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Article

Feasibility and Cost Analysis of Photovoltaic-Biomass Hybrid Energy System in Off-Grid Areas of Bangladesh

by
Nusrat Chowdhury
1,
Chowdhury Akram Hossain
2,
Michela Longo
3 and
Wahiba Yaïci
4,*
1
Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka 1207, Bangladesh
2
Department of Electrical and Electronic Engineering, American International University-Bangladesh, Dhaka 1229, Bangladesh
3
Department of Energy, Politecnico di Milano, 34–20156 Milano, Italy
4
CanmetENERGY Research Centre, Natural Resources Canada, Ottawa, ON K1A 1M1, Canada
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(4), 1568; https://doi.org/10.3390/su12041568
Submission received: 27 January 2020 / Revised: 17 February 2020 / Accepted: 17 February 2020 / Published: 19 February 2020
(This article belongs to the Special Issue Renewable Energies for Sustainable Development)
Browse Figures
Figure 1
<p>RETScreen technical evaluation structure flowchart [<a href="#B53-sustainability-12-01568" class="html-bibr">53</a>].</p> "> Figure 2
<p>Block diagram of proposed photovoltaics (PV)-biomass hybrid energy system.</p> "> Figure 3
<p>The specification of different components and energy produced by each source. Notation—Mono-Si: Monocrystalline silicon; CS4A: Multi-contact connector type 4.</p> "> Figure 4
<p>Initial cost of proposed system. Notation—BDT: Bangladeshi Taka.</p> "> Figure 5
<p>Cumulative cash flows.</p> ">
Versions Notes

Abstract

:
In this progressing technological advancement world, hybrid systems for power generation is one of the most promising fields for any researcher. In this context, photovoltaic-biomass hybrid systems with off-grid applications have become extremely popular with both Governments and individual users in rural areas of any part of the world. This system has gained popularity because of low cost, sustainability and very effective outcome with the use of natural resources at the rural areas. In this paper a proposed hybrid system which contains photovoltaics (PV) and biomass along with an additional storage has been considered to find the different aspects from an end user point of view. It also discusses the feasibility of the proposed model for an off-grid power system located in the remote areas of Ashuganj, Bangladesh. In order to analyse the pollutant emissions and calculate the cost parameters of the proposed system, RETScreen simulation software was deployed. This research also carries out a brief financial analysis considering the annual income of the end user and the payback periods for the installed system. It endeavours to provide complete information about different parameters which also includes the environmental impacts involved in establishing the proposed system. The conventional system in the pilot area is a kerosene-based system, hence in this research, a comparison between the proposed and the conventional system has been analysed using simulated results. The simple payback of the project was estimated to be 6.9 years and this model will be able to reduce the CO2 emissions by approximately 3.81 tonnes per year. The results have significantly supported the proposed system to be more reliable, environmentally-friendly and less costly than the conventional kerosene-based system.

1. Introduction

Electricity is the major source of energy in most urban systems worldwide. Since the first high-voltage Alternating Current (AC) coal power station was commissioned in London in 1890, electrification of residential and industrial installations has grown exponentially expanding toe 83% of all urban areas by 2010 [1]. Even with a projection of increased electricity use for future energy systems the electrification of rural areas still represents a relevant issue. Such expanded use will likely seriously affect different sectors of developing economies, ranging from industrial to transportation uses [2]. In particular, the augmenting of current electricity distribution grids in rural areas, which are located far from the main national grid, may result in excessive costs in terms of installation, transmission, distribution, and maintenance [3,4]. Indeed, the electrification of rural areas through the extension of grid connections may raise the overall generation costs, which could reach up to seven times the normal price obtainable in urban areas [4]. Some studies have been focussed on operational performance of the Bangladesh rural electrification program and its determinants with a focus on political interference [5,6,7,8]. Per [5] for instance, complete electrification through Bangladesh will take many years, and thus the diminishing returns to scale of incremental investment for further rural electrification will be faced in the long run. The authors suggested that both Bangladesh and international donors revisit the original principle of the Rural Electrification Program, eradicate political hindrances from the program, and make sustained efforts to develop more efficient infrastructure of delivering electricity to the rural poor and encouraging local economic development.
Another issue of concern is the unreliability in frequency, blackouts, power losses, and fluctuations of the grid voltage. In such cases, the proper use of Renewable Energy Sources (RESs) in remote areas could represent a viable and economical alternative to the extension of electricity grids [9,10,11].
Authoritative studies have shown that hybrid stand-alone electricity-generating systems are more economically feasible for off-grid consumers located in distant areas [12,13]. In addition, RES installations can also reduce the amount of CO2 emissions emanating from electrical energy generation. Studies in Bangladesh have demonstrated, in fact, that 1kWh of electricity generated by solar photovoltaic (PV) systems can reduce the amount of CO2 emissions by approximately 660 tonnes per year [14]. However, the use of stand-alone PV systems in off-grid applications also presents certain drawbacks, mainly related to the intrinsic intermittency and stochasticity of the solar source. To overcome these drawbacks, the use of electrical Energy Storage System (ESS) solutions is usually implemented [15,16], along with the adoption of hybrid configurations, i.e., by adding traditional controllable power generators (such as diesel-electric motors), to support the intermittency and unreliability of PV systems.
Currently, in off-grid PV systems, the use of large ESS solutions is usually considered economically unviable due to the high investment costs, that is, high costs of storage devices [17,18], whereas the combined adoption of PV with ESS and diesel-electric generators has been widely adopted [19]. Nevertheless, increasing concern about global warming and environmental pollution is propelling the replacement of generators that were traditionally powered by fossil fuels, while there is more interest in adopting greener solutions. To this end, the use of biomass generators could indeed represent an interesting alternative, due both to its low carbon impact and its lower investment and developmental costs.
As indicated in the literature, numerous feasibility and techno-economic studies were performed on micro-grid projects in different countries, specifically on stand-alone hybrid energy systems for applications in remote areas [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. The inference drawn is that it appears that no feasibility study or other similar work has been conducted on such a system in the remote communities of Bangladesh; thus, the present study is an original research initiative and a firm contribution to knowledge.
Considering the environmental and cost concerns described earlier, the purpose of this paper is a feasibility study of the potentials and likely impact of a hybrid PV-biomass system as a possible option for the provision of power in a rural area of Bangladesh. The primary objective of the study is to present a preliminary design of a hybrid PV-biomass system that is capable of satisfying the energy needs of the selected off-grid application, and to compare its economic performance in respect of existing solutions, by making use of discounted cash flow and payback period analyses. The paper examines the technical, economic and environmental feasibility, both the integration and sizing of a hybrid PV-biomass system, and the energy storage of microgrid for remote electrification within Ashuganaj, Bangladesh. In addition, the impact of related CO2 emissions is also analysed, and compared to traditional solutions. The study is essentially performed employing the simulation tool referred to as RETScreen [46].
The paper is organised as follows: Section 2 gives a full description of the proposed project location as well as the simulation methods; also provides the load profiles and climate data related to the project location and Section 3 presents details on the proposed PV-biomass hybrid system. Simulation results are discussed in Section 4, including the sizing of the system, the cost and emissions analyses, while conclusions are drawn in Section 5.

