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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. Centre for Water Systems, University of Exeter, Exeter, UK. Outline. Motivation WDS Failures Risk-based decision making Case study Future work Summary. 2. Motivation.

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Operation of Water Distribution Systems Using Risk-based Decision Making

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  1. 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

  2. Outline • Motivation • WDS Failures • Risk-based decision making • Case study • Future work • Summary 2

  3. Motivation Support operator’s decision making WDS operation under abnormal conditions Help prioritise actions of the operators Reduce impact on customers Meet regulatory requirements EPSRC Neptune project

  4. WDS Failures Exhibit abnormal flow/pressure patterns Focus on pipe bursts Exact cause typically unknown Operational risk assessment Failure risk is dynamic

  5. Risk Assessment Potential Incident 1 Internal Alarm List Impact 1 Likelihood Alarm 1 Potential Incident 2 Impact 2 Alarm 2 Type Size Location Timing Impact Risk Horizon Network State Forecasted Demands … Alarm M … Impact X … i.e. (water & energy losses, low pressure, supply interruption, discolouration, damage..) Potential Incident N

  6. Pipe Burst Occurrence Likelihood Combination of several bodies of evidence Dempster-Shafer Theory

  7. Pipe Burst Impacts Lost Water Water Utility Customers Low Pressure ECONOMIC SOCIAL ENVIRONMENTAL Supply Interruption Pipe Burst Discolouration Third Party Damage Energy Losses

  8. Risk-based Decision Making Risk maps Non-aggregated risk Pipe burst investigation Likelihood of burstoccurrence Impact of the burst over a given horizon Likelihood Low High Impact Low High

  9. Performance Considerations Risk assessment computationally demanding Database-centric distributed architecture

  10. Case Study 16 DMAs 25,000 properties 95% residential >300 km of mains Demand: 35 MLD >8,700 Nodes >9,000 Pipes 69% not metered • Urban DMA • 1,600 properties • 95% residential • 19 km of mains • Demand: 1 MLD • 447 Nodes • 468 Pipes

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

  12. Alarm 1 – Risk plot

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

  14. Alarms 1&2 Risk Comparison

  15. Future work Automated prioritisation of alarms Based on the risk of all potential incidents Further performance improvements Grouping of similar pipes using clustering Implementation in a near real-time DSS

  16. Summary Supporting control room personnel Non-aggregated risk presentation Risk-aware decision making Better insight into WDS behaviour Improved response to contingency situations Reduced failure consequences

  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

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