Integration of Renewables in the power sector

Integration of Renewables in the power sector

Rangan Banerjee

Dept of Energy Science and Engineering

Seminar at Dept of Electrical Engineering,Cambridge University , UK, May 29,2009

Overview of talk

Indian Context

What is the status and potential for renewables in the power sector?

How can we plan future power systems with high renewable shares?

India and UK (2005 Statistics)

Source: IEA

Population 1095 million 60 million

GDP (PPP) 3362 Billion US$ 1627 Billion US$

Primary Energy 22.4 EJ 9.8 EJ

Energy/person 20.5 GJ/person/year 163.3 GJ/person/year

Electricity/person

480 kWh/capita/year 6254kWh/capita/year

CO2 emissions

Per person

Per GDP

1147 Million tonnes 530 Million tonnes

1.05 tonnes /capita/year 8.8tonnes /capita/year

0.31 kg /US$ 0.34 kg /US$

Power Generation Installed Capacity

Coal

52%

Oil

1%

Gas

10%

Hydro

25%

Nuclear

3%

Renew able

9%

As on 31st March, 2009.

Total installed capacity = 149 GW

Source: Ministry of Power

Power Generation Options

Power Generation

Centralised Grid Connected

Cogeneration/Trigeneration

Decentralised Distributed Generation

Isolated

Demand Side Management (Solar Water Heater, Passive

Solar)

Need for Alternatives

Fossil fuel reserves limited

India - 17% of World population, 4% of primary energy

Present pattern – predominantly fossil based (87% comm, 64% total)

52% of households unelectrified

Linkage between energy services and quality of life

Renewable Energy Options

Wind

Solar Small

Hydro Biomass

Tidal

Energy

Wave Energy

Ocean Thermal

Energy

Solar Thermal

Solar

Photovoltaic

Geothermal*

Summary of Power Generation Technologies Using Renewable Energy

Technology

Status of

Technology

Largest

Installation

Capaci

ty

Factor

Cost of

Generati

on

(Rs./kWh)

CDM Projects Applications

No. of

project

s

MW Estimate of

GHG

abatement

in million

tCO2 eq.

Geothermal D 50 25%

OTEC D 1 MWa -

Wave P 150 kW -

Tide P 3.6 MWb 17%

Wind C 400 MW 14% 2.0-4.4 42 1186 20.5

Small Hydro C 25 MW 40% 1.0-4.5 37 317 8.4

Solar PV C 239 kW 15% 18.0-50.0 - - -

D - Demonstration

P - Prototype

C – Commerciala - Not operational till dateb – being planned

Renewable Installed Capacity trend

0

2000

4000

6000

8000

10000

12000

1992 1994 1996 1998 2000 2002 2004 2006 2008

Year

Insta

lled

Cap

acit

y

Estimated Renewable Generation in India

0

5000

10000

15000

20000

25000

2001 2002 2003 2004 2005 2006 2007

Year

An

nu

al G

en

era

tio

n

(M

iliio

n K

Wh

)

Renewable Share in Power

Perspective Plan (MNRE)

Source: 11th Plan proposal MNRE Govt of India

Renewable installed capacity and generation

Installed

Capacity*

(MW)

Estimated

Capacity factor

Estimated

Generation

(GWh)

Wind 7845 14% 9621

Biomass Power 606 70% 3185

Biomass Gasifier 86 60% 527

Bagasse

Cogeneration

720 60% 3784

Small Hydro 2046 40% 7169

Waste to Energy 55 50% 241

Solar PV 2.74 20% 5

Total 11360 25% 24380

*as on Jan 1, 2008

Geothermal/OTEC/Tidal/Wave

World Cost Estimates

Geothermal COMMERCIAL 8240 MW 4c/kWh

$2000/kW

No Indian experience

50 MW plant J & K planned

Tidal PROTOTYPE 240 MW

FRANCE

LF 20%

No Indian experience (3.6MW planned Sunderbans)

OTEC PROTOTYPE 50 kW

210 kW

NELHA

India 1MW gross plant attempted

Wave Energy

PROTOTYPE < 1MW

Grid Connected

India 150kW plant Thiruvananthpuram

OTEC plant schematic

Mooring Arrangement

Wave Energy

Source: Sukhatme

Wind Power

7800 MW installed

Single machine upto 2.1 MW

Average capacity factor 14%

Capital cost Rs 4-5crores/MW, Rs 2-3/kWh (cost effective if site CF >20%)

India 45000 /13000 MW potential estimated

32%/ year (5 year growth rate)

0

5

10

15

20

25

30

35

40

1991 1993 1995 1997 1999 2001 2003

An

nu

al L

oa

d F

ac

to

r (

%)

