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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