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Smart Real-time Control of Water Systems

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Smart Real-time Control of Water Systems

Henrik Madsen(1), Peter Steen Mikkelsen(2), Lasse Engbo Christiansen(3), Anne Katrine Falk(1), Morten Borup(2), Rune Juhl(3), Nadia Schou Vorndran Lund(2), Rasmus Halvgaard(1), Nina Donna Sto. Domingo(1), Lisbeth Birch Pedersen(1), Stephen J. Flood(1) & Lene Bassøe(4)

(1)DHI, Agern Alle 5, 2970 Hørsholm, DK
(2)DTU Environment, Bygning 115, , 2800 Kongens Lyngby, DK
(3)DTU Compute, Bygning 324, 2800 Kongens Lyngby, DK
(4)Aarhus Water, Bautavej 1, 8210 Aarhus V, DK

Delivered at: Urban Drainage Group Autumn Conference and Exhibition 2016, Blackpool, UK, November 9th–11th 2016

Presenter: Dr. Lisbeth Birch Pedersen (Product Owner, MIKE Powered by DHI)

Keywords: smart water, smart cities, urban drainage, climate change, surrogate modelling, weather radar, frameworks

Cities across the world are facing significant water system challenges related to urbanisation and climate change. To advance the concept of integrated real-time control of water systems, an innovative generalised technology framework has been developed as part of a collaborative research effort. The framework facilitates the global acceptance and improved implementation of smart real-time control of water systems which offers a proven, cost effective alternative to the conventional approach of increasing system capacity.

The core of the framework is a generalised data platform which can be used within all water domains. This platform integrates time series data and spatial data with numerical models, and offers a broad suite of generalised tools for data processing and reporting. Data tools can be executed as automated workflows that replace tedious and error prone manual tasks. A key element of the work is the development of fast and accurate surrogate models of complex physical systems, complementing standard deterministic high-fidelity models adapted to observations in real time in order to ensure efficiency and performance of the automated model predictive control algorithms. A second element is the utilisation of local area weather radar data, since distributed rainfall observations and accurate forecasts are essential for optimising the use of the system capacity.

This work focuses on the impacts of using the new smart real-time control of water systems framework on a full scale, real world example using the urban drainage system of the city of Aarhus, Denmark as a testbed.

Published in: Engineering
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Smart Real-time Control of Water Systems

