Diffusion of Renewable RB

8/3/2019 Diffusion of Renewable RB

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Diffusion of

Renewable Energy

Rangan Banerjee

Energy Systems Engineering

Lecture delivered at RENET Workshop, IIT Bombay, September 22, 2006


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Outline Technology forecasting, Diffusion

models

Example for Solar Water Heaters in

Pune Results for Pune

Extrapolation for India Conclusions


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Problem Context

Transition from fossil to renewable Potential Estimation

Decide Feasible growth rates

Set targets


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Technology Forecasting Trend Extrapolation

Growth Curve - limitless growth

y = y0

ekt (y0, k - from data)

competitors trying to outdo others

Co-efficients - obtained by regression -

report t, F statistic & uncertaintylimits.


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Technology Forecasting

Often growth limited by constraints Pearl Curve y = L/(1+ ae-bt)

S shaped Logistic CurveSymmetric about point of inflection


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Macro Level Diffusion Models

Logistic

or S curve

Exponential

growth

Limit LVariable

Being

Forecast

Time t

Growth curves (Linstone and Sahal, 1976)


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Technology Diffusion

Even if new technology better - will notreach 100% acceptance :

postponement of acceptance,

supply bottlenecks,

information gaps


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Fisher-Pry Model

Fisher-Pry model for substitution

df/dt = bf (1-f)

where f is the fractional market share

of the new improved technology

ln (f/(1-f)) = a + bt

where a, b are constants


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Time (Years)

Fractio

n(

f)

Fisher-Pry Curve


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Blackmans Model

Blackmans model

Final market share not 100% but F

ln (f/(F-f)) = a + bt

Determine a, b by method of leastsquare (regression) with initial

substitution dataa,b by analogy


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80

Year

Fracti

on

(f)

Blackman Curve


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Penetration ofWind Energy inGermany

Source:Peter Lund, Energy Policy,34, No17, 2006


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Accelerated Diffusion


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Solar Water Heaters

Estimate potential for solar water heaters in agiven area

Develop generic framework

On-going Ph.D. project of Indu R. Pillai

Diffusion of Renewable Energy Technologies

Indu R. Pillai and Rangan Banerjee Methodology forestimation of potential for solar water heating in a targetarea, Solar Energy, In Press, Available online 5 June 2006,


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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

Estimated Potential = 140million sq.m.(Basis not known) Installed Capacity = 1.5 million sq. m. (0.8% of estimated potential)


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Diffusion ProcessMicro-level decision model

(Decision of single user* or

beneficiary to adopt or not)

Macro-level diffusion model

(Aggregate market and diffusion ofproduct with respect to potential)

Integration of multiple users to

form aggregate market

Potential

Estimation

EstimatedPotential

*Single user may be a person, a household, a society, an institution or an industry


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Factors Affecting Diffusion

Of SWHS

Location- Insolation

Water Usage Pattern

Cost of electricity

Capital Cost

Reliability

Potential savings

Subsidies/ Financial Incentives

Mi L l D i i M d l


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Micro Level Decision Model(Parametric Analysis)

TRNSYS

INPUT DATA

Water usage pattern

Location(Monthly average hourly

temperature and radiation data)

Characteristics of SWHS Auxiliary heating requirement

(Monthly average hourly data)

Economic Analysis

MS EXCEL

Savings in Electricity Cost

Payback Period Analysis

Cost of electricity saved

Selection and sizing

of SWHS

TRNSYS (Transient System Simulation Program developed at SEL, University of Wisconsin)

Model for Potential Estimation of Target Area


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Model for Potential Estimation of Target Area

Target area

Weather 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 (m2of collector area)

Economic Potential (m2of collector area)

Market Potential (m

2

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


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Base load

for heating

Electricity/ fuel savings

Economicviability

Price of electricity

Investment for

SWHS

Technical

PotentialSWHS

capacity

Constraint: roof

area availability

Capacity ofSWHS

(Collector area)

TargetAuxiliaryheating

Single end use point

Micro simulation using TRNSYS

Hot waterusage pattern Weatherdata

SIMULATION

Auxiliary heating requirement

No. of end

use points

Technical

Potential

Economic

Potential

EconomicConstraint

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)

f l f


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Information Flow Diagram of

M icro simulation for SWHSWeather

data

COLLECTOR

SOLAR RADIATIONPROCESSOR

STORAGE

TANK

LOAD(Hourly hot water

usage pattern)

AUXILIARY

HEATER

Hourly Global Solar Radiation

& Diffuse Solar Radiation

Hourly Solar Radiation on

Collector Surface

Hourly ambientTemperature

Auxiliary

heating

requirement


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Potential Of SWHSTechnical potent ial Pij for sub-classjin sector iis

wherejdenotes sub-class of end use points in sector i.Psj is the simulation output for a single end use point

fjdenotes fraction of the end uses

m is the total number of sub-classes.

faj is fraction of roof area availabilityNiis the number of end uses points in sector i

Technical Potential for sector iis

where idenotes sector

Technical Potential of SWHS P(T)in the target area is

sjPiNajfjfijP =

=

=

m

1j

ijPiP

= iP)T(P


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Economic Potential

Economic potential of SWHS

P(E): subset of technical potential

ve = 0, if payback period > maximum

acceptable limitve = 1, if payback period < maximumacceptable limit

( ) ijij PEP ev=

Payback Acceptance Schedule


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Payback Acceptance Schedule

0

0.10.2

0.3

0.4

0.50.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12

Payback period (years)

Frac

tionMeeting

Economic

Criteria

MARKET POTENTIAL

fp,j

is fraction of potential adopters meeting economic criteria.

