cash flows equals the initial investment outlay. Equation (5) shows the calculation for the DPBP which is:

$${\sum }_{t=1}^{n=\text{DPBP}}\frac{C_{in,t}-C_{out,t}}{{\left(1+r\right)}^t}=C_0$$ (5)

Investors and managers use DPBP to evaluate the risk and liquidity aspects of an investment, as projects with a shorter DPBP are generally considered less risky and more liquid [71].

2.6.4. Profitability index (PI)

The Profitability Index (PI), a crucial metric in investment analysis, assesses the attractiveness of a project by comparing the present value of its future cash flows to the initial investment outlay [72]. Mathematically, the PI can be expressed by Equation (6):

$$\text{PI}=\frac{C_{in,t}-C_{out,t}}{C_0}$$ (6)

A PI greater than one signifies that the project is expected to generate positive returns, with higher values indicating greater potential profitability. Conversely, a PI below one suggests that the project may not yield sufficient returns to justify the initial investment and is therefore less desirable from a financial perspective [73].

2.6.5. Internal Rate of Return (IRR)

The Internal Rate of Return (IRR) is a financial measure used to estimate the profitability of potential investments. It is the interest rate at which the net present value of all the cash flows (both incoming and outgoing) from an investment equal zero [74]. The IRR is a critical value in financial analysis, providing a single number that accounts for the time value of money, risk, and investment opportunity. Equation (7) shows the formula for calculating the IRR which is based on the net present value (NPV) and is as follows:

$$\text{NPV}=-C_0+{\sum }_{t=1}^n\frac{C_{in,t}-C_{out,t}}{{\left(1+IRR\right)}^t}$$ (7)

Using the IRR value, decision-makers can make informed judgments about whether to proceed with an investment. The basic rule of thumb is that if the IRR of a project is greater than the required rate of return, the project is considered good and vice versa [75]. The required rate of return is often the company's cost of capital or the rate of return from alternative investments. A project with an IRR higher than the cost of capital is expected to cover its initial investment and generate additional value. However, if the IRR is below the desired threshold, it suggests that the project may not be worth investing in, given the potential risks and opportunity costs.

2.7. Environmental determinants

In the context of increasing global awareness about climate change, understanding and mitigating carbon dioxide emissions from electricity consumption is crucial. Renewable energy sources, such as solar modules, offer a promising solution to reduce these emissions. Quantifying the environmental and economic benefits of adopting solar energy systems is essential for driving policy and investment decisions. This financial advantage is quantifiable and can be systematically calculated using a specific equation, underscoring the economic benefits tied to our environmental efforts [76]. Equation (8) can be stated as:

$$C_{C{O}_2}=E{F}_{C{O}_2}\times G_{elec}\times {\varnothing }_{C{O}_2}$$ (8)

where $E{F}_{C{O}_2}$ represents the amount of $\mathrm{C}{\mathrm{O}}_2$ emission per unit of electricity consumed [kg $\mathrm{C}{\mathrm{O}}_2$/MWh], $G_{elec}$ is the annual energy saved [MWh] and ${\varnothing }_{C{O}_2}$ is a coefficient that accounts for the specific carbon dioxide emission characteristics of the energy source or process being considered. This equation is significant for the mitigation analysis of solar modules as it quantifies the carbon dioxide emissions associated with electricity consumption. By applying this equation, researchers can assess the reduction in $\mathrm{C}{\mathrm{O}}_2$ emissions achieved by using solar modules compared to conventional energy sources. This aids in evaluating the environmental benefits of solar energy, guiding policy development for renewable energy adoption, and promoting sustainable energy practices to mitigate climate change.

2.8. Constraints and uncertainties

In the context of our study comparing the techno-economic feasibilities of various solar modules in South Sakucia Union, several constraints must be considered. Firstly, the accuracy of our simulations conducted with software tools such as PV*SOL, PVsyst, and System Advisor Model (SAM) may introduce uncertainties, as the models may not fully capture real-world conditions. We have assumed to go with the default transposition models associated with each software. In addition, the solar modules were assumed to have an optimum tilt angle of 22°, which corresponded to the latitude of the site [77,78]. Additionally, our analysis assumes constant solar irradiance levels, although actual weather patterns and environmental factors may vary, leading to discrepancies between simulations and real-world performance. To simplify our economic calculations, we have assumed some values as well as we have rounded some financial values to their nearest decimal places. Moreover, variations in local installation conditions, maintenance practices, and economic factors such as fluctuating PV panel prices could introduce uncertainty in the practical implementation of the chosen solar module configurations.

2.9. Overall workflow

In the outlined workflow for our project, we initiated by carefully selecting an appropriate site, followed by the collection of essential meteorological data to inform our design process. Utilizing PV*SOL software, the system's design was meticulously crafted, beginning with the strategic placement of solar panels in a designated open area, complemented by the installation of inverters and necessary wiring. We developed four unique configurations, each incorporating different modules, which were then simulated in PV*SOL, PVsyst, and SAM to evaluate their performance under specified parameters. Through these simulations, we meticulously analyzed the energy output from the solar modules, conducting both a comparative study to evaluate energy generation differences among setups and a deviation analysis to identify any inconsistencies. Additionally, an economic analysis was undertaken to determine the most cost-effective module, alongside an environmental assessment to quantify CO₂ emission reductions attributable to the project. The culmination of these analyses will guide us in selecting the optimal system configuration that balances profitability with environmental benefits, as depicted in Fig. 11, which visually summarizes the project's comprehensive workflow.

Fig. 11.

Workflow diagram.

3. Software simulation & result analysis

3.1. Electricity yield from PV systems

To evaluate the energy production capabilities of different solar modules, we utilized four types: monofacial solar modules, bifacial solar modules, dual-axis solar trackers, and seasonal tilt solar modules. For simulation purposes, three software programs, namely, PV*SOL, PVsyst, and SAM were used. The comparison mainly focuses on the average energy output produced by each module type. Subsequently, we performed an in-depth deviation analysis on the data collected from these three software programs. Differences in system output across the software platforms were observed, with SAM typically factoring in more potential losses, which leads to varied energy yields under different conditions. Additionally, each software's statistical modeling, irradiation value measurement processes, and yield calculations have slight differences. The weather profiles utilized in the three software platforms also differ, particularly in terms of irradiation and temperature, which affects the comparison. A deviation analysis of the three PV software platforms is crucial for interpreting the collected data and for future estimations and planning efforts [79].