Satara, Maharashtra

0

200

400

600

800

1000

1200

1400

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug Se

pO

ctNov

Dec

Month of year

Actu

al

Mo

nth

ly G

en

era

tio

n

(*1

00

0k

Wh

)

0

2

4

6

8

10

12

14

Av

era

ge W

ind

Sp

eed

(m

/s)

Monthly GenerationWind Speed

Variation in Wind Output

Small Hydro Power

Classification - Capacity

-Micro less than 100 kW

Mini 100 kW - 3 MWSmall 3 MW - 15 MW

Micro and Mini - usually isolated,

Small grid connected

Heads as low as 3 m viable

Capital Cost Rs 5-6crores/MW ,

Rs 1.50-2.50/kWh

1846 MW (7%/year)

200 kW Chizami village, Nagaland

Aleo (3MW) Himachal Pradesh

BIOMASS

THERMOCHEMICAL BIOCHEMICAL

COMBUSTION GASIFICATION PYROLYSIS

RANKINE CYCLE

PRODUCER GAS

ATMOSPHERIC PRESSURISED

FERMENTATIONDIGESTION

BIOGAS ETHANOL

Duel Fuel SIPGEGas Turbines

Biomass Conversion Routes

Biomass Power

Higher Capacity factors than other renewables

Fuelwood, agricultural residues, animal waste

Atmospheric gasification with dual fuel engine -

500 kW gasifier - largest installation

Combustion – 5-7.5 MW

Rs 2.50-4/kWh

Kaganti Power Ltd. Raichur Distt. A.P. 7.5 MW

100 kWe Pfutseromi village, Nagaland

Biogas

45-70% CH4 rest CO2

Calorific value 16-25MJ/m3

Digestor- well containing animal waste slurry

Dome - floats on slurry- acts as gas holder

Spent Slurry -sludge- fertiliser

Anaerobic Digestion- bacterial action

Family size plants 2m3/day

Community Size plants 12- 150 m3/day

Rs 12-14000 for a 2m3 unit

Cooking, Electricity, running engine

Pura, Karnataka

Bagasse Cogeneration

Incremental Capital Cost

(Rs/kW)

30000

Life 20 years

Boiler Efficiency 70%

Bagasse NCV = 3400 kcal/kg (dry basis), Price Rs 1.50/kg

Discount rate = 10%, O&M cost = Rs 0.5/kWh

2500 tcd plant 9.5 MW export, 0.93 kg extra/ kWh

Load factor 0.4 0.5 0.6

Rs/kWh 2.60 2.40 2.27

0.5T/hr

Feed water

Process

Process

2 ata

~

STEAM

TURBINE

2.5 MW

6 ata

BAGASSE

58 T/hr 22 ata

330o C

4.5T/hr 27T/hr

26T/hr

Schematic of typical 2500 tcd Sugar factory

Flashed

Condensate

PRDS

PRDS

MILLING

0.5T/hr

FEED

WATER

BOILER

Feed water

Con

dens

er

2 ata

PROCESS

75 TPH, 65

ata, 480O

C

Process

Process

4.5 TPH

~

6 ata

BAGASSE (Alternate fuel)

2 ata

BFP

13 MW

BOILER

1.0 MW

Mill

drives

9.5 MW

Power export

2.5 MW

Captive

load

PROCESS

PROPOSED PLANT CONFIGURATION: OPTION 2

STEAM

TURBINE

CONDENSER

ESS

Solar PV

India -2740 kW Grid connected systems

(25-239 kW)

Array efficiency in field 12-15%

Cost Rs 26cr/MW

Rs 15-20 /kWh

Vidyut Saudha Building, 100 kWp , APTRANSCO

(2001) BHEL

Mousuni Island , 105 kWp, West Bengal

Renewable Energy Agency (2003 )

Solar Photovoltaic Power Plant

www.mnes.nic.in

Map of India showing geothermal provinces

Annual Insolation

kWh/m2/year

kWh/m2/year

kWh/m2/year

kWh/m2/year

Area for Power Generation

India’s present electricity

requirement approx. 500

billion kWh, can be met by

installing 2500 sq. km of

solar field.

A square of 50km x 50km,

or

4 smaller squares of 25km

x 25km.

