From Static to Adaptive Control of Network Topologies for Improving Systems Resilience (DMA v2.0] [The next generation water supply systems?

Transcription

1 From Static to Adaptive Control of Network Topologies for Improving Systems Resilience (DMA v2.0] [The next generation water supply systems?] Ivan Stoianov, Kevin Henderson SWIG, October,

2 Operational Environment Long-term Leakage Management Performance affected by: Ageing infrastructure Influence of Outcomes and Penalties & Incentives (SIM score) Operating under a Cost/Benefit (SELL) assessment Greater impact of customer on Outcome of Environmentally Sustainable Business More severe variability in weather patterns

3 DMA v1.0 (Static Topology) Flow Meter PRV Graphics adapted from i2o Kept-Shut Boundary Valve Leakage Resilience, QoS Water Quality

4 Flow Networks & Resilience Telephone Networks / Internet Water Supply Networks Customer Connections Customers Customers Local Exchanges ISPs Supply Zones & DMAs Long-Distance Offices Backbone Pumping & Storage

5 DMAv2.0: Dynamically Configurable Network Topology Diurnal Topology Control Segregating Aggregating 01:00AM-05:00AM 05:00AM-01:00AM Single Feed Cascading Multi-Feed

6 DMA v2.0 Integrated System Smart Sensors Pressure, Flow, Water Quality High-Fidelity Hydraulic Data (InfraSense TS) WDS as a Process Plant Feedback on System Control (Process Instabilities, Control Failures) Computationally Efficient ALGORITHMS for the hydraulic analysis of large scale systems Robust & Adaptive Control Multi-Function Network Controllers Adaptive Control Functions (flow & pressure modulation, position control, 2-way control)

7 Smart Water Networks Demonstrator (Field Lab)

8 Smart Water Networks Demonstrator (Field Lab) Smart Water Networks Demonstrator: InfraSense technologies for continuous high-resolution monitoring of the dynamic hydraulic conditions Boundaries of PMA 90m AOD 61m AOD Hospital CP Network Controller: Functionalities for Flow Modulation, Position Control & Fixed Outlet Pressure Control. 24m AOD Hospital 25m AOD 26m AOD InfraSense TS & RT moitoring systems for acquiring hydraulic (pressure and flow) data with unprecedented spatial and temporal resolution. 3D Network Diagram Not to Scale Network Controller & InfraSense RT: - Pressure monitoring 128 S/s - Flow monitoring 1S/s - Stem position sensor - Vortex flow meter (e-flow) InfraSense TS

9 Modelling & Control H: Nodal Heads Q: Pipe Flows A: Incidence Matrix Q: Customer Demand η: Valve Settings H0: Reservoir Profiles F(Q): Frictional Head Loss Equation NN: Number of Nodes NP: Number of Pipes NV: Number of Self-Powered Control Valves Reference: Wright, R., Stoianov, I., Parpas, P., Henderson, K., and King, J. (2014). Adaptive water distribution networks with dynamically reconfigurable topology. Journal of Hydroinformatics, Available from:

10 Real-Time High-Res Data Smart Water Networks Demonstrator:(BW-Imperial-ClaVal) Pressure Managed Areas (PMAs) Boundaries of PMA 90m AOD 61m AOD Hospital Hospital CP 24m AOD 25m AOD 26m AOD 3D Network Diagram Not to Scale

11 Near Real-Time Hydraulic Model Plotted at 5min intervals, with upper and lower bands using data at 128S/S S_Ln PRV Pressure Outlet Model InfraSense Max InfraSense Min

12 Near Real-Time Hydraulic Model NS Road (South) South Model InfraSense Max InfraSense Mean InfraSense Min

13 Near Real-Time Hydraulic Model LCway PRV Inlet South Model InfraSense Max InfraSense Mean InfraSense Min

14 Near Real-Time Hydraulic Model LCway PRV Outlet South Model InfraSense Max InfraSense Mean InfraSense Min

15 Benchmarking Performance Comparison Scenarios: 1. Closed DMAs, Fixed pressure outlet 2. Closed DMAs, Flow modulation 3. Dynamic DMAs

16 Pressure Variability

17 The real McCoy 30 main 24,000 homes without water Pipe burst 30 main. 24,000 homes without water. ~ 4,500 homes were kept with a minimum level of service due to the adaptive configurability of the Field Lab and the provided redundancy in connectivity. Real-time InfraSense data available to assist engineers in their incident management response. The enhanced resilience (adaptive incident management ) and improved pressure management of DMA v2 has demonstrated great potential.

18 Measuring the Success Impact (success) is measured through the long-term monitoring of: Burst rates. Leakage levels. Customer complaints (incl interruptions of supply). Discolouration / Water quality (incl chlorine residual). Man hours spent per sector. Maintenance cost (rehabilitation, flushing, repairs). Understand the cost/benefits of those elements you influence with smart technology so to assess the level of scalability of the solution The enhanced resilience (adaptive incident management ) and improved pressure management of DMA v2 has demonstrated great potential. Further work on system integration and cost-benefit analysis.

19 Conclusions The journey to achieve Sustainable leakage management by 2040 Research & Development and Innovation Better understanding of failure mechanisms and pre-cursors to failures Benefits of steady-state operation ( calm networks ) Increased spatial and resolution of sensor/telemetry data Self-cleaning networks (diurnal velocity variations and residence time) Near real-time modelling and visual analytics Through the implementation of Smart/Smarter Systems District Meter Areas with dynamically configurable topology

20 Thank you Acknowledgements EPSRC, And all the dedicated team members in the collaborative partnership from InfraSense Labs (Imperial), Bristol Water, Cla-Val and NEC. For further information: Wright, R., Stoianov, I., Parpas, P., Henderson, K., and King, J. (2014). Adaptive water distribution networks with dynamically reconfigurable topology. Journal of Hydroinformatics. available from: