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Geographical distribution of agricultural residues andoptimum sites of biomass based power plant in Bathinda,Punjab

Jagtar Singh a,*, B.S. Panesar b, S.K. Sharma c

aMechanical Engineering Department, SLIET, Longowal, District Sangrur, Punjab 148106, Indiab SCS Engineers, 11260 Roger Bacon Drive, #300, VA 20190, USAcMechanical Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

a r t i c l e i n f o

Article history:

Received 4 January 2008

Received in revised form

4 September 2011

Accepted 5 September 2011

Available online 22 September 2011

Keywords:

Geographical information systems

Mathematical model

Residues

Optimum location

Power plants and collection centres

etc

* Corresponding author. Tel.: þ91 9781122101E-mail address: j_s_ranu@rediffmail.com

0961-9534/$ e see front matter ª 2011 Elsevdoi:10.1016/j.biombioe.2011.09.004

a b s t r a c t

Agricultural residues are spatially scattered in Punjab. The spatial distribution and asso-

ciated costs on collection and transportation of this resource are the critical factor in

planning the biomass based power plants. This paper presents a case study of Bathinda

district of Punjab, using geographical information system to evaluate the feasibility of

setting up biomass based power plants and optimizing their location. Mathematical

models were developed to determine the storage and handling cost of agricultural residue.

Geographical information system and non-linear optimization techniques were employed

to locate appropriate sites and sizes of power plants. It was found that two power plants

and their two collection centres are financially feasible. Power generation capacity is

20 MW (e). The fuel procurement area was in the range of less than 20 km.

ª 2011 Elsevier Ltd. All rights reserved.

1. Introduction biomass power plant in a given region. It is a powerful tool to

Punjab state has large potential of agricultural biomass as

a resource for energy and is estimated as 231 TJ, Biomass

energy contributes about 15e20% of primary energy need in

Punjab [1]. As the availability this resource is spatially scat-

tered, the supply of agricultural residue to power plants can be

made secure by installing collection centres, where biomass is

to be collected, compacted and stored for future use in the

power plant. Optimizing the locations of power plants can

reduce transportation cost. Spatial information technologies,

particularly geographic information systems (GIS) can be

highly helpful in evaluating the feasibility of setting up new

; fax: þ91 1672 280057.(J. Singh).ier Ltd. All rights reserved

integrate data of various factors and to perform spatial anal-

yses for feasibility evaluation and location optimization [2].

The first GIS based decision support system in biomass energy

sector was introduced as BRAVO [3]. A decision support

system (DSS) for forest biomass exploitation for energy

production was introduced by Freppaz D et al [4]. Ma Jianguo

et al. [5] also use GIS analysis with an aims at finding the

optimal power plant locations and minimizes the trans-

portation cost. The location of appropriate site and size of

power plant can be identified, if the area based fuel availability

density is known. Some earlier studies reported the develop-

ment of computer programs and GIS data to identify the

.

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 4 5 5e4 4 6 04456

proper locations of power generation based on the

geographical availability of biomass fuel, and other energy

related parameters [6e8]. Collection and transportation of

biomass to biomass based power plants is crucial component,

which is being handled in the previous study [9].

By following the first part of this study [1,9], this paper

presents the geographical distribution of crop residue in

Bathinda district of an Indian state Punjab. The optimization

program was developed to locate the suitable sites for the

biomass based power plants and their collection centres.

2. Material and methods

Themethodology adopted for conducting the present study as

given below:

2.1. Geographical distribution of crop residue

The assessment of the resource base of agricultural residues

(i.e. the total amount of generated annually) is usually carried

out on the basis of information on crop residue index and the

total crop residue produced. The spatial distribution of this

resource has been evaluated in the study area on the basis of

recent survey of crop residue [10,11]. Spatial availability of

unused residues comprises area, yield, production of crops

and GIS software. These components have been integrated in

a spreadsheet. The spatial model has been implemented on

computer using ARC/INFO and spreadsheets [14]. The avail-

ability of unused residue in each block of Bathinda district was

determined. Maps of residue intensities for the study year are

developed. The SML (Simple Micro Language) programs used

to develop the maps of residue intensity potential.

2.2. Cost of biomass fuel

Handling of agricultural residue for power generation is an

integration of various operations namely: collection of residue

at field, preconditioning and storage at collection centres,

transportation from field to collection centre and from

collection centres to power plants. These operations are per-

formed manually as well as by machines. The costs involved

in these operations are fixed costs, operating costs and vari-

able costs. Here operating costs are the part of variable costs.

