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Operation of Water Distribution Systems Using Risk-based Decision Making. Josef Bicik , Dragan A. Savić & Zoran Kapelan. Centre for Water Systems, University of Exeter, Exeter, UK. Outline. Motivation WDS Failures Risk-based decision making Case study Future work Summary. 2. Motivation. - PowerPoint PPT Presentation
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Operation of Water Distribution Systems Using Risk-based Decision Making
Operation of Water Distribution Systems Using Risk-based Decision Making
Josef Bicik, Dragan A. Savić & Zoran Kapelan Josef Bicik, Dragan A. Savić & Zoran Kapelan
Centre for Water Systems, University of Exeter, Exeter, UKCentre for Water Systems, University of Exeter, Exeter, UK
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OutlineMotivationWDS FailuresRisk-based decision makingCase studyFuture workSummary
2
3/17
MotivationSupport operator’s decision making
WDS operation under abnormal conditions
Help prioritise actions of the operators
Reduce impact on customers
Meet regulatory requirements
EPSRC Neptune projectNEPTUNENEPTUNE
MONITORMONITOR CONTROLCONTROL OPTIMISEOPTIMISE
EPSRCEPSRC
ABBABBYorkshireYorkshire
WaterWaterUnited United UtilitiesUtilities
NEPTUNENEPTUNEMONITORMONITOR CONTROLCONTROL OPTIMISEOPTIMISE
EPSRCEPSRC
ABBABBYorkshireYorkshire
WaterWaterUnited United UtilitiesUtilities
4/17
WDS FailuresExhibit abnormal flow/pressure patterns
Focus on pipe bursts
Exact cause typically unknown
Operational risk assessment
Failure risk is dynamic
5/17
Risk Assessment
InternalAlarm List
Alarm 1
Alarm 2
Alarm M
Potential Incident 1
Potential Incident 2
Potential Incident N
…
Impact 1
Impact 2
Impact X
…
…
NetworkState
ForecastedDemands
RiskHorizon
i.e. (water & energy losses, low pressure, supply interruption,
discolouration, damage..)
Likelihood
ImpactTypeSize
LocationTiming
6/17
Pipe Burst Occurrence LikelihoodCombination of several bodies of evidenceDempster-Shafer Theory
7/17
Pipe Burst Impacts
Pipe Burst
Lost Water
Low Pressure
Discolouration
Supply Interruption
Energy Losses
Third Party Damage
Water Utility Customers
ECONOMIC
SOCIAL
ENVIRONMENTAL
8/17
Risk-based Decision MakingRisk mapsNon-aggregated riskPipe burst investigation
Likelihood of burstoccurrence
Impact of the burst over a given horizon
Low High
LikelihoodLikelihood
Low HighImpactImpact
9/17
Performance ConsiderationsRisk assessment computationally demandingDatabase-centric distributed architecture
10/17
Case Study16 DMAs25,000 properties95% residential >300 km of mains Demand: 35 MLD>8,700 Nodes>9,000 Pipes69% not metered
Urban DMA1,600 properties95% residential 19 km of mains Demand: 1 MLD447 Nodes468 Pipes
11/17
Alarm 1 – Risk map (Low Impact)WMS Order No. 7252193
12/17
Alarm 1 – Risk plotScatterplot of the Likelihood vs. Impact
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Likelihood
No
rmal
ised
Imp
act Alarm 1
13/17
Alarm 2 – Risk map (Medium Impact)WMS Order No. 6873187
14/17
Alarms 1&2 Risk ComparisonScatterplot of the Likelihood vs. Impact
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Likelihood
No
rmal
ised
Im
pac
t Alarm 1
Alarm 2
15/17
Future workAutomated prioritisation of alarms
Based on the risk of all potential incidents
Further performance improvementsGrouping of similar pipes using clustering
Implementation in a near real-time DSS
16/17
SummarySupporting control room personnelNon-aggregated risk presentationRisk-aware decision makingBetter insight into WDS behaviourImproved response to contingency situationsReduced failure consequences
17/17
Thank you!Questions?
The work on the NEPTUNE project was supported by the U.K. EPSRC grant EP/E003192/1 and Industrial Collaborators.
www.exeter.ac.uk/cws/neptune