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IRP and Demand Side Management-Basics
Rangan Banerjee
Department of Energy Science and Engineering
Indian Institute of Technology Bombay
Presentation at the Workshop on Demand Side Management - 7th December 2016, IIT Bombay
Goals for the Energy Sector
Energy Access – Energy for all
Energy and Equity
Sustainable Energy
Energy Security
Energy for improved quality of life
24/7 Electricity – at affordable prices
DSM/DR can play a major role in this solution
2
Electric Utility Role
Provision of Electricity to customers in the service area- supply customers with power they wish at whatever time
Planning under uncertainty of demand
Daily, Weekly & Seasonal Load Variation
Conventional power planning -Demand –exogenous, uncontrollable – build new supply to meet demand
Similar concept for Energy utilities
3
Redefining Role
Capacity additions costly – Shortages of peak power and energy
Low capacity utilisation of power plants –increased costs – high tariffs
Gestation Period
Provision of energy services(using electricity) to customers in the service area (lighting, cooling, motive power…)
Increased Variability in supply – more renewables
4
Energy Flow Diagram
PRIMARY ENERGY
ENERGY CONVERSION FACILITY
SECONDARY ENERGY
TRANSMISSION & DISTRN. SYSTEM
FINAL ENERGY
ENERGY UTILISATION EQUIPMENT & SYSTEMS
USEFUL ENERGY
END USE ACTIVITIES
(ENERGY SERVICES)
COAL, OIL, SOLAR, GAS
POWER PLANT, REFINERIES
REFINED OIL, ELECTRICITY
RAILWAYS,TRUCKS, PIPELINES
WHAT CONSUMERS BUY DELIVERED ENERGY
AUTOMOBILE, LAMP, MOTOR,
STOVE
MOTIVE POWER RADIANT ENERGY
DISTANCE TRAVELLED, ILLUMINATION,COOKED FOOD etc..
Energy End Uses
Boiler, GeyserFluid heatedHeating
Fans,AC, refrigSpace CooledCooling
motorsShaft workMotive Power
Cycle, car, train,
motorcycle, bus
Distance
travelled
Transport
Incandescent
Fluorescent, CFL
IlluminationLighting
Chullah, stoveFood CookedCooking
DeviceEnergy ServiceEnd Use
ELECTRIC MOTOR-PUMP
COAL
COAL MINING/TRANSPORT
TRANSMISSION & DISTRN. SYSTEM
Electricity to Farmer
MOTOR
Pump output
POWER PLANT
PUMP
cm
pp
T&D
m
p
90%
30%
78%
70%
60%
88%
75%
Power Plant capacity additions
0
50000
100000
150000
200000
250000
30000019
51
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
MW
Target (MW) Achieved (MW)8th Plan - 11th Plan
10% 13%15%
13%4%
4%
4%
8%
9%
10%
5%
15%
Coumpund annual Growth Rate of Actual Installed Capacity,every 5 Year Plan
0
200
400
600
800
1000
1200
1400
GW
Installed Capacity - Historical Trends and Future Projections
Historical Trend
5
3,4
2
1 - BAU :Capacity needed with current average reserve margin of 61%
1'- Scenario: Capacity Needed with an improved average reserve margin of 51%
2- IEP@8%
3,4 - MoP@ 8% & IEP @ 9%
5 - MoP @ 9%
1'
1
Load Profile
Consumers have usage patterns that vary with time
Load profile – aggregate pattern of all consumers
Peaks- periods of maximum demand
Valleys –periods of minimum demand
Shoulders (partial peak)- periods of intermediate demand
Analysis of System Load Curve
A load curve defines power vs time
Load Factor = (Average Power)
Peak Power
System Load Factor
Capacity Factor (plant load factor)
= Energy generated by a plant
Energy generated if operating at max capacity
Classification
Time intervals
Daily Load Curves (hourly/half hourly)
Seasonal (Winter/Summer/monsoon)
Annual Load Curves
User Classes - Residential
- Industrial
-Commercial
-Agricultural
End Uses – Lighting, pumping,motors, heating,AC
Load Duration Curve
Frequency Distribution of loads
Re-arrange data to obtain cumulative number of hours where demand specified value
Plot – Load Duration curve
Highest load period -15-20% of the hours-designated as peak
Base Load – present for 70-80% of time
Daily Load Curve
4000
4500
5000
5500
6000
6500
7000
7500
0 4 8 12 16 20 24
Hour
Sy
ste
m L
oa
d M
W
Series1
Load Duration Curve
0
1000
2000
3000
4000
5000
6000
7000
8000
0 4 8 12 16 20 24
Hours
System
Lo
ad
MW
Series1
Annual cost of Power
0
5000
10000
15000
20000
25000
0 0.2 0.4 0.6 0.8 1
Load Factor
Rs
/kW Series1
Series2
Series3