3.1.1. Monofacial solar module

Monofacial solar modules are photovoltaic panels with a single-sided active surface, that convert sunlight into electricity. Comprising layers of front glass cover, semiconductor material (usually silicon), back sheet, and electrical connections, they offer cost-effective solar solutions with slightly lower efficiency than bifacial modules. Optimally installed to capture sunlight effectively, they are commonly used in solar farms, rooftops, and power plants. While they cannot harness reflected light, ongoing research aims to enhance their performance, making them a viable choice for many solar installations.

3.1.1.1. Simulation parameters of monofacial solar module

The solar park under investigation utilized the LG450N2W-E6 monofacial module. To conduct the simulations, three software tools were employed, namely PV*SOL, SAM, and PVsyst. The simulation parameters employed in the study are presented in Table 4.

Table 4.

Simulation parameters for monofacial module in PV*SOL, SAM & PVsyst.

Parameters	PV*SOL	SAM	PVsyst
Panel rating	450W	450W	450W
No. of Panels	200	200	198
Total PV Generator	90kWp	90kWp	90kWp
Tilt	22°	22°	22°
Module area	2.1985 m2	2.20 m2	2.196 m2

3.1.1.2. Energy generation by monofacial solar module

The table provided presents the monthly energy generation data for three software programs: SAM, PV*SOL, and PVsyst, along with their corresponding average energy yield production. These numbers provide valuable insights into the performance and comparative analysis of these software programs in terms of energy generation. Throughout the year, SAM showed a varied range of energy outputs. It contributed to a total energy yield of 125,944 kWh, resulting in an average energy yield of 10,366 kWh. PV*SOL displayed a slightly different energy generation pattern when compared to SAM. The total energy yield for PV*SOL amounted to 127,631 kWh, resulting in an average energy yield of 10,409 kWh. On the other hand, PVsyst exhibited a distinct energy generation trend. The total energy yield for PVsyst was 119,680 kWh, with an average energy yield of 12,419 kWh. When comparing the three software programs, PV*SOL demonstrated the highest total energy yield of 127,631 kWh, while PVsyst had the lowest total energy yield of 119,680 kWh. However, its worth noting that the average energy yield production was relatively similar, with SAM at 10,366 kWh, PV*SOL at 10,409 kWh, and PVsyst at 12,419 kWh. Energy production for the specific months in the year is summarized in Table 5.

Table 5.

Monthly energy generation by monofacial module (in kWh).

Month	SAM (kWh)	PV*SOL (kWh)	PVsyst (kWh)	Average Energy Production (kWh)
January	10,514	10,735	9850	10,366
February	10,865	10,612	9750	10,409
March	12,757	12,980	12,100	12,612
April	11,595	12,571	11,820	11,995
May	11,757	11,469	11,210	11,479
June	8189	9510	8800	8833
July	9729	8775	7840	8781
August	8891	9714	9400	9335
September	8810	10,000	9130	9313
October	10,297	10,571	10,090	10,319
November	11,054	10,694	10,010	10,586
December	11,486	10,000	9680	10,389
Total	125,944	127,631	119,680	124,418

3.1.1.3. Deviation analysis for monofacial solar panel

The provided figure presents the deviation analysis of three software programs: PV*SOL, PVsyst, and SAM. The analysis focuses on the differences in energy estimations between these programs for each month. Examining the difference between PV*SOL and PVsyst, we observe deviations ranging from 259 to 935. In January, PV*SOL showed higher energy estimations than PVsyst, with a deviation of 885. Conversely, in May, PV*SOL underestimated the energy compared to PVsyst, with a deviation of 259. These deviations indicate variations in energy estimations between the two programs. Analyzing the difference between PVsyst and SAM, the deviations range from −1889 to 611. In February, PVsyst overestimated the energy generation compared to SAM, with a deviation of −1115. However, in June, PVsyst underestimated the energy, resulting in a deviation of 611. These deviations illustrate differences in energy estimations between PVsyst and SAM. Lastly, when exploring the difference between SAM and PV*SOL, the deviations range from −1321 to 1486. In April, SAM underestimated the energy compared to PV*SOL, with a deviation of −976. However, in December, SAM predicted significantly higher energy generation than PV*SOL, resulting in a deviation of 1486. Fig. 12 represents the graphical illustration of the deviations.

Fig. 12.

Deviation analysis of monthly energy generation by monofacial solar module using PV*SOL, PVsyst, and SAM.

3.1.2. Bifacial solar module

Bifacial solar modules are advanced photovoltaic panels capable of harnessing sunlight from both the front and rear sides, leading to enhanced energy yields and greater efficiency compared to traditional solar panels. These modules can capture sunlight reflected from surrounding surfaces through the albedo effect, converting indirect and scattered light into useable energy. Bifacial solar panels offer installation versatility and durability, making them suitable for various applications, including utility-scale solar power plants, commercial installations, and residential setups. As a promising technology in the solar industry, bifacial modules continue to contribute to improved energy generation and sustainability.

3.1.2.1. Simulation parameters of bifacial solar module

We employed the LG455N2W-E6 model of bifacial solar modules across all three software applications. Additionally, we utilized 200 monofacial modules in each software. As the solar irradiation can enter from both sides, so its efficiency is higher than that of a monofacial solar panel. The simulation parameters utilized in this study are outlined in Table 6.

Table 6.

Simulation parameters for bifacial module in PV*SOL, SAM & PVsyst.

Parameters	PV*SOL	SAM	PVsyst
No. of Panels	200	200	200
Total PV Generator	88 kWp	90kWp	90.2 kWp
Tilt	22°	22°	22°
Module area	2.1985 m2	2.20 m2	2.196 m2

3.1.2.2. Energy generation by bifacial solar module

Throughout the year, the systems energy production performance displayed fascinating fluctuations, providing valuable insights into how it operates. Based on the simulation results from the System Advisor Model (SAM), the total energy generated amounted to 128,836 kWh. The most significant amount of energy, reaching 12,892 kWh, was produced in March. Additionally, using PV*SOL, the annual energy generation was calculated to be 123,921 kWh, with the highest energy output of 12,645 kWh also occurring in March. On the other hand, PVsyst yielded a total energy generation of 12,522 kWh, with a peak energy output of 12,278 kWh recorded in April. These findings not only highlight the systems capability to harness renewable energy effectively but also emphasize its potential to contribute significantly to a sustainable future by reducing reliance on conventional power sources. Table 7 presents the energy output data obtained from the bifacial solar module, offering a quantitative overview of its performance.

