“trends in the consumption of electricity in the industry” trends in the consumption of...

“Trends in the consumption of electricity in the Industry”

RSA perspective

Presented by: Morore Mashao, Pr.Eng.

Email: [email protected]

Tel: +27 11 800 3822

Mobile:+27 78 457 1629

Contents

• Overview of Key drivers for electricity growth

• Forecast Methodology

• Electricity consumption Trends

• History

• Forecast

2012/04/02 2

2012/04/02 3

Some of Key Drivers of Electricity Growth

• Economic Growth (GDP)

• Large Industrial New Projects

• Weather (low winter temperatures)

• Commodity Prices

• Electrification Connections

•Population growth

2012/04/02 4

Historical events and uncertainties

• First and second oil crises in the 1970’s

• End of the Cold War and fall of the Berlin Wall 1989

• Globalisation

• Gulf War 1991

• Un-banning of the ANC 1990 and first elections in 1994

• Birth of the dot-com boom 1995

• Asian market crisis and Emerging market crisis 1997/8

• Y2k bug panic in 1999 and the dot-com crash

• September 11 attacks on the US in 2001

• It’s the psychology that leads to panics and recessions ~ Alan Greenspan, The Age of

Turbulence, 2007

Forecasting

Forecast Results

Models

Key Drivers

Assumptions

2012/04/02 5

Forecasting Models

• Causal models use some specific input to forecast some other entity.

• Depicted by the model in the previous slide.

• Extrapolative models take some trend or pattern and extrapolate it into the future.

• Intuitive models essentially use the experience of one or more key people to develop a forecast.

• This can be regarded as crystal ball gazing!

• Hybrid models use a combination of the methods.

2012/04/02 6

2012/04/02 7

Electricity-GDP relationships

• Electricity demand is a dependent variable

• Many factors impact on electricity demand – called population growth, interest rates, trade account, mixed of production sectors, energy efficiency initiatives, electricity prices, etc.

• These are either independent variables or exogenous variables

• Electricity demand = f (A, B, C, D, E, F)

• The golden rule in forecasting is: not to use too many independent variables

2012/04/02 8


• Also, there is no point using an independent variable for which there is no proper historical data or for which future values cannot be estimated

• Research, analysis and experience of the past 25 years had proven that GDP is a major driver of electricity demand

• This relationship had been used very successfully during this period

• Regression of historical electricity demand and SA GDP historical time series shows an R2 of 0.9

1

2012/04/02 9


Electricity growth vs GDP growthGDP at market prices - 2005=100

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

Perc

en

t

Electricity

GDP

2012/04/02 10


• What is important to note is the fact that economies recover after down swings, recessions or depressions

• These recoveries are sometimes very quick

• Some times there are periods of volatile economic growth and sometimes there are periods of relatively stable growth

2012/04/02 11

SA economic growth

SA GDP at market prices2005 = 100

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

1912 1926 1940 1954 1968 1982 1996 2010

R m

illio

n

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Perc

en

t

GDP

Growth %

2012/04/02

SA economic growth

SA GDP at market prices2005 = 100

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

1912 1926 1940 1954 1968 1982 1996 2010

R m

illio

n

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Perc

en

t

GDP

Growth %

2012/04/02 13

SA electricity demand

SA electricity sales

0

50000

100000

150000

200000

250000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

GW

h

-5

0

5

10

15

20

Perc

en

t

SalesSales growth

2012/04/02 14


• A second way is to use the concept of income elasticity

• Income elasticity =

The change in electricity demand with respect to the change in real GDP at market prices in real terms OR

% Change in electricity demand / % change in real GDP

• Income elasticity can be estimated using a demand function, or considering arc elasticity or point elasticity

• Next slide shows point income elasticity – the values of a few points have been reduced for purposes of smoothing

2

2012/04/02 15


Income elasticity

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

• Income elasticity = The change in electricity demand with respect to the change in real GDP at market prices in real terms OR

% Change in electricity demand / % change in real GDP

• Income elasticity can be estimated using a demand function, or considering arc elasticity or point elasticity

2012/04/02 16


• In the early years income elasticity was greater than 1.0 while in later years it was lower than 1.0

• Income elasticity of greater than 1.0 means that electricity demand growth is higher than GDP growth

• Income elasticity of less than 1.0 implies that the growth in GDP is greater than the growth in electricity demand