2. Project Location and Simulation Methods

2.1. Simulation Method

Many studies have analysed renewable energy systems using RETScreen [47,48,49,50,51]. RETScreen software was employed in this design to achieve the least energy costs that people will find affordable [52]. The RET Screen Clean Energy Management Software (usually shortened to RET Screen) is a clean energy software package developed by Ministry of Natural Resources Canada (Government of Canada) for evaluating both financial and environmental costs and benefits of different renewable energy technologies for any location in the world. This software uses visual basic and C language as working platform. RETScreen PV model also covers off-grid PV applications and include stand-alone, hybrid and water pumping systems also. It has a global climate data database of more than 6000 ground stations (month wise solar irradiation and temperature data for the year), energy resource maps (i.e., wind maps), hydrology data, product data like solar photovoltaic panel details and wind turbine power curves. It also provides link to NASA climate database. It enables comprehensive identification, assessment and optimization of the technical and financial viability of potential renewable energy and energy-efficient projects. It also allows measurement and verification of the actual performance of facilities and identification of energy savings/production opportunities. The software tool can determine the technical and financial viability of renewable energy, energy efficiency and cogeneration projects. The area number of worksheets for performing detailed project analysis includes Energy Modelling, Cost Analysis, Emissions Analysis, Financial Analysis and Sensitivity and Risk Analyses sheets. The analysis of different types of energy efficient and renewable technologies (RETS) covers mainly energy production, life-cycle costs and greenhouse gas emission reduction. In summary, RETScreen Plus is a Windows-based energy management software tool to study the energy performance [51,52].
This simulation process empowers experts and decision-makers to identify and evaluate the technical and financial viability of potential clean energy projects and also to measure and verify the actual and ongoing energy performance of energy-efficiency projects. The process also enables the evaluation of energy production, life-cycle costs and greenhouse gas emission reductions for the proposed Hybrid system [46]. The flowchart used in the simulation tool is shown in Figure 1.
Ashuganj city located in the Brahmanbaria District of Chittagong Division in Bangladesh, was selected as the reference site for the application and evaluation of the proposed hybrid PV-biomass system. The city was selected because of its residents’ pervasive dependence on fossil fuels to meet their energy needs, due to the absence of a power grid extension. Residents situated close to the city depend mainly on horticulture and animal husbandry for their livelihood. Therefore, in rural areas, it is economical to use hybrid systems comprising solar and biomass, provided the biomass supply is consistently available. In most of the areas, manure, crop wastes and cooking wastes are accessible free of charge.

2.2. Load Profile and Climate Data of Project Location

The load demand of the proposed system is estimated by reference to a household load (4 LED light, 2 DC fan and 1 DC TV) for a typical lower middle-class family. The study evaluated fifty houses for provision of electricity for the proposed hybrid system (Table 1). The actual daily average energy demand for fifty houses is calculated as 45.6 kWh.
The load characteristics are calculated on the basis of three seasons: summer (March–June), spring (July–October) and winter (November–February). The RETScreen software calculates the monthly and yearly energy consumptions for the load based on the following indices: appliance used, season, weather condition based on location, number of people per house and number of total houses. Considering the system loss, the daily average energy demand is estimated as 47.2 kWh which is higher than the actual demand and annual energy demand is 14.161 MWh for both the base case and the proposed case. The detailed data of monthly and yearly energy consumption is shown in Table 2. In winter season the load demand is comparatively lower than the other two seasons. Therefore, it is considered 54%. In spring season considering the weather condition, the percentage of energy use is 93%. Due to the extreme hot weather in summer the percentage of energy is considered 99%.
As mentioned earlier, this paper discusses the feasibility study of the proposed hybrid system with RETScreen. The financial analysis has been carried out for systematic progression towards a PV- Biomass-based hybrid system. We need to determine the latitude and longitude of the study area and analysis is done to derive the climate data. The RETScreen software gives complete weather details based on climate location. Table 3 indicates the climate data location and the project location which has been collected from NASA by RETScreen. The daily solar radiation, air temperature, humidity and earth temperature data are also collected for the reference location to check the feasibility of solar project implementation. The detailed data are shown in Table 4.