Table 7.

Monthly energy generation by bifacial module (in kWh).

Month	SAM (kWh)	PV*SOL (kWh)	PVsyst (kWh)	Average Energy Production (kWh)
January	10,622	10,387	10,380	10,463
February	10,946	10,297	10,390	10,544
March	12,892	12,645	12,990	12,842
April	11,865	12,329	12,640	12,278
May	12,108	11,200	11,910	11,739
June	8621	9258	9190	9023
July	10,135	8535	8110	8927
August	9297	9393	9890	9527
September	9027	9664	9690	9460.
October	10,459	10,252	10,760	10,490
November	11,189	10,342	10,680	10,737
December	11,675	9619	10,180	10,491
Total	128,836	123,921	126,810	126,522

An important observation is that the overall energy production of the bifacial solar module surpasses that of the monofacial solar module. This finding underscores the advantages of the bifacial design, which allows for additional energy generation through the utilization of reflected and diffuse light from the ground or surrounding surfaces. The enhanced energy output of the bifacial module signifies its potential to provide increased efficiency and contribute to a more sustainable energy generation system.

3.1.2.3. Deviation analysis for bifacial solar panel

The graph provided offers valuable insights through a deviation analysis of energy generation from bifacial solar panels, utilizing three distinct software programs: SAM, PV*SOL, and PVsyst. By examining the PV*SOL-PVsyst deviations, which span from −710 to 425 units, we can observe significant variations in energy estimations between the SAM and PV*SOL software. Similarly, the PVsyst-SAM deviations, ranging from −2025 to 775 units, highlight substantial differences in energy predictions between the PVsyst and SAM software. Furthermore, the SAM-PV*SOL deviations, spanning from −637 to 2056 units, provide additional evidence of differences in energy estimations between the SAM and PV*SOL software. This suggests that the two programs may utilize different methodologies or algorithms in their calculations. Fig. 13 visually depicts the graphical illustration of these deviations. These deviations emphasize the significance of software selection, as it can significantly impact the accuracy of energy estimation for bifacial solar panels. Understanding these deviations can assist in making informed decisions and selecting the most suitable software for energy analysis and project planning.

Fig. 13.

Deviation analysis of monthly energy generation by bifacial solar module using PV*SOL, PVsyst, and SAM.

3.1.3. Dual-axis solar tracker

A dual-axis solar tracker is an advanced device used in solar photovoltaic systems to maximize energy generation by automatically orienting solar panels or collectors towards the sun in both horizontal and vertical directions. The tracker employs precise motors and control systems to continuously adjust the panels position, ensuring it remains perpendicular to the sun's rays throughout the day. This increased efficiency leads to up to 40 %–50 % higher energy output compared to fixed solar panels. Although more expensive, dual-axis trackers are ideal for regions with significant seasonal variations and are commonly used in large-scale solar power plants and commercial installations to achieve optimal returns on investment while promoting sustainable energy practices.

3.1.3.1. Simulation parameters of dual-axis solar tracker

The dual-axis solar tracker utilizes the LG450N2W-E6 solar panel, which exhibits similarities with the monofacial solar module employed in our study. Consequently, all parameters of the dual-axis solar tracker will be identical to those of the monofacial solar module, as listed in Table 4.

3.1.3.2. Energy generation by dual-axis solar tracker

The monthly energy production exhibited a distinct pattern of fluctuation throughout the year. In January, the system generated relatively lower energy, followed by an increase in February. March witnessed a notable surge in energy production, indicating a clear upward trend. Subsequently, April and May maintained relatively high energy outputs. However, June experienced a decline in energy production. The trend continued with July recording below-average energy generation. From August to September, the system's energy production saw a moderate increase. October, November, and December witnessed further growth in energy output. The analysis of the dual-axis solar tracker data reveals significant trends in energy generation over the course of the year.

The average annual energy production was 149,070.3 kWh, with individual software contributions as follows: 152,382 kWh (SAM), 138,199 kWh (PV*SOL), and 156,630 kWh (PVSYST). Table 8 contains comprehensive data on the energy production for each month. This data highlights the system's performance and provides valuable insights into the impact of seasonal variations on energy generation.

Table 8.

Monthly energy generation by dual-axis solar tracker (in kWh).

Month	SAM (kWh)	PV*SOL (kWh)	PVsyst (kWh)	Average Energy Produced (kWh)
January	12,335	11,632	12,780	12,249
February	12,705	11,272	12,520	12,166
March	15,289	13,897	15,860	15,015
April	14,037	13,949	15,950	14,645
May	14,671	12,868	15,300	14,280
June	10,511	10,397	11,230	10,713
July	12,335	9625	9540	10,500
August	10,556	10,397	12,180	11,044
September	10,627	10,757	11,840	11,075
October	12,417	11,221	13,200	12,279
November	13,314	11,272	13,410	12,665
December	13,585	10,912	12,820	12,439
Total	152,382	138,199	156,630	149,070

3.1.3.3. Deviation analysis for dual-axis solar tracker

The deviation analysis focused on the energy generated by a dual-axis solar tracker using three software programs: PV*SOL, PVsyst, and SAM. By comparing the energy values obtained from each software, insights were gained into the differences and variations in their estimations. When examining PV*SOL and PVsyst, it was evident that PV*SOL consistently yielded lower energy values than PVsyst across the analyzed months. The deviations ranged from −1148 kWh in January to −2432 kWh in May. These deviations indicated that PV*SOL tended to underestimate the energy generation in comparison to PVsyst. The deviations between PVsyst and SAM were notable, with variations in energy estimations. PVsyst occasionally generated higher energy values than SAM, as observed in April with a deviation of 1913 kWh. Conversely, PVsyst sometimes produced lower energy values than SAM, such as in September with a deviation of 1213 kWh. These discrepancies indicated that the two software programs exhibited divergent estimations of energy generation for the dual-axis solar tracker. SAM consistently outperformed PV*SOL in energy estimations. The deviations between SAM and PV*SOL ranged from 703 kWh in January to 2673 kWh in December, with positive values indicating that SAM predicted higher energy generation compared to PV*SOL throughout the analyzed period. In summary, the deviation analysis highlighted variations in energy estimations among PV*SOL, PVsyst, and SAM for the dual-axis solar tracker. PV*SOL consistently underestimated the energy, PVsyst showed mixed results compared to SAM, and SAM generally predicted higher energy generation. The research findings on the monthly energy generation deviation of the dual-axis solar module, as simulated by PV*SOL, PVsyst, and SAM, are presented in Fig. 14. These findings underscore the significance of software selection and its impact on energy estimations when analyzing the performance of dual-axis solar trackers.

