Barry McDonnell Sydney Water

1. Validating the
Robustness of an
Optimised Water
Infrastructure
Investment Plan
Barry McDonnell, Strategic Investment
Optimisation Manager - Sydney Water
with

2. Themes...
Digital Twins to
inform Long Term
Capital Operating
Plan
Need for agile /
adaptive /
quantifiable
planning processes
1 2 3
Business
strategy and
asset challenges

3. 3

4. Multiple drivers & challenges drive the need for
holistic & integrated infrastructure planning.
SW
challenges
Rapid Growth
Significant population growth
Renewals of Aging Assets
Aging asset profile
Service Level Enhancement
Increased service and
regulatory pressure
Asset Capacity
Assets are reaching capacity
Financial Impact
Pressure to manage
customer bill inflation
Enabling Our Vision
A liveable, sustainable,
productive city requires
long-term integrated planning
Stakeholders
Need long term investment
plans
Address scenarios with
adaptive planning options
$$
Cost drivers
Constraints

5. Optimisation & Simulation Digital Twins provided
the tools we needed to move up the analytics
maturity curve...
Complexity/Competitive
Advantage
Time/Value/Insight
What might happen?
How should I act?
What is the best
that could happen?
Cleansed
data
Ad-hoc
BI/Standard
reports
Data
exploration
Data
analytics
Predictive
Artificial
Intelligence
Optimisation
Prescriptive
Individual and
collective asset
models in
spreadsheet
Sense & Respond Enabled by
Digital Twins
Predict & Act
What happened and why?

6. …and were used at key points in a Strategic
Planning Process that was clear to all
stakeholders.

7. The two Digital Twins are complementary; they
model the same network….
• SCIP (Strategic Capital Investment Plan)
model was built in 2018 to support long-
term (30+ years) investment pathway
evaluation for Sydney Water
• SWIFT (stochastic) model was developed
in 2020 to stress test long-term investment
pathways and policy settings (business
rules) against a broad range of variable
future conditions
• Both SCIP and SWIFT models
simultaneously consider costs / benefits
and ALL critical elements of the Greater
Sydney Water Cycle

8. 02
03
04
05
01
Stochastic
Assumptions
Economic Gap
Assessment
SWIFT @ Measure
Network Performance
SCIP @ Optimise
Network Configuration
Test “Average”
conditions
Steady State
Assumptions
… and they work together as part of a cyclical
evaluation process.
Test “Variable”
conditions and policy
settings

9. SCIP is a steady-state network model with a
deterministic investment optimiser.
• Mixed integer programming model
• Built on River Logic Enterprise Optimiser
Platform
• Optimises by balancing supply and
demand for water at minimum cost
• Produces infrastructure investment plans
based on forecast water yield, demand
and investment strategy
• Recommends and sequences major asset
investment in annual buckets
• DOES NOT model variability or water
inventory

10. The SWIFT simulation tool models input variability,
system storage & policy settings (business rules).
• Built on AnyLogic modelling platform
• High-level digital representation of
the Greater Sydney water supply /
wastewater treatment network
• Catchments & reservoirs
• Major canals & rivers
• Non-potable, potable & recycled water
• Water filtration, waste treatment, water
recycling
• Climate forecast-based water production
• Water demand forecasts
• Operating rules, water restrictions
• Infrastructure construction
• Measurement & metrics collection

11. In SWIFT, the investment portfolio is fixed, inputs are
varied & outputs & policy settings are tested against
service level targets.
Each SWIFT simulation takes prescribed network configurations, testing time-frame
(from SCIP or elsewhere) & operating rules, randomised inflows & demand profiles
& records a range of performance metrics as it simulates the system operation
through time.
Outputs
• Restrictions – depth
frequency / duration
• Nodal Supply Failures
• Asset / Transfer Scheme
Utilisation
• Capital / Operational Costs
• Wet / Dry weather Overflows
Each “scenario” is executed a large number of times (iterations)
Each iteration produces a different result due to the modelling of random events
Analysis of the iteration set enables us to quantify performance statistically
Inputs
• Inflow Profiles
• Demand Profiles
• Network (current / proposed)
• Transfers
• Restrictions
• Capital ‘response’ items

12. Each investment portfolio performance vs service
levels is quantified.
• Mandatory KPI’s:
• Reliability – proportion of total time not requiring water
restrictions
• Robustness – the proportion of years that did not require
water restrictions
• Security – the proportion of years in which system reserves
are above a threshold
• Exploratory KPI’s:
• Used to gain further understanding of scenario performance
e.g.: for 10,000 possible weather realisations, what proportion
of iterations exceed a given threshold?
They provide the ability to
optimally plan for the most
likely scenario while
understanding the range of
potential outcomes, thus
providing a risk-based
understanding of the long-
term infrastructure
investment plan
Many scenarios are investigated to provide an understanding of the planned system’s
performance should the climate or demand predictions prove incorrect

13. It’s all brought together into an overall Adaptive
Planning process.
• The Adaptive Pathways help communicate key decision points
• Baseline plans can be updated in a systematic, yet agile way to update
pathways and provide strategic investment forecasts/planning context
internally and for a range of stakeholders
• The planning process and data/solution integration is continually evolving
and improving
Key decision/investment adaptive pathways are identified
Not a once-off – the tools enable a repeatable, agile approach and are continuously
improving

14. Let’s take a look

16. Questions...

17. Thank you
sydneywater.com.au