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Article

A Techno-Economic Analysis of a Hybrid Microgrid System in a Residential Area of Bangladesh: Optimizing Renewable Energy

by
Md. Feroz Ali
1,*,
Md. Alamgir Hossain
2,
Mir Md. Julhash
1,
Md Ashikuzzaman
1,
Md Shafiul Alam
3 and
Md. Rafiqul Islam Sheikh
4
1
Department of Electrical and Electronic Engineering, Pabna University of Science and Technology (PUST), Pabna 6600, Bangladesh
2
Queensland Micro- and Nanotechnology Centre (QMNC), Griffith University, Nathan 4111, Australia
3
Department of Electrical and Electronic Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh
4
Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi 6204, Bangladesh
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(18), 8051; https://doi.org/10.3390/su16188051
Submission received: 29 July 2024 / Revised: 8 September 2024 / Accepted: 10 September 2024 / Published: 14 September 2024
Browse Figures
Figure 1
<p>Architecture of HOMER Pro software [<a href="#B38-sustainability-16-08051" class="html-bibr">38</a>].</p> "> Figure 2
<p>Methodology flowchart of the proposed work.</p> "> Figure 3
<p>Schematic diagram of different cases.</p> "> Figure 4
<p>Geographic positioning of the study area.</p> "> Figure 5
<p>Load Profile: (<b>a</b>) daily and (<b>b</b>) monthly total.</p> "> Figure 6
<p>Monthly AC primary load profile.</p> "> Figure 7
<p>Hourly load profile for the community.</p> "> Figure 8
<p>Solar daily radiation and clearness index at the location.</p> "> Figure 9
<p>The monthly average wind speed at the location.</p> "> Figure 10
<p>The daily temperature at the location.</p> "> Figure 11
<p>Daily available biomass resources.</p> "> Figure 12
<p>Comparison of various factors of different cases: (<b>a</b>) capital cost and NPC, (<b>b</b>) COE and operating cost.</p> "> Figure 13
<p>Energy purchased and sold for different cases.</p> "> Figure 14
<p>Comparison of GHG emissions of different cases: (<b>a</b>) carbon dioxide (kg/year), (<b>b</b>) carbon monoxide (kg/year), (<b>c</b>) sulfur dioxide (kg/year), (<b>d</b>) nitrogen oxide (kg/year).</p> "> Figure 15
<p>NPC and COE plots, examining impacts of sensitivity variables: (<b>a</b>) solar radiation, (<b>b</b>) wind speed, (<b>c</b>) hub height, and (<b>d</b>) biomass quantity on the microgrid system.</p> "> Figure 16
<p>Spider plot of sensitive variables based on COE.</p> "> Figure 17
<p>Visual comparison among cases considering different parameters: (<b>a</b>) COE, (<b>b</b>) NPC, (<b>c</b>) payback period, (<b>d</b>) CO<sub>2</sub>, (<b>e</b>) return on investment.</p> ">
Versions Notes

Abstract

:
In the face of a significant power crisis, Bangladesh is turning towards renewable energy solutions, a move supported by the government’s initiatives. This article presents the findings of a study conducted in a residential area of Pabna, Bangladesh, using HOMER (Hybrid Optimization of Multiple Energy Resources) Pro software version 3.14.2. The study investigates the feasibility and efficiency of a grid-connected hybrid power system, combining photovoltaics (PV), a biomass generator, and wind energy. The simulation produced six competing solutions, each featuring a distinct combination of energy sources. Among the configurations analyzed, the grid-connected PV–biomass generator system emerged as the most cost-effective, exhibiting the lowest COE at USD 0.0232, a total net present cost (NPC) of USD 321,798.00, and an annual operating cost of USD 6060.59. The system presents a simple payback period of 9.25 years, highlighting its economic viability. Moreover, this hybrid model significantly reduces CO2 emissions to 78,721 kg/year, compared to the 257,093 kg/year emissions from a solely grid-connected system, highlighting its environmental benefits. Sensitivity analyses further reveal that the system’s performance is highly dependent on solar irradiance, indicating that slight variations in solar input can significantly impact the system’s output. This study underscores the potential of integrating multiple renewable energy sources to address the power crisis in Bangladesh, offering a sustainable and economically viable solution while also mitigating environmental impacts.

