smart real-time control of water systems

Smart Real-time Control of Water SystemsHenrik 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 2016Blackpool, November 9th – 11th

Dr. Lisbeth Birch PedersenProduct OwnerMIKE Powered by DHI

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

Who am I?

© DHI

Tax payer

Project consultant

End user

#3

Motivation for MPC and surrogate models

in real-time control of urban water systems

© DHI #4

Integrated Urban Water Management

© DHI #5

Different Solutions for Different Maturity Levels and Different

Challenges

Optimised MPC

Forecast model

On-line model

On-line Data

© DHI #6

© DHI

Local control

System-wide Model Predictive Control (MPC)

#7

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

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

MPC-surrogate modelling framework

© DHI

Modular framework for building surrogate models

Qin

Qout

Qlat

QinQout

Qout

Qin

#10

MPC-surrogate modelling framework

© DHI

MIKE

model

Surrogate

model1

2

Formulation of optimisation model• Constraints

• Operation targets

• Objective function

3Behind the scenes• Automatic setup of MPC optimisation model

• Efficient optimisation solver

#11

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

From vision to operation

Real-time control of urban drainage system,

Aarhus, Denmark

© DHI #15

© 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

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 modelbasedcontrol 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

http://www.youtube.com/watch?v=pHqm8v55R9k

http://www.youtube.com/watch?v=pHqm8v55R9k

http://www.prepared-fp7.eu/prepared-arhus-denmark

http://mikepoweredbydhi.com/smart-realtime-control

http://www.dhigroup.com/global/news/2016/03/water-smart-cities

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

© 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

© DHI

HiFi

system stateLevels

Flows

Gate positions

Optimisedcontrol 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

© DHI

Rainfall forecast

ensemble

Radar data processing:

• Conversion to

catchment rainfall

Surrogate rainfall-

runoff modelCatchment runoff

time series

MPC control

modelOptimised control

Observed radar and

rain gauge data


• dBZ adjustment

• Bias adjustment

Ensemble radar

nowcast model

MIKE URBAN

model

Operation data

Water level/flow

measurements

Data assimilation

system


• Conversion to

catchment rainfall

MIKE OPERATIONS workflow

#21

© DHI

Layer 3

PLC/SCADA

Sensors/Actuators

WISYS

Short-term ensemble

rainfall forecastGlobal 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

© DHI

Control action

Output variable(s)

Uncontrolled boundaries

Radar-rainfall

Rainfall-runoff

model

Control

model

#23

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

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

smart real-time control of water systems

Engineering