Making the South African TIMES (SATIM) Energy System Model ‘Water Smart’, by Adrian Stone from the University of Cape Town

Making the South African TIMES (SATIM)Energy System Model ‘Water Smart’

Adrian Stone, Bruno Merven & James Cullis (Aurecon) UN-Water Annual International Zaragoza ConferenceZaragoza, Spain – 13th January 2014

·ERC

Energy andclimate change

Energyefficiency

EnergySystems Analysis

Research Groups in ERC

Energy, poverty and

development

South Africa~52 million people.GDP ~6000 USD per capita (2011).~1.2 million m2 (12%) of total land cover suitable for crop production.~60% of allocated water consumed for irrigated agriculture.~90% of population with access to electricity.Increasing urbanisation of the population.Increasing demand for housing and water and sanitation services.

Low-in-

come; 42%

Mid-in-

come; 36%

High-income;

22%

Population distribution by income

at a glance…

Gauteng34%

Western Cape14%

KwaZulu-Na-tal

16%

Eastern Cape8%

Provincial share of GDP

DBNIrrigation; 60%

Municipal-urban; 24%

Munic-ipal-ru-ral; 3%

Indus-try; 3%

Mining; 3%

Electric-ity; 2% Other; 6%

Estimate of sectorial water use

JHB

CPT

National Water Resource Strategy

4

Water Marginal Cost Curves

5

Reconciliation of future demand and potential augmentation options for the Lephalale WMA

Water Supply Cost Curves

6Provisional costs for future water supply augmentation in the Lephalale catchment

Water Supply Cost Curves under Climate Change Risk

7

Potential impact of climate change on cost of future water supply options in the Lephalale WMA.

National Energy Model – Key Points• South African TIMES Model (SATIM)• Partial equilibrium linear least-cost optimisation model

capable of representing the whole energy system, including its economic costs and its emissions.

• A number of years of development - 2003 IEP, 2007 LTMS• Sectoral Representation - Electricity & Transport sector

represented in most detail.• Methodology & Assumptions in the public domain -• http://www.erc.uct.ac.za/Research/Otherdocs/Satim/SATIM%20Methodology-

v2.1.pdf• http://www.erc.uct.ac.za/Research/esystems-group-satim.htm• http://www.erc.uct.ac.za/Research/publications/12-Merven-etal_Quantifying_

energy_needs_transport%20sector.pdf8

Main Features• Bottom-up Energy Systems Least-Cost Optimization Long-Range

(>10 years) Planning Model (similar to the one used for the IEP)• Full Sector: Includes and allows trade-off between demand and

Supply• End-use type model:

– Gives a detailed description of how the energy is used.– Describes the types of equipment used and how much energy is used by

each type of equipment to satisfy demand.– Can be used to forecast useful energy as well as final energy demand– Can capture:

• structural changes/ shocks• mode switching (transport)• fuel switching• Technical improvement/ improved efficiency• Intensity changes e.g. mines have to dig deeper

Objective – Minimise the cost of supplying an energy service

TIMES – represent & cost entire energy system – cost optimal pathways under constraints

10

Energy model components

• Made up of 2 simple components:– Energy Carriers (e.g. fuels, demand)– Technologies (e.g. Light bulb, power plant) all

characterized in the same way:– Input and Output Carrier (Commodity)– Efficiency– Investment Costs per unit of capacity– Activity Costs– Existing Capacity– Annual Availability– Expected Life– Emissions

These are the parameters that affect the cost of supplying the energy service

Simple Reference Energy System

Commodities are input to and output from technologies along competing chains to supply an energy service. Water can be one of these commodities if we know enough about supply.

Scenarios we will look at…..• Optimisation results with and without water

costs.• Climate change impacts on water supply cost

curves• 4 GHG constrained scenarios – contrasting 275

Mton cap on the power sector with CO2 tax

options based on National Treasury’s proposed tax structure.

Shortcomings of TIMES

• No demand response (unless used with elastic demand, in which case a price elasticity is needed for each end-use)

• Impacts on the rest of the economy, and socio-economic indicators not quantified

• For answering broad techno-economic planning questions – neither a lot of engineering or economic detail. Can be reliant on good micro-analysis.