  1. 1. Smart Real-time Control of Water Systems Henrik Madsen, Peter Steen Mikkelsen, Lasse Engbo Christiansen, Anne Katrine Falk, Morten Borup, Rune Juhl, Nadia Schou Vorndran Lund, Rasmus Halvgaard, Nina Donna Sto. Domingo, Lisbeth Birch Pedersen, Stephen J. Flood & Lene Bassøe Urban Drainage Group Autumn Conference and Exhibition 2016 Blackpool, November 9th – 11th Dr. Lisbeth Birch Pedersen Product Owner MIKE Powered by DHI
  2. 2. Outline • MPC and surrogate models in real-time control of urban water systems • Aarhus – full scale test and implementation • Status and what’s next © DHI #2
  3. 3. Who am I? © DHI Tax payer Project consultant End user #3
  4. 4. Motivation for MPC and surrogate models in real-time control of urban water systems © DHI #4
  5. 5. Integrated Urban Water Management © DHI #5
  6. 6. Different Solutions for Different Maturity Levels and Different Challenges Optimised MPC Forecast model On-line model On-line Data © DHI #6
  7. 7. © DHI Local control System-wide Model Predictive Control (MPC) #7
  8. 8. Why think of a new optimisation approach? © DHI Current optimisation system • Uses simplified optimisation model (few decision variables) • Includes execution of many hundreds of MIKE 11 simulations • Takes hours on a 16-core server New optimisation system • Uses detailed optimisation model (thousands of decision variables) • Model dynamics described by a simplified (surrogate) model • Takes few minutes on today’s laptop #8
  9. 9. What is a surrogate model? • Derived from the HiFi model (MIKE model) • Sufficiently accurate for modelling the most important characteristics relevant for the problem at hand • Computationally fast © DHI Automated conceptualisation #9
  10. 10. MPC-surrogate modelling framework © DHI Modular framework for building surrogate models Qin Qout Qlat Qin Qout Qout Qin #10
  11. 11. MPC-surrogate modelling framework © DHI MIKE model Surrogate model 1 2 Formulation of optimisation model • Constraints • Operation targets • Objective function 3 Behind the scenes • Automatic setup of MPC optimisation model • Efficient optimisation solver #11
  12. 12. Benefits • New technology enables solving large system-wide optimisation and control problems in real-time − Problems that we cannot solve today • Significant value propositions within several business areas − Creating value to real-time forecasting and operations solutions and services • The generic basis of the technology allows to efficiently develop new solutions within new areas. © DHI More foodReduced flooding Environmental protection More hydropower More money #14
  13. 13. From vision to operation Real-time control of urban drainage system, Aarhus, Denmark © DHI #15
  14. 14. © DHI Drivers …Integrating water into the urban space … Recreational use of water …New housing area on the harbor front …Rapid city development ©Politiken©Aarhus.dk #16
  15. 15. DHI & Aarhus Water - Leading the way © DHI Samstyrring I • Analysis and design • 2006-2007 • More Info Samstyrring II • Implementation of infrastructure • 2007-2012 • More Info Samstyrring III • Integrated modelbased control and warning • PREPARED • 2009-2013 • More Info Klimaspring • MPC and surrogate modelling for real-time control • REAL-DANIA • 2015-2017 • More Info Water Smart Cities • Climate change adaptation through intelligent software technologies • MPC in real-time control • 2016-2020 • More info #17
  16. 16. Prerequisites for Smart Real-Time Control • Remote controllable elements • SCADA system • Data integration in real-time across “silos” • Common framework for automatic execution of data and models © DHI #18
  17. 17. © DHI Physical system HiFi model (MIKE model) Optimiser Allows formulation of simplified optimisation Physical system HiFi model (MIKE model) Optimiser Allows formulation of very large optimisation problem Surrogate model (Linear) MPC optimisation framework 100.000’s of decision variables10’s of decision variables #19
  18. 18. © DHI HiFi system state Levels Flows Gate positions Optimised control set-points Gates, pumps, valves MIKE URBAN RR CATCHMENT RUNOFF MIKE URBAN HD Rainfall (incl. forecast) HiFi Models Optimizer Surrogate model RR Model CATCHMENT RUNOFF Surrogate Models Surrogate model Initial state data #20
  19. 19. © DHI Rainfall forecast ensemble Radar data processing: • Conversion to catchment rainfall Surrogate rainfall- runoff model Catchment runoff time series MPC control model Optimised control Observed radar and rain gauge data Radar data processing: • dBZ adjustment • Bias adjustment Ensemble radar nowcast model MIKE URBAN model Operation data Water level/flow measurements Data assimilation system Radar data processing: • Conversion to catchment rainfall MIKE OPERATIONS workflow #21
  20. 20. © DHI Layer 3 PLC/SCADA Sensors/Actuators WISYS Short-term ensemble rainfall forecast Global Model Predictive Control Optimised control WWTP max. hydraulic load Data validation and filtration Software sensors: Flow, Elevations, tank filling, etc. PID (flows) at each storage tank PID (elevations) at each storage tank PID output: 0-100% distributed to set- points for pumps, weirs/gates at each storage tank Layer 2 Layer 1 Levels, flows and weir/gate positions Set-points Real-Time Control System Set-points #22
  21. 21. © DHI Control action Output variable(s) Uncontrolled boundaries Radar-rainfall Rainfall-runoff model Control model #23
  22. 22. © DHI #24
  23. 23. Trøjborg overflow model validation © DHI #25 Basin water level Overflow discharge Outflow set point
  24. 24. Status and what to come © DHI #26
  25. 25. © DHI Consolidated in… #27
  26. 26. Currently ongoing at DHI • Working MPC - surrogate modelling framework within MIKE WORKBENCH • Demonstration of framework for reservoir flood control and optimisation of large-scale irrigation system • First tests of control of urban storm and wastewater systems © DHI 2. MPC 1. RR 3. HD-HiFi 0. Rainfall- Forecast #28
  27. 27. Water Smart Cities project 2016-2020 • MPC-surrogate modelling framework − Extension of surrogate model class − Extension of methods for handling forecast uncertainty (incl. short to medium range weather forecasts) − Adaptive control automatically shifting between different control strategies depending on system state and rainfall forecast − Combined control of drainage system and WWTP • Forecast models − Probabilistic forecasting − Use of surrogate models − Data assimilation • Automatic surrogate model builder − MIKE URBAN -> surrogate model suitable for different purposes (e.g. forecast, control) • MIKE OPERATIONS implementation © DHI #29
  28. 28. Thank you Dr. Lisbeth Birch Pedersen lpe@dhigroup.com © DHI #30

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