Traditional Power Planning
Projections of Demand growth
Expansion Planning to determine available resources and when they are needed
Production- cost analysis to rank supply -options by costs
Calculation of required revenues and rates
DEFENDUS (Reddy et al)
Estimate true demand in base year
Frozen efficiency scenario
Development focussed - growth rates
Estimation of energy saving by different end-uses
Karnataka (EPW, 1991)
UCOE Unit Cost of Energy (CSE)
DSM
OPTION
DEMAND
(MW)
ENERGY
(GWh)
PROG. COST
(MILLION Rs)
UTILITY
Rs/kW
CSE
p/kWh
PF 40.2 - 41 1000 -
TOD 110.4 - 190 1700 -
EAF 17.8 94.9 36 2000 20
CFL 1.1 4.7 3 2900 61
GHK 55.2 228.8 208 3800 86
HPSV 1.4 7.2 9 6500 -10
PUMPF 16.1 80.5 140 8700 77
EEM 9.3 46.4 83 9000 63
VSD 37.4 333.1 381 10200 105
VARS 11.2 79.1 119 10600 64
ELB 2.2 9.8 28 12400 100
TOTAL 302.3 885.3 1238 4100 82
COGEN 242.3 1358.0 1162 4800 76
TOTAL 544.6 2243.3 2400 4400 78
DSM HT Industrial Plan Results Maharashtra
0 50 100 150 200 250 3000
2000
4000
6000
8000
10000
12000
14000
Pf
Cos
t of D
eman
d S
aved
(Rs/
kW)
Demand Saving (MW)
Least Cost Curve for DSM
Screening Curves
Screening Curves to Compare Efficiency Investments to Power Plants-LBL 27286
Kooney, Rosenfield, Gadgil
Conservation Load Factor (CLF)
= Average annual load savingsPeak Load Savings
Utility co-incident peak demand savings
Present Demand DSM options after
preliminary screeningExisting supply side
resource
Capacity addition plan for
new power projects
Demand forecasting
Characterizing as supply side
resources
COMMON POOL OF SUPPLY
SIDE AND DSM RESOURCES
Cost, Emission and Reliability
(LOLE) characteristics of systemCOMPROMISE PROGRAMMING APPROACH
OPTIMAL CHOICE OF RESOURCES
1. DSM OPTIONS AND USAGE 2. POWER SELECTION
3. COAL MINES CAPACITY EXPANSION 4. COAL TRANSPORTATION PLAN
INTEGRATED RESOURCE PLANNING OF UTILITY
Steps Involved and Inputs Required
Integrating DSM & Supply
Optimisation framework
Objectives -
Minimise annual system cost
Minimise annual emissions
Minimise Loss of Load Probability
Constraints -Coal& Gas production, transportation capacity, Hydro availability, Electricity demand
Integration Approach
Chattopadhyay et al (1995,IEEE)
Preliminary Screening of DSM options
Applicability - potential, CSE
Characterising as supply side resources Non Dispatchable Technologies NDT(Lighting) -only specified hours
Limited Energy Plant - LEP
Pump Storage Type- Direct Load Control
LCP/IRP Experience
Least Cost Planning /Integrated Resource Planning
Deferred or Avoided Demand equivalent to Future Supply
Benefits - reduced costs, emissions
1992- Energy Policy Act US - required all electric utilities to employ IRP & ratify plans before their state PUC
DSM Concept
Demand Side Management (DSM) - co-operative action by the customer & the utility(Distribution company) to modify the customer load
DSM benefits utility, consumer & society
Energy Conservation
Fuel Switching
Peak Clipping/Valley Filling/Load Shifting
Utility Load shape objectives
Peak Clipping
(Reducing system peak loads)
Valley Filling
(Increase off-peak loads without affecting peak period energy and demand)
Load Shifting
(Shifting of loads to off-peak periods)
Strategic Conservation
(Reduce end-use consumption through increased efficiency)
Strategic Load Growth
(Increase end-use consumption resulting in increased sales beyond valley filling)
Flexible Load Shape
Traditional LM Options
Staggering of working hours of large consumers
Staggering of holidays of large consumers
Specified energy and power quotas for major consumers
Rostering of agricultural loads
Curtailment of demand - service interruptions (load shedding)
DSM Programmes
Efficient Pumping Systems –Agricultural/ Municipal /Industry
Efficient Motor-Drive Systems - Industrial
Efficient Lighting - Commercial/Residential
Process Improvements- Industrial
Solar Water Heaters –Residential/Commercial
Efficient AC – Commercial/ Residential
Cogeneration/Captive Power-Industry/commercial
Demand Side Management: History of its origin and growth
Before 1970s: Supply oriented Power Planning.
