model predictive control · 2018-02-02 · anfis •flows hydraulic ... o robust mpc methods: mmpc,...
TRANSCRIPT
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Real-time river flood control
with Model Predictive Control (MPC)Implemented for the river Demer basin (Belgium)
Patrick Willems
KU Leuven
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Flood of September 1998
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Mitigation strategies
Local solutionsDykes, rectifying river stretches, …
1959: ir. Roovers “enhanced water flow”
Give space to the river1970’s: Schulensmeer
Regulated hydraulic infrastructure
Early warning systems2004: Operational basin model
2014: Launch waterinfo.be
Intelligent real-time control
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Study case & Approach
OBM Demer
(InfoWorks RS)
Conceptual model
Model Predictive
Control
Optimal control
strategy
Observations
Rainfall
forecasts
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3 PhDs on the topic
Toni Barjas-Blanco, 2010. ‘The Demer controlled by MPC’, KU Leuven – Faculty of
Engineering (prom. B. De Moor, J. Berlamont, P. Willems)
Maarten Breckpot, 2013. ‘Model predictive control of river flooding’, KU Leuven –
Faculty of Engineering (prom. B. De Moor, P. Willems)
Evert Vermuyten, 2018. ‘Real-time flood control by means of model predictive
control and a reduced genetic algorithm’, KU Leuven – Faculty of Engineering
(prom. P. Willems)
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Operational implementation at this moment
By Flemish Environment Agency (VMM)
Approach developed by KU Leuven PhDs
Companies involved in current operational implementation:
Antea Group, IMDC, Fabricom, IPCOS
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Conceptual models
• Integrated approachInclude hydrology, rivers, sewers
and other processes
• Super fast
• Similar accuracy as full HD
model if properly set up
and calibrated
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Conceptual model: network
Hydrodynamic InfoWorks model (OBM) Conceptual model
• Data-based mechanistic approach
• Storage cell concept
• Modular setting
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Conceptual model: different levels of aggregation
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Conceptual model: different levels of aggregation
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Conceptual model structures & parameters
Linear
reservoir
River / Floodplain
Sewer
Other• Flows
• Water levels
PWL
• Close water balance
Continuity
equation
• Flows
• Rainfall runoff
Transfer
function
• Flows
Static /
Dynamic
• Flows
• Water levels
• Rating curves
ANN
• Flows river -
floodplain
ANFIS
• Flows
Hydraulic
structures
• Gate regulations
PLC
• Water levels
• Rating curves
M5’ trees
• Rating curves
SDP
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Conceptual model: results
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Conceptual model: results
InfoWorks
RS
Conceptual
model
WL nodes > 3 500 466
Hydraulic
structures> 1 700 391
Reservoirs / 90
Computation
time1h15m 0.8s
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Conceptual model: results
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Conceptual model: results
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Real time control by means of RGA-MPC
• Model Predictive Control (MPC)
o River model
o Flow & water level observations
o Rainfall forecast
o Optimizer
• Reduced Genetic Algorithm (RGA)
o Based on standard GA
o Objectives:
• Retention basin dikes
• Damage cost
• Critical dikes
• Retention basins
timet t+Δt
RIVER
MODEL
SCENARIO
GENERATOR
SELECTOR
rainfall
forecasts
new GL
scenario
observations
predicted
system states
best GL
scenario
optimal GL
scenario
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Gate Level (GL) scenario generation
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RGA versus GA
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MPC results (perfect forecasts assumed)
Event
Economic damage
cost [€]Damage
reduction
[%]PLC MPC
Sept1998 3.0M 2.1M 30Aug2003 0 0 /Dec1999 0 0 /Jan1995 0 0 /Jan2002 0 0 /Nov2010 300 100 (67)
VMM 4.7M 3.5M 26T1000 2.0M 1.7M 15
Sept1998x1.3 28.0M 26.8M 42xSept1998 28.1M 27.5M 2
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MPC results Sept 1998
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MPC resultsRiver Herk subbasin:
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MPC results: after climate change ?River Herk subbasin:
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Uncertainties involved
• Hydrodynamic model uncertainty
o Model structure, calibration errors, seasonal vegetation, …
o Data Assimilation: State Estimators, Prediction Error Methods
• Input uncertainty
o Rainfall forecasts, hydrological model uncertainty, …
o Robust MPC methods: MMPC, AMMPC, TB-MPC, …
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Influence rainfall forecast uncertainty
River Herk subbasin:
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Influence rainfall forecast uncertainty
River Herk subbasin:
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Conclusions
• Conceptual modelling
o Integrated approach
o Flexible model detail
o Super fast
o Accuracy similar to full hydrodynamic model
• RGA-MPC for real time flood control
o Outperforms PLC
o Computationally very efficient
o Can handle large and complex networks
o Uncertainties to be dealt with
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Main references:
On conceptual modelling method:
Wolfs, V., Meert, P., Willems, P. (2015). Modular conceptual modelling approach and software for
river hydraulic simulations. Environmental Modelling and Software, 71, 60-77
On RGA-MPC approach:
Vermuyten E., Meert P., Wolfs V., Willems P. (2018). Combining model predictive control with a
reduced genetic algorithm for real-time flood control. Journal of Water Resources Planning and
Management, 144(2), doi:10.1061/(ASCE)WR.1943-5452.0000859 (in press)
Contact:
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Other references:
Chiang, P., Willems, P. (2013), ‘Model conceptualization procedure for river (flood) hydraulic computations: Case study of
the Demer River, Belgium’, Water Resources Management, 27(12), 4277–4289
Chiang, P., Willems, P. (2015). ‘Combine evolutionary optimization with Model Predictive Control in real-time flood control of
a river system’, Water Resources Management, 29(8), 2527-2542
Wolfs, V., Van Steenbergen, N., Willems, P. (2012), ‘Flood probability mapping by means of conceptual modeling’, River
Flow 2012 (Ed. R.M. Muñoz), International Conference on Fluvial Hydraulics, Costa Rica, 5-7 Sept. 2012; Volume 2, CRC
Press, Taylor & Francis Group, London, UK, 1081-1085; ISBN 979-0-415-62129-8
Breckpot, M., Agudelo, O.M., Meert, P., Willems, P., De Moor, B. (2013), ‘Flood control of the Demer by using Model
Predictive Control’, Control Engineering Practice, 21(12), 1776–1787
Barjas Blanco, T., Willems, P., Chiang, P.-K., Haverbeke, N., Berlamont, J., De Moor, B. (2010), ‘Flood regulation using
nonlinear model predictive control’, Control Engineering Practice, 18(10), 1147-1157