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Renewable Energy 68, (2014): 35–45 DOI: 10.1016/j.renene.2014.01.030

Hybrid application of biogas and solar resources for fulfilling household energy needs: A

potentially viable option in rural areas

Md. Mizanur Rahmana,b,

, Mohammad Mahmodul Hasana, Jukka V. Paatero

a, Risto Lahdelma

a

aDepartment of Energy Technology, Aalto University School of Engineering, FI-0076 Aalto, Finland

b

Rural Electrification Board (REB), System Operation Management, Savar, Dhaka-1344, Bangladesh

Abstracts

Absence of clean cooking facilities and electricity makes billions of rural people vulnerable to life

threatening diseases and pushes women and children into miserable life. Biogas from anaerobic

digestion of cattle residue (dung) and solar radiation are the two renewable resources abundantly

found in the rural areas. These two resources separately unfeasible to provide both thermal

(cooking) and electric loads. Despite appealing potential, hybrid applications of biogas and solar

resources are extremely limited. To facilitate integrating these two resources in rural energy

planning, and to promote dissemination by exploiting the full potential, it is necessary to evaluate

their economic merits, and assess their aptitudes to deal with the demands and physical constraints.

In this paper, we used HOMER® software tool to examine the techno economic performance of

hybrid application of these two resources and find the extent they can handle the cooking and

electric loads in rural areas in the context of Bangladesh. This paper also performed sensitivity

analysis on some variables to see how their changes affect on the performance of different energy

options. We furthermore examined the practical applicability of the biogas system through a

structured survey on 72 ongoing household biogas plants. This study found that households who

domesticate 3-6 cattle can potentially be served cooking and electric loads through hybrid

utilization of these two sources. Households can achieve saving from replacing conventional fuels,

which are more than the total annualized costs to incur for instating new services.

Keywords: Biogas engine, Solar PV, Monetary saving

Corresponding author: Tel: +358 (0) 505709911; fax: +358 (0) 9 47023419; e-mail: mdmizanur.rahman@aalto.fi

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Abbreviations

DGE

Dgen

CFL

COE

HHC1

HHC2

HHC3

Digester Gas Engine

Diesel Generator

Compact Florescent Light

Cost of Energy

Household Category 1

Household Category 2

Household Category 3

LNG

NPC

PV

SCR

TAC

TAS

Liquefied Natural Gas

Net Present Cost

Photovoltaic

Saving to Cost Ratio

Total Annualized Cost

Total Annual Saving

1. Introduction

The lack of access to electricity and clean cooking facility are the two key elements measure the

magnitude of energy poverty. These two modern forms of energy are essential for furnishing the

basic human needs such as clean water, sanitation, and healthcare and for eradicating the worst

effects of poverty [1]. Electricity and clean cooking facility bring enormous improvement to the

user’s living standard through the provisions of effective lighting, clean cooking, space cooling,

cold storage, and modern day communication. In spite of high essential, these two forms of energy

services are still missing for more than 1 billion people in the world.

The rural households usually use conventional cook-stoves and relatively wet solid biomass as fuel

for their cooking purposes. The low efficient stove with wet solid biomass produces a high level of

smoke that is hazardous for the human health. Women and children are the main victims of these

hazards as they are generally responsible for cooking and collection of biomass. Women suffer long

term health problems and children get deprived of going to school. Lighting through paraffin candle

and kerosene lantern emits smokes and they produce poor lighting intensity per unit of consumed

wattage. The kerosene smoke causes many health diseases like tuberculosis, cancer and other

associated risk like fires, being drunk by the children. Thus, cooking with fuel-wood and other solid

biomass fuels using traditional stoves contributes significantly to degrading the quality of life. The

other basic energy applications such as lighting, entertaining/ leisure, communication, and space

cooling (fan) need electricity. Thus, there is a crucial need to provide clean gaseous fuel for

cooking, and electricity for other basic applications at the rural households.

Bangladesh, which has been focused in this study, like other developing countries is highly reliant

on solid biomass for cooking, and massive lack of access to electricity in rural areas [2]. The

country produces a plenty of bio-wastes from livestock every day and is endowed with many hours

of daily sunshine (3 to 11 hours having 200 W/m2 intensity or more). These two renewable energy

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sources i.e. bio-wastes and solar radiation can play a leading role in meeting the energy demand of

households in the rural areas of Bangladesh [3]. K Hossain and Badr [3] estimate that rural

households of Bangladesh have potential to generate in an average of 0.84 t/y recoverable wastes

every year from animal manure and poultry droppings. About one million households are keeping

more than 5 heads of cattle per household, and two million households are keeping 3-4 cattle per

household [4].

Several studies such as [5–11] have reported on the techno-economic viability of biogas plants in

rural areas of South Asian countries. Katuwal and Bohra [8] reported that biogas model has been

one of the most successful for the production of clean, environmentally friendly, cost effective

energy in Nepal and offers multiple benefits. Bond and Templeton [12] examined the potential of

biogas technology and also reviewed the ecological, social, cultural, and economic impacts from its

application. Srinivasan [13] inspected the positive externalities for domestic biogas initiatives and

implications for financing to these initiatives. Gwavuya [14] made a cost benefit analysis of biogas

for household energy applications in rural Ethiopia and reported that the main drawback for

disseminating this technology is insufficient understanding of household’s energy use pattern.

Urmee and Harries [15], Asif and Barua [16], and M Ziaur [17] intensively studied the

performance and features of solar PV program in Bangladesh and concluded that the PV has

been highly successful with a large number of rural households installed solar PV system. They

found that the users use PV electricity for operating their lighting, home-appliances, and mobile

charger, and resulted in a significant increase in their quality of life.