Trend of wind energy development in India

0

1000

2000

3000

4000

5000

6000

7000

Till

03/9

2

1992-

93

1993-

94

1994-

95

1995-

96

1996-

97

1997-

98

1998-

99

1999-

2000

2000-

01

2001-

02

2002-

03

2003-

04

2004-

05

2005-

06

Till

09/0

6

Year

Inst

all

ed

Ca

pa

cit

y (

MW

)

Generation from wind

0

500

1000

1500

2000

2500

3000

3500

Till 0

3/92

1992-93

1993-94

1994-95

1995-96

1996-97

1997-98

1998-99

1999-2000

2000-01

2001-02

2002-03

2003-04

Year

Po

wer

Gen

era

ted

An

nu

aly

(G

Wh

)..

Diffusion curve

L wind potential estimate.

P cumulative installed capacity

dP/dt increment in installed capacity

)( PLPdt

dP

Diffusion curve

BtAP

L

ln1ln

BtAe

LP

1 Value of coefficients R2

A B

641.99 -0.03 0.88

Diffusion Curves for wind energy

0

10000

20000

30000

40000

50000

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Year

Inst

all

ed C

ap

aci

ty (

MW

)

Actual Installation

Diffusion curve

Upper limit of uncertainityLow er limit of uncertainity

Forecast Values by MNRE

Potential = 45000MW

a1

a2

a

am

Year Projection by

MNRE

Projection by

diffusion curve

Values in the uncertainty limit of

5%

Lower

limit Higher limit

2007 7000 8700 2000 24800

2012 17500 23000 5800 39600

Wind Diffusion

Hydro Diffusion

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

1990 2010 2030 2050 2070 2090 2110 2130

Year

Inst

all

ed

Ca

pa

cit

y (

MW

)..

Actual InstallationPotential 15000 MWPotential11000 MWPotential 20000 MWValues by MNRE Potential = 15000MW

Potential =20000MW

Potential = 11000MW

Projected Renewable installed capacity and generation (2022)

Installed

Capacity*

(MW)

Estimated

Capacity factor

Estimated

Generation

(GWh)

Wind 42900 17% 63400

Biomass Power 2500 70% 15300

Biomass Gasifier 100 60% 527

Bagasse

Cogeneration

5500 60% 39400

Small Hydro 6500 40% 22,800

Waste to Energy 100 50% 241

Solar PV 100 20% 175

Total 59,800 27% 142600

Aggregate Grid connected

Integrated Energy Policy Committee

2022 – 425000 MW Installed capacity 2118000 Generation

Renewables 14% of Installed capacity

6.7% of generation

Trends of overall growth lower than forecast values

Trends of renewable capacity higher than forecast value

Technology cost reductions

Need for refinement of methods

Total installed capacity (GW)

Renewable power

Wind power (GW)

Solar power (GW)

Biomass power (GW)

Planning Commission

425 4 4 -- --

TERI BAU 441 4.5 4.2 -- --

TERI HYB 395 12 9 1.05 2

Greenpeace BAU

294 7 4.5 -- 2

Greenpeace ER

290 72 51 12 2

Diffusion model

-- -- 38 -- --

Scenarios for 2021-22

Issues in grid integration

Conventional power planning- hydro-thermal scheduling

How do we deal with renewables?

Capacity credit

New methodology based on Load Duration Curve

Illustrated for Wind in Tamil Nadu

Tamil Nadu location

Source Installed

capacity

(MW)

Annual

Energy

generated

(MU)

Annual

average

capacity

factor (%)

Coal 2970 21230 81.6

Gas 424 1945 52.4

Hydro 2187 6290 32.8

Firm central share 2825 17785 71.9

Wind (state + private) 3856 5270 18.6

Other renewables (solar PV,

biomass and Bagasse based

cogeneration)

556 1220 25.1

Independent power projects

(coal, lignite, diesel or gas

based)

1180 6360 61.5

Assistance from other regional

grids519 2280 50.1

Total 14517 63370 49.8

Tamil Nadu – Grid Details

Installed wind power

Wind energy

generated

0

500

1000

1500

2000

2500

3000

3500

4000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

Insta

lled

cap

acit

y (

MW

)

0

1000

2000

3000

4000

5000

6000

En

erg

y g

en

era

ted

(M

U)

TN – Installed wind power and wind energy generated

5000

5500

6000

6500

7000

7500

8000

8500

9000

9500

0 4 8 12 16 20 24

Jan-07

june

july

august

sept

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 4 8 12 16 20 24

hours

Po

we

r g

en

era

ted

in

MW

january

June

July

August

September

Wind Generation

Total Generation

Tamil Nadu 2006-7

0

500

1000

1500

2000

2500

0 4 8 12 16 20 24

Hours

Po

wer

gen

era

ted

in

MW January

June

September

Mean value

0

200

400

600

800

1000

1200

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Months

Win

d e

ne

rgy g

en

era

ted

(M

U)