1. Introduction

The quest for sustainable and reliable energy solutions has become a critical agenda for countries worldwide, especially for those experiencing acute power shortages [1]. The globe faces the dual challenges of preventing climate change and guaranteeing energy security, particularly in light of the rising cost of petroleum due to geopolitical conflicts such as the conflict between Russia and Ukraine [2,3]. Global energy markets have been volatile as a result of these disputes, underscoring the risk associated with a heavy reliance on fossil fuels. Energy costs surged globally immediately after the invasion, rising by 20% for five months in a row [4]. Bangladesh, a country confronting a formidable energy crisis, is no exception [5]. With its burgeoning population and escalating energy demand, the traditional energy infrastructure, heavily reliant on non-renewable resources, is proving inadequate [6]. As a result, the country is turning to renewable energy sources, driven by both international environmental objectives and official backing [7].
Several studies have explored the design and optimization of hybrid renewable energy microgrids in various regions, using HOMER software to evaluate different energy configurations and their feasibility. The study in [8] examines the feasibility of implementing a renewable-energy-powered microgrid at the Tulalip Tribes’ Administration Building, employing HOMER software to evaluate solar PV panels, a battery energy storage system (BESS), and diesel generators within the community’s unique constraints and goals. This study focuses on resilience, emergency power, and carbon emission reduction, ensuring system design is tailored to the tribal community’s specific conditions and needs. Baston A et al. in [9] explore solar PV and BESS to power remote areas, revealing an island in Maine can nearly rely on renewables, needing a 400 kW PV and 2 MWh BESS. Despite its potential, low solar periods and cost challenges in reducing fossil fuel use were noted. The study in [10] presents an economic evaluation and optimal design of a community microgrid via HOMER, analyzing levelized cost of energy (LCOE), NPC, and operating cost (OC). It highlights the microgrid’s ideal size using hybrid renewable energies and a diesel generator for cost efficiency. The research in [11] proposes a hybrid renewable energy microgrid (HREM) for a South Indian residential community, focusing on techno-economic and environmental modeling. Using HOMER, the HREM configuration was optimized, including PV, a diesel generator (DG), and BESS components. The paper [12] outlines the development of a hybrid solar/mini-hydro energy system for a rural community, utilizing HOMER Pro software for simulation and optimization. Challenges include remote terrain and low population density affecting revenue generation despite the system’s cost effectiveness. G. K. Suman et al. in [13] examine a microgrid in Gurmia, India, optimized by HOMER for a small community’s renewable energy needs. It assesses stability and transient behavior during faults, utilizing MATLAB Simulink. The study in [14] explores integrating solar, wind, and micro-hydro power into an existing micro-hydro plant to ensure constant electricity in remote rural regions using HOMER Pro simulations, revealing an optimal configuration with additional costs of USD 0.070 per kW including 84 kW solar PV panels, 84 batteries, and a 27 kW converter. However, limitations include the absence of main grid access and the high cost of expansion for dependable electricity in rural areas. Li He et al. in [15] examine the techno-economic viability of a renewable energy microgrid in a Beijing residential area using HOMER software, finding high potential. It suggests that a hybrid system combining wind power and moderate solar PV is cost effective. A feasibility study presented in [16] explores a hybrid microgrid for rural Ethiopian communities on Dek Island, suggesting an optimal configuration of photovoltaic/diesel generator/battery at a net present cost of USD 4.13 million, achieving a cost of energy of USD 0.149 through a multi-agent system with a fuzzy logic controller. T. O. Araoye et al. in [17] explore the University of Abuja’s hybrid microgrid system, utilizing PV, wind, diesel, and battery resources, which achieves the lowest energy cost, with PV contributing the most energy, followed by wind and diesel, modeled and optimized using HOMER software. However, limitations regarding both the software and renewable energy resources were not addressed. The work presented in [18] reveals that hybrid installation, incorporating multiple renewable energy sources, achieved impressive energy performance in a moderate climate, with solar collectors providing substantial heat generation. The study demonstrates that the system’s efficiency was comparable to standalone devices, highlighting the potential for maximizing renewable energy use in residential applications. The study in [19] reveals that the energy-reliability-constrained optimization method significantly improves the performance of photovoltaic–wind hybrid systems with battery storage, achieving higher energy reliability and efficiency, particularly in urban settings, while balancing the trade-offs between system components and energy output.
The diversity of hybrid energy systems in the literature highlights the importance of contextual factors in system design and performance evaluation. Each system analyzed in the literature was designed with specific users, resources, geographical locations, and types of loads in mind, making direct comparisons based on their intended purposes impractical. The outcomes of each system, such as COE and system sizing, are best understood within the context of their unique architectural designs. For instance, Table 1 summarizes the results from additional studies conducted using HOMER Pro, offering a broad overview of these findings. Each system’s configuration and parameters play a crucial role in determining its efficiency and suitability for various applications, emphasizing the need to consider these factors comprehensively when evaluating their performance and feasibility in different scenarios.
The research highlights a significant gap in the optimal utilization and economic analysis of hybrid microgrid systems, particularly in residential areas of Bangladesh, with a focus on the Pabna District. While the study demonstrates the feasibility and economic benefits of integrating PV and biomass generator systems, it lacks a detailed exploration of how these systems can be applied and scaled across different geographical and climatic conditions within the Pabna region. The broader applicability of such systems remains underexplored, limiting the potential for wider adoption in varied environments. Moreover, the literature review indicates that most studies on hybrid microgrids in Bangladeshi community areas did not incorporate sensitivity analyses. This omission is critical, as sensitivity analyses are essential for identifying key factors that significantly impact the performance and feasibility of specific system configurations. The absence of these analyses restricts the understanding of how diverse environmental and operational factors influence hybrid microgrid systems. To address this gap, further research should explore different renewable energy configurations under varying environmental conditions. Such studies would provide crucial insights for policymakers and stakeholders, enabling informed decisions on renewable energy investments that are tailored to specific local contexts.
This research article investigates the feasibility of a grid-connected hybrid power system in Pabna, Bangladesh, utilizing HOMER Pro software. By integrating photovoltaics, biomass generation, and wind energy into a microgrid, the study explores sustainable electricity generation for residential areas. It finds that a photovoltaic–biomass system offers the lowest energy cost and a favorable payback period, highlighting economic viability and the potential to bridge the energy gap. The hybrid system’s reduced CO2 emissions contribute to climate change mitigation, though its performance is sensitive to solar irradiance, underscoring the need for localized environmental assessments. This study provides insights into renewable energy’s role in Bangladesh’s energy resilience and sustainable development, advocating for diverse renewable sources for environmental stewardship and economic sustainability. The purpose of this study is to evaluate the feasibility and economic viability of a grid-connected hybrid power system in Pabna, Bangladesh, using HOMER Pro. The objectives are to optimize the integration of photovoltaics, biomass, and wind energy, assess cost effectiveness, and explore the system’s environmental impact under varying local conditions.

