model results: water demand

MODELLING OF DEMAND FOR SERVICES THROUGH THE CHARACTERISATION OF TOWNS AND AREAS THROUGH STANDARD DWELLING TYPES
Presented by:
Tian Claassens
Jon Lijnes
CONTENTS
1. BACKGROUND
2. BIGEN AFRICA
3. RISK MANAGEMENT
4. COMMON MISTAKES
5. BIGEN AFRICA METHODOLOGY
6. CASE STUDY
7. USING DEMAND MODEL FOR RISK MITIGATION
8. CONCLUSIONS
BACKGROUND
•
In 1994 South Africa launched an initiative to extend basic services (water,
sanitation & electricity supply) to all communities
•
Large parts of the country and communities remained “un-serviced” due to
the previous dispensation of apartheid
•
Government has also launched a housing initiative to:
– Convert informal settlements to formal settlements
– Convert shacks to formal dwellings
– Provide each citizen with a decent house
BACKGROUND cont’d
• Also driven by the UN Millennium Development Goals
• South Africa has committed inter alia to:
– Target 9: integrate the principles of sustainable development into country
policies and programmes and reverse the loss of environmental resources
– Target 10: halve by 2015 the proportion of people without sustainable access
to safe drinking water
– Target 11: by 2020 to have achieved a significant improvement in the lives of at
least 100 million slum dwellers
BACKGROUND cont’d
MAIN AIM IS TO TURN THIS
INTO THIS!
BACKGROUND Cont’d
•
“BACKLOG” – Quantified expenditure to meet the millennium
goals
•
15 years later and extent of backlog is growing:
– Limited government resources
– Inefficient expenditure
– Unsustainable expenditure/investments
•
Widely recognised that extent of backlog is too large to be
eradicated through government resources only
•
Commercial finance (project finance) will play a key role in next
15 years
•
Risk identification, management & mitigation becomes key
factors for success
BIGEN AFRICA
•
Development activist company
•
Multi-disciplinary project teams including:
•
–
Engineers (civil, electrical, township etc.)
–
Project managers
–
Town planners
–
Project finance specialists
Package major infrastructure and housing projects for implementation and
finance
•
Focus on risk management
•
Mitigate risks that impact on “bankability” of projects
RISK MANAGEMENT
•
What are the key risks in this context?
–
Estimating demand for service in a target area
–
Growth in demand?
–
Cost of service versus affordability
–
Ability/strategy to recover costs
–
Sustainability
credit risk
•
Broadly referred to as “Demand Risk”
•
What are the consequences of Demand Risk?
–
Inappropriate system design
–
Capital inefficiency (scarce resource!)
–
Low cost recovery
–
Unsustainable systems manifested through low maintenance expenditure
–
Failure of supply
–
Total system failure
–
“Non-bankable” projects
high default rate
default
COMMON MISTAKES REFLECTING DEMAND RISK
•
•
Existing supply as basis
–
Ignores current restrictions in systems
–
Ignores losses in systems (can be major e.g. > 40%)
–
Ignores incorrect data (readings)
Population numbers as basis
–
Ignores inaccuracy of census info (RSA)
–
Ignores differences in consumption patterns of socio economic groups (housing
typologies)
–
•
Ignores changes in demand because of socio economic shifts
Inappropriate growth modelling
–
Uniform growth rate applied in “perpetuity”
–
Measured short-term spurts projected over the long term
THE BIGEN AFRICA METHODOLOGY
•
Demand risk is encountered on every infrastructure project
– Key risk that inhibits availability of project finance
•
Bigen Africa has developed a methodology to:
– Quantify demand risk
– (Partially) mitigate demand risk
– Price demand risk (in a municipal context)
•
This methodology is based on the following premises:
– Key drivers of demand in a region or area:
• Total number of households in the region
• Total commercial/institutional floor area in the region
– Household demand is a function of the housing typology
– Key typology characteristic is the number of toilets
– Growth in demand is primarily driven through growth in the