Hourly variation of

wind power

Monthly variation of

wind energy

generated

Capacity Credit Methodology

Mean Value

0

5

10

15

20

25

30

35

40

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Months

Cap

acit

y c

red

it (

%)

Variation of CC

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Monthly capacity factor (%)

Mo

nth

ly c

ap

acit

y c

red

it (

%)

Capacity Credit and Capacity factor

Percentage penetration of

wind power (%)

Wind power installed

capacity (MW)Capacity credit (MW)

0 0 0

5.5 500 130

11.1 1000 240

16.7 1500 350

22.2 2000 460

33.3 3000 675

44.4 4000 895

61.1 5500 1220

77.8 7000 1550

88.9 8000 1750

100 9000 1965

Variation of Capacity credit with wind penetration

Methodology – Wind (Micro level)

Select major sitesHourly wind speed data at

sensor height

Wind turbine characteristics

Extrapolated hourly wind

power generated in the

state

Installed capacity at each

site

Hourly wind power at

sensor height

Hourly wind power at hub

height

Hub height, power law

index at each site

Continue to LDC

Methodology

Input n and

n discrete

wind

capacities

Select major sites

Extrapolated hourly wind

power generation

Effective load curve

Divide load curve into 100

MW bins

Record number of hours in

each bin

Calculate effective base and

peak load savings from

different LDCs obtained

Evaluate for n discrete wind

capacities

Sum up to obtain annual load

duration curve

Frequency distribution of load

over the year

Hourly

wind

speed

data

Wind

turbine

characteris

ticsInstalled

capacity of

wind power

Hourly

load

curve

Methodology – Wind

Impacts on Load Duration Curve

Wind power installed capacity

(MW)

Base load capacity saved

(MW)

Peak load capacity saved

(MW)

0 0 0

500 60 70

1000 100 355

1500 150 1105

2000 240 1265

3000 470 1475

4000 770 1625

5500 1150 1775

7000 1460 1975

8000 1630 2085

9000 1855 2125

Effect of Wind penetration on LDC

LDC Methodology for future scenarios

Present LDC

GDP growth rate

Elasticity

Projected LDC

Projected year

Projected LDC with

renewables

Projected renewables

installed capacity

Evaluate of discrete renewable

energy capacities

Compute base and peak

capacity saved

TN – Wind energy scenarios for 2021

Simulation for UK

Site: Valley, Hollyhead, Anglessy (Wales)

Mean monthly wind speed

0

1

2

3

4

5

6

7

8

9

1 2 3 4 5 6 7 8 9 10 11 12

month

win

d s

pe

ed

(m

/s)

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25

Hrs.

no

rma

lis

ed

po

we

r g

en

era

ted

UK load

wind power

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25

Hrs.

No

rmali

sed

w.r

.t.

peak

Load

wind power

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25

Hrs.

No

rmali

sed

gen

era

tio

n w

.r.t

.

peak Load

Wind power

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25

Series1

Series2

Wind power - load curve correlation in UK

July – 0.88 Jan – (-)0.51

Mar – 0.48 Oct – 0.39

Average correlation factor over the year = 0.38

No wind power

3200 MW wind (present installed

capacity)

6000 MW wind (2011 estimate)

Results for UK

% wind powerInstalled cap

MWbase saved peak saved

TN

6.9 620 1.50% 0.30%

12.9 1140 2.80% 1.20%

UK

6.9 3200 5.90% 0.90%

12.9 6000 10.30% 6%

Comparison of UK and TN impact

Installed solar power

capacity

Hourly total renewable

energy generated

Installed wind power

capacity

Installed biomass

power capacity

Hourly solar energy

generated

Hourly wind energy

generated

Hourly biomass energy

generated

Effective load curve

Divide load curve into equal-sized bins

Record no. of hrs. in each bin

Sum up to obtain annual LDC

Frequency distribution of load over the year

Evaluate for n discrete renewable energy capacities

Hourly load curve

Compute base and peak load savings from diff. LDCs

Input n and n

discrete capacities

of W, S and B

Wind resource model

WECS performance model Solar PV performance model Biomass power generation

Solar resource model

Utility generation model Load model

Economic scenarioCapacity expansion model

Output: Capacity Savings

Micro level model

Macro level model

TN Solar Methodology (Micro-level)

Select major sites

Hourly solar power

generation at each site

Correction for panel

inclination

Correction for temperature

effect

Extrapolate based on

installed capacity at each

site to get hourly solar

power generation for the

state

Continue to LDC

Methodology

Hourly average

insolation

Solar module

characteristics (efficiency

vs insolation)

Power coefficient

(W/deg. C)

Cos ()