Fig. 14.

Deviation analysis of monthly energy generation by dual-axis solar module using PV*SOL, PVsyst, and SAM.

3.1.4. Seasonal tilt solar module

A seasonal tilt solar module is a specialized solar panel system designed to optimize energy generation throughout the year by manually adjusting its tilt angle. Unlike fixed-tilt solar panels, this module allows users to change the angle based on the suns position during different seasons, maximizing solar energy absorption and increasing overall electricity output. By increasing the tilt angle during the winter months and decreasing it in the summer, the module aligns itself optimally with the sun's rays, resulting in improved energy yield and cost-effectiveness. Regular maintenance and consideration of geographic location are important for ensuring the system functions optimally, and some setups may integrate both manual adjustments and automated tracking systems for even better energy capture. For this particular project, we have used the LG450N2W-E6 which is similar to the monofacial and dual-axis solar trackers. We have used mounting structures with scissor jacks, shown in Fig. 15, which allows us to change the tilt angle efficiently with minimal hassle.

Fig. 15.

The design of a manually adjustable tilt mechanism for Seasonal Tilt Solar Module (adopted from Ref. [80]).

3.1.4.1. Simulation parameters of seasonal tilt solar module

The seasonal tilt solar module is equipped with the LG450N2W-E6 solar panel, which shares similarities with the monofacial solar module and dual-axis solar tracker used in this study. As a result, all parameters of the seasonal tilt solar module will be identical to those of the other two modules, as provided in Table 4.

However, in the previous cases there was a fixed tilt angle of 22°, whereas in this case the tilt angle is not fixed. By adjusting the tilt angle for different months and inputting location-specific data, users can evaluate energy generation potential and system efficiency. These simulations offer valuable insights for designing efficient solar installations that adapt to changing irradiation angles and maximize energy capture across all seasons.

3.1.4.2. Finding optimal angle for seasonal tilt solar module

For the seasonal tilt solar panels, we conducted simulations to identify the optimal tilt angle, adjusting the angle from 0 to 90° in 5-degree increments. For each month, we recorded the tilt angle that maximized energy production. For instance, in January, a tilt angle of 40° was found to yield the highest solar energy output. These simulations were carried out using PV*SOL software. Table 9 presents the energy generation in kWh for various tilt angles in the seasonal tilt solar module.

Table 9.

Energy generation for different tilt angle in seasonal tilt solar module.

Angle	Energy Generation (kWh)
	January	February	March	April	May	June	July	August	September	October	November	December
0	9754	9870	11,176	10,890	12,108	10,500	9095	9333	8982	9645	10,112	9016
5	9654	10,178	11,542	11,000	11,590	10,650	9150	9567	9287	9782	9976	8976
10	9800	10,190	11,986	11,782	11,110	10,343	9206	9755	9565	9855	9812	8845
15	9990	10,200	12,078	12,342	10,580	10,200	9150	9782	9833	9900	9600	8543
20	10,000	10,347	12,675	12,574	9888	9800	9000	9645	9756	10,100	9640	8200
25	10,123	10,467	12,760	12,090	9000	8750	8700	9545	9666	10,282	9764	8346
30	10,212	10,510	12,750	11,872	9550	8500	8650	9382	8965	10,392	9805	8432
35	10,353	10,526	12,542	11,567	10,210	9666	8500	8956	8554	9876	9850	8590
40	10,632	10,426	11,382	10,986	11,150	9780	8660	8734	7945	9756	9907	8610
45	10,567	10,330	11,200	10,564	11,500	9950	8800	8860	8000	9660	9967	8715
50	10,403	10,267	11,009	10,675	11,780	10,000	8960	8942	8203	9542	10,326	8917
55	9876	10,110	10,956	10,764	12,019	10,261	8990	9203	8367	9326	10,215	9175

The analysis of energy generation data for various tilt angles of a seasonal tilt solar module reveals significant insights into optimizing solar energy capture throughout the year. The optimal tilt angle varies seasonally, with higher angles favoring the winter months (December–February) to maximize energy capture when the sun is lower in the sky. During the summer months (June–August), the data does not show a simple pattern, suggesting that mid-range to higher tilt angles might sometimes be more effective, reflecting the sun's higher position. For the transition months (March–May and September–November), mid-range tilt angles (around 20–30°) are generally most effective, with peak energy generation observed at a 30-degree tilt in March. This indicates an optimal balance for spring conditions. The analysis highlights the complexity of solar energy system design, emphasizing the need for seasonal adjustment of panel tilt angles to optimize energy generation throughout the year, acknowledging the sun's varying angles and intensity across seasons.

3.1.4.3. Energy generation by seasonal tilt solar module

The table provided unveils the captivating performance of a sophisticated seasonal tilt solar module across all twelve months of the year. With meticulous precision, this module adjusts its tilt angle to optimize solar energy capture in accordance with the changing seasons. Such dynamic adaptation ensures a continuous and remarkable energy production throughout the year. Table 10 presents the solar energy output obtained from the seasonal tilt solar module. It reveals that SAM generates the highest solar energy in March, reaching 12,737 kWh, contributing to a total of 129,784 kWh. In comparison, PV*SOL records a maximum energy generation of 12,760 kWh, contributing to a total energy output of 127,964 kWh. On the other hand, PVsyst achieves the highest energy output of 13,170 kWh in March, with a cumulative solar energy generation of 133,860 kWh.

Table 10.

Monthly energy generation by seasonal tilt solar module (in kWh).

