International Conference on TIM, 2012, Nepal 1 Electricity Demand Side Management in Residential...

International Conference on TIM, 2012, Nepal

1

Electricity Demand Side Management

in

Residential Sector of Kathmandu Valley

Sujan Adhikari

Prof Amrit Man Nakarmi

Institute of Engineering Pulchowk Campus ,Lalitpur, Nepal


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• Determine the current electricity consumption pattern of

residential sector of Kathmandu Valley.

• Forecast the electricity demand of residential sector

Kathmandu Valley.

• To propose suitable Demand Side Management strategies.

Research Objectives


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Electricity consumption pattern• 13% of energy consumed in Nepal is through commercial source

(WECS,2010)• 48% of population have access to electricity of which 8% of people

reside in rural areas (MOF,2007) • Increasing residential consumer base• Decreasing per capita electricity consumption

Year Total No of

Domestic

Consumers

Domestic

Electricity

Consumption

(GWh)

Average

Household

Size

Per capita

electricity

consumptio

n (KWh)

2011 1948968 1170.77 4.7 127.81

2001 713307 518.36 5.44 133.58

(Source: NEA Annual Report, 2011; CBS Preliminary Survey, 2011)


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• Demand Side Management

Demand side management (DSM) also known as Energy demand management is the modification of consumer demand for energy through various methods.

• Alternative to supply side “overspending” in energy systems (NILSSON, 2007).

Concept of Demand Side Management


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• In context of Nepal, residential accounts for the major share

of energy consumption (89.1%) (Wecs, 2010).

• 44% of the total electricity consumption in Nepal is in

residential sector

Transport0.20%

Industrial38%

Residential43%

Agricultural2%

Commercial7% other

9%

Share of electricity consumption

Residential Sector


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• Limitations

Urban areas of Kathmandu Valley.

Municipal boundaries of Kathmandu Valley considered as

the urban limits.

• Electricity consumption characteristics

29.2% of total electricity distributed by the Nepal Electricity

Authority is consumed in Kathmandu Valley alone. (NRB,2012)

Kathmandu municipalities alone constitutes about 36% of the

total urban households (CBS,2011)

Kathmandu Valley


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• Questionnaire Design

• Sample Design

• Household Survey

• Analysis and conclusion

Methodology


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• Household Characteristics

Characteristics DescriptionHousehold Income Total household income per month

Nepalese Rupees (Nrs)

Household size

Number of individuals in the household

Rooms Number of rooms in the household

No of Children Number of children in the household

• Variables Number of Appliance Operating hour of the ApplianceRating of the appliance

Questionnaire Design


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• Average electricity consumption was regressed with the household

characteristics

ID CoefficientsStandard

Error t StatIntercept -61.28 29.20 -2.10

Monthly Income (Nrs) MI 0.0018 0.0002 7.49

Family size FS 8.81 7.16 1.23

Number of children NC 21.90 16.40 1.34Number of rooms R 6.85 3.43 2.00

R2= 0.765Therefore the regression equation is E= -61.28+0.002 MI + 8.811FS+21.9 NC+6.85R

Regression Result


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• Size of the sample, n

2*N*(1-P)

ME2(N-1) + ( 2*P*(1-P))

Source: Source: Morgan et all.1970

Where

n = required sample size

2 = Chi square for the specified confidence level at 1 degree of freedom

N = Population size

ME = Desired Marginal error (expressed as a proportion)

Sample Design


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Household sample surveys have become a key source of data on social phenomena in the last 60-70 years

S.N Income Class Qty of electricity used (Units/Month)

Share (%)

1 <15,000 Poorest 47.9 82 15,000-25,000 Second

74.7 403 25,000-40,000 Third

103 394 40,000-80,000 Fourth

132 95 80,000-

2,00,000Richest

200 4Total 100

Household Survey


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LEAP Modelling Framework

Modeling Conditions

The base year for the model was 2012 and horizon of

18years was used, thus modeling until 2030.

Discount rate used in model was 12%.


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LEAP Modelling Framework

• Household projection

Y=119519*exp (0.6025*x) where x is the number of year.

• Household income

• Appliance ownership

Energy ladder hypothesis

Regression between household income and appliance number

Result showing R2 value greater that 75% is considered

Year 2012 2015 2020 2025 2030

Households No (Lakhs)

3.9 4.9 6.7 9.2 12.6

Projection


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• Business As Usual (BAU) Scenario

5.4% household income growth rate• Medium Income Growth (MG) scenario

5.5 % national GDP growth rate

8.7% household income growth rate • High Income Growth (HG) scenario

7% national GDP growth rate

11.1% household income growth rate

• DSM Scenario

Scenario Selection


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Scenario Electricity Consumption (Million Kilowatt Hours)

2012 2015 2020 2025 2030HG 1252 1816 3155 5368 9066MG 1252 1766 2952 4853 7909BAU 1252 1691 2660 4148 6427

2010 2015 2020 2025 2030 20350

100020003000400050006000700080009000

10000Scenario Comparison

HGYear

Elec

trici

ty C

onsu

mpti

on(M

illio

n Ki

low

att H

ours

)

BAU-9.5%MG-10.7%HG- 11.6%

Scenario Result


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• DSM 1 Replacement of all of the widely used incandescent lamps of rated power 40 W

and 100 W to CFL of rated power 20W in the poorest households by the end of 2030.

Replacement of widely used 36 W fluorescent lights and 40 W and 100 W incandescent light in the above poorest households by LED lights of 13 W by the end of 2030.

• DSM 2 Replacement of electrical water heater by solar water by the end of 2030.

• DSM 3 All light will be replaced by solar powered LED lights.

DSM OptionsDSM Criteria


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• Electricity consumption under DSM implementation scenario was compared with the MG scenario

• DSM1

7320.6 GWh of electricity savings by the end of modeling

period.

• DSM 2

146.2 GWh of electricity savings by the end of modeling period.

• DSM3

7885 GWh of electricity can be saved across the modelling

period.

DSM Result (Energy Savings)


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B/C Ratio

Discount rate of 12%

Benefit – NPV of avoided supply cost

Cost- NPV of programme cost

The B/C of the DSM1 programme was 5.09, 4.43, 7.61, 11.38 and 51.2 for

poorest, second, third, fourth and richest class of households respectively





DSM Results (Economic Analysis)


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• Electricity consumption mostly depend on household income.

• Second and Third is the highest electricity consuming category

of households and continue in future on all scenarios

• Implementation of effective DSM options would assist in

managing Nepal’s electricity deficit.

Conclusion


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• Government should consider the adverse effects of unchecked increasing electricity

consumption trend and create separate DSM unit to ensure coordinated efforts for

DSM projects implementation.

• Major steps should be taken to establish robust database of energy consumption

pattern of the region, management of available secondary information from various

relevant authorities and carries out frequent customer perception survey and energy

consumption pattern.

• Detailed energy audit survey needed for very high end households with higher

electricity consumption

• Special attention has to be given to the no cost/low cost energy conservation

measures such as use of daylight and other measures like energy labeling on

electric cookers ,Refrigerators etc

Recommendations


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Thank You

International Conference on TIM, 2012, Nepal 1 Electricity Demand Side Management in Residential...

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