0

20

40

60

80

100

120

140

160

0 4 8 12 16 20 24

hrs

MW

March

Julyl

September

December

Solar power – hourly variation

100

125

150

175

200

225

250

jan feb mar apr may jun jul aug sep oct nov dec

months

Mo

nth

ly g

en

era

tio

n f

or

10

00

MW

so

lar

PV

(M

U)

Solar power – monthly variation

0

20

40

60

80

100

120

140

160

180

jan feb mar apr may jun jul aug sep oct nov dec

months

en

erg

y g

en

era

ted

in

MU

Biomass power – monthly variation

Installed capacity – 450 MW (340 MW from bagasse cogen)

Wind and solar power required for 1000 MW avg. peak saving

y = -0.8666x + 2798.1

R2 = 0.9834

0

500

1000

1500

2000

2500

3000

3500

0 500 1000 1500 2000 2500 3000 3500 4000

Installed wind power (MW)

Ins

talle

d s

ola

r p

ow

er

(MW

)Wind and solar power installations to replace 1000 MW base power

Slope = 0.8666 which is almost equal to ratio of capacity factors (0.18/0.21 = 0.857)

Base capacity savings with wind + solar

Peak capacity savings with wind + solar

Hybrid scenarios – Impacts on LDC

A

B

C

Impacts on LDC – capacity savings

Scenario Wind

(MW)

Solar

(MW)

Biomass

(MW)

Base capacity

saved (MW)

Base capacity

saved (% of

installed RE

capacity)

Peak

capacity

saved

(MW)

Peak

capacity

saved(% of

installed

RE

capacity)

A 4000 1000 1000 1568 30.31 1252 27.22

B 5000 1000 1500 2137 28.66 1423 26.79

C 3000 3000 3000 2984 33.15 2276 25.29

Only 44% of households have access to

electricity

Capacity of village electrification systems

vary from 1 kW to 100 kW

Supply for 4-6 hrs/day

~ 80,000 villages to be electrified

Village Electrification

State Type of Renewable Energy No. of Isolated Power systems

Chattisgarh

PV

Bio-Diesel

Hybrid

800 (1 to 6 kW)

05 (10 kW)

01

MaharashtraPV

Bio-mass

2 (5 kW)

1 (15 kW)

West Bengal

PV

Wind-Diesel

Bio-mass gasifier

17 (25 to 110 kW)

1 (510 kW)

2 (500 kW)

AssamPV

Micro-hydel

1

1

Andhra Pradesh PVSolar home lighting systems

only

Karnataka PVSolar home lighting systems

only

Kerala micro-hydel2 (55 kW) – but recently got

synchronized with the grid

Isolated SPV power plants in India

Installed capacity (As on 31/01/2009) : 2.8 MWp

Total number of SPV power plants : 230 Nos.

S.No StatePV Capacity

( kWp)No. of power

plants

1 Maharashtra 5 2

2 Chattisgargh 1 to 6 108

3 Rajasthan 17.2534.5

821

4 Jharkhand 28 1

5 Orissa 2 11

6 Haryana 10 -

7 Mizoram 25 1

8 UP (NTPC) 11.9 kW 1

9 West Bengal 25 kW 15

Isolated biomass gasifier power plants in India

S.No StateCapacity

( kW)

No. of power plants

1 Maharashtra 2010

42

2 Chattisgargh 1020

100

551

3 Arunachal Pradesh 10-20 5

4 Jharkhand 50 1

5 Orissa 10010

110

6 Karnataka 1205020

232

7 Tamilnadu 940

120250

60524

8 Bihar 50 2

9 West Bengal 500 2

10 UP 10 15-20

11 Uttaranchal 50 4

12 MP 80 1

13 Nagaland 25010-20

45

14 Meghalaya 10-20 5

Issues- Low plant capacity factor

Name of the plant

PV Capacity( kWp)

Inverter capacity

(kVA)

Charge controller

(kW)

Battery capacity

(Ah)

Battery Voltage(Volts)

DistributionVoltage(Volts)

ConnectedLoad (kW)

PlantCapacity

factor (%)

Dound II,Chattisgargh

1 1.5 2 400 48 230 0.3 5.8

Latdadar,Chattisgargh

2 3 3 500 48 230 0.7 7.1

Chatal,Chattisgargh 3 5 5 800 48 230 0.7 4.4

Gudagarh, Chattisgargh

4 5 5 800 48 230 1.2 6.3

Rajmachi, Maharashtra

5 7.5 5 800 120 230 1.4 8.2

Sura, Udaipur, Rajasthan

17.25 15 20 1200 120 230 5.0 7.3

Nurda village, Jharkhand

28 20 25 1200 120 230 9.5 8.5

Anandgarh, Bikaner, Rajasthan.