Biogas and solar resources separately unfeasible to provide both thermal (cooking) and electric

loads. Interestingly, these two resources can potentially generate both thermal and electric loads

while they are in a hybrid configuration. Despite the abundance in resources and appealing

potential, there appears lack of studies on hybrid use of these two alternative energy sources. In this

paper, we perform techno-economic evaluation of hybrid biogas and solar PV system with the help

of NREL’s HOMER tool to find how potentially these two renewable energy resources serve the

households’ demand in the context of rural Bangladesh. We develop a cost-saving equation to

quantify the monetary saving from replaced traditional fuels such as fuelwood, kerosene, and car-

batteries. We also perform sensitivity analysis on some variables to know the extent how they affect

on the system’s viability, and to find their (variables) threshold values over diesel generator and grid

system. Although ongoing solar PV performances have been reported by several case studies,

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performance of biogas plants are not as such clear as PV; therefore, we also extend the work to a

structured survey to examine the performance of ongoing biogas plant and their practical validity.

2. Technologies

Considering the availability, complementary characteristics and load conditions, this paper focuses

on two technologies namely Digester Gas Engine (DGE), and solar PV (Photovoltaic) system.

2.1 Digester gas engine system (DGE)

It consists of two components- Biogas plant (sometimes referred to as anaerobic digester) and

Biogas engine. Biogas plant is an assembly of few containers (tanks) and pipes, which converts

organic waste (e.g. cattle dung, poultry litter, food waste etc.) into biogas, and slurry. Biogas engine

converts the energy in biogas into electricity. Household-scale biogas plant usually consists of an

airtight underground digester tank, a gas holder, two inlet outlet tanks, a mixing device, few pipes,

and gas regulator valves (see Fig. 1 (a)). The digester tank gets feed in with properly mixed

livestock wastes and water. The outlet of the biogas pipe is connected to the kitchen or power house

where biogas is supplied to the gas stove and/or gas engine. Size (sometimes referred to as

capacity) of biogas plant corresponds to the quantity of biogas (m3) the plant can produce in 24

hours. One kilogram of fresh cattle dung has the potential to generate about 0.04 m3 of biogas, in

other words 1 m3 of biogas corresponds to 25 kg of cattle dung [18]. The biogas comprises of 50-

75% methane gas, 25-50% carbon dioxide, 0-10% nitrogen, and small quantity of other gases like

hydrogen sulphide (H2S), hydrogen. Biogas can be used for cooking through gas stoves, which

burns biogas with a blue flame without emitting smoke, and for electricity generation through

biogas engine [8]. Rural areas have an abundant source of livestock manure, and there is immense

potential for biogas plants in most of the Asian and African countries.

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(a)

(b)

Fig. 1. Main components of (a) biogas plant and (b) solar PV system.

2.2 Solar PV System

Solar PV system is an assembly of photovoltaic (PV) module, rechargeable battery, charge

controller, inverter, and few small wires and sockets (see Fig. 1(b)). It is a fixed installation

designed for domestic household applications. The PV module is installed in the open place on a

roof or terrace that is exposed to sunlight while the charge controller, inverter, and battery are kept

inside a protected place in the house. Household-level solar PV system can have a wide range of

capacities from few watts to hundreds of watts depending on the power requirements. The capacity

of solar PV system is specified by watt peak (Wp), which is the power generated under standard

conditions. The watt peak multiplied by panel generation factor (PGF) gives the energy generated

per day by the PV panel [19]. The rechargeable battery stores energy and serves the load when

generation shortfall occurs. PV power can be served directly to DC or AC loads with the help of an

inverter. More than one million standalone solar PV systems have been installed in rural areas of

Bangladesh [20].

3. Current applications of fuels in rural areas

The main energy use in rural households is in cooking and lighting purposes. The other basic energy

requirements beyond cooking and lighting are space cooling, home-appliances for leisure, and cell

phone charging. Because of geography and climate conditions, space and water heating needs in

rural households are very small in the developing countries [21]. Households use a wide range of

energy sources for cooking such as fuelwood, agricultural residues, kerosene, Liquefied Natural

Gas (LNG), Liquefied Petroleum Gas (LPG), and biogas. The uses of other fuels such as plant oils,

biomass briquettes, charcoals, and electricity are very small or negligible. The lighting services are

PV module

Charge controller Inverter

AC

Loads

Battery

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provided by some form of external sources such as kerosene, paraffin candle etc. The other energy

services such as leisure/entertainment, cell-phone battery charging are served with car-battery or

dry-cell battery.

Three-stone mud burners are used for cooking by biomass fuels, and kerosene stoves are used for

cooking by kerosene, LPG/LNG fuels. Lighting services in the rural households are provided by

paraffin candles, hurricane lantern or wicks lamps. The common appliances for

leisure/entertainment and communication are radio, cassette player, TV, and mobile phone. Beyond

theses appliances, some other home-appliances such as refrigerator, hair drier, rice cooker, iron are

also used in few households. Households’ current energy applications, fuel sources,

devices/appliances, and possible alternative forms are presented in Fig. 2. Two forms of renewable

fuels (e.g. biogas and solar) have been focused in this study as the alternative fuels to meet the

households’ energy needs.

Fig. 2. Narrative of fuel applications in rural households

Fuel applications

Alternative appliances

Alternatives fuels

Current fuels

Current appliances

Household category

Radio/Tap

Television

Fan (space cooling)

Fuel wood

Residues

LPG/LNG

Kerosenne stove

Three-stone wood

stove

Solar and biogas

Kerosene

Wick lamp

Hurrican lantern

Paraffin candle

Car battery

Dry-cell battery

Radio/Tap

Television

Mobile crg.

Computer

Fan (space cooling)

Others

Gas stove CFL bulb

Cooking Lighting Other

appliances

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4. Methodologies

In this paper, we first designed optimized hybrid systems from biogas and solar resources with the

help of appropriate simulation tool. Then we performed techno-economic evaluations on the

resulted hybrid systems to examine how potentially these two renewable energy resources serve the

households’ energy demands. We also quantified the saving in monetary worth from replaced

traditional fuels by using proposed cost saving equations. At last we extended this work to a

structured survey to examine the performance of ongoing biogas plants and their practical

implications to the users.

4.1 Simulation tool

The aim of this simulation was to design a hybrid energy system which can serve both electric and

thermal demands with complying technical and resource constraints. The simulation method can be

accomplished by many tools and models and such most common are BP neutral network, Simulink,

LINDO Systems, and HOMER [22,23].