3. The Proposed PV-Biomass Hybrid System

The proposed system consists of electric DC loads, solar PV, biomass generator, battery, and converter. Figure 2 demonstrates the block diagram of the proposed system. The system is fed by PV arrays and a biomass generator. There is no grid connection between the systems. Biomass is an abundant source of energy around the world, which is composed of organic matter including agricultural residues, and wood, animal and human wastes. Use of biomass for the purpose of power generation has become very popular, especially since it is an easily obtainable source of energy in the rural parts of Bangladesh. Additionally, it is a cleaner source of energy than fossils throughout the world. Its relative abundance makes it a viable option for use as a potential source of energy for electricity-generation in the country where it comprises animal manure that can either be converted through the absorption process or its residues extracted through the combustion process. In this system hybrid solar and biomass system was chosen as biomass is accessible effectively in the form of manure throughout the year. This hybrid system using biomass and solar with a battery as a storage system for electricity generation is more economical, because it can generate electricity during cloudy days also.
In the proposed system, the AC generator is used. Here, the output of the generator is converted into 24 V DC using the AC to DC converter and then connected to 24 V DC bus bar. Finally, DC to DC converter is used in every house to convert 24 V DC to 12 V DC to operate the DC house load.
The total capital cost of 1 kW biogas fuel-based generator considered as BDT (Bangladeshi Taka) 60,000/kW (USD $714.29/kW) and the lifetime of the generator is specified in hours of operation. The lifetime of the generator is considered as 15,000 hr. The efficiency of the generator is considered as 80%. We estimate the cost per tonne of biogas at BDT 70 (USD $0.833) based on biomass resources being obtainable almost free of charge.

4. Results and Discussion

RETScreen software has been used to analyse the different parameters of the proposed case and finally, the proposed case was compared with the base case. After the comparison, based on the financial viability, annual savings and evaluation of GHG emissions it can be easily deduced that the proposed case is more beneficial than the existing one. Analysis types and methods selected in RETScreen are mini-grid and method two respectively.

4.1. Base Case Power Study

Having chosen an off-grid area, the kerosene lamp was evaluated as a power source in the base case. Estimated total power capacity for base case is 4.70 kW. We estimated the cost of a litre of kerosene to be BDT 65 (USD $0.77) and so the total cost of electricity is calculated as BDT 474,702 (USD $5651.21) for the existing kerosene-based system by RETScreen. Table 5 indicates the unit cost and total electricity cost for the base case.
From the simulation, it was evident that the unit cost of electricity for the existing system is very high and also harmful to the environment. A new system has been proposed for this reason.

4.2. Proposed Case Power Study

In the proposed case analysis, mono-crystalline silicon PV solar cell with a power capacity of 12.9 kWp, and an efficiency of 13.1% was used. In this model maximum power point tracker is used as a control method and miscellaneous losses are considered as 5%. To fulfil the peak time energy demand biomass generator is used where the biomass rate is BDT 70/tonne (USD $0.83/tonne) and capacity of the generator is considered as 1 kW. As a storage system and an emergency backup, battery has been used. In this system, a total of 9 batteries are considered where each battery has a capacity of 24 V, 200 Ah. A one-day autonomy has been estimated for reliable power supply. The total capacity of the battery bank of 1800 Ah and 43 kWh is considered. Figure 3 presents the battery, PV and biomass generator specification that is given as an input in RETScreen and also shows that for the given combination approximately 81.2% of total energy comes from PV while the remainder of the energy comes from biomass generator. Thus, the total annual energy delivered to the load from PV and biomass generator are 13.98 MWh and 3.2 MWh respectively. Therefore, from the proposed system yearly 17.185 MWh energy can be produced which can easily fulfil the required load demand of 14.161 MWh.
In the proposed system, since most of the energy comes from PV, it produces clean energy and reduces CO2 emissions compared to the existing system.

4.3. Cost Analysis

In order to analyse the costs of the proposed system, the initial, annual, and periodic costs as well as credits for any base case costs that are avoided in the proposed system are analysed. Before implementing the project in Ashuganj it is necessary to test the feasibility of the project to determine its suitability for the selected area. To check the climate feasibility, some testing is necessary which comes at a cost. For a “Feasibility analysis,” more detailed and more accurate information is usually required. The calculations performed by the RETScreen Software for this step are straightforward and relatively simple (addition and multiplication). Figure 4 shows the detailed cost calculation for the project.
In cost analysis, it has been observed that the total initial cost is BDT 2,190,089 (USD $26,072.49) where 89.1% cost comes from power system sources such as PV, battery, biomass generator while the remaining cost components are from feasibility study and system miscellaneous. In the proposed system the lifetime of the PV, battery and converter have been based on 25 years, 5 years and 10 years respectively. Therefore, at full project life in 25 years the battery will require replacement 4 times, while the converter will be replaced twice. For the proposed system the total annual cost is BDT 167,696 (USD $1996.38). In periodic cost, it is seen that the battery replacement cost in full project life is BDT 403,200 (USD $4800), and converter cost is BDT 89,600 (USD $1066.67). After all the expenses the annual savings from the project is BDT 474,702 (USD $5,651.21) which is provided in Table 6.