34.5 2*15 2*20 2*1200 120 230 10 7.5

Lonarwadi

Dissoli

Rajmachi

Maharashtra

INDIA

More issues- Case study analysis

5 kWp Solar PV power plant at Rajmachi Village, Maharashtra

No. of house holds: 29

Connected load : 1.4 kW

0

200

400

600

800

1000

1200

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

Time (hrs)

Pow

er(

Watt

s)

and V

oltage (

Volts)

VOLTAGE POWER

Measurements

Performance of solar PV power plant at Rajmachi

Performance parameter Value

Connected load 1.4 kW

Peak load 1.04 kW

Average energy /day 10 kWh

Voltage drop across 500m line at peak load 12 Volts

Daily load factor 0.46

Plant capacity factor 8.2%

Levelised unit cost of energy* Rs 32/ kWh

* Capital cost= Rs 2,75,000/kWp; O&M cost = Rs 0.25/kWh;

Discount rate =10%; life= 20 years

20 kWe biomass gasifier plant at Lonarwadi

Gasifier

20 kW

Gasifier

20 kW

Natural

Gas engine

20 kW

Alternator

15 kVA

1500 rpm

Natural

Gas engine

20 kW

Alternator

15 kVA

1500 rpm

A A A

V

Hz

Energy

meter

32 A, 415 V, 4 pole switch

Control pannel

Three phase

Distribution

feeder

Performance of biomass gasifier plant at Lonarwadi


Connected load (kW) 10.7

Peak load (kW) 6.0

Average energy /day(kWh/day) 20-25

Voltage drop across 2 km line at peak load 42 Volts


Plant operating efficiency* 14-15 %

Phase voltage variation (Volts) 250.10.4%

251.00.5%

241.00.5%

Frequency variation (Hz) 51.40.4%

Plant capacity factor (%) 4.3 - 5.6

Voltage unbalance (%) 2.66%

Current unbalance (%) 42.94%

Voltage THD (%) 7.82%, 11.01%, 7.91.6%

Current THD (%) 16.04%, 17.41.4%, 10.83%

Levelised unit cost of energy** Rs 45-66/ kWh

* Caloric value of fuel= 20.3 MJ/kg

** Capital cost = Rs 1, 10,000/kW; O&M cost= Rs 0.5/kWh; Discount rate =10%;

Life= 20 years; Fuel cost = Rs 0.5/kg

Three phase distribution network of Lonarwadi (ViPOR)

Distribution network

Measurements

0

10

20

30

40

50

19:20 19:40 20:00 20:20 20:40 21:00 21:20 21:40 22:00

Time (Hrs)

Unbala

nce (

%)

Voltage unbalace (%) Current unbalance (%)

0

4

8

12

16

20

19:20 19:40 20:00 20:20 20:40 21:00 21:20 21:40 22:00

Time (Hrs)

To

tal H

arm

on

ic D

isto

rtio

n (

%)

Voltage A phase Voltage B phase

Voltage C phase Current A phase

Current B phase Current C phase

10 kWe biomass gasifier plant at

Dissoli village, Maharashtra

Gasifier

20 kW

Natural

Gas engine

20 kW

Alternator

15 kVA

1500 rpm

Three phase

Distribution

feeder

100 A

FUSE

MCCB

100 A, 415 V

Dissoli Gasifier

Performance of biomass gasifier plant at Dissoli


Connected load (kW) 6.9

Peak load (kW) 6.345

Average energy /day(kWh/day) 20-22

Plant capacity factor(%) 9.2


Plant operating efficiency* 14-15 %

Phase voltage variation (Volts) 231.30.3%.

234.20.3%

245.60.3%

Frequency variation (Hz) 49.40.3%

Plant capacity factor (%) 8.3-9.5

Voltage unbalance factor (%) 3.63%

Current unbalance factor (%) 604%

Voltage THD (%) 4.78%, 10.32%, 8.92%

Current THD (%) 4.48%, 10.13%, 8.92%

Levelised unit cost of energy** Rs 29.5- 36.5/ kWh

* Caloric value of fuel= 20.3 MJ/kg

** Capital cost = Rs 1, 40,000/kW; O&M cost= Rs 0.5/kWh; Discount rate =10%;

Life= 10 years; Fuel cost = Rs 0.5/kg

Optimum

source location

ViPOR distribution network of Dissoli village power system

Distribution Network

S. No. Parameter Standard value 10 kWe plant at

Dissoli

20 kWe plant at

Lonarwadi

1 Voltage

Phase ‘A’