2. Materials and Methods

2.1. HOMER Pro

HOMER Pro is the industry-standard software for microgrid design and optimization, developed from decades of expertise in creating distributed power systems that integrate fossil fuel generation, storage, and renewable energy sources [32]. The National Renewable Energy Laboratory (NREL) in the United States developed the simulation program HOMER to help in the design and construction of microgrids powered by renewable energy [33]. HOMER simulates energy system performance and lifecycle costs, including capital and operational expenses. It evaluates distributed generation and grid options for remote areas, tackling microgrid design challenges through simulation, optimization, and sensitivity analysis, considering factors like load growth and future fuel prices. [34]. The system’s total yearly cost affects NPC and COE. HOMER calculates annualized costs, considering component costs and excluding other expenses [35]. To simulate using HOMER, various input parameters are required, including component costs, load demand, renewable resource data, and component specifications [36]. The whole architecture of HOMER Pro is shown in Figure 1, which enables consumers to choose the best option in terms of both technical and financial benefits [37]. The best system was selected using HOMER to simulate several system designs and compare their LCOE and overall net present cost [37]. The process of optimization involves determining the sources of renewable energy that may be utilized in the selected region (such as solar radiation and wind resources), estimating the required electric demand, and selecting the appropriate technical parameters for system components.
The total NPC and COE are determined using Equations (1) and (2) [32]:
C N P C = C a C R F i , N p
COE   = C a t E p + E g s + E d
where C N P C represents the NPC, C a represents the total cost (annualized), and CRF is the capital recovery factor, which is dependent on i (annual real interest rate in percent) and N p (project lifetime). However, CRF is constant here as it is dependent on i and N p . For each project, i and N p are constant as different projects have different project lifetimes and annual real interest rates. C a t represents the annual cost, E p represents the primary load, E g s represents the sold energy to grid (yearly), and E d represents the deferrable load.
The Figure 2 flowchart explains a methodology in a HOMER Pro simulation for microgrid evaluation, incorporating location, load, resource, and tariff data. The process includes system design, baseline simulation, and performance assessment, followed by sensitivity analysis for variable inputs, and optimization focusing on cost, emissions, renewable fraction, and reliability, ensuring robust microgrid performance.
An HRES integrates two or more renewable energy sources to provide increased system efficiency as well as greater balance in energy supply [39]. These systems are designed to exploit the complementary nature of different renewable energy sources. Figure 3 presents a schematic diagram illustrating various configurations that integrate wind turbines, utility grid, biomass, solar PV, and inverters, showcasing potential microgrid system designs and energy flow pathways.

2.2. Site Location

In this research, we focus on a residential area situated in Pabna, Bangladesh, specifically at the coordinates 24.00 N, 89.26 E. Residential areas are crucial for microgrid research using HOMER Pro because they represent significant, unique energy consumption patterns, which help in designing efficient, sustainable, and cost-effective distributed energy systems. Analyzing residential zones also helps in understanding demand profiles, peak usage, and potential for distributed generation, enabling more accurate simulations of the energy efficiency, cost effectiveness, and sustainability of microgrids, thereby facilitating tailored solutions for reliable and green energy supply. In the study, a residential area encompassing 61,374.8 square meters was analyzed. The area consists of 100 residential flats (units), distributed across approximately 15 buildings, with each building housing 6 units. On average, each flat accommodates five residents. Each flat is considered an individual energy consumer, resulting in a total of 100 energy consumers. The total daily energy consumption for all the flats combined is estimated at 1114.50 kWh. Figure 4 depicts the site location for our study.

2.3. Load Profile

The significant element in the simulation and optimization processes is the load profile unique to each site. Facilities like educational institutions, hotels, hospitals, and industrial zones often provide actual load consumption data for simulations, inputted into HOMER as time-series data. In contrast, residential areas, remote, and rural areas lacking real consumption statistics necessitate estimated load profiles, crafted with the area’s characteristics in mind, and these estimations are then fed into HOMER as daily profiles for power balance calculations. This study examines a community with 15 buildings, comprising 100 residential units, exhibiting significant variation in peak demand across seasons and between weekdays and weekends. Despite its modest scale, the community’s peak load reaches 239.31 KW, with daily consumption of 1114.50 KWh, attributed to substantial residential loads. Daily and monthly load profiles are constructed using average statistical data from a 5-year period. Figure 5 depicts the daily and monthly load profile, Figure 6 Shows the monthly ac primary load profile, and Figure 7 shows the hourly load profile for the community.