4.4. Financial Viability & Cumulative Cash Flow Analysis

The RETScreen Software enables a user to input various forms of financial data such as discount rates, etc., which it automatically calculates to produce key financial feasibility indicators such as simple payback, equity payback, and net present value. Based on the data entered by the user, financial indicators for the project being analysed are provided, thus deriving vital information which facilitates the project evaluation process for planners and decision-makers [54].
The simple payback SP is the number of years it takes for the cash flow (excluding debt payments) to equal the total investment (which is equal to the sum of debt and equity):
S P = C − I G ( C e n e r g y + C c a p a c i t y + C R E + C G H G ) − ( C O & M + C f u e l )
where, C is the total initial cost of the project and IG is the value of incentives and grants. C e n e r g y , C c a p a c i t y ,   C R E , and C G H G are annual energy saving or income, annual capacity saving or income, annual renewable energy production credit income and greenhouse gas reduction income respectively. C O & M , C f u e l represent the yearly operation and maintenance cost and yearly cost of fuel or electricity respectively.
Similarly, the year-to-positive cash flow (also equity payback), NPCF is the first year that the cumulative cash flows for the project are positive. It is calculated by solving the following equation for NPCF:
0 = ∑ n = 0 N P C F C n ﹀
where, C n ﹀ is the after tax cash flow in year n.
The net present value NPV of a project is calculated by discounting all cash flows as given in the following equation:
N P V = ∑ n = 0 N C n ﹀ ( 1 + r ) N
where, N is the project life in years and r is the discount rate.
Discounted payback period, DPBP can be calculated that can be calculated that the discounted cash flow method discounts each inflow considering the time value of money until NPV equals zero at the certain year n of the system operation as indicated in Equation (4) [55,56].
∑ n = 0 D P B T C I n − C O n ( 1 + c ) n = 0
where, CI: cash inflow; CO: cash outflow; C: cost opportunity of capital; n: time period.
The annual life cycle savings ALCS is calculated using the following formula:
A L C S = N P V 1 − 1 ( 1 + r ) N
From the cash flow diagram depicted in Figure 5 using RETScreen, it can be estimated that it takes 7.2 years for cash flow to become positive and that the simple payback period will be 6.9 years. From the financial viability analysis, we get a Net Present Value (NPV) of BDT 855,428 (USD $10,183.67) and annual life cycle saving is BDT 73,405 (USD $873.87) and equity payback period is 7.2 years. In terms of the project’s economics, we can say that the proposed hybrid system is the most economical one because after 7 years the project will start to generate profit and reduce the system’s overall costs.

4.5. Emissions Analysis

The emissions’ analysis estimates the greenhouse gas emission-reduction (mitigation) potential of the proposed case. RETScreen estimates the annual GHG emission reduction, ΔGHG of electricity by utilising the following Equation:
∆ G H G = ( e b a s e − e p r o p o s e d ) E p r o p o s e d ( 1 − λ p r o p o s e d ) ( 1 − e c r e d i t )
where e b a s e : base case GHG emission factor; e p r o p o s e d : proposed case GHG emission factor; E p r o p o s e d : proposed case annual electricity produced; λ p r o p o s e d : the fraction of electricity lost in transmission and distribution (T&D) for the proposed case. For off grid system consider the value of λ p r o p o s e d is zero; e c r e d i t : the GHG emission reduction credit transaction fee.
For a single fuel type or source, the following formula is used to calculate the base case electricity system GHG emission factor,   e b a s e :
e b a s e = ( e C O 2 G W P C O 2 + e C H 4 G W P C H 4 + e N 2 O G W P N 2 O ) 1 η 1 1 − λ
where e C O 2 , e C H 4 , and e N 2 O are respectively the CO2, CH4 and N2O emission factors for the fuel/source considered, G W P C O 2 , G W P C H 4 , and G W P N 2 O are the global warming potentials for CO2, CH4 and N2O, η is the fuel conversion efficiency, and λ is the fraction of electricity lost in transmission and distribution. For standard analysis consider G W P C O 2 , G W P C H 4 , and G W P N 2 O as 1, 21, and 310.
In cases for which there are a number of fuel types or sources, the GHG emission factor e p r o p o s e d for the electricity mix is calculated as the weighted sum of emission factors calculated for each individual fuel source:
e p r o p o s e d = ∑ i = 1 n f i e p r o p o s e d , i
where n is the number of fuels/sources in the mix, f i is the fraction of end-use electricity coming from fuel/source i, and e p r o p o s e d , i , is the emission factor for fuel i, calculated through a formula similar to Equation (8):
e p r o p o s e d , i = ( e C O 2 , i G W P C O 2 + e C H 4 , i G W P C H 4 + e N 2 O , i G W P N 2 O ) 1 η i 1 1 − λ i
where, e C O 2 , i , e C H 4 , i , and e N 2 O , i are respectively the CO2, CH4 and N2O emission factors for fuel/source i, η i is the fuel conversion efficiency for fuel i, and λi is fraction of electricity lost in transmission and distribution for fuel i. Consider all λi are zero in case of mix of duel/sources [54].
Table 7 reports the estimated GHG emissions reduction in the proposed system. From the result, it is evident that the proposed system reduces the CO2 emissions by 0.269 tonne/MWh compared to the base case.
Thus, using Equation (6), we can calculate the yearly CO2 reduction in the proposed system for 14.161MWh annual electricity production which is 3.81 tonnes. Therefore, we can say that the proposed system is cost effective and also environmentally-friendly.
∆ G H G = ( 0.269 − 0 ) X   14.161 = 3.81   t o n n e s

5. Conclusions

This paper highlighted the benefits of using a hybrid energy system consisting of both solar energy and biomass energy to reduce energy costs and CO2 emissions. The design was compared with data from the RETScreen data and also with the existing kerosene-based system. Due to the lack of the regional power grid, and available local resources, photovoltaic panel, biogas generator along with battery storage bank are the best solution for providing electricity in future. The paper demonstrated that the hybrid mini-grid system is the most economical and reliable for rural areas. Another useful part of employing a hybrid system is the minimal use of biomass generator which ultimately reduces the greenhouse gas emissions. The only drawback in this system is the battery cost. So, to make this proposed system a reliable one the government should take step to reduce the battery cost. In this hybrid energy model, simulation results showed that 81.2% of total energy is produced by PV and the rest of the energy comes from biomass generator. The simple payback of the project was estimated to be 6.9 years and this project will be able to reduce the CO2 emissions by approximately 3.81 tonnes per year. The study proved that the proposed system is more reliable and cost-effective and also more environmentally friendly when compared with the kerosene-based system.

