Recent IAM presentation by Dr J Hagan of Severn Trent Water, providing an overview of the modelling process STW go through for regulatory planning within the water sector.
2. INTRODUCTION
• Decision Support Tools (DSTs), predictive and prescriptive analytics, enable the processing of
large, complex datasets. For asset-focussed organisations, DSTs provide two key benefits:
• Insight and foresight for deterioration, service, performance, safety and risk
• Forecasting of the impact of different investment patterns or maintenance strategies
• DST’s allow us to find the appropriate balance between proactive and reactive investment
• Severn Trent Water (STW) has a suite of mature models that help the business make
investment decisions
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• In Water Infrastructure, STW have used asset modelling since PR09.
• STW use a number of other asset models, e.g. Sewer infrastructure model, non-infra models.
• Outputs from asset models are transferred to a “Portfolio Optimiser”
• Last year STW upgraded from WiLCO to Enterprise Decision Analytics (EDA)
IAM June 2017
7. PR19 BUSINESS
PLANNING PROCESS
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Modelled Schemes
Business driven needs-
Bespoke Schemes
Portfolio
Optimiser Price Controls
Water
Waste
Bioresources
Water
Resources
Retail
Risks
Totex
costs
Performance
measures
Willingness
to pay
IAM June 2017
8. 7
Decision
Support Tool
Assets
Included
Water, Waste
& Sludge
Mechanical,
Electrical &
Instrumentation
Treatment,
Pumping &
Storage
Distribution
Mains
Gravity Sewers
& PDaS
Portfolio
Optimiser
Water Infra
Strategic Model
Non Infra
Models
Waste Infra
Strategic Model
Genetic
Algorithm
Linear
Optimisation
Linear
Optimisation
MODELLED SCHEMES
Linear
Optimisation
Increasing Complexity
Trigger Prioritiser Linear Optimisation Genetic Algorithm
Solver Type
IAM June 2017
9. WATER INFRASTRUCTURE SUPPLY
DEMAND MODEL (WISDM)
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Our approach uses a single model (WiSDM) to derive the least cost plan to achieve Supply Demand
and Maintenance needs.
Inputs WiSDM
Pipe Maintenance
Plan
Supply Demand
Plan
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10. INPUTS TO WISDM
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WiSDM
Aquator
Demand
Schemes
Outage
Allowance
Target
Headroom
WAFU
Total
WAFU
11. WISDM
IAM June 2017
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Our approach uses a single model (WiSDM) to derive the least cost plan to achieve Supply Demand
and Maintenance needs.
Pipe Maintenance Plan Supply Demand Plan
12. 11
UKWIR guidance describes ability and
sophistication of modelling approaches
We applied the UKWIR problem
characterisation approach to assess the size
and complexity of the supply demand
situation from 2020 to 2045.
We have a large, difficult to solve problem
and therefore need to enhance our modelling
methods.
IAM June 2017
Output of our Problem Characterisation assessment
(March 2016)
WRMP19 PROBLEM CHARACTERISATION
WiSDM can be configured to build Dynamic ‘adaptive’ pathways. We
call this enhancement to WiSDM the “Decision Making Upgrade”
(DMU).
13. DMU OVERVIEW
IAM June 2017
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Results
ResultsVirtual
Model
Virtual
Model
Model
Input data & Modelling Processing Stage 1
Schemes
Results
Post-
Processing
Analyse & identify
Dynamic Plan(s)
Processing Stage 2 Visualise & Report
Virtual
Model
Optimser
Real
Options /
Scenarios
Pipe Plan
Results
Results
(Results Service)
Calculate
Scheme
Uncertainty
parameters
Define inputs:
• Schemes
• Pipe plan options
• Uncertainty Parameters
• Import WRMP & SDB schemes
• Define uncertainty around cost, time to benefit & DO
• Generate uncertainty using Latin Hypercube
Existing WiSDM model that combines WRMP,
SDB & Pipe investment optimisation
14. • Define SDB scenarios (eg WFD, climate change, growth etc)
• Combine scenarios & uncertainty
• Solve each with non-linear optimiser
~ over 40 ‘Alternative futures’
~ over 100 scheme options
~ 100-1000 Latin Hypercube samples
= ~ 4,000 - 40,000 optimisations
DMU OVERVIEW
IAM June 2017
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Results
ResultsVirtual
Model
Virtual
Model
Model
Input data & Modelling Processing Stage 1
Schemes
Results
Post-
Processing
Analyse & identify
Dynamic Plan(s)
Processing Stage 2 Visualise & Report
Virtual
Model
Optimser
Real
Options /
Scenarios
Pipe Plan
Results
Results
(Results Service)
Calculate
Scheme
Uncertainty
parameters
15. SCENARIOS – ALTERNATIVE
FUTURES
We are planning to consider ~40 scenarios through the DMU to test a range of possible
futures with by varying:
• WFD No Deterioration
• RSA
• Demand
• Climate change (2030s)
• Climate change (2080s)
• Extreme Drought
14IAM June 2017
16. DMU OVERVIEW
IAM June 2017
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Results
ResultsVirtual
Model
Virtual
Model
Model
Input data & Modelling Processing Stage 1
Schemes
Results
Post-
Processing
Analyse & identify
Dynamic Plan(s)
Processing Stage 2 Visualise & Report
Virtual
Model
Optimser
Real
Options /
Scenarios
Pipe Plan
Results
Results
(Results Service)
Calculate
Scheme
Uncertainty
parameters
Build rich
visualisations
• Build automated pipeline to
process input, optimise & extract
results.
• Process becomes quick and easy
to re-run
• Frequency analysis of results to define
new ‘dynamic plans’
• Plan/Pathway details & Uncertainty
visualisation
• Demonstrate ‘low regret’ options that
make sense in multiple futures
• Build rich visualisations & report
17. RESULTS
IAM June 2017
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• These are NOT real scenarios; the presented results consider only 6 scenarios using numbers
generated to test the functionality and methodology of the DMU.