Oil crisis of 1970s and accompanying recession led to rapid increase in the electricity prices.
First Wave: Late 1970s to Mid 1980s. Conservation and Load management (CL&M) initiated. DSM term coined for the first time. PURPA and National Energy Conservation policy Act of 1978 introduced DSM.
Second Wave: Mid 1980s to mid 1990s. Cash rebate and low interest financing for DSM programs. Term “Negawatts” coined.
Third Wave: Early 2000s.
Demand Response programs introduced into electricity market by CPUC.
Barriers to Energy Efficiency
Risk
Imperfect Information
Hidden Costs
Access to Capital
Split Incentives
Bounded Rationality
Early ESCO concept
"We will leave a steam engine free of charge to you. We will install these and will take over for five years the customer service. We guarantee you that the coal for the machine costs less, than you must spend at present at fodder (energy) on the horses, which do the same work. And everything that we require of you, is that you give us a third of the money, which you save.“
[James Watt, 1736-1819]
Efficiency and DSM
DSM – on the margins, to happen automatically, sub-critical programmes
Rebound Effect
Transaction Costs
Level Playing Field
Needed a Paradigm Change – Focus on Energy Services
Shortage of Supply to Longage of Demand
47
Carbon Dioxide Emissions
Kaya identity: Total CO2 Emissions
= (CO2/E)(E/GDP)(GDP/Pop)Pop
CO2/E – Carbon Intensity
E/GDP- Energy Intensity of Economy
Mitigation – increase sinks, reduce sources-aforestation, fuel mix,energy efficiency, renewables,nuclear, carbon sequestration
Adaptation
The cost of carbon dioxide saved for the options in A is negative
This implies:
i)There is an increase in the carbon dioxide emissions as compared to the BAU scenario
ii)There is an error in the calculation, the costs cannot be negative
iii)In the initial period there are no savings , savings occur after a few years
iv)The costs are negative since these relate to options for the developing countries
v)None of the above
Standard Fan vs Efficient Fan
Standard Fan Efficient FanPower 70 W 35 WPrice Rs 1300 Rs 2600
BLDC motorLife : 10years Sweep 1200 mm RPM – 350-400Similar air delivery 230 m3/min
Cost Of Saved Energy – Efficient Fan
53
0
2
4
6
8
10
12
14
16
18
0 0.1 0.2 0.3 0.4 0.5
CSERs/kWh
Discount Rate
1000 hours
2000 hours
4000 hours
3000 hours
Macro-Level Barriers
Distorted Energy Prices
Lack of Human Capital Infrastructure
Lack of Technical Infrastructure
Lock-in effects
Lack of External access to Capital
Institutional Factors
Estimation of Rooftop Solar Photovoltaic Potential of a City
R Singh and R Banerjee, 2015: Estimation of Rooftop Solar Photovoltaic Potential of a City, accepted for publication in Solar Energy
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Da
ily L
oa
d P
rofi
le a
nd
Da
ily P
V G
en
era
tio
n(M
illi
on
kW
h)
Process Scheduling Summary
Example Structure Results Saving
Flour Mill
Continuous
Linear, IP
120 variables
46 constraints
Flat- 2 shift -25%store
TOU-3 shift
1%
6.4%
75%peak
reduction
Mini Steel Plant
Batch
Linear, IP
432 variables
630 constraints
Flat
TOU
Diff loading
8%
10%
50% peak reduction
57
30 T MeltingArc furnace
Bar mill
Wire mill
40 T Melting Arc
furnace
St. steel Scrap mix or
