OF A REGIONAL TIMES MODEL : L ESSONS FROM A CASE STUDY IN G AUTENG , - - PowerPoint PPT Presentation

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OF A REGIONAL TIMES MODEL : L ESSONS FROM A CASE STUDY IN G AUTENG , - - PowerPoint PPT Presentation

Sep 10, 2022 21 likes •197 views

A SSESSING THE IMPACT OF ENERGY POVERTY IN THE ENERGY SYSTEM THROUGH THE APPLICATION OF A REGIONAL TIMES MODEL : L ESSONS FROM A CASE STUDY IN G AUTENG , S OUTH A FRICA Audrey Dobbins, Uli Fahl, Kai Hufendiek Institute of Energy Economics and

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ASSESSING THE IMPACT OF ENERGY POVERTY IN

THE ENERGY SYSTEM THROUGH THE APPLICATION OF A REGIONAL TIMES MODEL:

LESSONS FROM A CASE STUDY IN GAUTENG, SOUTH AFRICA

Audrey Dobbins, Uli Fahl, Kai Hufendiek

Institute of Energy Economics and Rational Energy Use, University of Stuttgart

International BE4 Workshop

20-21st April, 2015 in London

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Background: Gauteng Province, South Africa

Source: http://www.southafrica.info

Gauteng

Population 10.4 mil Households 3.2 mil Urbanisation 98% Electrification 84%

www.enerkey.info

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Residential energy consumption

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Residential sector – Assumptions and drivers of energy demand

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Income group Poor-Income Low-Income Mid-Income High-Income Annual Income R1 – R9,600 R9,601 – R76,800 R76,801 – R307,200 R307,201 + Number of HH 705,224 1,430,872 651,292 388,191 % HHs 22.2% 45.1% 20.5% 12.2% % total Energy use 4.2% 22.3% 32.2% 41.3% GJ/HH/a 12.3 13.9 34.7 51.5 Dominant Energy carriers Electricity (60%), paraffin Electricity (71.5%), paraffin Electricity (86.4%), LPG Electricity (89.1%), LPG Energy service priorities cooking, water heating, appliances cooking, water heating, appliances water heating, space heating, appliances water heating, space heating, lighting Future demand dependent on population and income

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

COOKING

Living standards characteristics

Gas stove elec stove SPACE HEAT WATER HEATING TECHNOLOGIES / end-uses FUELS

DEMOGRAPHICS

POLICIES HOUSING TYPE / building materials Gas Electricity Wood income GDP population households Energy efficiency Renewable energy Carbon emissions Access to energy Free standing Semi- detached Informal Flats Energy subsidies Typical consumption profiles for end-use for each of the different housing types, income groups, living standards (hot water, space heating, cooking profiles) Elec geyser SWH

Typical appliances and efficiencies Building type & efficiency Household size (ppl/HH) Living space size (m2) Energy sources + + + +

Oil Solar ... … ... Elec GAS ... ...

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Residential sector energy system

  • 2. Income specific building

types, income specific consumption profiles

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  • 1. Four income

groups High income Medium income Low income Poor income

Energy demand

  • 3. Income specific

technologies

Energy supply For example, income specific SWH, elec. geyser, other technologies

  • 4. Energy carriers

Gas/LPG Electricity Paraffin Coal Wood Solar Biomass,

  • ther
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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Other factors influencing behaviour

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Barriers Impact Access Policies impacting fuel choice (e.g., subsidies, VAT removal) Infrastructure (electrification, gas) Acceptance Perception / cultural tradition (e.g. smoke) Perception (e.g., SWHs) perception (e.g., gas is dangerous) Affordability High upfront costs of efficient technologies Lifestyle choices and purchasing priorities Suppressed demand and disposable income

Modelled with a mix of user constraints and discount rates

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

The reference scenario: Residential results

  • Different solutions for different income groups
  • Each income group has a different motivation for engaging – higher income groups can

afford to meet GHG targets, become more efficient, increase comfort and act as forerunners, while lower income households are trying to afford a better living standard and want to save money

  • The best solutions are still not necessarily what people do. (e.g. SWH).

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10 20 30 40 50 60 70 80 90 2007 2010 2020 2030 2040 2007 2010 2020 2030 2040 2007 2010 2020 2030 2040 2007 2010 2020 2030 2040 high medium low poor Final residential energy consumption by income group [PJ] Electricity Candles Coal LPG NG Paraffin Solar Wood

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Income specific transport characteristics

  • Analysis of availability of passenger cars by income class
  • Assessment of mode of transport by employment level
  • Travel demand characteristics

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www.enerkey.info

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Transferability to German context

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  • Currently population represented as homogenous
  • Available data disaggregation possible by:

i. type of building, ii. number of people per household,

  • iii. energy carrier,
  • iv. end-use
  • but not all in combination with income -> data gymnastics required
  • Current monitoring of effect of energy transition on energy

affordability done through means of „sample households“ with the same energy consumption but with a different household income

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

What do we know about energy poverty in Germany?

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Residential electricity prices in Germany

Sources: Prognos 2014, BMWi 2014, VZBZ 2014, Eurostat 2014

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Geographical mapping of proxy energy poverty indicators

http://www.insightenergy.org/ 12

Germany estimated 5.5 – 11 million people in energy poverty EU estimated 50 – 120 million ppl

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

Conclusion

  • Value in disaggregation
  • Income specific recommendations for households and/or countries (?)
  • Scale to best capture aspects of energy poverty considering data

requirements/availability

  • Highlights implications for energy planning and monitoring of the energy

transition

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Thank you

Photo: Mark Lewis

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty

SUVs

(incl. ‚bakkies’)

Motor- cyles Minibus Taxi High speed train

(Gautrain)

Small Bus Aircraft

HDV Train

Cars

Individual Passenger Public Passenger Road

Train

LDV

(incl. ‚bakkies’)

Big Bus BRT

Motorised Transport Non motorised Transport Freight Transport Road Rail Air

Walking

Rail

Bicycles

Transport sector:

modes considered

Integration of Gauteng specific transport modes: e.g. minibuses, BRT and Gautrain

Source: Tomaschek, 2013

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Audrey Dobbins 20-21 April 2015 Modelling energy poverty Fuels

Ethanol (E85) Biodiesel (B100)

LPG CNG/SNG Kerosene Vehicle technology/ powertrain

Combustion engine Mild hybrid Full hybrid Plug-in hybrid Combustion engine Mild hybrid Full hybrid Plug-in hybrid Combustion engine Combustion engine Combustion engine Combustion engine Battery electric Trolley/ Grid Combustion engine Fuel-cell electric Jet turbine

Motorcycle

  • Car (small)
  • Car big (SUV)
  • Minibus
  • Bus (small)
  • Bus (big)
  • BRT
  • Train (passenger)
  • Light rail (Gautrain)
  • LDV
  • Truck
  • Train (freight)
  • Aviation
  • Petrol

Diesel Hydrogen Electricity 16

TIMES-GEECO: The transport sector

LPG = liquefied petroleum gas SNG = substitute natural gas CNG = compressed natural gas H2 = Hydrogen

Advanced features:

Driving profiles i. Highway

  • ii. Urban
  • iii. Rural

Transport infrastructure investments i. Bus rapid transit

  • ii. High speed train
  • iii. Trolley bus

Carbon capture and storage (CCS) Vehicle-to-grid (V2G) energy storage