This paper considers two energy generation technologies- Digester gas engine system and Solar PV

system. A suitable sizing of these two technologies is required for performing techno-economic

evaluations on the resulted system. Optimization technique can determine appropriate sizing of

these technologies as well as other auxiliary components that balance the local demands and meet

physical constraints. Among different approaches, simulation-based optimization is a widely

utilized approach for designing small-scale energy systems and performing economic evaluations

on them [24]. Some common optimization tools are HOMER [25], Hybrid2 [26], and HOGA [27].

This article, however, applied HOMER computer tool to design a hybrid renewable energy system

and perform techno-economic evaluation. HOMER was chosen because it has distinct capability to

handle small-scale renewable based energy systems. Renewable based power systems entail

complexity due to the transient nature of power outputs and variation of availability of renewable

resources. HOMER perform time-series (hourly) simulation and can incorporate the effects from

uncertainties of different input variables such as load sizes, fuel price, resource availability etc.

HOMER simulates, optimizes and evaluates a wide range of equipment options based on their

technical and economic merits and can quantify the uncertainty effects [25,28]. The small and

micro-power systems involve varieties of resource options, HOMER can handle all major

renewable resources including solar and biomass. HOMER also compatible to evaluate the energy

systems which involve both electric and thermal energies, AC and DC loads, hydrogen and battery

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storages, grid connected and standalone. HOMER is found as an appropriate and sophisticated tool

to simulate energy systems like this one concerned in this paper.

HOMER tool performs three principal tasks: simulation, optimization, and sensitivity analysis. In

the simulation process, it first simulates the performance of a vast number of system configuration

based on energy balance calculation for each hour of the year to determine whether these

configurations are feasible. HOMER tool considers the system to be feasible if it can adequately

serve the electric and thermal loads and satisfy all technical constraints imposed by the model users.

Then it estimates the total net present cost (NPC), which is the present value of all costs for

installing and operating of the system minus the present value of all revenue over its lifetime.

The total NPC of the system is calculated as below:

, & , , , ,0

(T

cap t O M t replace t fuel t salvage tt

NPC C C C C R (1)

where T is the lifetime of the project, Ccap,t is the present value of capital costs for year t, CO&M,t is

the present value of operation & maintenance cost for year t, Cfuel,t is the present value of fuel cost

for year t, Creplace,t is the present value of the replacement costs for year t, Rsalvage,t is the present

value of salvage price for year t.

In the optimization process, it determines the best possible system configuration based on lowest

total NPC. In the sensitivity analysis, HOMER accomplishes multiple optimizations on the basis of

input variables to explore the effects of uncertainties or changes of input parameters. Model user

enters a range of values for a single variable and HOMER shows the effects for changing of each

value. Though HOMER ranks the system on total NPC, it also calculates the COE (electricity)

value for each of the optimized system and extends the opportunity to prioritize the system on COE.

The COE (electricity) is the average cost of producing per kWh of useful electrical energy and does

not include the thermal part and is calculated through the following equation.

, .( )

a tot boiler served

served

C c HCOE electricity

E

(2)

where, Ca,tot is the total annualized cost of the system (US$/y), cboiler is the boiler marginal cost

(US$/kWh), Hserved is the total thermal load served (kWh/y) , Eserved is the total electrical load

served (kWh/y).

4.2. Input data for HOMER simulation tool

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HOMER simulation requires a plenty of data sets as input which are not necessarily ready but their

correct entries are essential. In the following section, we have described the techniques how the

input data were processed.

4.2.1 Loads

HOMER load consists of three components; primary load, thermal load, and deferrable load.

Primary load is the electrical demands that the power system must meet at any specific time.

Thermal load is the heat demand that must be served, and deferrable load is the electrical demand

that can be served at any time within a certain time span.

We have categorized rural households of Bangladesh into three categories based on energy use

information from Grameen Shakti survey report [29] (details are presented in section 4.5) and few

other studies namely Asaduzzaman et al. [2], Miah et al. [30] and World Bank [31].

Study [2] was based on a national-level comprehensive survey, whose main objective was to

determine the overall energy-use pattern in rural areas of Bangladesh. This survey used cluster

sampling strategy, and total 120 villages were selected randomly from all four older divisions

(proportionate to population) of the country. The resulting sample closely resembled overall rural

population of Bangladesh. Study [30] was conducted through stratified random sampling technique

using the semi-structured questionnaire. Total 120 households were randomly selected from 12

villages of a sub-district of Bangladesh. The study determined the energy consumption pattern for

six household categories (based on income) for different fuel sources (e.g. biomass, kerosene,

electricity). The data of the study [31] were obtained from Household Income and Expenditure

Survey,2005 (HIES) of Bangladesh. The sample size was 10 054 households of whom 64% were

from rural areas. The sample data corresponds to 139 million people (out of country’s total 153

million) based on the weights provided in the survey.

In this paper, we hypothesized daily electric and thermal energy demands for the three household

categories and presented in Table 1 and 2. All three household categories have been considered to

have basic electric appliances and use 2-6 hours a day. The gas-burner with 60% efficiency has

been considered as cooking device (as Boiler in HOMER) with biogas as fuel. The gas-burner

operates 4-8 hours a day with final thermal output per burner 1.6 MJ/h [30,32,33]. Household

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category 1 (HHC1) is assumed to have one gas-burner and uses 4 burner-hour1 a day while

household category 2 and 3 (HHC2 and HHC3) use 6 and 8 burner-hour respectively [29]. We have

utilized these average demands for each household category as the scaled annual average in

HOMER. The deferrable load was not allowed in this study.

Table 1

Thermal (cooking) energy demand per household (HH) for each of 3 categories.

Load

type

Appliance Thermal

output per

Burner

HHC1 HHC2 HHC3

(MJ/h) Burner-

hour a

Daily final heat

consumption

(kWh/d)

Burner-

hour

Daily final

heat

consumption

(kWh/d)

Burner-

hour

Daily final heat

consumption

(kWh/d)

Cooking Gas burner 1.6 b 4 1.776 6 2.664 8 3.552

a A Burner-hour is the thermal (cooking) load served by 1 Burner in 1 hour.

b A Gas burner approximately gives final thermal output of 1.6 MJ/h.