2.4. Resources

Data on renewable resources are required for the HOMER simulation, such as statistics on solar radiation, clearness index, temperature and wind speed for particular places. The NASA surface meteorological and solar energy database provided the sun irradiation for the recommended site [40]. HOMER Pro software uses long-term average climate data from NASA, typically spanning 22 years (1983–2005), to calculate average temperature, wind speed, and solar radiation for specific locations [41].

2.4.1. Solar Irradiations and Clearness Index

The total annual solar radiation for the recommended location is shown in Figure 8 along with a clearness index. For the site under consideration, 4.75 kWh/m2/day of yearly average irradiation is used. Solar PV output is directly influenced by solar radiation levels and clearness index. Higher solar radiation increases PV output, while clearness index, indicating atmospheric clarity, affects the efficiency of PV systems by altering the amount of sunlight reaching solar panels [40].

2.4.2. Wind Speed

Figure 9 shows that the monthly average wind speed at the location is 4.32 m/s. Higher wind speeds can cool solar panels, increasing their efficiency [42]. Cloud cover decreases solar irradiance, reducing PV output, while wind can disperse clouds, potentially increasing solar production. On the other hand, wind speed directly influences wind turbine (WT) output; higher speeds generate more electricity or vice versa [43].

2.4.3. Temperature

Figure 10 depicts the daily temperature at the location. The average scaled temperature is 26.34 °C. Solar PV output is directly affected by temperature. As temperature rises, PV efficiency decreases due to increased resistance [44].

2.4.4. Biomass Resources

Figure 11 illustrates the daily availability of biomass resources sourced from Pabna Municipality for utilization in a biomass generator aimed at community service. Biomass generators provide a sustainable energy source by converting organic waste into electricity, offering a reliable and environmentally friendly solution to meet the energy needs of communities, especially in rural areas. Initially, 5 tons per day of biomass from municipal solid waste (MSW) is considered. However, this quantity is varied to conduct sensitivity analysis on the impact of biomass amount on electricity production and the COE.

2.5. Solar PV

Equation (1) can be used by HOMER to calculate the power produced by the solar cells [45]. In this equation, P P V is the power output from the PV system, C P V represents the rated capacity of the PV array, and D P V denotes the PV derating factor. I T is the incident solar radiation, while I r e f refers to the incident solar radiation under reference conditions (1 kW/m2). K T indicates the temperature coefficient of maximum power, T c is the PV cell temperature, and T r e f is the PV cell temperature under reference conditions ( T r e f = 25 °C).
P P V = C P V D P V I T I r e f 1 + K T T c − T r e f

2.6. Wind Turbine

Equation (2) enables estimation of wind power at specific heights [45]. With known wind turbine density, one can calculate wind acceleration variation and vice versa. The wind turbine power equation is expressed as follows:
P = 1 2 C p A ρ v 3
where P denotes wind turbine power (Watt), ρ denotes wind power density (W/m2), v denotes wind turbine velocity (ms−1), C p denotes rotor efficiency, and A denotes rotor area (m2).

2.7. Biomass Generator

A typical model equation for the output of a biomass generator, which converts biomass into electricity through combustion or gasification, can be represented as follows [46]:
P B i o m a s s = η H M
where P B i o m a s s is the power output from the biomass generator (in kW), η is the efficiency of the biomass conversion system, H is the heating value of the biomass material (in kWh/kg), and M is the mass flow rate of the biomass consumed (in kg/h).

2.8. Inverter

The PV inverter employed in microgrid systems efficiently converts direct current (DC) electricity generated by solar photovoltaic panels into alternating current (AC) electricity for use within the microgrid [47]. It regulates voltage and frequency, ensuring seamless integration of solar power with the grid and optimizing energy distribution. Here is a simplified version of such an equation:
P A C = P D C η I n v e r t e r
where P A C is the output power in AC (Watts), P D C is the input power in DC (Watts), and η I n v e r t e r is the efficiency of the inverter, which may vary depending on the power level, inverter technology, and operating conditions.

2.9. Utility Grid

The microgrid integrates solar PV, biomass generator, and wind turbine sources, augmented by a utility-grid-connected net metering system. Electricity is supplied to the community, while excess energy can be sold back to the grid at USD 0.04/kWh, with purchases from the grid priced at USD 0.060/kWh, as regulated by the Bangladesh Energy Regulatory Commission [48,49]. This arrangement incentivizes renewable energy production and fosters sustainable community energy management.

2.10. Technical Specifications

Table 2 shows the specification of the components, and Table 3 shows the per-unit cost of the components. To get the best possible results, prices are attentively gathered from current references [42,47]. For the study, 100 residential flats were considered, distributed across approximately 15 buildings with 6 units each. Each building has a 2000 sq feet rooftop, totaling 30,000 sq feet or 2787 sq meters. Following the guideline of 10 sq meters per 1 kW of solar capacity [50], a potential installation of 278 kW of rooftop solar PV is feasible.
The estimated national grid emission factor over a six-year period is 530–570 tCO2/GWh, which is excessively high when compared to developed nations [51]. The grid emission factor in HOMER Pro has been adjusted to 632 tCO2/GWh for this investigation. To assure correctness, careful consideration was given to obtaining up-to-date rates from trustworthy sources. In order to optimize system design and financial planning in renewable energy projects, these extensive data sets are a useful resource.