Month	Angle (°)	SAM (kWh)	PV*SOL (kWh)	PVsyst (kWh)	Average Energy Produced (kWh)
January	40	10,996	10,632	11,230	10,953
February	35	10,991	10,526	10,930	10,816
March	25	12,737	12,760	13,170	12,889
April	20	11,677	12,574	13,150	12,467
May	0	12,394	12,108	12,640	12,381
June	5	8862	10,650	9850	9787
July	10	10,219	9206	8660	9362
August	15	9043	9782	10,410	9745
September	15	8792	9833	9990	9538
October	30	10,316	10,392	11,160	10,623
November	50	11,589	10,326	11,500	11,138
December	55	12,168	9175	11,170	10,838
Total		129,784	127,964	133,860	130,536

3.1.4.4. Deviation analysis for seasonal tilt solar module

The deviation analysis was performed on the energy generated by three software programs, namely PV*SOL, PVsyst, and SAM, for a seasonal tilt module. The analysis involved calculating the differences between the energy values obtained from each software. When comparing PV*SOL and PVsyst, it was observed that PV*SOL consistently yielded lower energy values than PVsyst. The deviations ranged from −1995 kWh in December to −404 kWh in February. These negative values indicate that PV*SOL underestimated the energy generation compared to PVsyst for most months. On the other hand, when comparing PVsyst and SAM, the deviations varied significantly. PVsyst generated more energy than SAM in some months, such as April with a deviation of 1473 kWh, while in other months, SAM outperformed PVsyst, like in June with a deviation of −1788 kWh. These deviations show that the two software programs produced different energy estimations, with PVsyst sometimes overestimating and sometimes underestimating the energy compared to SAM. The deviations between SAM and PV*SOL were also notable. SAM consistently produced higher energy values than PV*SOL, with deviations ranging from 364 kWh in January to 2993 kWh in December. These positive deviations indicate that SAM predicted higher energy generation than PV*SOL throughout the year. The outcomes are visually represented in Fig. 16, providing valuable insights into the observed trends.

Fig. 16.

Deviation analysis of monthly energy generation by seasonal tilt solar module using PV*SOL, PVsyst, and SAM.

3.2. Economic analysis

Investing in a solar photovoltaic (PV) system represents a significant upfront financial commitment, making economic analysis a crucial step in assessing the viability of such projects. This economic study focuses on a detailed simulation of a 90 kW grid-integrated solar PV system planned for Bhola, leveraging insights from previous implementations in similar geographic and economic contexts, as well as current market data, to derive an accurate cost estimate. The foundational cost components for initiating this project encompass the expenses associated with PV modules, inverters, mounting structures, and electrical wiring. Notably, the costs for PV modules and mounting structures are subject to variation depending on the chosen system orientation, which is a critical factor in the overall financial planning. Based on these factors, key financial parameters are summarized and stated in Table 11.

Table 11.

Financial parameters.

Description	Monofacial	Bifacial	Seasonal Tilt	Dual-axis Solar Tracker
PV Module ($)	86,000	90,000	86,000	86,000
Inverter ($)	20,120
Mounting ($)	6000	6000	6600	29,500
Wiring ($)	2500
Lifespan (years)	25
Total System Capacity (kW)	90	91	90	90
Performance Degradation	1 %
Discount Rate	5 %
O&M	1 % of the Initial Cost			5 % of Initial Cost
Contingency	1 % of the Revenue
Electricity Price ($)	0.10
Average Energy Produced/year (kWh)	124,418	126,522	130,536	149,070
Actual Energy Produced (kWh)	2,657,640	2,824,472	2,900,230	3,302,528

For the purposes of our analysis, we have selected LG450N2W-E6 solar panels, each with a power output capability of approximately 450W and a unit cost of around $430 [81]. The project requires a total of 200 units of these panels, culminating in an investment of $86,000 specifically for the solar panels. To address the needs of bifacial analysis, a different model, the LG455N2W-E6, priced at $450 per unit, has been chosen [82]. This adjustment brings the investment for bifacial solar modules to $90,000, highlighting the nuanced financial considerations specific to the system's design.

Inverters, which are crucial for converting the DC electricity produced by the solar modules into useable AC power, represent another significant financial consideration. This project requires the initial installation of two inverters to ensure uninterrupted power conversion, with an anticipated inverter lifespan of 12–15 years. Given the necessity of replacing these inverters at least once over the system's lifetime, we project the need for a total of four inverters throughout the project's duration [83]. Each inverter, specified as the CSI-60KTL-GS model, is priced at $5,030, for all of the four different orientations [84].

To mitigate the risk of unforeseen expenses, a contingency fund amounting to 1 % of the project's revenue has been established, underscoring the project's comprehensive financial planning strategy [85]. The cost of the mounting structure, which is pivotal for the secure installation of solar panels, is estimated at $6000 for this project size [86]. This cost escalates for systems that incorporate dual-axis tracking due to the additional requirement for motors, sensors, and actuators, which facilitate the tracking of the sun's movement, enhancing the system's efficiency [87]. The project also explores the manual adjustment of seasonal tilt solar modules, which can be efficiently managed with the use of perforated mounting brackets or scissor lift jacks, estimating the need for approximately 30–40 jacks to accommodate the entire system. This introduces an additional 10 % cost increase for the mounting structure, accounting for the manual adjustment capabilities [80].

The electrical wiring and connectors, essential for the system's operational integrity, are projected to cost around $2500 [87]. Operational and maintenance (O&M) costs form a critical component of the project's ongoing financial considerations. For fixed and manually adjustable systems, O&M expenses are conservatively estimated at 1 % of the system's annual cost [88,89]. In contrast, dual-axis tracking systems, due to their complex electromechanical nature, incur higher O&M costs, estimated at 5 % of the tracking equipment's annual cost [90,91]. We've opted to exclude additional labor costs for manual tilt adjustments, considering that minimal training enables existing power plant staff to manage these adjustments quickly. The process is fast, taking less than a minute per array, with the potential to adjust all arrays within a day. Consequently, the annual operational and maintenance (O&M) costs remain unchanged for arrays requiring periodic adjustments. This approach also integrates tilt adjustments with routine dust removal tasks, particularly in dusty areas, avoiding extra labor costs [64].

Cash flows of PV systems are dependent on electricity charges. Right now, the price of electricity in Bangladesh is almost $0.1/kWh [92]. The price is subjected to change depending on the currency rate. This consideration is crucial for projecting the project's revenue and assessing its economic viability.