Source: [33]

1 Household owing to 4 burner-hour means it uses 1 burner for 4 hours or 2 burners for 2 hours and so

on.

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Table 2

Electric energy demand per household (HH) for each of 3 categories.

Load type Appliances Power

(W)

Operation

time (h/d)

HHC1 HHC2 HHC3

Number

of

appliances

Daily

electricity

consumption

(kWh/d)

Number

of

appliances

Daily

electricity

consumption

(kWh/d)

Number

of

appliances

Daily

electricity

consumptio

n (kWh/d)

Lighting CFL bulb 10 6 3 180 4 240 6 360

Leisure/

Entertainme

nt

TV 40 4 1 160 1 160 1 160

Radio/

Cassette

player

10 4 1 40 1 40 1 40

Mobile

charger 5 2 1 10 1 10 1 10

Computer 120 6 1 720 1 720 1 720

Others 50 2 0 0 1 100 3 300

Cooling Ceiling fan 50 6 2 600 3 900 3 900

Total

electric load

(kWh/d)

1.71

2.17

2.49

Sources: [2,30,34]

4.2.2 Electrical load profile

In addition to the average energy demand, HOMER model also requires a hourly load profile to

enable hourly simulate the operation of the system by making energy balance calculations for each

of the 8760 hours in a year. We have collected a monthly averaged daily load profile for rural areas

of Bangladesh for the year 2010 and presented in Fig. 3. This load profile are based on the real

loads of 8.2 million rural consumers connected to electric distribution network of Bangladesh [35].

The primary load varies over 24 hours, and different months of the year. These monthly averaged

12 sets of load data were utilized to input into HOMER model. Homer creates hourly load values

from the scaled load data based on the monthly averaged daily load profiles. The daily and hourly

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noise inputs allow adding randomness to the load data, and enabling the load profile to be more

realistic. We have incorporated the randomness by applying daily 15% and hourly 10% noise

inputs.

Fig.3. Monthly averaged hourly load profile for rural households.

4.2.3 Solar data

Solar inputs data for HOMER were taken as monthly averaged daily insolation incident on a

horizontal surface (kWh/m2/day) from NASA Surface Meteorology and Solar Energy (SSE) website

[36]. NASA gives monthly averaged values from 22 years of data. The solar insolation were taken

for a site of 24º latitude and 90º longitude in Bangladesh. The annual average solar insolation in this

area was found 4.65 kWh/m2/day. Figure 4 shows the solar resource profile over a one year period.

The data generated for a particular location may not perfectly replicate the characteristics of the real

solar radiation, however, test have shown that the synthetic solar data produces virtually the same

simulation results as real data [37].

0.175

0.195

0.215

0.235

0.255

0.275

0.295

0.315

0.335

0.355

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Dem

and

(k

W)

Hour of the day

January

February

March

April

May

June

July

August

September

October

November

December

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Fig. 4. Daily solar radiation profile for a typical site in Bangladesh

4.2.4 Costs of components

Capital cost for solar PV system

For obtaining the capital cost of PV systems of various sizes, we have developed a generalized cost

function equation and results from the equation are applied in HOMER Model. The generalized cost

function equation (Eq. 4) are developed according to linear regression model (Eq. 3) by solving for

unknown coefficients c and d with Least Squares (LSQ) method and costs data obtained from

Grameen Shakti [38].

, 0, 0,[ ( / )cap PV PV PV PVC C c d S S

(3)

, 60.6 6.14cap PV PVC S ; {Applicable for: 20 500PVW S W } (4)

where, Ccav,PV (US$) is the capital cost of solar PV system of size SPV (W), C0,PV (US$) is the

capital cost of solar PV system of reference size, S0,PV (W) is the reference PV system size, c and d

are curve fit coefficients. The solar PV system package includes PV module, battery and other

accessories. The breaks down of capital cost for each component are as follows: 60% cost for PV

module, 25 % cost for battery, and remaining 15% cost for converter.

Operation and maintenance (O&M) costs of PV system

The solar module requires no maintenance during their lifetime of over 20 years [39] but the battery

unit needs to be replaced a number of times [40]. Batteries with various lifetimes and types are

available in the markets. Hence it will be appropriate to obtain the annual operation and

maintenance cost of battery from its lifetime maintenance cost per kWh and lifetime throughput

(kWh). We have obtained the operation and maintenance cost of battery Ca,O&M,bat (US$/y) from the

following equations:

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, & , & , /a O M bat bat O M bat batC B P t

(5)

bat bat bat dodB S f

(6)

where, batB (kWh) is the lifetime throughput of battery, & ,O M batP (US$/kWh) is the battery lifetime

maintenance cost per kWh, batt (y) is the battery life, batS (kWh) is the nominal capacity of battery,

batf is the number of charge-discharge cycles of the battery for acceptable depth of discharge (ɳdod).

Capital cost of digester gas engine (DGE) system

The capital costs of digester and gas engine have a significant effect on the overall production cost.

The capital cost of small size gas engine that are suitable for bio-gas utilization vary over the

regions. The cost function equations for gas engines in the range of 0.6-5 kW and bio-digesters in

the range of 1.6 - 77 m3 are taken from Rahman and Paatero [41] that are based on costs data

obtained by reviewing the markets price in Bangladesh. We have applied the aggregated capital

costs of gas engine and digester in HOMER as digester gas engine capital cost. The biogas

requirements against different gas engine sizes are obtained from Table 3.

Table 3

Biogas consumption rate in gas engine.

Engine power

(kW)

Average gas

consumption

(m3/d)

1 14.47

2 29.59

3 72.06

4 101.66

Source:[42]

Operation and maintenance cost of digester gas engine system

The operation and maintenance cost of the digester gas engine system has been obtained from

methodology developed by Rahman and Paatero [41].

4.3 Saving in monetary worth

Saving is the hypothetical cost that would incur if the household consumed the same energy with

conventional fuels. It is measured by the value of the conventional fuels displaced by the new fuels.

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Total annual saving are computed from the equations developed based on the methodology of

Kandpal, Joshi, and Sinha [7], and Bala and Hossain [10].