Author Contributions

N.C. and C.A.H. proposed the core idea, developed the models. N.C. performed the simulations, exported the results and analysed the data. N.C., C.A.H., M.L. and W.Y. contributed to the design of the models and the writing of this manuscript. M.L. and W.Y. revised the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Pasetti, M.; Rinaldi, S.; Manerba, D. A Virtual Power Plant Architecture for the Demand-Side Management of Smart Prosumers. Appl. Sci. 2018, 8, 432. [Google Scholar] [CrossRef] [Green Version]
  2. Rinaldi, S.; Pasetti, M.; Sisinni, E.; Bonafini, F.; Ferrari, P.; Rizzi, M.; Flammini, A. On the Mobile Communication Requirements for the Demand-Side Management of Electric Vehicles. Energies 2018, 11, 1220. [Google Scholar] [CrossRef] [Green Version]
  3. Zomers, A. The challenge of rural electrification. Energy Sustain. Dev. 2003, 7, 69–76. [Google Scholar] [CrossRef]
  4. Alavi, S.M. Techno-Economic Pre-Feasibility Study of Wind and Solar Electricity Generating Systems for Households in Central Finland. Master’s Thesis, University of Jyväskylä, Jyväskylä, Finland, 2014. [Google Scholar]
  5. Taniguchi, M.; Kaneko, S. Operational performance of the Bangladesh rural electrification program and its determinants with a focus on political interference. Energy Policy 2009, 37, 2433–2439. [Google Scholar] [CrossRef] [Green Version]
  6. Islam, A.; Chan, E.S.; Taufiq-Yap, Y.H.; Mondal, M.A.H.; Moniruzzaman, M.; Mridha, M. Energy security in Bangladesh perspective—An assessment and implication. Renew. Sustain. Energy Rev. 2014, 32, 154–171. [Google Scholar] [CrossRef]
  7. Mandelli, S.; Barbieri, J.; Mereu, R.; Colombo, E. Off-grid systems for rural electrification in developing countries: Definitions, classification and a comprehensive literature review. Renew. Sustain. Energy Rev. 2016, 58, 1621–1646. [Google Scholar] [CrossRef]
  8. Bhattacharyya, S.C.; Palit, D. Mini-grid based off-gridelectrification to enhance electricity access in developing countries: What policies may be required? Energy Policy 2016, 94, 166–178. [Google Scholar] [CrossRef] [Green Version]
  9. Kobos, P.H.; Erickson, J.D.; Drennen, T.E. Technological learning and renewable energy costs: Implications for US renewable energy policy. Energy Policy 2006, 34, 1645–1658. [Google Scholar] [CrossRef]
  10. Nguyen, K.Q. Alternatives to grid extension for rural electrification: Decentralized renewable energy technologies in Vietnam. Energy Policy 2007, 35, 2579–2589. [Google Scholar] [CrossRef]
  11. Longo, M.; Hossain, C.A.; Roscia, M. Smart Mobility for Green University Campus. In Proceedings of the Asia-Pacific Power and Energy Engineering Conference (APPEEC), Kowloon, China, 8–11 December 2013; pp. 1–6. [Google Scholar]
  12. Bernal-Agustín, J.L.; Dufo-López, R. Simulation and optimization of stand-alone hybrid renewable energy systems. Renew. Sustain. Energy Rev. 2009, 13, 2111–2118. [Google Scholar] [CrossRef]
  13. Ho, W.S.; Hashim, H.; Hassim, M.H.; Muis, Z.A.; Shamsuddin, N.L.M. Design of distributed energy system through Electric System Cascade Analysis (ESCA). Appl. Energy 2012, 99, 309–315. [Google Scholar] [CrossRef]
  14. Chowdhury, N.; Hossain, C.A.; Longo, M.; Yaïci, W. Optimization of solar energy system for the electric vehicle at university campus in Dhaka, Bangladesh. Energies 2018, 11, 2433. [Google Scholar] [CrossRef] [Green Version]
  15. Dedé, A.; Della Giustina, D.; Massa, G.; Pasetti, M.; Rinaldi, S. A Smart PV Module with Integrated Electrical Storage for Smart Grid Applications. In Proceedings of the IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Anacapri, Italy, 22–24 June 2016; pp. 895–900. [Google Scholar]
  16. Marchi, B.; Pasetti, M.; Zanoni, S. Life Cycle Cost Analysis for BESS Optimal Sizing. In Proceedings of the Energy Procedia, Special issue of the 2016 International Scientific Conference on Environmental and Climate Technologies (CONECT), Riga, Latvia, 12–14 October 2016; Volume 113, pp. 127–134. [Google Scholar]
  17. Marchi, B.; Zanoni, S.; Pasetti, M. A Techno-Economic Analysis of Li-ion Battery Energy Storage Systems in Support of PV Distributed Generation. In Proceedings of the 21st Summer School F. Turco of Industrial Systems Engineering, Naples, Italy, 13–15 September 2016; pp. 145–149. [Google Scholar]
  18. Marchi, B.; Pasetti, M.; Zanoni, S.; Zavanella, L.E. The Italian Reform of Electricity Tariffs for Non Household Customers: The Impact on Distributed Generation and Energy Storage. In Proceedings of the 22nd Summer School F. Turco of Industrial Systems Engineering, Palermo, Italy, 13–15 September 2017; pp. 103–109. [Google Scholar]
  19. Hossain, C.A.; Chowdhury, N.; Longo, M.; Yaïci, W. System and Cost Analysis of Stand-Alone Solar Home System Applied to a Developing Country. Sustainability 2019, 11, 1403. [Google Scholar] [CrossRef] [Green Version]
  20. Khan, M.J.; Iqbal, M.T. Pre-feasibility study of stand-alone hybrid energy systems for applications in Newfoundland. Renew. Energy 2005, 30, 835–854. [Google Scholar] [CrossRef]
  21. Akella, A.K.; Sharma, M.P.; Saini, R.P. Optimum utilization of renewable energy sources in a remote area. Renew. Sustain. Energy Rev. 2007, 11, 894–908. [Google Scholar] [CrossRef]
  22. Shaahid, S.M.; Elhadidy, M.A. Technical and economic assessment of grid-independent hybrid photovoltaic–diesel–battery power systems for commercial loads in desert environments. Renew. Sustain. Energy Rev. 2007, 11, 1794–1810. [Google Scholar] [CrossRef]
  23. Kenfack, J.; Neirac, F.P.; Tatietse, T.T.; Mayer, D.; Fogue, M.D.; Lejeune, A. Micro hydro–PV–hybrid system: Sizing a small hydro–PV–hybrid system for rural electrification in developing countries, technical note. Renew. Energy 2009, 34, 2259–2263. [Google Scholar] [CrossRef]