Fixed costs are the depreciation and interest on investment

and costs for housing, insurance, taxes etc. Operating costs

include wages of the operator, labour costs for loading,

unloading and stacking, cost of fuels, lubricants and mainte-

nance etc. In the present study, variable costs include the cost

of preconversion of residue, storage and transportation cost of

residue etc. The mathematical modelling is helpful to solve

real world problems. Mathematical model has to be developed

for appropriate location of collection centres and power

plants. This model is being developed on the basis of the

comprehensive model for collection analysis, location anal-

ysis of collection centres, cost analysis such as fixed cost,

variable cost and transport cost. Some assumptions have been

considered for development of a model as follows:

Number of collection centres ‘n’, and amount of residue

collected (Qci) in dry form should always be greater than zero.

Demand for biomass at power plants should be less than the

supply of biomass from all the collection centres. The dry

biomass moved from various collection centres to various

power plants should be less than the biomass collected at

each collection centre. The briquetted residue is only being

transported to power plants. All the costs are given in United

States Dollars for the year 2007, when exchange rate was, 100

INR ¼ 2.5394 USD. Residue would be transported from

collection centre to power plants by trucks only. It has also

assumed that the sum of the radius of two adjacent collection

centres should be greater than the distance between these

two collection centres.

2.2.1. Model for storage and handling cost of residueAs discussed earlier, various costs are associated with storage

and handling cost of agricultural residue such as fixed cost,

variable cost, straw collection cost, transportation cost etc.

2.2.1.1. Fixed cost. Fixed cost is not depending on amount of

residue managed at the collection centre. The cost of fencing,

gates, weighing facility, watch and ward including watch

tower, water supply, fire fighting, machinery shed, workshop,

and land for building, offices, etc. are considered as fixed

costs. Eq. (2.1), used to evaluate fixed cost [11].

FCCSi ¼ ½LCF þ GCF þWPCF þ FCF þ FCOB þWTCF

þWSCF þ FFCF þMSCF þ PCF� (2:1)

Where LCF is fixed land cost at collection centre, GCF is fixed

cost for gates, WPCF is the cost of weighing facility, FCF is the

cost for fencing at each collection centre, FCOB is the fixed cost

for office buildings, WTCF is cost for watchtowers at each

collection centre, WSCF is cost for water supply, FFCF cost for

fire fighting,MSCF is cost formachinery shed, PCF, fixed cost for

human resource.

2.2.1.2. Variable cost. Variable cost is directly associate with

the amount of residue handled at collection centre. The pre-

conversion process has to be applied for conversion of losses

biomass into compact for on each collection centre. It is useful

for reducing the storage and transportation cost. The pre-

conversion process can be conducted with two types of tech-

nology i.e. Briquetting or Baling. In this study only briquetted

biomass is to be considered. Briquetting cost dependsupon the

energy required for machinery, labour cost and repair &

maintenance cost of the machinery used for the purpose.

BriquettingCost¼½EnergycostþLabourcost

þRepairandMaintenancecost�

BRC¼�ðPBR�UP�TOBR�NBRÞþðWL�NL�TOBR�NBRÞþðRMTCÞ

�

(2.2)

Where BRC is briquetting cost in $ t�1, PBR is power required

to briquettingmachine (kwh),UP is unit cost of power $ kwh�1,

TOBR is total operating time of briquetting machine in hours

year�1, NBR is the number of machines required, WL is wages

rate of labour in $ hour�1, NL is number of labour persons are

required. RMTC is the repair and maintenance cost ($ t�1) is to

be calculated on the basis of life of machine, depreciation cost

and rate of interest on investment [12].

Fig. 1 e Spatial distribution of unused biomass potential in

Bathinda.

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 4 5 5e4 4 6 0 4457

2.2.1.3. Straw collection cost. Straw collection cost is evalu-

ated on the basis of the earlier studied [1,13] and same is

considered herewith as follows:

SSCi ¼ 23CctQci �

ffiffiffiffiffiffiffiffiffiffiffiffiqc

p� r

r(2.3)

Where SSCi is the cost of residue transport within the

catchments area of ith centre, Cct is the transport cost from

farmer’s field to collection centre, $ t�1km�1, qc is capacity of

the carrier in ton [average value of qc ¼ 5 tonne]; r is the spatial

density of residue availability in t ha�1.