3. Result and Discussion

HOMER Pro software has been employed to assess the operational and economic dimensions of the proposed microgrid. Its streamlined, non-derivative optimizer allows for swift execution of numerous simulations. By discarding impractical options, it prioritizes feasible ones based on total NPC. A 25-year planning horizon is considered, with hourly time-series simulations exploring each feasible microgrid design. Seven potential cases including only grid connection as the base case were analyzed to identify the most advantageous technique for microgrid planning. Here, the sources are solar PV, wind turbine (WT), biomass generator (Biogen), and grid.

3.1. Techno-Economic Assessment of the Microgrid

Figure 12 shows the comparison of various factors of different cases: (a) capital cost and NPC, and (b) COE and operating cost. Among the configurations analyzed, Case-I stands out as the most viable option, offering the lowest net present cost (NPC) of USD 321,798.00 and the lowest cost of energy (COE) at USD 0.0232/kWh. With a renewable energy fraction of 80.1%, Case-I not only ensures economic feasibility but also promotes environmental sustainability. Furthermore, its relatively short payback period of 9.25 years enhances its attractiveness for renewable energy projects, making it a compelling solution for sustainable development. This combination of economic and environmental advantages positions Case-I as the optimal choice for future energy initiatives.
Figure 13 showcases energy procurement and sales across different configurations, with Case-I and Case-II standing out notably. While the base case relies solely on the grid without selling energy, other cases exhibit diverse patterns of energy transactions, with notable differences in purchase and sale quantities, suggesting varying degrees of self-sufficiency and renewable energy integration. Figure 13 showcases energy procurement and sales across different configurations, with Case-I and Case-II standing out notably. Despite lower energy purchases, both cases demonstrate significant energy sales, indicating potential efficiency improvements. These findings underscore the importance of optimizing energy management strategies, offering valuable insights for various applications.
Figure 14 illustrates the comparative analysis of GHG emissions across various cases: (a) carbon dioxide (kg/year), (b) carbon monoxide (kg/year), (c) sulfur dioxide (kg/year), and (d) nitrogen oxide (kg/year). Cases-I and Case-II notably demonstrate substantial reductions in carbon dioxide emissions compared to the base case, alongside minimal emissions of carbon monoxide, sulfur dioxide, and nitrogen oxides. These findings suggest promising avenues for mitigating environmental impact through optimized energy management strategies. The visual representation highlights the significant reductions achieved in emissions across different pollutants, offering insights into the environmental implications of different energy management strategies.
To sum up, among the evaluated microgrid configurations, Case-I emerges as the optimum choice due to its lower NPC at USD 321,798.00 and reduced COE at USD 0.0232/kWh, coupled with an impressive renewable fraction of 80.1%. With substantial reductions in greenhouse gas emissions, particularly carbon dioxide, alongside significant energy sales, Case-I presents a compelling solution for sustainable energy projects, highlighting the importance of efficient energy management strategies in minimizing environmental impact and promoting economic viability.

3.2. Sensitivity Analysis Results

Sensitivity analysis in HOMER Pro identifies how changes in input variables affect microgrid performance, which is crucial for optimizing design and ensuring reliability under diverse conditions in hybrid systems. Table 4 presents crucial variables affecting microgrid simulations in HOMER Pro, including solar radiation, wind speed, hub height, and biomass quantity, essential for accurate energy system modeling.
Figure 15 displays NPC and COE graphs, analyzing how solar radiation, wind speed, hub height, and biomass affect the microgrid, through plots (a) to (d). In examining the sensitivity of diverse microgrid configurations that integrate solar PV, wind turbines (WT), biomass generators (Biogen), and grid connectivity, this study assesses the influence of various factors on NPC and COE. A summarized insight derived from the analysis of the data tables follows: The sensitivity analysis reveals that an increase in solar radiation from 3 to 6 kWh/m2/day significantly lowers both NPC and COE, underscoring the pivotal role of solar PV in enhancing cost effectiveness. Conversely, alterations in wind speed between 2 and 5 m/s show no effect on NPC and COE, hinting at either a saturation in wind energy’s impact or a system design adept at handling these variations. Similarly, modifications in hub height from 6 to 14 m do not influence NPC and COE, suggesting optimal wind turbine technology or a design less sensitive to hub height changes. Lastly, variations in biomass input from 2 to 6 tonnes/day have no visible effect on economic outcomes, indicating a system design resilient to fluctuations in biomass availability. Therefore, the sensitivity analysis highlights solar radiation’s significant impact on microgrid economics, with wind speed, hub height, and biomass showing stable NPC and COE values.
Table 5 summarizes the sensitivity analysis across various microgrid configurations combining solar PV, wind turbine (WT), biomass generator (Biogen), and grid components. The analysis shows that configurations with solar PV (Cases I–IV) are sensitive to changes in solar radiation, significantly impacting COE and NPC. In contrast, cases without solar PV (Cases V, VI, and the Base Case) are less sensitive to variations in wind speed, hub height, and biomass, highlighting solar PV’s crucial role in economic performance.
Figure 16 presents a spider plot illustrating the sensitivity of four variables on the COE, with point 1 considered the most accurate evaluation. The intersection of these variables is observed within the range of USD 0.02 to USD 0.03, highlighting key insights into their impact on COE.
The sensitivity analysis reveals that solar radiance has a significant impact on the performance of a hybrid microgrid system, as it directly influences the electricity generated by photovoltaic panels, thereby affecting the system’s NPC and COE. In contrast, hub height and wind speed show negligible effects on these economic outcomes, possibly due to the system’s design or the limited contribution of wind energy. Likewise, available biomass resources do not significantly alter the economic metrics, indicating the system’s resilience to fluctuations in biomass supply. This underscores the pivotal role of solar energy in enhancing the microgrid’s cost-effectiveness and sustainability. Therefore, the proposed microgrid design, optimized for solar input, reveals that solar PV’s role is paramount, while the contributions of wind and biomass are less sensitive to their respective variable changes.
Figure 17 provides a detailed comparison across different system configurations, assessing their performance based on COE, NPC, payback period, CO2 emissions, and return on investment (ROI). Notably, Case-I emerges as the optimum configuration, offering the lowest COE (USD 0.0232/kWh), a competitive NPC (USD 321,798), and significantly reduced CO2 emissions (78,721 kg/year), striking a balance between economic and environmental performance. This case exemplifies an effective strategy for achieving lower operational costs and environmental impact within the analyzed microgrid systems.