  24. Silva, S.B.; de Oliveira, M.A.G.; Severino, M.M. Economic evaluation and optimization of a photovoltaic–fuel cell–batteries hybrid system for use in the Brazilian Amazon. Energy Policy 2010, 38, 6713–6723. [Google Scholar] [CrossRef]
  25. Ma, T.; Yang, H.; Lu, L. A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island. Appl. Energy 2014, 121, 149–158. [Google Scholar] [CrossRef]
  26. Okedu, K.E.; Al-Hashmi, M. Assessment of the cost of various renewable energy systems to provide power for a small community: Case of Bukha, Oman. Int. J. Smart Grid 2018, 2, 3. [Google Scholar]
  27. Sepulveda, T.T.; Martinez, L. Optimization of a hybrid energy system for an isolated community in Brazil. Int. J. Renew. Energy Res. 2016, 6, 1476–1481. [Google Scholar]
  28. Bhattarai, P.R.; Thompson, S. Optimizing an off-grid electrical system in Brochet, Manitoba, Canada. Renew. Sustain. Energy Rev. 2016, 53, 709–719. [Google Scholar] [CrossRef] [Green Version]
  29. Lipu, M.S.H.; Hafiz, M.G.; Ullah, M.S.; Hossain, A.; Munia, F.Y. Design Optimization and Sensitivity Analysis of Hybrid Renewable Energy Systems: A case of Saint Martin Island in Bangladesh. Int. J. Renew. Energy Res. 2017, 7, 2. [Google Scholar]
  30. Brenna, M.; Longo, M.; Yaici, W.; Abegaz, T.D. Simulation and Optimization of Integration of Hybrid Renewable Energy Sources and Storages for Remote Communities Electrification. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Torino, Italy, 26–29 September 2017. [Google Scholar]
  31. Longo, M.; Yaïci, W.; Foidelli, F. Hybrid Renewable Energy System with Storage for Electrification—Case Study of Remote Northern Community in Canada. Int. J. Smart Grid 2019, 3, 63–71. [Google Scholar]
  32. Ahmed, S.; Islam, M.T.; Karim, M.A.; Karim, N.M. Exploitation of renewable energy for sustainable development and overcoming power crisis in Bangladesh. Renew. Energy 2014, 72, 223–235. [Google Scholar] [CrossRef]
  33. Islam, M.T.; Shahir, S.A.; Uddin, T.M.I.; Saifullah, A.Z.A. Current energy scenario and future prospect of renewable energy in Bangladesh. Renew. Sustain. Energy Rev. 2014, 39, 1074–1088. [Google Scholar] [CrossRef]
  34. Kanagawa, M.; Nakata, T. Assessment of access to electricity and the socio-economic impacts in rural areas of developing countries. Energy Policy 2008, 36, 2016–2029. [Google Scholar] [CrossRef]
  35. Siddaiah, R.; Saini, R.P. A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications. Renew. Sustain. Energy Rev. 2016, 58, 376–396. [Google Scholar] [CrossRef]
  36. Guo, S.; Liu, Q.; Sun, J.; Jin, H. A review on the utilization of hybrid renewable energy. Renew. Sustain. Energy Rev. 2018, 91, 1121–1147. [Google Scholar] [CrossRef]
  37. Islam, M.S.; Akhter, R.; Rahman, M.A. A thorough investigation on hybrid application of biomass gasifier and PV resources to meet energy needs for a northern rural off-grid region of Bangladesh: A potential solution to replicate in rural off-grid areas or not? Energy 2018, 145, 338–355. [Google Scholar] [CrossRef]
  38. Murugaperumala, K.; Vimal Raj, P.A.D. Feasibility design and techno-economic analysis of hybrid renewable energy system for rural electrification. Sol. Energy 2019, 188, 1068–1083. [Google Scholar] [CrossRef]
  39. Rad, M.A.V.; Ghasempour, R.; Rahdan, P.; Mousavi, S.; Arastounia, M. Techno-economic analysis of a hybrid power system based on the cost-effective hydrogen production method for rural electrification, a case study in Iran. Energy 2020, 190, 116421. [Google Scholar] [CrossRef]
  40. Tanim, M.M.; Chowdhury, N.A.; Rahman, M.M.; Ferdous, J. Design of a Photovoltaic-Biogas Hybrid Power Generation System for Bangladeshi Remote area Using HOMER Software. In Proceedings of the 2014 3rd International Conference on the Developments in Renewable Energy Technology ICDRET, Dhak, Bangladesh, 29–31 May 2014. [Google Scholar]
  41. Habib, M.A.; Chungpaibulpatana, S. Utilization of Solar and Biomass for Rural Electrification in Bangladesh. In Proceedings of the 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development ICUE, Pattaya City, Thailand, 19–21 March 2014. [Google Scholar]
  42. Ahsan-Uz-Zaman, K.M.; Wahed, A.; Sayam, A.S.M.; Faruk, O.; Sarker, B.C. Solar-Biomass Hybrid System, an Approach for Rural Electrification in Bangladesh. In Proceedings of the 4th International Conference on Electrical Engineering and Information and Communication Technology iCEEiCT, Dhaka, Bangladesh, 13–15 September 2018; pp. 187–192. [Google Scholar]
  43. Das, B.K.; Hoque, N.; Mandal, S.; Pal, T.K.; Raihan, M.A. A techno-economic feasibility of a stand-alone hybrid power generation for remote area application in Bangladesh. Energy 2017, 134, 775–788. [Google Scholar] [CrossRef]
  44. Khan, E.U.; Mainali, B.; Martin, A.; Silveira, S. Techno-economic analysis of small scale biogas based polygeneration systems: Bangladesh case study. Sustain. Energy Technol. Assess. 2014, 7, 68–74. [Google Scholar] [CrossRef]
  45. Mandal, S.; Das, B.K.; Hoque, N. Optimum sizing of a stand-alone hybrid energy system for rural electrification in Bangladesh. J. Clean Prod. 2018, 200, 12–27. [Google Scholar] [CrossRef]
  46. Natural Resources Canada, RETScreen Data Analysis Software and Modelling Tool. Available online: https://www.nrcan.gc.ca/energy/software-tools/7465 (accessed on 15 January 2020).
  47. Afzal, A. Performance Analysis of Integrated Wind, Photovoltaic and Biomass Energy Systems. In Proceedings of the World Renewable Energy Congress 2011, Linköping, Sweden, 8–13 May 2011; pp. 818–825. [Google Scholar]
  48. Liqun, L.; Chunxia, L. Feasibility analyses of hybrid wind-PV-battery power system in Dongwangsha, Shanghai. Przegląd Elektrotechniczny 2013, 89, 239–242. [Google Scholar]
  49. Kalinchyk, I.; Pfeiffer, C.F.; Inshekov, E. RETScreen Modeling for Combined Energy Systems Fertilizers Plant Case. In Proceedings of the 55th Conference on Simulation and Modelling, Modelling, Simulation and Optimization (SIMS 55) 2014, Aalborg, Denmark, 21–22 October 2014; pp. 7–15. [Google Scholar]