Alloy steel scrap mix
Alloy steel
scrap mix
Convertor (only for
St Steel)
Ladle Arc
furnace
VD or VOD
station
Bloom caster
Billet caster
Bloom mill
ooo
ooo
Reheat furnace
Reheat furnace
Reheat
furnace
Wire products
for final finish
Rods, Bars for final
finish
Open store
Open store
Open store
Open store
Steel Plant Flow Diagram
58
0
10
20
30
40
50
60
Time hours
Lo
ad
MW
Optimal with TOU tariff
Optimal with flat tariff
2 4 6 8 10 12 14 16 18 20 22 24
Steel Plant Optimal Response to TOU tariff
59
Process Scheduling Summary
Example Structure Results Saving
Flour Mill
Continuous
Linear, IP
120 variables
46 constraints
Flat- 2 shift -25%store
TOU-3 shift
1%
6.4%
75%peak
reduction
Mini Steel Plant
Batch
Linear, IP
432 variables
630 constraints
Flat
TOU
Diff loading
8%
10%
50% peak reduction
60
- Integrated approach
Operating
cost
structure
Optimal
operating
strategy of
captive/
cogeneration
plant
Captive/Cogeneration
power model
Grid tariff, fuel costs,
Grid conditions
Modified process
demand profile
Process demand profile,
Cooling electric load
profile, Steam load profile
Process load
model Air conditioning
(cooling) load model
Optimal process load
schedule Optimal cool storage
Plant
/measured
input data
Modified cooling electric
load profile
Modify steam load
profile for process
related loads
61
Analytical support
Improved understanding of load profiles, variations
Need for Statistical Analysis, New Framework
BAU vs DSM
Free rider effect
Diffusion models
Public Domain reports, load profiles
Optimisation, Simulation models
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
Fra
cti
on
(f)
Time (Years)
62
Generalised lighting end use model
Framework of model proposed for construction of end use load profiles 63
Potential for domestic lighting energy efficiency, 2004-5
Potential estimation for different household categories
Potential estimation by different end use devices
0
200
400
600
800
1000
1200
1400
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours
MW
Energy efficient lighting load profile
Conventional Lighting Load Profile
Peak Demand Reduction : 703 MW and
Annual energy saving : 2335 MU
0
200
400
600
800
1000
1200
1400
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
HoursM
W
Energy efficient lighting load profile
Conventional lighting load profile
Peak demand reduction : 663 MW and
Annual energy saving : 2121 MU
A) Lighting energy efficiency potential forrural households
B ) Lighting energy efficiency potential forurban households
64
Economic analysis from different perspectives
Economic analysis from perspective of Utilities
0
0.5
1
1.5
2
2.5
3
0 500 1000 1500 2000 2500 3000 3500 4000
Energy Saved/ Purchased in GWh
Co
st
of
Saved
En
erg
y in
Rs/k
Wh
0
0.5
1
1.5
2
2.5
3
Avera
ge E
nerg
y P
urc
hase C
ost
in R
s/k
Wh
Energy Purchased by MSEDCL
Energy Saved by Lighting Energy Efficiency
Highest Power generation cost of MSPGCL = 1.81 Rs/kWh
DSM – Effect on load profiles
0
10
20
30
40
50
60
70
80
12:0
0 A
M
1:0
0 A
M
2:0
0 A
M
3:0
0 A
M
4:0
0 A
M
5:0
0 A
M
6:0
0 A
M
7:0
0 A
M
8:0
0 A
M
9:0
0 A
M
10:0
0 A
M
11:0
0 A
M
12:0
0 P
M
1:0
0 P
M
2:0
0 P
M
3:0
0 P
M
4:0
0 P
M
5:0
0 P
M
6:0
0 P
M
7:0
0 P
M
8:0
0 P
M
9:0
0 P
M
10:0
0 P
M
11:0
0 P
M