The project is designed with a minimum lifespan of 25 years [93,94], during which the system's performance is expected to degrade by 1 % annually. A discount rate of 5 % is applied to the economic evaluation to assess the project's feasibility accurately [95].

Taking all these into consideration, a thorough economic analysis is done for four different modules, cashflow diagrams are generated which helps us to find key financial indicators. A comparison of yearly discounted cashflow in these systems are presented in Table 12. Analyzing the annual cash flow helps assess the economic viability and profitability of each solar module option within the project, providing valuable insights into their long-term financial performance and potential returns on investment. Hence, this comprehensive economic analysis provides a solid foundation for evaluating the investment in a 90 kW grid-integrated solar PV system in Bhola, considering both initial costs and ongoing operational expenses.

Table 12.

Comparison of discounted annual cashflow (in $).

Year	Discounted Annual Cashflow ($)
	Monofacial	Bifacial	Seasonal Tilt	Dual-axis Solar Tracker
0	(114,620.00)	(118,620.00)	(115,220.00)	(138,120.00)
1	11,942.65	12,124.97	12,577.87	13,643.71
2	10,753.27	11,483.87	11,848.72	12,801.11
3	10,128.91	10,817.36	11,161.63	12,051.72
4	9540.67	10,189.47	10,514.38	11,346.00
5	8986.54	9597.97	9904.49	10,681.43
6	8464.41	9040.61	9329.92	10,055.60
7	7972.59	8515.59	8788.62	9466.27
8	7509.27	8020.97	8278.61	8911.32
9	7072.81	7555.00	7798.10	8388.75
10	6661.59	7115.97	7345.45	7896.67
11	6274.23	6702.40	6918.94	7433.32
12	5909.29	6312.81	6517.19	6997.02
13	5565.55	5945.78	6138.66	6586.20
14	5241.73	5600.05	5782.06	6199.39
15	4936.68	5274.31	5446.14	5835.17
16	4649.37	4967.49	5129.65	5492.24
17	4378.69	4678.47	4831.49	5169.36
18	4123.72	4406.21	4550.59	4865.36
19	3883.51	4149.71	4286.01	4579.14
20	3657.31	3908.15	4036.77	4309.66
21	3444.18	3680.54	3801.94	4055.96
22	3243.45	3466.16	3580.77	3817.11
23	3054.40	3264.25	3372.40	3592.24
24	2876.29	3074.05	3176.10	3380.55
25	2708.56	2894.89	2991.21	3181.26

For economic analysis, four solar panel configurations are analyzed: Monofacial, Bifacial, Seasonal Tilt, and Dual-axis Solar Tracker. Their economic performances are assessed through various financial indicators, namely Levelized Cost of Electricity (LCOE), Net Present Value (NPV), Discounted Payback Period (DPBP), Internal Rate of Return (IRR), and Profitability Index (PI). A summary of the key findings is presented in Table 13.

Table 13.

Results of economic performance evaluation.

Financial Metrics	Monofacial	Bifacial	Seasonal Tilt	Dual-axis Solar Tracker
LCOE ($/kWh)	0.0492	0.0473	0.0452	0.0487
NPV ($)	38,359.67	44,167.06	52,887.70	42,616.56
DPBP (years)	14.97	13.93	12.69	14.53
IRR (%)	8.074	8.661	9.460	8.323
PI	1.309	1.372	1.459	1.335

Starting with LCOE, a measure that encapsulates the average cost per kWh of electricity produced, the Seasonal Tilt configuration demonstrates the greatest cost-efficiency at $0.0452/kWh. Bifacial stands next to the seasonal tilt with a LCOE of $0.0473. In contrast, the Dual-axis Solar Tracker, despite its advanced technology, is less cost-effective, reflected in its higher LCOE of $0.0487/kWh. This is because of the initial upfront cost that is incurred to set up a system of this kind. This contrast may not only reflect operational efficiency but also encapsulates the total expenditure over the life span of the projects, from installation to decommissioning.

Shifting the focus to NPV, which considers the value of future cash flows in today's dollars, the Seasonal Tilt's superiority is underscored by an NPV of $52,887.70, which indicates a strong potential for profit when looking at the long-term horizon. This figure surpasses the NPV of the Monofacial setup, which is at the lower end with $38,359.67, suggesting that the traditional Monofacial approach may not be as profitable over time, despite potentially lower initial costs.

The DPBP metric highlights the time it takes for an investment to break even. Here, the Seasonal Tilt shines again, with a payback period of 12.69 years, the shortest among the configurations. Bifacial module stands next in the list with a payback period of 13.93 years. This rapid return on investment contrasts with the Monofacial technology, which trails behind with a DPBP of 14.97 years, indicating investors would have to wait longer for the initial outlay to be recovered.

As for the IRR, which indicates the efficiency of the investment, the Seasonal Tilt panels yield an impressive 9.460 %, dwarfing the Dual-axis Solar Tracker's return of 8.323 %. This substantial difference suggests that the Seasonal Tilt is likely to be the more compelling choice for investors seeking robust yields on their investments.

Finally, the PI offers a ratio that summarizes the profitability of the project, where again, the Seasonal Tilt panels score highest with a ratio of 1.459. This means that for every dollar invested, the Seasonal Tilt panels are expected to return $1.459. The Dual-axis Solar Tracker, while still above the breakeven point with a PI of 1.335, indicates less relative profitability than its counterparts.

Incorporating these numeric details into the analysis presents a clear economic narrative: the Seasonal Tilt panels, with the lowest LCOE, highest NPV, and most favorable PI, IRR, and DPBP, emerge as the most economically promising technology amongst the ones evaluated. This configuration seems to strike an optimal balance between efficiency and cost, leading to a quicker and higher return on investment. Conversely, while the Dual-axis Solar Tracker and Monofacial panels may have their own merits, their economic performance as indicated by the data suggests they are less attractive in our case from a purely financial standpoint.

4. Environmental benefits

Climate change has become a focal issue worldwide, leading to efforts aimed at curbing fossil fuel usage and enhancing renewable energy contributions. Specifically, the generation of electricity from fossil fuels is a significant source of pollutants, accounting for approximately 35.29 % of emissions that drive climate change and global warming [96,97]. Transitioning to renewable sources, such as solar energy, has proven effective in significantly reducing $\mathrm{C}{\mathrm{O}}_2$ emissions. Each solar module, depending on its efficiency and technology, saves a certain amount of electrical energy that would otherwise be produced using fossil fuels.