  50. Hasan, M.M.; Chowdhury, N.; Hossain, C.A.; Longo, M. State of Art on Possibility & Optimization of Solar PV-Wind Hybrid System. In Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 10–12 January 2019; pp. 598–601. [Google Scholar]
  51. Sinha, S.; Chandel, S.S. Review of software tools for hybrid renewable energy systems. Renew. Sustain. Energy Rev. 2014, 32, 192–205. [Google Scholar] [CrossRef]
  52. Dwivedy, D.; Singh, S.K.; Choudhury, M.K.; Pradhan, S.R. Study of Cost Analysis and Emission Analysis for Grid Connected PV Systems using RETSCREEN 4 Simulation Software. Int. J. Eng. Res. Technol. 2015, 4, 203–207. [Google Scholar]
  53. Wattblock; Witheridge, S.; Stegen, S. Comparison of Solar PV vs Solar Thermal Hot Water Systems to Provide Energy Solutions for Strata Buildings. Master’s Thesis, Griffith School of Engineering Griffith University, Mount Gravatt, Australia, 22 May 2017. [Google Scholar]
  54. Clean Energy Project Analysis: RETScreen Engineering & Cases Textbook. Available online: http://msessd.ioe.edu.np/wp-content/uploads/2017/04/Textbook-clean-energy-project-analysis.pdf (accessed on 15 January 2020).
  55. D’Adamo, I. The Profitability of Residential Photovoltaic Systems. A New Scheme of Subsidies Based on the Price of CO2 in a Developed PV Market. Soc. Sci. 2018, 7, 148. [Google Scholar] [CrossRef] [Green Version]
  56. Nam, H.; Mukai, K.; Konishi, S.; Nam, K. Biomass gasification with high temperature heat and economic assessment of fusion-biomass hybrid system. Fusion Eng. Des. 2019, 146, 1838–1842. [Google Scholar] [CrossRef]
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Figure 1. RETScreen technical evaluation structure flowchart [53].
Figure 1. RETScreen technical evaluation structure flowchart [53].
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Figure 2. Block diagram of proposed photovoltaics (PV)-biomass hybrid energy system.
Figure 2. Block diagram of proposed photovoltaics (PV)-biomass hybrid energy system.
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Figure 3. The specification of different components and energy produced by each source. Notation—Mono-Si: Monocrystalline silicon; CS4A: Multi-contact connector type 4.
Figure 3. The specification of different components and energy produced by each source. Notation—Mono-Si: Monocrystalline silicon; CS4A: Multi-contact connector type 4.
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Figure 4. Initial cost of proposed system. Notation—BDT: Bangladeshi Taka.
Figure 4. Initial cost of proposed system. Notation—BDT: Bangladeshi Taka.
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Figure 5. Cumulative cash flows.
Figure 5. Cumulative cash flows.
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Table
Table 1. Load profile.
Table 1. Load profile.
Daily Load Profile
ApplianceTypeUnitWattHour of Operation
(h)		Total Power
(W)		Total Energy Demand
(Wh)
LEDDC46824192
FanDC2201420560
TVDC140440160
Daily Demand for each house84912
Daily Demand for 50 houses4.2 kW45.6 kWh
Table
Table 2. Load characteristics of base and proposed cases.
Table 2. Load characteristics of base and proposed cases.
Load Characteristics
Electricity-DCBase CaseProposed Case
Daily47.2 kWh47.2 kWh
Annual14.161 MWh14.161 MWh
Peak Load-Annual4.7 kW
Percentage of Month Used
MonthBase CaseProposed Case
January54%54%
February54%54%
March93%93%
April93%93%
May93%93%
June93%93%
July99%99%
August99%99%
September99%99%
October99%99%
November54%54%
December54%54%
Table
Table 3. Ashuganj site reference data.
Table 3. Ashuganj site reference data.
ParameterUnitClimate Data LocationProject Location
Latitude°N24.124.1
Longitude°E91.991.9
Elevationm1414
Heating design temperature°C13
Cooling design temperature°C30.9
Earth temperature amplitude°C13.5
Table
Table 4. Site reference conditions.
Table 4. Site reference conditions.
MonthAmbient Air Temperature
°C		Relative Humidity
%		Daily Solar Radiation
kWh/m2/d		Earth Temperature
°C
January20.454.74.4221.6
February22.755.34.9823.9
March25.261.75.4427
April26.373.15.5128.1
May27.179.15.1128.8
June27.584.74.1628.4
July27.385.94.0427.9
August27.185.54.1827.8
September26.784.14.0227.6
October2677.94.2826.8
November23.869.44.2524.4
December21.360.14.2822.1
Annual25.172.74.5526.2
Table
Table 5. Base case power system.
Table 5. Base case power system.
ParameterValue
Grid TypeOff-grid
Fuel typeKerosene-L
Fuel rate65 BDT/L
Capacity4.7 kW
Heat rate8 kJ/kWh
Annual O& M costBDT 474,500
Electricity rate-base case33.512 BDT/kWh
Total electricity costBDT 474,702
Table
Table 6. Annual saving of proposed system.
Table 6. Annual saving of proposed system.
Project Costs and Saving/Income Summary
Initial Cost
Feasibility study0.3%BDT6000
Development0.3%BDT6000
Engineering0.4%BDT8000
Power system89.1%BDT1,951,200
Balance of system & misc.10.0%BDT218,889
Total initial costs100%BDT2,190,089
Annual Costs & Debt Payments
O & M BDT159,375
Fuel cost-proposed caseBDT0
Debt payments-25 yrs BDT8321
Total Annual costs BDT167,696
Periodic Costs (credits)
Battery-5 yrs BDT403,200
Converter-10 yrs BDT89,600
End of project life-costBDT376,832
Annual Savings and Income
Fuel cost-base case BDT474,702
Total annual savings and incomeBDT474,702
Table
Table 7. Emissions analysis.
Table 7. Emissions analysis.
Base Case System GHG Summary (Baseline)
Fuel typeFuel Mix
%		CO2 emission factor
Kg/GJ		CH4 emission factor
Kg/GJ		N2O emission factor
Kg/GJ		Fuel Consumption
MWh		GHG emission factor
tCO2/MWh
Kerosene100%73.90.00700.002000.269
Total100%73.90.00700.002000.269
Proposed Case System GHG Summary (Power Proposed Project)
Fuel typeFuel Mix
%		CO2 emission factor
Kg/GJ		CH4 emission factor
Kg/GJ		N2O emission factor
Kg/GJ		Fuel Consumption
MWh		GHG emission factor
tCO2/MWh
Biomass0.100.0320.00400.007
Solar99.9000140
Total100%000140