Energy savings from
DSM
New Load curve
Old Load curve(kW)
Total peak demand savings = 20.5 kW = 20.7 kVA (@0.99 pf lag)
Energy savings = 161 kWh/day
66
0
10
20
30
40
50
60
70
80
12:0
…
2:0
0…
4:0
0…
6:0
0…
8:0
0…
10:0
…
12:0
…
2:0
0…
4:0
0…
6:0
0…
8:0
0…
10:0
…
MB TotalLoad
MB totallighting
Total fans
Totalcomputers
Total AC
Integration of DSM with PV
67
100 HouseholdsResidential loads: Incandescent bulbCeiling Fan,TelevisionRadio/Music load,Agricultural pumpsetIsolated systemPV- BatteryPV-Battery-DSM
Sector Load % contribution to total load
EE option
Residential Lighting 41.1 60W incandescent to 15WCFL
Fan load 12.7 Ceiling Fan of 65Wreplaced by BLDC fan
TV load 2.1 19 inch CRT TV replacedwith 22W LCD
Agricultural Motor & pump loads 29.55 Energy efficient motor-56%
Community Street lighting 8.45 HPSV lamps by LEDlighting
Annual energy savings(kWh/year) 25610
Load EE option
Demand
savings(kWh/year
)
Hours of
operation/Year
Lighting load
Replacing Incandescent with 15W
CFL 13140 1460
Replacing CFL with LED 2044 1460
Replacing with 8W LED 15184 1460
FAN load
Replacing with 35 W BLDC motor 3240 1460
Replacing with energy efficient
blades(60W) 540 1460
Improved AC induction
motor(50W) 1620 1460
TV Replacing with 19 inch LCD TV 635 1095
Agriculture Energy efficient motor-56% 5748 2008
Street lighting HPSV lamps by LED lighting(40W) 2847 4745
68
0
5
10
15
20
25
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Laod
of
are
a(k
W)
Time of day(h)
Total demand summer(kWh)
Total load Average Load(DSM) Average(DSM)
0
2
4
6
8
10
12
14
16
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Laod
of
are
a(k
W)
Time of day(h)
Total demand Winter(kWh)
Total load Average Load(DSM) Average(DSM)
69
EE technologies, systems
Technology Development
Research
Innovation
Grand Challenges
Market transformation
Capacity and Institution Building
71
Concluding Remarks
Redefining utility role
Load and Supply Variability
Analysis of Load profiles and consumer price elasticities
Participation rates, Transaction costs
Flexibility and Resilience in Power system
Innovation
DSM to be integrated into Energy planning
DSM in curriculum
Thank you 72
References
FERC, 2009 http://webarchive.nationalarchives.gov.uk/20121217150421/http://www.decc.gov.uk/ass
ets/decc/statistics/publications/trends/articles_issue/560-trendssep10-electricity-demand-article.pdf
Power System Statistics 2012-13, KSEB: KERALA STATE ELECTRICITY BOARD LIMITED J.K. Parikh , B.S.Reddy and R.Banerjee, Planning for Demand Side Management in the
Electricity sector, Tata McGraw Hill , New Delhi,1994. S Ashok and Rangan Banerjee, Optimal Operation of Industrial Cogeneration for Load
Management, IEEE Transactions on Power Systems, Vol 18, No. 2, May 2003. S. Ashok and Rangan Banerjee, Optimal cool storage capacity for load management,
Energy 28 (2003) 115-126. R.Banerjee, "Load management in the Indian power sector using US experience", Energy,
Vol 23, 1998 , pp 961-973 Puradbhat, S. and Banerjee, R., "Estimating Demand Side Management Impacts on
Buildings in Smart Grid." The 2014 IEEE ISGT Asia Conference on 'Innovative Smart Grid Technologies (ISGT Asia 2014), Berjaya Times Square Hotel, Kuala Lumpur, Malayasia, May 20-23, 2014.
73