The total cost for handling and storage of biomass is to be

based on “n” number of collection centres and “p” number of

power plants. It can be obtained by integrating the different

cost components (Eq. (2.1)e(2.3)) as given below.

ACT ¼

�FCCSi � nþ ðAVCi þ SSCiÞ �

Pni¼1 Qci þ TTC

�Pn

i¼1 Qci(2.4)

Table 1 e Residue storage and handling cost for 20 MW (e),30combination in US$ tL1.

Collectioncentre v/sPower plant

Onecollectioncentre

Twocollectioncentre

Two Power

plant (20 MW)

26.89 26.50

Three Power

plant (30 MW)

e 26.68

Four Power

plant (40 MW)

e e

Note: Capacity of each power plant 10 MW (e).

Where ACT is the total cost of handling and storage at “n”

collection centres and supply of this biomass to “p” number of

powerplants.AVCi is the annual variable cost, SSCi is the straw

collection cost in the field, TTC is the total transport cost from

collectioncentres topowerplants [11], FCCSi thefixedcost forn

collection centres. AVCi can be obtained by integrating the

price of biomass given to the farmers, collection cost, unit

briquetting cost, variable land cost for storage of biomass.

2.3. Optimum location of biomass based power plant

In order to apply the geographical information system in real

practice, the digitized map is used to pinpoint suitable loca-

tions for power plants. It is assumed that the crop residue is

uniformly distributed in each block of study area. The crop

residue procurement area is assigned to be a circle having the

power plant at its centre. The centre of the circles moves with

in the Bathinda district. The developed program calculates the

area based fuel availability density and suggests the optimum

size of power plant. By including the collection cost in the

field, transportation cost, conversion cost and optimum

collection centre radius, the optimum location and minimum

storage and handling cost of crop residue are obtainable.

The calculation of proper location of power plant and

storage, handling cost is basically an iteration process. At the

initial stage, radius of collection centre, number of power

plants, number of collection centres and their x-y coordinates

are given to computer programming of non-linear optimiza-

tion (Nelder Mead)method [15,16]. This program is interlinked

with ARC/INFO. This program gives the optimum location of

biomass-based power plant, radius of collection centres, with

minimum storage and handling cost of residue.

3. Results and discussion

3.1. Geographical distribution of residue in Bathinda

Bathinda district is situated in southern parts of Punjab state,

in the heart of malwa region. The total area of this district is

3401 km2. It is situated between 29o-330 & 30o-360 longitudesand 74o-380 & 75o-460 latitudes. It has seven blocks namely;

Sangat, Talwandi Saboo, Phool, Rampuraphool, Bathinda,

Maur and Nathana. As per study the availability of unused

MW (e) and 40 MW (e) power generation for various

Threecollectioncentre

Fourcollectioncentre

Fivecollectioncentre

26.62 26.71 e

26.63 26.75 e

27.38 26.83 26.90

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 4 5 5e4 4 6 04458

agricultural biomass is 0.62 Mt year�1. The maximum power

generationcapacity fromthis biomass is approximately 40MW

(e). Annual biomass requirement for biomass power plants

calculatedon thebasis of averageheatingvalueof biomassand

overall efficiencyof the system[1,17]. It is assumedthatplant is

to be working for 24 h per day for 300 days per year at full load.

The biomass intensity in this district varies from 0.90 to

3.00 t ha�1. Average availability of biomass in district has been

considered by taking a variety of crops grown in the area. It has

been further suggested that biomass will be collected and

stored in the specific collection centres and used as per the

requirement of the power plant. This will help in supply of

biomass for production of electricity throughout the year. The

available GISmap of the state has been converged for Bathinda

district only by ARC-INFO programming tools [11].

Fig. 1 shows spatial distribution of unused residue in

Bathinda district. The analysis of the data presented in this

figure shows that area under low range is block Sangat, area

under semi medium range in Talwandi Saboo, Bathinda

blocks; medium range have Rampura, Nathana, Maur blocks

and high range have only one block Phool. The analysis has

been done for three systems for generating power 20 MW (e),

30 MW (e) and 40 MW (e) and also compares the residue

storage and handling cost. Assume that the capacity of each

power plant should be 10 MW (e).

Fig. 3 e Optimum location of three power plants and three

collection centres.