• An electricity demand forecast can be developed by using estimated future income elasticity

2012/04/02 17


• When a demand function is used the algorithm becomes more complex

• When arc income elasticity is used the % growth in electricity demand can be calculated through using the estimated income elasticity and the estimated GDP growth

• Experience is that following two relationships are seen as easier, more user friendly and more practical to use

2012/04/02 18


• A to show the relationship is to use the concept of electricity intensity

• Electricity intensity =

electricity demand / GDP at market prices in real terms

and expressed in kWh / rand

• It is an overall measure used by the International Energy Agency for energy efficiency of a country

3

2012/04/02 19


Electricity intensitySA electricity sales / GDP at market prices - 2005=100

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

kWh

/ R

Electricity intensity in later years had started to decline as the SA economy

had matured

Electricity Intensity

2012/04/02 20

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

1980 1990 2000 2010 2020 2030 2040 2050 2060

kW

h/R

an

d

RSA electricity intensity SA GDP 2005 = 100

2012/04/02 21


• GDP forecasts exist – done by many institutions or companies

• Future electricity intensity can be estimated based on its historical trend, research and information about the future

• The electricity intensity trend changes gradually

• Hence future electricity demand can be estimated and

= estimated future GDP (Y) x estimated future electricity intensity (Z)

= R Y x Z kWh/rand = X kWh

2012/04/02 22


• Fourthly consider electricity demand-GDP growth margin

• Growth margin = Electricity demand % growth less GDP % growth at market prices in real terms

• When growth in electricity demand is higher than GDP growth electricity intensity is on the increase

• When growth in electricity demand is less than GDP growth electricity intensity decreases

• This is the more user friendly way to express the relationship and to calculate electricity demand growth

4

2012/04/02 23


Electricity growth vs GDP growthGDP at market prices - 2005=100

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

Perc

en

t

Electricity

GDP

2012/04/02


Electricity-GDP growth marginGDP at market prices - 2005=100

-10.0

-5.0

0.0

5.0

10.0

15.0

1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

Perc

en

t

Used in Top down approach

Electricity Consumption by RSA Economic Sectors

2012/04/02 25

0

100000

200000

300000

400000

500000

600000

700000

800000

1980 1990 2000 2010 2020 2030 2040 2050 2060

GW

h

RSA sales by economic sector

Residential

Agriculture

Commerce

Industry

Mining

Transport World Economic

recession Very Long Term

View Forecast

Mining

2012/04/02 26

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

1980 1990 2000 2010 2020 2030 2040 2050 2060

GW

h

Eskom sales to Mining

Rest

Manganese

Asbestos

Chrome

Diamond

Copper

Iron Ore

Coal

Platinum

Gold

Very Long Term

View Forecast

Industry

2012/04/02 27

0

50000

100000

150000

200000

250000

300000

1980 1990 2000 2010 2020 2030 2040 2050 2060

GW

h

Eskom sales to Industry

Rest/Balance

Basic Metals

N.M.Minerals

Chemicals

Wood & W.Prods

Text & Cloth

Food Bev & Tob Very Long Term

View Forecast

Basic Metals

2012/04/02 28

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

1980 1990 2000 2010 2020 2030 2040 2050 2060

GW

h

Eskom sales to Basic Metals

Other/new

Zinc

I & Titanium

Aluminium

FeMn

I & Steel

FeSi

FeCr Very Long Term

View Forecast

Chemicals

2012/04/02 29

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

1980 1990 2000 2010 2020 2030 2040 2050 2060

GW

h

Eskom sales to Chemicals

Rest of Chemicals

Petrochemicals

Very Long Term

View Forecast

Municipalities and Water sector (AAG of 2.7%)

2012/04/02 30

0

50000

100000

150000

200000

250000

300000

350000

1980 1990 2000 2010 2020 2030 2040 2050 2060

GW

h

Eskom sales to the Electricity and Water sector

Water purific, pump & distr

Municipalities

Very Long Term

View Forecast

AAG of 2.7% since 1994

Complexities

• New norms in the world and RSA economy

• Supply and demand balance of RSA electricity

• IPAP 2 implications on the RSA economic structure

• NPC planning scenarios

• RSA political climate in the build-up towards 2014

• All these factors will certainly impact the trends presented!

2012/04/02 31

Thank you

“trends in the consumption of electricity in the industry” trends in the consumption of...

Documents