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MDPI and ACS Style

Chowdhury, N.; Akram Hossain, C.; Longo, M.; Yaïci, W. Feasibility and Cost Analysis of Photovoltaic-Biomass Hybrid Energy System in Off-Grid Areas of Bangladesh. Sustainability 2020, 12, 1568. https://doi.org/10.3390/su12041568

AMA Style

Chowdhury N, Akram Hossain C, Longo M, Yaïci W. Feasibility and Cost Analysis of Photovoltaic-Biomass Hybrid Energy System in Off-Grid Areas of Bangladesh. Sustainability. 2020; 12(4):1568. https://doi.org/10.3390/su12041568

Chicago/Turabian Style

Chowdhury, Nusrat, Chowdhury Akram Hossain, Michela Longo, and Wahiba Yaïci. 2020. "Feasibility and Cost Analysis of Photovoltaic-Biomass Hybrid Energy System in Off-Grid Areas of Bangladesh" Sustainability 12, no. 4: 1568. https://doi.org/10.3390/su12041568

APA Style

Chowdhury, N., Akram Hossain, C., Longo, M., & Yaïci, W. (2020). Feasibility and Cost Analysis of Photovoltaic-Biomass Hybrid Energy System in Off-Grid Areas of Bangladesh. Sustainability, 12(4), 1568. https://doi.org/10.3390/su12041568

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