3.6 3.62.15 365

apc Lfw k bat bg w b u

fw fw k k bat dod

LL LTAS p p p V d Nh p

Q Q

(7)

where, TAS (US$/y) is the total annual saving, Lc (kWh/d) is the daily cooking load, Qfw (MJ/kg) is

the calorific value of fuelwood, ηfw is the efficiency of fuelwood for combustion by cook-stove, Pfw

(US$/kg) is the price of fuelwood, LL (kWh/d) is the daily lighting load, Qk (MJ/kg) is the calorific

value of kerosene, ɳk is the efficiency of kerosene for lighting, Pk (US$/kg) is the price of kerosene,

Lap (kWh/d) is the daily appliances load, ɳbat is the efficiency of battery, Pbat (US$/kWh) is the

battery lifecycle cost per kWh until the battery reached the maximum limit of depth of discharge

(ɳdod), Vbg (m3/d) is the daily biogas consumption, dw (kg/m

3) is fresh dung required to produce 1

m3 of biogas, N is the nitrogen available in fresh dung, hb is nitrogen retention factor, Pu (US$/kg) is

the price of urea.

Annual saving to cost ratio (SCR)

SCR is the ratio of the total annual saving from displacing the conventional fuels to the total

annualized cost for adopting modern fuels. The annual savings are calculated from the Eq. 7 and the

annualized costs are taken as the total annualized cost results from HOMER model. The SCR can

be calculated as below:

(8)

4.4. Other data applied to HOMER model

The values of physical and economic parameters which were used as input in HOMER model are

presented in Table 4 and 5.

/SCR TAS TAC

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Table 4

Parameters related to biogas digester and cooking fuels.

Parameters Symbol Values Variations

Calorific value of biogas Qb 23 MJ/m3

Density of biogas from digester - 1.2 kg/m3

Calorific value of fuelwood

(15% moisture)

Qfw 16 MJ/kg

Calorific value of kerosene Qk 43 MJ/kg

Biogas cook-stove efficiency ηbg 60%

Efficiency of kerosene for

lighting

ηk 6%

Fresh dung required to produce

1 m3 of biogas

dw 25 kg

Nitrogen available in fresh dung N 2%

Nitrogen retention factor hb 60%

Price of kerosene Pk 1.0 US$/kg

Efficiency of fuelwood for

cooking

ηfw 15%

Price of fuelwood Pfw 0.02 US$/kg 0.01-0.07 US$/kg

Price of urea Pu 0.25 US$/kg

Price of dunga Pd 0.25 US$/m

3 0.10-0.50 US$/m

3

Price of diesel - 0.75 US$/L 0.50-0.90 US$/L

Lifetime of the project tproj 20 years

Real interest rate - 5%

a Note: The price of cattle dung is expressed in US$/m3, which directly corresponds

to price of 25 kg of cattle dung.

Sources: [2,7]

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Table 5

Parameters used in the economic analysis of solar PV system

Parameters Symbol Values

Derating factor (sometimes

refer to as efficiency factor)

for PV system

- 90%

Battery efficiency ηbat 85%

Acceptable depth of discharge

of battery

ηdod 60%

Battery lifecycle maintenance

cost per kWh

Po&m,bat 0.01 US$/kWh

Lifetime of the battery tbat 5 years

Real interest rate - 5%

4.5. Household biogas plants survey

The survey data are based on a primary data collection survey on biogas plants households

undertaken by Grameen Shakti (a private organization serving renewable energy)[29]. The survey

covered 72 households from three districts of Bangladesh (i.e. 39 from Gazipur, 20 from Joipurhat,

and 13 from Naogaon). The households were selected in a way that they could be representative of

average features of households who own and operate the biogas plants from Grameen Shakti. A

structured questionnaire form, which consisted of five sections and 36 questions, were used for this

survey. The objectives of the survey were to get the users’ appraisal on acceptability and practical

applicability of biogas plants.

5. Results and Discussion

5.1. Optimized system architecture

Through HOMER simulation, we have determined the optimal configurations of the power

generating components that match with the energy demands (both electric and thermal) of three

types of households. In this simulation, biogas from cattle manure and solar radiation were

considered as the primary fuels. The optimized system components, total annualized cost, annual

electricity production, and biogas consumption for all three category households are presented in

Table 6. The presented configurations are capable to serve both heat and electricity to the

18

households at electricity price (COE) of 0.384 US$/kWh, 0.354 US$/kWh, 0.341 US$/kWh

respectively for HHC1, HHC2, and HHC3 without facing any capacity shortage. Biogas required

by each household corresponds to 30 kg, 45 kg, and 56 kg cow dung per day for HHC1, HHC2 and

HHC3 respectively. Solar PV systems were found to serve almost half of daily electricity demand

for each household category.

The thermal energy for cooking was met only by biogas thus a significant share of biogas was

consumed for cooking load. In the simulation search space, the minimum size of digester gas

engine (DGE) system was restricted to 0.6 kW that resulted over-sizes of DGE and converter. The

oversized DGE system forced the batteries to receive energy from both PV and DGE system. This

has a significant effect to increase the COE (electricity) as it causes more losses due to bidirectional

power flows in batteries and converters. Thus, availability of gas engine of size less than 0.6 kW

would cause further decrease of COE (electricity).

Table 6

System architecture for meeting energy demands for three household categories.

House-

hold

category

System architecture Total

annualized

cost

(US$/y)

Levelized

COE

(electricity)

(US$/kWh)

Electricity

production

(kWh/y)

Biogas

consumption

for thermal

energy

production

(m3/y)

Biogas

consumption

for electric

energy

production

(m3/y)

HHC1 PV 0.2 kW

Converter 0.3 kW

DGE 0.6 kW

Battery (360 Ah, 6V) 3

Gas Burner

282 0.384 PV = 333,

DGE = 461

170 279

HHC2 PV 0.2 kW

Converter 0.3 kW

DGE 0.6 kW

Battery (360 Ah, 6V) 3

Gas Burner

344 0.354 PV=333,

DGE=670

254 403

HHC3 PV 0.2 kW

Converter 0.3 kW

DGE 0.6 kW

Battery (360 Ah, 6V) 3

Gas Burner

394 0.341 PV=333,

DGE=817

339 489

19

The cost break-up for the optimized system architecture for each household category is presented in

Fig. 5. The annualized capital cost for all three household categories were found same (i.e. 152

US$/y). The only cost, which increases significantly with increases of load, is the fuel cost thus

system with higher load yields lower COE (electricity). Each household incurs almost half of their

total cost for fuel which also another reason to make the DGE system superior over diesel system.