3.3. Comparison with Others Published Work

Table 6 shows the comparison of the proposed work with others’ published work. This table illustrates how the proposed work compares to other published works.
The proposed work presents a groundbreaking techno-economic analysis of a hybrid microgrid system for a residential area in Bangladesh, showcasing a novel integration of photovoltaics, biomass generation, and wind energy optimized through HOMER Pro software. This study identifies a grid-connected PV–biomass system as the most cost-effective configuration, achieving a low cost of energy, significant CO2 emissions reduction, and a viable payback period. The main contributions include the demonstration of the system’s economic viability and environmental benefits, and the emphasis on optimizing renewable energy integration to mitigate Bangladesh’s power crisis. This analysis highlights the potential for sustainable and economically feasible energy solutions in developing countries, setting a precedent for future renewable energy projects.

4. Conclusions

This study presents a comprehensive techno-economic analysis of hybrid microgrid systems in a residential area of Pabna, Bangladesh, utilizing HOMER Pro software. Among the configurations examined, the integration of photovoltaics with a biomass generator and grid connection (Case-I) emerged as the most sustainable and cost-effective solution, offering the lowest cost of energy, reduced CO2 emissions, and an optimal payback period, highlighting its economic and environmental benefits. The key outcomes of this study can be summarized as follows:
  • The integration of photovoltaics with a biomass generator and grid connection (Case-I) is the most cost-effective configuration, with a COE of USD 0.0232/kWh and an NPC of USD 321,798.00.
  • Case-I demonstrates environmental benefits by reducing CO2 emissions to 78,721 kg/year and has an attractive payback period of 9.25 years.
  • Sensitivity analysis confirms Case-I’s robustness, highlighting its dependence on solar irradiance.
  • This study emphasizes the scalability and viability of hybrid microgrids for addressing energy challenges in Bangladesh.
This research demonstrates the potential of hybrid microgrid systems as a scalable solution to Bangladesh’s energy challenges. The study underscores their financial viability, especially in residential areas. These findings have broader implications for similar regions facing energy issues.