Electric energy production in Bangladesh has been heavily reliant on fossil fuels, with gas and coal being the primary sources. As of 2022, 98 % of the electricity generated in Bangladesh came from fossil fuels, with around 59 % reliance on gas and an increasing share from coal, rising from 3 % in 2015 to 15 % in 2022 [14]. Such dependence has led to increased greenhouse gas emissions, underscoring the urgent need to shift towards renewable energy alternatives [98]. In response to this, the Bangladeshi government has set an ambitious target to obtain 40 % of its electricity from clean energy sources by 2041 [99]. This initiative is aimed at meeting the growing demand for electricity while pursuing environmental sustainability goals. The adoption of solar energy, in particular, is anticipated to play a significant role in reducing $\mathrm{C}{\mathrm{O}}_2$ emissions within the power sector by replacing the electricity that would otherwise be generated through fossil fuels.

The shift to renewable energy like solar is crucial for Bangladesh, not only to meet its growing energy demands but also to reduce its carbon footprint. With the right investments and policy support, the integration of solar energy into the national grid could lead to a significant decrease in $\mathrm{C}{\mathrm{O}}_2$ emissions, aiding in the global efforts to combat climate change. The current solar energy market in Bangladesh is moderately fragmented, with a number of companies leading the charge in solar PV installations. The government is actively promoting renewable energy from diverse sources, including solar, to secure a substantial portion of its electric energy capacity from renewables.

Our project places a strong emphasis on environmental sustainability, actively addressing the impacts within our scope of work. By steering away from fossil fuel dependency, our proposed initiative stands to significantly diminish the carbon footprint, curbing $\mathrm{C}{\mathrm{O}}_2$ emissions that would have prevailed in the absence of this shift. The reduction in $\mathrm{C}{\mathrm{O}}_2$ emissions not only contribute to environmental health but also presents an opportunity for cost mitigation.

The comparative analysis of different solar module types, shown in Table 14, reveals distinct variations in performance. Dual-axis Solar Trackers lead in energy production with a total output of 3,302,528 kWh, which surpasses the Monofacial, Bifacial, and Seasonal Tilt modules by 24.25 %, 16.93 %, and 13.88 % respectively. This high yield correlates with the greatest $\mathrm{C}{\mathrm{O}}_2$ emissions avoidance, with Dual-axis Solar Trackers preventing 1658.40 tons, outperforming the other types by a margin of approximately 19.82 % over Monofacial, 17.82 % over Bifacial, and 14.22 % over Seasonal Tilt modules. Financially, this translates to the Dual-axis Solar Trackers achieving the highest cost savings at $99,504, marking a considerable increase over the others – 19.81 % more than Monofacial, 17.84 % more than Bifacial, and 14.16 % more than Seasonal Tilt modules.

Table 14.

$C{O}_2$ mitigation analysis for different solar modules.

Solar Module Type	Total Energy Produced (kWh)	CO2 Emissions Avoided (tons)	CO2 Mitigation Cost Savings ($)
Monofacial	2,657,640	1384.15	83,049
Bifacial	2,824,472	1407.56	84,453.60
Dual-axis Solar Tracker	3,302,528	1658.40	99,504
Seasonal Tilt	2,900,230	1452.21	87,132.60

The analysis takes into account a performance degradation of 1 % per year, a standard estimation for solar panels. It is important to note that avoidance of $\mathrm{C}{\mathrm{O}}_2$ emissions and subsequent cost savings are based on the assumption of replacing traditional, non-renewable energy sources with solar power. However, this substitution effect may differ based on local energy compositions and the operational efficiency of the fossil fuel-based power plants being replaced, with an emission factor of 0.5 kg $\mathrm{C}{\mathrm{O}}_2$/kWh factored into the calculations [100]. Additionally, the $\mathrm{C}{\mathrm{O}}_2$ mitigation cost savings are predicated on a valuation of $60/ton, a figure subject to change due to the volatility of carbon pricing and regulatory changes [101]. Therefore, while Dual-axis Solar Trackers show superior performance in this analysis, the final module selection should also consider site-specific factors, installation expenses, and operational efficiencies.

The importance of transitioning to solar energy in reducing the global carbon footprint cannot be overstated. Each step towards solar implementation not only represents progress in $\mathrm{C}{\mathrm{O}}_2$ emission reduction but also signifies a move towards a more sustainable and environmentally friendly energy landscape. The ongoing efforts and future plans outlined by the Bangladeshi government underscore a dedicated approach to embrace renewable energy, setting a precedent for other nations in the global fight against climate change.

5. Discussions

The comprehensive analysis conducted in this study provides invaluable insights into the techno-economic viability of deploying four different solar module configurations, namely monofacial modules, bifacial modules, dual-axis solar trackers, and seasonal tilt solar panels, in South Sakucia Union, Bangladesh. By integrating findings from energy generation capabilities, economic performance, and environmental impact assessments, this discussion emphasizes the paramount importance of considering economic factors in the selection of solar module configurations to ensure both financial viability and environmental sustainability.

The economic analysis stands as a cornerstone of this study, revealing that while the dual-axis solar tracker configuration exhibits superior energy generation capabilities of 149,070 kWh, its financial viability is compromised by its high initial costs. This discrepancy between energy performance and economic feasibility underscores the critical need for a balanced approach that considers both aspects when selecting a solar module configuration. The seasonal tilt solar module emerges as the most economically promising option, offering the lowest Levelized Cost of Electricity (LCOE) at $0.0452/kWh and the highest Net Present Value (NPV) of $52,887.70. These financial metrics are complemented by its favorable Internal Rate of Return (IRR) of 9.460 % and Profitability Index (PI) of 1.459, indicating a robust return on investment and positioning it as a financially prudent choice among the evaluated configurations.

The decision to prioritize the seasonal tilt configuration is further bolstered by its environmental benefits. While the dual-axis solar tracker leads in energy production and CO₂ emissions avoidance, the economic analysis suggests that the additional financial outlay required for these systems may not be justified in all contexts, particularly given the seasonal tilt module's competitive performance in both energy generation and environmental impact mitigation at a lower cost.