Fig. 5. Cost components and annual savings.

5.2 Monetary saving by adopting new services

We have weighted the saving in monetary term for shifting from the conventional fuels such as

fuelwood, kerosene, and battery to new fuels solar, and biogas. The annual saving for displacing

the conventional fuels for HHC1, HHC2, HHC3 were found 312 US$/y, 381 US$/y, and 412US$/y

respectively (Fig. 5). HHC1 incurred total annualized cost 282 US$/y for new service while it

would save 312 US$/y, it means that the saving is more than the cost if HHC1 shifts it energy

service from conventional to new technologies.

20

The costs and savings are dependent on the price of feedstock materials, which varies significantly

over locations and seasons. The total annualized cost (TAC) increases almost linearly with increases

of the dung price that result decreases of saving cost ratio (SCR) (Fig. 6). For example, for HHC2,

the TAC is 234 US$/y for dung price of 0.10 US$/m3 while the TAC is 439 US$/y for dung price of

0.50 US$/m3, which yield SCR 1.63 and 0.87 respectively. This sensitivity results show that even

for higher dung cost, for example, 0.30 US$/m3, the saving cost ratio still remain more than 1 for

household category 1 and 2. The COE (electricity) is also varied significantly with the variations of

dung price. For example, COE (electricity) is 0.263 US$/kWh for dung price of 0.10 US$/m3 while

it is 0.39 US$/kWh) if dung price is set to 0.50 US$/m3 for HHC2. These results mean that the

economics of the new energy services still be better even they collect or buy dung with slightly

higher price if energy consumption level remains same.

Fig. 6. Variations of COE (electricity), SCR and TAC against various dung prices.

The variation of prices of current fuel (fuelwood) also have a significant effect on the annualized

saving and thus also on saving cost ratios. The SCR increases linearly with increases of fuelwood

prices (Fig. 7). At instance, the SCR is .0.82 for the fuel-wood price of 0.01 US$/kg and is 1.21 for

the fuel-wood price of 0.07 US$/kg for HHC3. Thus, price increasing trend of fuelwood (which

indeed are the reality) makes the PV and gas engine hybrid system even more attractive in terms of

monetary saving.

21

Fig. 7. Variation of SCR with fuel-wood prices.

5.3. Threshold dung and diesel prices to keep PV/DGE system superior over Dgen system

The optimal system type (OST) varies with the variation of dung and diesel prices. Figure 8 shows

that the Optimal System Type graphs for various values of diesel and dung price for HHC1, and

HHC3. The OST graph means, the area occupied by each option is the least cost situation with the

corresponding sensitivity variables. For example, if the biomass price is less than 0.22 US$/m3, the

PV/DGE/Battery system is superior to PV/Dgen/Battery system for whole diesel price range of 0.5-

0.90 US$/L for HHC1. As the diesel price increases on the y axis, the area where the

PV/DGE/Battery system is the least cost solution expands over larger biogas price. For HHC3,

PV/DGE/Battery system is expanded to a larger area that means this configuration is feasible over

higher diesel and dung prices. If diesel price of 0.50 US$/L is applied, PV/DGE/Battery is a better

option than PV/Dgen/Battery system up to the dung price of 0.21 US$/m3 and 0.24 US$/m

3 for

HHC1 and HHC3 respectively. This results show that these households can preferably be served by

the PV/DGE/Battery system even diesel price become as low as 0.50 US$/L.

22

(a)

(b)

Fig. 8. Threshold dung prices for gas engine system to be superior to Diesel system for (a) HHC1,

and (b) HHC3

5.4. Grid breakeven extension length

The breakeven distance for grid extension can be a crucial factor for selecting the energy supply

options. The Fig. 9 shows the break even distance for grid expansion against PV and gas engine

based off-grid system for various dung prices. This breakeven distance is the distance beyond which

the standalone system has lower NPC than that of grid expansion. The results show that each

individual households deserved to be accessed with grid service if they require very small line

length. For example, HH1 can be economically served with grid service if it requires only 0.35 km

line or less for biogas price of 0.25 US$/m3.

PV/DGE/Battery

PV/Dgen/Battery

System Types

Dung price (US$/m3)

Die

sel p

rice

(US

$/L

)

PV/DGE/Battery

PV/Dgen/Battery

Dung price (US$/m3)

Die

sel p

rice

(US

$/L

)

System Types

23

.

Fig. 9. Breakeven grid extension lengths.

5.5. Effects on COE (electricity) for changing loads

The changing of load has a significant effect on the COE (electricity), and the COE (electricity)

curves for each of the alternatives show their threshold load to be superior over others (Fig. 10).

For the assumed diesel and dung prices, the PV/DGE/Battery system has the lowest COE

(electricity) for the load range 1.6-10 kWh/d. As the load increases, the COE (electricity) for

Dgen/Battery decreases sharply than other two options, but still the PV/DGE/Battery and

PV/Dgen/Battery system remain as the better option than Dgen/Battery system. The

PV/DGE/Battery system is clearly the best option if the electrical load is 1.6 kWh/d or more.

Fig. 10. Effects on COE (electricity) for changing of loads.

24

5.6. Optimal System Type for changing loads

COE (electricity) curves changes its shape for changing both loads and dung prices and

consequently the area of optimal system type also changes. The Fig. 11 shows the Optimal System

Type graph for various values of load conditions and dung prices. The lower fuel cost allows the

PV/DGE/Battery and DGE/Battery system to be more attractive than PV/Dgen system when serving

larger loads. As the dung price increases on the y axis, the area where the PV and gas engine system

(black, and gray areas) are the least cost solution constricted to fewer areas. At an average load of 4

kWh/day, for example, the PV/DGE/Battery system would be the optimal system type for dung

price of 0.30 US$/m3. If the dung price is less than 0.21 US$/m

3, electric load more than 6 kWh/d

can even be served economically by only DGE/Battery system without PV.