Author Contributions

Conceptualization, M.F.A., M.A.H., M.M.J. and M.A.; methodology, M.F.A., M.A.H., M.A. and M.R.I.S.; software, M.F.A., M.M.J. and M.A.; validation, M.F.A., M.A.H., M.M.J., M.A. and M.R.I.S.; formal analysis, M.F.A., M.M.J. and M.A.; investigation, M.F.A., M.M.J., M.A. and M.R.I.S.; resources, M.F.A. and M.M.J.; data curation, M.F.A.; writing—original draft preparation, M.F.A., M.M.J. and M.A.; writing—review and editing, M.F.A., M.A.H., M.M.J., M.A., M.S.A. and M.R.I.S.; visualization, M.F.A. and M.R.I.S.; supervision, M.F.A. and M.R.I.S.; project administration, M.F.A. and M.R.I.S.; funding acquisition, M.A.H. and M.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Architecture of HOMER Pro software [38].
Figure 1. Architecture of HOMER Pro software [38].
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Figure 2. Methodology flowchart of the proposed work.
Figure 2. Methodology flowchart of the proposed work.
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Figure 3. Schematic diagram of different cases.
Figure 3. Schematic diagram of different cases.
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Figure 4. Geographic positioning of the study area.
Figure 4. Geographic positioning of the study area.
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Figure 5. Load Profile: (a) daily and (b) monthly total.
Figure 5. Load Profile: (a) daily and (b) monthly total.
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Figure 6. Monthly AC primary load profile.
Figure 6. Monthly AC primary load profile.
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Figure 7. Hourly load profile for the community.
Figure 7. Hourly load profile for the community.
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Figure 8. Solar daily radiation and clearness index at the location.
Figure 8. Solar daily radiation and clearness index at the location.
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Figure 9. The monthly average wind speed at the location.
Figure 9. The monthly average wind speed at the location.
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Figure 10. The daily temperature at the location.
Figure 10. The daily temperature at the location.
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Figure 11. Daily available biomass resources.
Figure 11. Daily available biomass resources.
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Figure 12. Comparison of various factors of different cases: (a) capital cost and NPC, (b) COE and operating cost.
Figure 12. Comparison of various factors of different cases: (a) capital cost and NPC, (b) COE and operating cost.
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Figure 13. Energy purchased and sold for different cases.
Figure 13. Energy purchased and sold for different cases.
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Figure 14. Comparison of GHG emissions of different cases: (a) carbon dioxide (kg/year), (b) carbon monoxide (kg/year), (c) sulfur dioxide (kg/year), (d) nitrogen oxide (kg/year).
Figure 14. Comparison of GHG emissions of different cases: (a) carbon dioxide (kg/year), (b) carbon monoxide (kg/year), (c) sulfur dioxide (kg/year), (d) nitrogen oxide (kg/year).
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Figure 15. NPC and COE plots, examining impacts of sensitivity variables: (a) solar radiation, (b) wind speed, (c) hub height, and (d) biomass quantity on the microgrid system.
Figure 15. NPC and COE plots, examining impacts of sensitivity variables: (a) solar radiation, (b) wind speed, (c) hub height, and (d) biomass quantity on the microgrid system.
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Figure 16. Spider plot of sensitive variables based on COE.
Figure 16. Spider plot of sensitive variables based on COE.
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Figure 17. Visual comparison among cases considering different parameters: (a) COE, (b) NPC, (c) payback period, (d) CO2, (e) return on investment.
Figure 17. Visual comparison among cases considering different parameters: (a) COE, (b) NPC, (c) payback period, (d) CO2, (e) return on investment.
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Table 1. List of findings using HOMER pro.
Table 1. List of findings using HOMER pro.
System StructureSystem TypeLocationCategoryFindings
PV-WT-DG-BioGen-BESS [20]Off-gridChapainawabgonj, BangladeshResidentialThe study highlights the potential of hybrid renewable energy systems in remote areas of Bangladesh, emphasizing the importance of solar, wind, and biogas sources. By integrating these resources, the system can significantly reduce CO2 emissions, provide cost-effective solutions, and meet the energy demands of the community effectively.
PV-Biogas-BESS [21]Off-gridKohgiluye and Boyer-Ahmad Province, IranResidentialThe study assessed the feasibility of an off-grid solar–biomass system for remote rural areas in Iran, considering factors like solar radiation, available biomass, and economic aspects. HOMER Pro software optimized system sizing, resulting in a proposed configuration with a biogas-fueled generator, PV panels, battery storage, and a converter.
PV-WT-BESS [22]On-gridRemote Island, BangladeshResidential CommunityThe study employs fuzzy logic for load modeling and optimization, designing a hybrid microgrid for a residential community in Bangladesh. By integrating solar and wind energy, the proposed system achieves a low energy cost of USD 0.035/kWh, a high renewable fraction of 90%, a significant emission reduction of 78%, and enhanced reliability.
PV-WT-DG-BESS [23]Off-gridKandhkot, Sindh, PakistanCommunityThe study analyzed the energy demand of a village in Sindh, Pakistan, and proposed an optimized hybrid sustainable energy system using wind and solar resources. The results indicated that an on-grid hybrid system was the most economical solution, with sensitivity analysis showing a decrease in costs with increased storage capacity.