2405%

231.30.3% 250.10.4%

Phase ‘B’ 234.20.3% 251.00.5%

Phase ‘C’ 245.60.3% 241.00.5%

2 Frequency 503% 49.40.3% 51.40.4%

3 Voltage unbalance <2% 3.63% 2.66%

4Current unbalance <20% for current

<40A

604% 42.94%

5 Voltage THD

Phase ‘A’

<5%

4.78% 7.82%

Phase ‘B’ 10.32% 11.01%

Phase ‘C’ 8.92% 7.91.6%

6 Current THD

Phase ‘A’ 4.48% 16.04%

Phase ‘B’ 10.13% 17.41.4%

Phase ‘C’ 8.92% 10.83%

IEEE 1159-1995 IEEE 519

Performance of isolated power system- Summary

Design approach

Case study

of sample

systemsAnalysis

Decision

making

Integrated

design

method

Sample

design

INPUT OUTPUT

Develop

generic

guidelines

for

design

Compare

with

Existing

design

methods

Integrated design of Isolated power system

Load estimation

Sizing

Distribution network

using ViPOR

Load flow analysis

Is the current

location of

source gives

minimum loss

Relocate the source

No

Yes

End

Name of

the plant

Connected

Load

(kW)

Plant

Capacity

Distribution loss

(%)

Plant capacity

factor (%)

Energy cost

Rs / kWh

Existing Designed Existing Designed Existing Designed Existing Designed

Solar PV,

Rajmachi1.4 5 kWp 4 kWp 4.6 0.5 8.3 11.5 32 25

Biomass

gasifier,

Dissoli

6.9 10 kW 10 kW 12.3 2.0 8.8 12 29-37 21-25

Biomass

gasifier,

Lonarwadi

10.7 20 kW 10 kW 14.6 2.7 5.6 14 43-54 16-25

Integrated design-Summary

Photovoltaic array

AC busDC bus

Charge controller

Battery bank

Inverter Load

Sizing of Photovoltaic-Battery Systems

Objective:

To arrive at the set of all feasible configurations (Array rating and Battery

capacity) to meet a given demand following a time-series simulation of the system

Schematic of the System

Mathematical model

Energy balance

Repeatability of battery energy

Non negativity of battery energy

For a small time step, battery energy:

Hourly energy balance

Battery storage requirement

Power from the photovoltaic array

Photovoltaic-Battery System Sizing (Deterministic Approach)

Inputs: Hourly solar insolation data, Hourly load data,

Photovoltaic system efficiency, Power conversion

efficiency

Estimation of the solar insolation incident on the array

System simulation to obtain the minimum array size

and the corresponding battery capacity

Calculation of the minimum storage capacity for

different array sizes greater than the minimum

Plot of sizing curve and the identification of the

design space

Array rating (W)

Ba

tte

ry c

ap

acity (

Wh

)

Infeasible design

region

Minimum array

size

Feasible design region

(design space)

sizing curve

Battery capacity

corresponding to

the minimum array

size

Graphical representation

Sizing curve for given solar insolation profile, load curve and system

characteristics

Mathematical Formulations

Chance constraint:

Incorporating energy conservation equation of the storage:

Deterministic equivalent:

Generation of design space incorporating other constraints

)}()({ tDtDyProbabilit actual

)()(

)(

)(

)(

)(tPtD

ttf

tQ

ttf

ttQyProbabilit actual

BB

))(()(

)(

)(

)()()( ztD

ttf

tQ

ttf

ttQtPactualtP

BB

PV-Battery System

0.6

0.8

1

1.2

1.4

0 1 2 3 4 5 6 7

Array rating (kWp)

Battery

capacity

(kW

h)

α = 0.5

α = 0.7

α = 0.8

α = 0.95

Solar Water Heating System

COLLECTOR

STORAGE

TANK

FROM

OVERHEAD

TANK

TO USAGE

POINT

AUXILIARY

HEATER

STORAGE

TANK

COLLECTOR

PUMP

FROM

OVERHEAD

TANK

TO USAGE

POINT

Schematic of solar water heating system

AUXILIARY

HEATER

Solar Water Heating Systems in India

– Installed Capacity = 1.5 million sq. m. (0.8% of estimated potential)

Model for Potential Estimation of Target Area

Target areaWeather data, area details

Identification and Classification of different end uses by sector (i)

Residential (1) Hospital

(2)Nursing

Homes (3)

Hotels

(4)

Others

(5)

POTENTIAL OF SWHS IN TARGET AREA

Technical Potential (m2 of collector area)

Economic Potential (m2 of collector area)

Market Potential (m2 of collector area)

Energy Savings Potential (kWh/year)