Fig. 11. Optimal System Type for changing of loads and dung prices.

5.7 Appraisal from ongoing biogas plants (by practical case survey)

To validate the practical applicability of the biogas, we have investigated this matter through

practical case findings from a questionnaire survey of 72 biogas plant households. The household’s

annual incomes were found varied depending on many factors such as assets owned by the

households, livelihood of the household members, and ownership of livestock. More than 70% of

surveyed households have an annual income more than 1600 US$/y, and 21% have an annual

income between 801 US$ and 1600 US$. About half (48%) of the households installed biogas

plants to serve their cooking energy needs and relieve from problems relating to collecting and

burning of biomass. About 60% households maintained that they have achieved time saving by

installing biogas plant, and household members could do other works when they cook foods and

could spend more time with members of the family or guests. They also advocated that use of

PV/Dgen/Battery

PV/DGE/Battery

Electric load (kWh/d)

Du

ng

pri

ce

(US

$/m

3)

System Types

DGE/Battery

25

biogas increased the spare time for children. Around 50% of the households observed that the

biogas brought positive impact on their health by creating pollution free environment in the kitchen

and relieved their members from health issues like inhalation, skin diseases, fire accidents etc. and

improved their food habits and reduced health-care costs.

(a)

(b)

Fig. 12. Percent of household’s opinions on (a) gas sufficiency, and (b) satisfaction on services.

About 55% of the households expressed that the biogas amount is sufficient to meet their cooking

needs. Some households (27%) even maintained that the biogas amount is surplus to meet their

demands (Fig.12 (a)).

The causes for the insufficiency of biogases ( 5% of HHs) were also investigated and the main

reasons were:

a) Household had sold their cattle,

b) Cattle had died, and

c) Number of family members increased thus demand increased.

The households were also asked about their satisfaction on the biogas plant services. Majority of

the households (i.e. 73%) were found satisfied with the biogas plant (Fig. 12(b)). The main issues

those made some households (25%) unsatisfied are:

a) Lack of guarantees,

b) Lack of maintenance supports,

c) Jamming in the piping networks,

26

d) Inconvenience in handling of slurry, and

e) Lack of marketing facilities for manure.

The survey also found that studied household possessed at an average 4.5 cattle per household.

6. Conclusions

Conventional cooking and lighting facilities are inefficient and cause much harm to the public

health, particularly to women and children. Therefore, provision of clean cooking facilities and

electricity, the two basic energy services, must be ensured for billions of people who still lack them.

Interestingly, abundant amount of renewable resources such as biomass and solar radiation are

available across the rural areas where these deprived population are dwelling.

We have proposed hybrid systems of biogas and solar resources to serve both cooking (thermal) and

electric loads as to replace the conventional facilities. We then determine the techno economic

competence of hybrid systems of these two resources. A hybrid system of 0.2 kW PV, 0.6 kW

DGE (Digester Gas Engine), 0.3 kW Converter, 3 Batteries (360 Ah, 6 V), and a Gas burner can

potentially serve the households cooking and electrical loads if the households possess at least 3

cattle per household. This result is impressive because more than 2 million households in

Bangladesh keep 3 cattle per household.

The households can achieve saving of monetary worth 309 US$, 381 US$ and 412 US$ per year

against annualized costs 282 US$, 344 US$ and 394 US$ for adopting new technologies for HHC1,

HHC2, and HHC3 respectively. This means that households do not incur extra costs if they shift

from traditional to new technologies while they are benefitted by many folds improved services.

The costs and savings are dependent on the price of feedstock materials. The saving to cost ratio

remains at 1.63 for dung price of 0.10 US$/m3, while it is 1.04 for dung price of 0.30 US$/m

3 for

household category 2. The COE (electricity) is 0.263 US$/kWh for dung price 0.10 US$/m3,

whereas it is 0.36 US$/kWh if dung price was set to 0.30 US$/m3. These means that the economics

of new technologies will still be better even the households collect or buy the materials with slightly

higher prices. The PV/Gas engine system proves superior to the grid and diesel system for a large

extent of diesel prices i.e. 0.5-0.90 US$/L for HHC1. The survey results from ongoing biogas plants

suggest that biogas technology users recognize many benefits and its application already gets

27

acceptance by most of the users. Almost all households are satisfied with the biogas plants if some

supportive measures were taken by the biogas plant providers.

The limitation of these results is that, those households who do not have livestock, cannot meet

their cooking demand because solar PV alone is practically unable to serve cooking loads. This

research shows that hybrid solar PV and biogas engine system can be potentially adopted by the

households, which have cattle resources and situated beyond the economic grid extension distances.

Successful implementation of few cases can explore hybrid use of these two resources and expedite

their dissemination.

Acknowledgement

The authors are grateful to Fortum Foundation, and Aalto University School of Engineering

Doctoral Apprenticeship Program for providing scholarship support to carry out this research. The

authors are also grateful to Grameen Shakti, Bangladesh for providing biogas plant survey database-

2011 to use in this paper.

References

[1] IEA. Energy Poverty: How to make modern energy access universal. Paris, France: International Energy

Agency; 2010.

[2] Asaduzzaman M, Barnes DF, Khandker SR. Restoring Balance: Bangladesh’s Rural Energy

Realities. Washington, DC, USA: World Bank; 2010.

[3] K Hossain A, Badr O. Prospects of renewable energy utilisation for electricity generation in

Bangladesh. Renewable and Sustainable Energy Reviews 2007;11:1617–49.

[4] IDCOL. Final survey report on biogas plants installed in Bangladesh. Dhaka, Bangladesh:

Infrastructure Development Company Ltd (IDCOL); 2005.

[5] Bhat PR, Chanakya HN, Ravindranath NH. Biogas plant dissemination: success story of Sirsi, India. Energy for

Sustainable Development 2001;5:39–46.