number of dwellings or commercial floor space
THE BIGEN AFRICA METHODOLOGY
•
From these premises it follows that:
– If we know the total number of houses in an area
– And each house has been characterised in terms of housing typology
– Then demand for a service in the area can be determined
•
Similarly:
– If we forecast how the number of houses of each typology in the area will
grow
– Then we can forecast growth of demand in the area
•
For characterisation of houses in terms of typology we use standard dwelling
types (“SDT’s”)
Example: Low Capacity SDT
Water
Demand
EFFLUENT
FLOW (kl/month)
(%
WATER DEMAND)
Seasonality
ofOF
demand
• Typically found in all towns & villages as well as most urban
areas
• Enjoys a full level of service:
─ Water supply (house or yard connection)
─ Sanitation (water borne)
─ Electricity supply
• Serviced through dirt or paved roads
• Dwelling sizes typically range between 34 m2 and 80 m2
• In metropolitan areas, erf sizes will typically be smaller than
350m2
• Units feature a single toilet
• Housing 2 to 8 people
• 1 out of 3 units uses electricity for cooking
• 9 out of 10 units feature an electrical geyser
LIST OF SDT’S
•
•
•
•
•
•
•
•
Low level of service (“LLOS”)
Intermediate level of service (“ILOS”)
Low capacity (“L‐CAP”)
Medium capacity (“M‐CAP”)
High capacity (“H‐CAP”)
Medium capacity type 2 (“M‐CAP2”)
High capacity type 2 (“H‐CAP2”)
Commercial/institutional (“COMM‐INST)
SDT Examples: LLOS & ILOS
SDT Examples: L‐Cap
WITH BACK YARD DWELLINGS
SDT Examples: M‐Cap
SDT Examples: H‐Cap
SDT Examples: Comm/Inst
SHOPS
OFFICES
SCHOOLS
MUNICIPAL
ADVANTAGES OF USING SDT’S
• Rapid model development
• Model simplicity
• Relative ease of obtaining an accurate modelling basis – aerial photography and counts
• Elimination of individually driven estimation errors
• Understanding of the model by a wider audience is enhanced as most people can generally and readily identify with the SDT’s and their associated demand for services
• Integration is facilitated with a logical connection to tariff and other policies
• Standardisation of models across projects, municipalities and regions
• Benchmarking of models across projects, municipalities and regions GENERAL SET‐UP OF MODEL
•
Identify area to be modelled
– Identify/set‐up sub‐areas if required
• Quantify total number of dwellings in area:
– Aerial photography
– Physical counts
– Area based extrapolation
– Other estimates
• Classify dwellings in terms of SDT’s
• Formulate growth model for each SDT
• Run @Risk simulation
• Extract 5 demand scenarios from simulation data
CASE STUDY
SOL PLAATJE LOCAL MUNICIPALITY
LOCALITY
GROUND COVER INDICATES ARID NATURE OF AREA
CONTEXT
FLAMINGO ISLAND
KIMBERLEY
HADISON
RETSWELELE
CITY
ROODEPAN
CENTRE
BIG
PARK
HOLE
GALESHEWE
KAMFERS DAM
ECOLOGICAL
Large
ANDVariation
ENVIRONMENTAL
in Land UseCRISIS
CONTEXT
Drive to establish bulk infrastructure to alleviate housing backlogs
•
All developments constrained due to inadequacy of services
•
Overloading of existing reticulation infrastructure (back‐yard dwellings)
•
Shortfall of income due to inadequate tariff structures
•
Inability to raise loans due to shortfall of income
•
Unaccounted for water and sewage spills
•
Environmental effects on Kamfers dam and flamingo breeding colony
•
Influenced by specific interest groups, resulting in wrong decisions
MODELLING PROCESS
•
Municipal area was divided into “demand‐zones”
– Based on
• Natural drainage areas for sewage
• Electrical supply zones and • Water supply zones
• Road service areas
– Reasonably uniform dwelling characteristics
– Spatial constraints
•
Physical count of dwellings carried out
– In each demand zone
– Based on aerial photography (Dec 2006)
MODELLING PROCESS (Ctd)
•
Estimate of back‐yard dwellings in each demand zone determined
– Direct field observations (samples) for each zone
– Statistical estimation – known variance & error
•
Commercial/industrial /institutional structures/dwellings
– No bulk data available from municipality
– Direct field observations (sample) for each zone