Load Shaving Potential (kWh/ hour for a monthly average day)

Sub-class (i, j)

Classification based on factors* (j)

Technical Potential

Economic Potential Market Potential

Potential for end use

sector (i = 1)

Potential

for i = 2

Potential

for i = 5

Potential

for i = 4

Potential

for i = 3

Base load for heating

Electricity/ fuel savings

Economic viability

Price of electricity

Investment for

SWHS

Technical

PotentialSWHS

capacity

Constraint: roof

area availability

Capacity of SWHS

(Collector area)

TargetAuxiliary heating

Single end use point

Micro simulation using TRNSYS

Hot water

usage pattern

Weather

data

SIMULATION

Auxiliary heating requirement

No. of end

use points

Technical

Potential

Economic

Potential

Economic

Constraint

Market

Potential

Constraint:

market

acceptance

Potential for end use sector (i = 1)

* Factors affecting the adoption/sizing of

solar water heating systems

Sub-class (i, j)

Classification based on factors* (j)

Single end use point

POTENTIAL

SECTOR (i)

Load Curve Representing Energy Requirement for Water Heating for Pune

0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14 16 18 20 22 24Hour of day

En

erg

y C

on

sum

pti

on

(M

W)

Typical day of January

Typical day of May

Total Consumption =760 MWh/day

Total Consumption = 390 MWh/day

53%

Electricity Consumption for water heating of Pune

Total Consumption =14300 MWh/day

Total Consumption = 13900 MWh/day

Total Electricity Consumption of Pune

Solar Water Heater Diffusion

0

10

20

30

40

50

60

70

80

1988 1998 2008 2018 2028 2038 2048 2058 2068 2078

Year

Inst

all

ed

Ca

pa

cit

y o

f S

oa

lr W

ate

r H

ea

tin

g

Sy

stem

s (m

illi

on

sq

. m

.)

Actual installed (million sq. m.)Diffusion curve Upper limit of uncertainityLower limit of uncertainity Potential =60 million sq.m.

Diffusion of SWH

0

50

100

150

200

250

300

1990 2010 2030 2050 2070 2090

Year

So

lar W

ate

r H

ea

tin

g C

ap

acit

y (

co

llecto

r a

rea

in

mil

lio

n

sq. m

.)..

Actual installed (million sq. m.)Potential 140 million sq. m.Potential 60 million sq. m.Potential 200 million sq. m.Extrapolated Potential (million sq.m.)

Potential = 60 million m 2



Estimated Potential in

2092 = 199 million m2

Conclusions

Renewables – status in India –mainstreaming

Diffusion curve- Potential estimation

Integration – Load Duration Curve approach –Base and peak capacity

Isolated systems –System design, sizing

DSM options – Micro-macro model

Mainstreaming of renewables- Need for better analysis, planning tools

Acknowledgment

Balkrishna Surve

Project Assistant

Indu R. Pillai

Ph.D - 2008Mel George A.

M.Tech - Ongoing

M.B. Sivapriya

M.Tech - 2008

Thank youManojkumar M.V.M.Tech - Ongoing

Santanu B. Faculty

Arun P. Ph.D. - 2009

References MNES Annual Reports, 2001-2009 Integrated Energy Policy Report, Planning Commission, 2006 11th Five year plan proposal, MNRE, Govt of India www.mnes.nic.in S.P.Sukhatme, Solar Energy, Tata McGraw Hill, Delhi,1997 Banerjee, Comparison of DG options, Energy Policy, 2006 Manish, Pillai, Banerjee, ‘Sustainability analysis of renewables’, Energy for

Sustainable Development , December 2006 Arun P., R. Banerjee, and S. Bandyopadhyay, Solar Energy, in press, 2009. Arun P., Santanu Bandyopadhyay and R. Banerjee, ‘Sizing curve for design of

isolated power systems’, Energy for Sustainable Development,Volume XI, No. 4, December 2007.

George, R. Banerjee,Analysis of impacts of wind integration in the Tamil Nadu grid, in press, Energy Policy

Indu R. Pillai and R. Banerjee, ‘Methodology for estimation of potential for solar water heating in a target area’, Solar Energy, Vol.8, No.2, pp 162-17, 2007.

Pillai and Banerjee,Renewable Energy in India, Status and Potential, Energy, in press

UK Wind speed data: GWEFR Cyf Hourly-mean wind speed datasets for sites in the European Wind Atlas, available at http://www.gwefr.co.uk/datasets.htm

UK load curves: Demand Data, UK National Grid, available at http://www.nationalgrid.com/uk/Electricity/Data/

Integration of Renewables in the power sector

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