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

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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

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WDS FailuresExhibit abnormal flow/pressure patterns

Focus on pipe bursts

Exact cause typically unknown

Operational risk assessment

Failure risk is dynamic

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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

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Pipe Burst Occurrence LikelihoodCombination of several bodies of evidenceDempster-Shafer Theory

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Pipe Burst Impacts

Pipe Burst

Lost Water

Low Pressure

Discolouration

Supply Interruption

Energy Losses

Third Party Damage

Water Utility Customers

ECONOMIC

SOCIAL

ENVIRONMENTAL

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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

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Performance ConsiderationsRisk assessment computationally demandingDatabase-centric distributed architecture

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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

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Alarm 1 – Risk map (Low Impact)WMS Order No. 7252193

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Alarm 1 – Risk plotScatterplot of the Likelihood vs. Impact

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0.1

0.2

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0.5

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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

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Alarm 2 – Risk map (Medium Impact)WMS Order No. 6873187

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Alarms 1&2 Risk ComparisonScatterplot of the Likelihood vs. Impact

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0.1

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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

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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

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SummarySupporting control room personnelNon-aggregated risk presentationRisk-aware decision makingBetter insight into WDS behaviourImproved response to contingency situationsReduced failure consequences

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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