– Statistical estimation – known variance & error
KIMBERLEY: DEMAND ZONES
KIMBERLEY: RESIDENTIAL SDT DATA
COMM/INST SDT DATA
MODEL RESULTS: TOTAL HOUSING
MODEL RESULTS: TOTAL HOUSING
30-Sep-14 / Total Dwellings
59.93
5.0%
2.5
66.15
90.0%
5.0%
30-Sep-14 / Total Dwellings
1.5
Minimum
Maximum
Mean
Std Dev
Values
1.0
0.5
Values in Thousands
74
72
70
68
66
64
62
60
58
0.0
56
Values x 10^-4
2.0
56455.1467
72604.6100
62915.0590
1898.1005
1000
MODEL RESULTS: TOTAL NEW HOUSING
MODEL RESULTS: INFORMAL HOUSING
MODEL RESULTS: TOTAL HOUSES PER ZONE
MODEL RESULTS: NEW HOUSES PER ZONE
MODEL RESULTS: NEW TYPES OF HOUSES
MODEL RESULTS‐COMM UNITS PER ZONE
MODEL RESULTS: WATER DEMAND
ORIGINAL PLANNING WAS TO INCREASE
THE EXISTING TREATMENT WORKS
CAPACITY BY 60Ml/D
MODEL RESULTS: WATER DEMAND (1)
30-Sep-14 / Total water consumption (Ml/day)
62.95
5.0%
0.09
80.21
90.0%
5.0%
0.08
0.07
30-Sep-14 / Total water
consumption (Ml/day)
0.06
0.05
Minimum
Maximum
Mean
Std Dev
Values
0.04
0.03
0.02
0.01
95
90
85
80
75
70
65
60
55
50
0.00
54.7288
91.1906
71.3600
5.3609
1000
MODEL RESULTS: WATER DEMAND (2)
MODEL RESULTS: WATER DEMAND (3)
RESULTS: ABNORMAL WATER LOSSES
MODEL RESULTS: SEWAGE
ORIGINAL PLANNING WAS TO REHABILITATE
THE EXISTING HOMEVALE WORKS WITHOUT
PROVIDING ADDITIONAL CAPACITY
MODEL RESULTS: SEWAGE (2)
MODEL RESULTS: SEWAGE (3)
46.74
61.41
5.0%
0.10
90.0%
5.0%
0.09
0.08
0.07
30-Sep-14 / Total sewage
effluent (Ml/day)
0.06
Minimum
Maximum
Mean
Std Dev
Values
0.05
0.04
0.03
0.02
0.01
75
70
65
60
55
50
45
40
0.00
40.4875
72.7238
53.7802
4.5141
1000
MODEL RESULTS: SEWAGE (4)
MODEL RESULTS: ENERGY DEMAND
MODEL RESULTS: ENERGY DEMAND
MODEL RESULTS: ENERGY DEMAND
MODEL RESULTS: POWER DEMAND
ORIGINAL PLANNING WAS TO ADD
EXTENSIVE BULK ELECTRICAL
INFRASTRUCTURE TO ABOVE 200 MVA
INTERPRETATION OF RESULTS
• Water supply: – No need to increase capacity of main supply system
– Focus on elimination of losses
– Demand management through appropriate tariffs & tariff structure
• Sewage treatment:
– Critical to expand capacity to 70 Ml/d vs 48 Ml/d as planned
– Critical to divert treated effluent away from Kamfers dam
– New capacity to be provided on new site
INTERPRETATION OF RESULTS cont’d
• Electricity supply: – No need to increase capacity to 200 MVA
– Capital expenditure should be limited to:
• Refurbishment
• Enhancing firm capacity
• Enhancing reliability of system
USING DEMAND MODEL FOR RISK MITIGATION
• In a project finance scenario 2 key risks must be mitigated:
– Demand risk
– Cost recovery risk
• Demand model is a key tool to quantify these 2 risks:
– Linked to detailed Financial model – Used to design suitable mitigation mechanisms
MITIGATING DEMAND RISK
This
risk is mitigated as follows:
Difference quantifies demand risk
1. Infrastructure is sized for HIGH SCENARIO
2. Income projections in Financial model
based on EXPECTED SCENARIO
3. Project tested for financial robustness at
LOW SCENARIO
4. Key parameter to adjust robustness:
TARIFF
MITIGATING COST RECOVERY RISK
•
Cost recovery risk (perceptions) vary for SDT’s:
– LLOS, ILOS & L‐CAP: High Risk
– M‐CAP, H‐CAP & COMM/INST: Low risk
• Two key problems:
– Financiers typically confuse population size & demand
– Municipalities typically use uniform cost recovery strategies across the board
• Through the demand model we shift paradigms to:
– Prove the 80:20 rule
– Understand that risk determined by the ‘Zone’ not the ‘SDT’
– Accept different cost recovery strategies for different zones
MITIGATING COST RECOVERY RISK
CONCLUSIONS
•
Using the model we now understand:
– What drives demand for services (housing)
– Where the demand for services are – Where the demand will be in future
– Who will use the services
• Model forms the basis of:
– Engineering/planning
– Financial model
– Revenue model & strategy
– Affordability analysis
– Integration between services – housing, water, sanitation, electricity etc.
• Model is the critical tool for risk analysis in all of these applications
THANK YOU FOR YOUR
ATTENTION