Furthermore, the study's focus on economic indicators such as LCOE, NPV, DPBP, IRR, and PI facilitates a nuanced understanding of the trade-offs involved in the adoption of solar technologies. The seasonal tilt configuration's superior economic indicators, particularly its low LCOE and high NPV, highlight its potential for widespread adoption in the context of Bangladesh's emerging solar market. These findings suggest that the economic performance of solar technologies is as crucial as their technical capabilities in driving the transition towards renewable energy.

The analysis also underscores the importance of considering site-specific factors, installation expenses, and operational efficiencies in the final module selection process. The variability in solar irradiance, local energy prices, and the environmental footprint of different technologies necessitates a holistic approach that goes beyond mere technical performance or initial cost considerations. The incorporation of environmental impact assessments, including CO₂ emissions avoidance and cost savings related to mitigation, enriches the decision-making process by aligning economic viability with environmental stewardship.

Incorporating Fig. 17, the comparison of average monthly energy profiles for the different solar module technologies provides further insights into their performance dynamics over the course of the year. This visual representation enhances our comprehension of how each technology responds to varying solar angles and climatic conditions, reinforcing the findings of this study.

Fig. 17.

Comparison of average monthly energy profile for different solar modules.

Fig. 18 provides a detailed comparison of the financial performance of four different solar module technologies by charting their cumulative cash flow over a 25-year period. This graph enables us to evaluate key financial metrics such as payback period, total revenue, and long-term profitability. The technologies compared are monofacial, bifacial, seasonal tilt, and dual-axis solar trackers. The visualization of cash flow trajectories allows for a clear assessment of the return on investment for each type of solar module, highlighting the time it takes for each to break even and the potential financial benefit over the life of the project, which in turn helps to take more informed decisions.

Fig. 18.

Comparison of Cashflow for different solar modules.

This study's findings advocate for a judicious selection of solar module configurations, emphasizing the economic viability and environmental benefits of the seasonal tilt configuration in the specific context of South Sakucia Union, Bangladesh. It highlights the necessity of integrating economic assessments with energy generation and environmental impact analyses to identify solar technologies that offer the best balance between cost, performance, and sustainability. This approach not only aids in maximizing the financial returns on investment in solar projects but also contributes to the broader objective of mitigating climate change through the adoption of renewable energy technologies.

6. Limitations and future prospects

This study has several limitations that should be acknowledged. Firstly, the analysis relies heavily on simulations using PV*SOL, PVsyst, and SAM software. While these tools provide valuable insights, they may not fully capture real-world conditions, such as local weather variations, dust accumulation, and maintenance practices. Additionally, the financial analysis assumes constant electricity prices and a specific discount rate. Fluctuations in these parameters could impact the economic viability of the solar modules.

Future research could address these limitations by incorporating several key directions. Firstly, conducting field studies to validate the simulation results would provide more accurate and reliable insights by capturing real-world data on energy generation and economic performance. Exploring the integration of solar modules with other renewable energy sources, such as wind or biomass, could assess the benefits of hybrid systems in terms of energy stability and cost-effectiveness. Additionally, investigating the potential of emerging solar technologies, such as perovskite solar cells or tandem solar cells, may offer higher efficiencies and lower costs in the future. Analyzing the impact of different policy frameworks and incentive structures on the adoption of various solar module configurations could help in formulating policies that promote sustainable energy solutions. These directions will not only address current limitations but also pave the way for more robust and applicable solutions in the solar energy sector.

7. Conclusion

The comprehensive techno-economic analysis of solar module configurations in South Sakucia Union, Bangladesh, underscores the critical balance between energy generation, economic viability, and environmental sustainability in selecting solar technologies. This research paper employed a software-based approach to comprehensively compare the performance of four distinct solar module configurations. The comparison was based on the results obtained from three reliable software tools: PVSOL, PVsyst, and SAM. The dual-axis solar module emerged as the clear frontrunner in terms of energy generation, exhibiting an impressive average output of 149,070.3 kWh. This configuration's ability to track the sun's movement throughout the day, maximizing its exposure to sunlight, undoubtedly played a significant role in its superior performance. However, among the evaluated configurations - monofacial, bifacial, dual-axis solar tracker, and seasonal tilt - the seasonal tilt solar module emerges as the most economically favorable option. It offers the lowest Levelized Cost of Electricity (LCOE), the highest Net Present Value (NPV), and the most advantageous financial metrics, including a compelling Internal Rate of Return (IRR) and Profitability Index (PI). This configuration not only promises competitive energy generation and significant environmental benefits but also aligns with financial and sustainable objectives crucial for the region.

The findings suggest that choosing the best solar technology should not only focus on how much energy it produces. It should also consider long-term costs and environmental benefits. The seasonal tilt configuration is efficient, cost-effective, and reduces $\mathrm{C}{\mathrm{O}}_2$ emissions. This makes it a good choice for renewable energy projects in Bangladesh and similar areas. Policymakers can benefit from these findings, especially if they plan to build solar parks in the southern part of the country. The seasonal tilt configuration combines economic, technical, and environmental benefits, making it a valuable choice. Based on these findings, several areas of Bangladesh can be identified to further support the development and optimization of solar energy projects.

To further support the development and optimization of solar energy projects in various areas of Bangladesh, integrating other renewable energy sources such as wind energy, hydropower, and biogas with solar parks can create hybrid energy systems that will enhance overall reliability and efficiency. Additionally, evaluating the impact of policy changes and financial incentives on the adoption and scalability of these solar technologies will provide valuable insights for policymakers to support renewable energy growth. Comprehensive sensitivity analyses should be conducted to examine the effects of varying economic parameters, such as interest rates and discount rates, on the financial viability of solar power plants. This will help in understanding the economic robustness of the configurations under different market conditions. These future research directions will help ensure that the chosen solar technology remains optimal over time and continues to deliver economic and environmental benefits.

Funding information

The authors did not receive any funding for this study.

Data availability

The data used for this study are embedded in the manuscript.

CRediT authorship contribution statement

Kashfia Rahman Oyshei: Resources, Methodology, Formal analysis, Data curation, Conceptualization. K. M. Sazid Hasan: Writing – review & editing, Writing – original draft, Visualization, Validation, Software. Nazmus Sadat: Writing – original draft. Md. Ashraful Hoque: Validation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.