[6] Biswas WK, Lucas NJD. Economic viability of biogas technology in a Bangladesh village.

Energy 1997;22:763–70.

[7] Kandpal TC, Joshi B, Sinha CS. Economics of family sized biogas plants in India. Energy

Conversion and Management 1991;32:101–13.

[8] Katuwal H, Bohara AK. Biogas: A promising renewable technology and its impact on rural

households in Nepal. Renewable and Sustainable Energy Reviews 2009;13:2668–74.

[9] Ghimire PC. SNV supported domestic biogas programmes in Asia and Africa. Renewable Energy 2013;49:90-

94.

[10] Bala BK, Hossain MM. Economics of biogas digesters in Bangladesh. Energy 1992;17:939–44.

[11] Biswas WK. Application of renewable energy to provide safe water from deep tubewells in rural Bangladesh.

Energy for Sustainable Development 2011;15:55–60.

[12] Bond T, Templeton MR. History and future of domestic biogas plants in the developing world. Energy for

Sustainable Development 2011;15:347–54.

[13] Srinivasan S. Positive externalities of domestic biogas initiatives: Implications for financing.

Renewable and Sustainable Energy Reviews 2008;12:1476–84.

28

[14] Gwavuya SG, Abele S, Barfuss I, Zeller M, Müller J. Household energy economics in rural

Ethiopia: A cost-benefit analysis of biogas energy. Renewable Energy 2012;48:202–9.

[15] Urmee T, Harries D. Determinants of the success and sustainability of Bangladesh’s SHS program. Renewable

Energy 2011;36:2822–30.

[16] Asif M, Barua D. Salient features of the Grameen Shakti renewable energy program. Renewable and Sustainable

Energy Reviews 2011;15:5063–7.

[17] Mohammad Ziaur R. Multitude of progress and unmediated problems of solar PV in Bangladesh. Renewable and

Sustainable Energy Reviews 2012;16:466–73.

[18] Singh KJ, Sooch SS. Comparative study of economics of different models of family size biogas plants for state

of Punjab, India. Energy Conversion and Management 2004;45:1329– 41.

[19] Al-Bustam H, Mahbub MZ, Shahriar MMS, Rahman MS, Hossain MI. Solar Energy Home System for the rural

areas of Bangladesh. International Journal of Engineering Research and Applications 2012;Vol. 2:614–7.

[20] Rahman MM, Paatero JV, Lahdelma R. Evaluation of choices for sustainable rural electrification in developing

countries: A multicriteria approach. Energy Policy 2013;59C:588–98.

[21] IEA. World energy outlook 2006: Focus on key topics. Paris, France: International Energy Agency; 2006.

[22] Bernal-Agustín JL, Dufo-López R. Simulation and optimization of stand-alone hybrid renewable energy

systems. Renewable and Sustainable Energy Reviews 2009;13:2111–8.

[23] Liu SQ, Lu XF, Liu RM, Zeng WH, He MC. The application of the Bp neutral network in the

estimation of non-point source pollution. Procedia Environmental Sciences 2012;13:263–73.

[24] Subhes C. B. Review of alternative methodologies for analysing off-grid electricity supply.

Renewable and Sustainable Energy Reviews 2012;16:677–94.

[25] HOMER. Hybrid Optimization Model for Electric Renewables. CO,USA: HOMER Enertgy, LLC; 2012.

[26] HYBRID2. The hybrid power system simulation model. MA, USA: Renewable Energy Research Laboratory

(RERL); 2012.

[27] HOGA. Hybrid Renewable Optimization by Genetic Algorithms. Spain: University of Zaragoza; 2012.

[28] Lambert T, Gilman P, Lilienthal P. Micropower System Modeling with Homer. In: Farret FA, Simoes MG,

editors. Integration of Alternative Sources of Energy, John Wiley & Sons, Inc.; 2005, p. 379–418.

[29] Grameen Shakti. Biogas plant survey report-2011. Dhaka, Bangladesh: Grameen Shakti; 2011.

[30] Miah MD, Kabir RRMS, Koike M, Akther S, Yong Shin M. Rural household energy consumption pattern in the

disregarded villages of Bangladesh. Energy Policy 2010;38:997–1003.

[31] World Bank. Expenditure of Low-Income Households on Energy. Washington, DC, USA: World Bank; 2010.

[32] Barnes DF, Khandker SR, Samad HA. Energy poverty in rural Bangladesh. Energy Policy

2011;39:894–904.

[33] Miah MD, Al Rashid H, Shin MY. Wood fuel use in the traditional cooking stoves in the rural floodplain areas

of Bangladesh: A socio-environmental perspective. Biomass and Bioenergy 2009;33:70–8.

[34] Bala B, Siddique SA. Optimal design of a PV-diesel hybrid system for electrification of an isolated island--

Sandwip in Bangladesh using genetic algorithm. Energy for Sustainable Development 2009;13:137–42.

[35] REB. Load Information System. Dhaka, Bangladesh: System Operation Management, Rural Electrification

Board; 2010.

[36] NASA. Surface meteorology and Solar Energy: A renewable energy resource website. available at

http://eosweb.larc.nasa.gov/sse/ [Accessed 02 November, 2012]

[37] Mondal AH, Denich M. Hybrid systems for decentralized power generation in Bangladesh. Energy for

Sustainable Development 2010;14:48–55.

[38] Grameen Shakti. Price list of solar home system. Dhaka-Bangladesh: Grameen Shakti; 2012

[39] Schleicher-Tappeser R. How renewables will change electricity markets in the next five years. Energy Policy

2012;48:64–75.

[40] Givler T, Lilienthal P. Using HOMER Software to Explore the Role of Gen-sets in Small Solar Power Systems:

Case study-Sri Lanka. Washington, DC, USA: National Renewable Energy Laboratory; 2005.

[41] Rahman MM, Paatero JV. A methodological approach for assessing potential of sustainable agricultural

residues for electricity generation: South Asian perspective. Biomass and Bioenergy 2012;47:153–63.

[42] Farret FA, Simões MG. Integration of Alternative Sources of Energy. John Wiley & Sons; 2006.