PV-Grid [24]On-gridMohammadpur, Dhaka, BangladeshResidentialThe study concluded that the PV-Converter-Grid configuration was the most cost-effective for a residential microgrid, with higher electricity sales than purchases and lower operating costs compared to the PV-Converter-Battery-Grid setup. This configuration efficiently met the annual electrical load with minimal losses, making it a viable solution for residential energy supply.
PV-BESS-Grid [25]On-gridLarkana, PakistanResidentialThe study found that a grid-tied PV system with battery storage significantly reduced greenhouse gas emissions compared to an unreliable grid system. The optimized configuration with no power outages had the lowest COE of USD 0.135/kWh. Outage duration directly impacted COE, increasing from USD 0.23/kWh to USD 0.55/kWh with 2 to 8 h of outages.
PV-BESS-DG [26]Off-gridCape Town,
South Africa		CommunityThe study evaluated a community microgrid in Cape Town using distributed energy resources like diesel generators and rooftop solar PV. HOMER software optimized system sizes for technical and economic feasibility. Comparing grid-forming and grid-connected modes, the grid-forming microgrid with 80% renewable energy penetration had a levelized cost of USD 0.509/kWh and a net present cost of USD 1.64 million.
PV-RESS [27]Off-gridNorth-West of Western AustraliaCommunityThe study found that integrating hydrogen storage in a stand-alone microgrid improved system stability and increased renewable energy penetration. Techno-economic analysis revealed that the hybrid system reduced the cost of energy and achieved high renewable fractions. HOMER Pro simulations showed the feasibility of the system under various scenarios, emphasizing its potential for sustainable energy solutions.
PV-DG-BESS [28]Off-gridEdem Urua,
Nigeria		CommunityThe research highlighted Scenario 3 as the optimal choice due to its cost effectiveness, zero CO2 emissions, and 100% renewable penetration. Despite its capacity shortage and unmet load, Scenario 3 stood out as a sustainable solution. This study’s outcomes aligned with global climate action goals, emphasizing the significance of solar PV and wind components in achieving efficient hybrid renewable energy systems.
PV-DG-BESS-Hydro [29]Off-gridChipendeke, ZimbabweCommunityThe study on Chipendeke Micro-Hydro in Zimbabwe revealed that a hybrid system combining hydro, solar PV, energy storage, and diesel generator can address energy crises and fluctuations. Results showed the hydro-only system met only 164 kWh/day, while the optimized Hydro/PV/DG/Battery system met the community’s 310 kWh/day demand efficiently.
PV-WT-Grid [30]Off-grid and on-gridChitradurga District, south Indian state of KarnatakaCommunityThe study compared on-grid and off-grid hybrid renewable energy system (HRES) configurations for a rural community. The grid-connected model achieved a minimum COE of USD 0.109, with a renewable energy contribution of 37.8%. The optimal NPC for the grid-connected system was USD 633,352, showcasing cost-effective and sustainable energy solutions.
PV-BESS-DG [31]Off-gridFulchari Union, Gaibandha District, BangladeshCommunity SchoolThe study successfully demonstrated that the proposed HRES effectively meets the electricity demand of a remote site with a peak load of 3.3 kW. Through sensitivity and multiyear analyses, the system’s economic viability was confirmed, outperforming grid extension. Further research with precise meteorological data is recommended for enhanced accuracy.
Table 2. Technical specifications of the components.
Table 2. Technical specifications of the components.
ParametersPVWTBiogenInverter
Rated capacity278 kW3 kW25 kW166 kW
Efficiency18.7%-30%95%
Hub height-10 m--
Lifetime25 years25 years2.28 Years15 Years
Table 3. Per-unit cost of components.
Table 3. Per-unit cost of components.
ParametersPVWTBiogenInverter
Capital costUSD 410/kWUSD 12,000/unitUSD 1000/kWUSD 290/kW
Replacement costUSD 410/kWUSD 10,000/unitUSD 835/kWUSD 290/kW
Operating and maintenance costUSD 8/yearUSD 100/yearUSD 0.2/hUSD 3/kW
Table 4. List of input-sensitive variables with values.
Table 4. List of input-sensitive variables with values.
Input Sensitive VariableValues
Solar radiation (kWh/m2/day)3, 4, 4.75, 5, 6
Wind speed (m/s)2, 3, 3.5, 4.32, 5
Hub height (m)6, 8, 10, 12, 14
Biomass quantity (tonnes/day)2, 3, 4, 5, 6
Table 5. The outcome of sensitive analysis for all cases.
Table 5. The outcome of sensitive analysis for all cases.
SystemCasesSensitive Outcome
HybridCase-ISensitive
HybridCase-IISensitive
Not hybridCase-IIISensitive
HybridCase-IVSensitive
Not hybridCase-VNot sensitive
HybridCase-VINot sensitive
Base CaseNot sensitive
Table 6. Comparison of the proposed work with others’ published work.
Table 6. Comparison of the proposed work with others’ published work.
System StructureSystem TypeCategoryRF (%)COE (USD/kWh)
PV-WT-BESS [52]Off-gridCommunity980.824
PV-BESS-Grid [53]On-gridResidential55.10.041
PV-Grid [54]On-gridCommunity70%0.0357
PV-WT-BESS-Grid [55]On-gridResidential90.10.0296
PV-BESS [56]Off-gridCommunity1000.173
PV-Gen [57]Off-gridCommunity-0.140
PV-WT-Grid [58]On-gridCommunity97.80.0751
PV-WT-DG-BESS [59]Off-gridCommunity88.51.01
PV-WT-BioGen-Grid-BESS [60]On-gridResidential820.059
PV-WT-BESS-Grid [61]On-gridCommunity29.30.165
PV-Grid [32]On-gridCommunity57.50.0442
The Proposed WorkOn-gridResidential80.10.0232
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Ali, M.F.; Hossain, M.A.; Julhash, M.M.; Ashikuzzaman, M.; Alam, M.S.; Sheikh, M.R.I. A Techno-Economic Analysis of a Hybrid Microgrid System in a Residential Area of Bangladesh: Optimizing Renewable Energy. Sustainability 2024, 16, 8051. https://doi.org/10.3390/su16188051

AMA Style

Ali MF, Hossain MA, Julhash MM, Ashikuzzaman M, Alam MS, Sheikh MRI. A Techno-Economic Analysis of a Hybrid Microgrid System in a Residential Area of Bangladesh: Optimizing Renewable Energy. Sustainability. 2024; 16(18):8051. https://doi.org/10.3390/su16188051

Chicago/Turabian Style

Ali, Md. Feroz, Md. Alamgir Hossain, Mir Md. Julhash, Md Ashikuzzaman, Md Shafiul Alam, and Md. Rafiqul Islam Sheikh. 2024. "A Techno-Economic Analysis of a Hybrid Microgrid System in a Residential Area of Bangladesh: Optimizing Renewable Energy" Sustainability 16, no. 18: 8051. https://doi.org/10.3390/su16188051

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