( ) ijPpjfij

MP =

Input Data For Potential


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Input Data For PotentialEstimation Of SWHS in Pune

Target Area Pune

Area 138 sq.km

Total Number of households 5.17 lakhs

Number of households with more than three rooms 1.41 lakhs

Average number of persons in each household 5

Number of hospitals 394

Capacity range of hospitals 1-570 beds

Number of nursing homes 118

Capacity range of nursing homes 1-50 beds

Number of hotels 298

Capacity range of hotels 10-414 inmates

Number of households residing in single ownership houses 35250

6 floors 1400

10 floors 880Number of buildings (4 flats in each floor)

11 floors 840

Residential 2.80Cost of electricity(Rs./kWh) Commercial 4.00

H t W t U P tt (P )


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Hot Water Usage Patterns (Pune)

(a) Residen tial (1) [Gadgil , 1987]

0

10

2 0

3 0

4 0

5 0

6 0

7 0

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

Hour of day

litres/h

Te m pe r a t ure = 4 0

o

C

(b) Residential (2) [Narkhe de, 2001]

0

20

40

60

80

100

120

140

160

180

200

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

litres/h

Te m pe r a t ure = 4 0

o

C

(c) Hospital (1 bed)

0

5

10

15

20

25

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

litres/h

Temperature = 50o

C

(d) Nursing Home (1 bed)

0

2

4

6

8

10

12

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

litres/h

Temperature = 50o

C

(e) Hotel - 1 guest

0

5

10

15

20

25

30

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

Time of day (Hour)

itres/h

Te mpe r at ure = 6 0 o C

M thl A A bi t


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Monthly Average Ambient

Conditions in Pune

0

5

10

15

20

25

30

35

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

MonthlyAverageSolar

Radiation(kWh/m2/day)

0

5

10

15

20

25

30

35

MonthlyAve

rageAmbient

Temperature(oC)

Incident Solar Radiation

Ambient Temperature

Mani, A. (1980) Handbook of solar radiation data for India.


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Sample simulation output and potentialestimation for hospital w ith 5 beds

(a) Ene rgy flow/ Solar Radiation for a typical day

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

10 0 0 0

12 0 0 0

0 2 4 6 8 10 12 14 16 18 2 0 2 2 2 4

Ho ur o f d ay

So lar Ra diatio n

So lar Energy

Aux.heating

(b) Temperature profiles for a typical day

0

10

2 0

3 0

4 0

50

6 0

70

8 0

9 0

10 0

0 2 4 6 8 10 12 14 16 18 2 0 2 2 2 4

Ho ur o f t he d ay

Amb. Tem p.

Tem p.at co llec to r o utlet

Tem p. at tank o utlet

Tem p. at lo ad

(c) Monthly variation in el ectricity savings

0

5 0

100

150

2 0 0

2 5 0

3 0 0

3 5 0

4 0 0

4 5 0

M o n th o f ye a r

Annual Electric ity Savings = 4290 kWh

P t ti l E ti ti f S t


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Potential Estimation of Sectors

(Pune)

Technical Potential Market Potential

Sector Collector

area (m2)

Annual

Electricity

savings

(kWh)

Collector

area (m2)

Annual

Electricity

savings

(kWh)Single houses 106000 37200000 2100 740000

ResidentialMulti-storeyed 227400 165000000 41000 29700000

Hospitals 5500 5900000 1700 1600000Nursing homes 600 500000 300 280000

Hotels 13800 15900000 9300 10740000

TOTAL 353300 224500000 54400 43100000


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Load Curve Representing Energy

Requirement for Water Heating

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

EnergyC

onsumption(MW

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

A hi bl P t ti l Of SWHS F Diff t


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Achievable Potential Of SWHS For DifferentPayback Periods (Pune)

0

50000

100000

150000

200000

250000

300000

350000

400000

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

Payback Period (years)

TechnicalPote

ntialofSWHS(sq.m.

Total Technical Potential = 353300 sq. m.

Economic potential (limit=payback period of 5 years) =19700 m2 collector area

Framework for Potential Estimation of


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Solar Water Heating Systems in a CountryCountry details

(Area, Average Weather Data)

Locations whereweather data available

Locations where weatherdata unavailable

Selection of base city

Methodology for potentialestimation for a target area

Weather data

End use details for each sub-class

Identification of sectors andclassification within each sector

Potential of SWHS in base city

Potential of SWHS indifferent location

Identification of variablesfor a different location

Weather data

End use details

SpatialInterpolation

Potential of SWHS innearby area where

weather data is notavailable location

Aggregation for all the locations

Potential of SWHS in the country

Technical potential Electricity savings


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Conclusions

Models for Substitution, diffusion to establish

feasible growth rates Framework developed linking micro-macro

simulation

Example of Pune city (significant reduction inmorning peak)

Framework to be extrapolated for country Can be used for all renewables


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References

Indu R. Pillai , R BanerjeeMethodology for estimation ofpotential for solar water heating in a target area, Solar

Energy, In Press J.P.Martino, Technological Forecasting for Decision

Making, 3rd edn, McGraw Hill, 1993.

Linstone. H.A. and Turoff,M, The Delphi Method, AdisonWesley, Reading -MA,1975.

Linstone and Sahal, Technology Substitution,1976

Mani, A. Handbook of solar radiation data for India.,

1980 Peter Lund, Market penetration rates of new energy

technologies, Energy Policy, Volume 34, Issue 17,November 2006, Pages 3317-3326.

Data Source- MEDA,MNES, PMC

Documents

Diffusion of Renewable RB