3.2. Optimum analysis

The detailed analysis of power generation capacity of 20 MW

(e), 30 MW (e) and 40 MW (e) with different combinations of

collection centres has been done. The storage and handling

cost of residue has been reported in Table 1.

Fig. 2 e Optimum location of two power plants and two

collection centres.

It is observed that the minimum storage and handling cost

of residue for generation of 20 MW (e) (System I) is 26.50 $ t�1

and the optimum location of two power plants and their

collection centres are shown in Fig. 2.

Fig. 4 e Optimum location of four power plants and four

collection centres.

Table 2 e Optimum cost of agricultural residue for generation of 20 MW (e) in Bathinda district in US$ tL1.

Collection centre v/s Powerplant

Onecollectioncentre

Twocollectioncentre

Threecollectioncentre

Fourcollectioncentre

Fivecollectioncentre

One Power plant

(each of capacity 20 MW)

e 26.81 26.70 26.60 26.78

Two Power plant

(each of capacity 10 MW)

26.74 26.50 26.62 26.71 e

Four Power plant

(each of capacity 05 MW)

e 26.76 26.70 26.86 e

Five Power plant

(each of capacity 04 MW)

e e 26.85 26.78 26.90

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 4 5 5e4 4 6 0 4459

In system II (generation of 30 MW (e)) the minimum cost of

residue storage and handling was observed 26.63 $ t�1 and

optimum locations of three power plants and their collection

centres are shown in Fig. 3.

In system III (generation of 40 MW (e)), the minimum cost

of residue storage and handling was observed 26.83 $ t�1 and

optimum location of four power plants and their collection

centres are shown in Fig. 4.

After comparison of all the three systems, it is observed

that the minimum cost of residue storage and handling in

System I having two power plants (each of capacity 10 MW)

and two-collection centres. The optimum location of collec-

tion centres I and II are (7.55, 5.2) and (7.51, 4.03) in GIS units

respectively. Collection centre I cover maximum area of Phool

and Nathana block, small area of Rampura and Bathinda

block. Collection Center II covers maximum area of Maur,

Talwandi Saboo block and small area of Rampura, Nathana,

Sangat and Bathinda blocks. Optimum location of Power Plant

I overlap on collection centre I and lying near the boarder of

Nathana and Phool block. Power plant II overlaps on collection

centre II and lying in Maur block.

3.3. Optimum location of power plants and collectioncentres

In second part of optimization, emphasis has been laid on the

number of power plants and their capacity for economical

generation of 20 MW (e). Four different combinations were

studied viz: One power plant (capacity 20 MW (e)), two power

plants (each of capacity 10 MW (e)), four power plants (each of

capacity 5 MW(e)) and five power plants (each of capacity

4MW (e)). The results of all the four subsystems for generation

of 20 MW (e) power, has been shown in Table 2 with the

variation of number of power plants and collection centres.

Minimum storage and handling cost of biomass in each

combination such as one, two, four and five number of power

plants were 26.60 $ t�1, 26.50 $ t�1, 26.70 $ t�1, and 26.78 $ t�1

respectively. It is observed that theminimum cost from all the

four systems, 26.50 $ t�1. The most efficient and economical

system for generation of 20 MW (e) was observed two collec-

tion centres and two power plants. The optimumsizes (radius)

of the collection centres observed 15.55 and 13.51 km

respectively. This system is an optimum system. The costs in

case of IIIrd and IVth systems are high, because in these two

cases, transportation cost as well as fixed costs is more for the

same amount of biomass handling.

4. Conclusion

Although the agricultural residues seem to be plentiful in

agricultural based states in India, it still needs careful study

for power generation projects. A case study has been

presented to utilize geographical information system in

planning biomass based power plants and demonstrating

a procedure to optimize the location of power plants and

collection centres to minimize the storage and handling cost

of residue. In the present study, emphasis has been laid on the

number of power plants and their capacity for economical

generation of 20 MW (e). Four different combinations were

studied. The most efficient and economical system was

observed of a power generation capacity 20 MW (e) with the

combination of two collection centres and two power plants

(each of capacity 10 MW (e). The optimum sizes (radius) of the

collection centres observed 15.55 and 13.51 km respectively.

Acknowledgements

The authors are express their gratitude to Director, School of

Energy Studies for Agriculture, Punjab Agricultural University

(PAU) Ludhiana, Punjab, for their cooperation, guidance and

facilities provided for completion of this work.

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