TEL - Thèses en ligne - Accueil · 2014. 10. 5. · Towards smart grid M. Y. Lamoudi -...
Transcript of TEL - Thèses en ligne - Accueil · 2014. 10. 5. · Towards smart grid M. Y. Lamoudi -...
Distributed Model Predictive Control for energy management in buildings
Ph.D. thesis presented by:
Mohamed Yacine Lamoudi
Supervised by:
Mazen Alamir - Directeur de recherche CNRS
Patrick Béguery - Schneider-Electric / Strategy & Innovation
November 29th 2012
Introduction
IntroductionEnergy consumption in the world - the facts
The challenge ...
3
Energydemand
2050Now
The challenge ...
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
The challenge ...
4
Energydemand
2050Now
CO2 emission
2050Now
The challenge ...
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
The challenge ...
5
Energydemand
CO2 emission
More efficient
The challenge ...
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
Primary energy consumption split per sector
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
31%Industry& Infrastructure
21%Residential
18%Buildings
28%Transportation
Primary energy consumption split per sector
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
40%
21%Residential
18%Buildings
Primary energy consumption split per sector
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
Electrical
GridNuclear plants
Thermal plants Power
Towards smart grid
Towards smart grid
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
Electrical
GridNuclear plants
Thermal plants
Wind farms
Solar plants
Power
Towards smart grid
Towards smart grid
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
Electrical
GridNuclear plants
Thermal plants
Wind farms
Solar plants
Power
Towards smart grid
Towards smart grid
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
Electrical
GridNuclear plants
Thermal plants
Wind farms
Solar plants
PowerD/R signals
Towards smart grid
Towards smart grid
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
Electrical
GridNuclear plants
Thermal plants
Wind farms
Solar plants
PowerD/R signals
Demand-side
management
Towards smart gridM. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
IntroductionEnergy consumption in the world - the facts
1 40 % of world-wide primary energy consumption isdue to buildings
2 Buildings play a key role in smart grid
40%
21%Residential
18%Buildings
Electrical
GridNuclear plants
Thermal plants
Wind farms
Solar plants
PowerD/R signals
Demand-side
management
Objectives1 Reduce Buildings energy consumption2 Make them smart grid ready
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 2 / 52
Introduction
The HOMES programLargest funded program on buildings active energy efficiency inEurope ...
8
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 3 / 52
Introduction
The HOMES programLargest funded program on buildings active energy efficiency inEurope ...
9
September 2008 > September 201226 Work Packages – 80 M€39 M€ funded by OSEO (French Agency) incl. Schneider 26 M€
“Equip each building with Active Energy Efficiency solutions, to achieve the best possible energy performance”
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 3 / 52
Introduction
Thesis objectives
Design control algorithms able to improve energymanagement in buildings
1 Reduce energy and maintain comfort2 Make buildings "smart grid ready" (variable
energy prices, power limitations)3 Design generic, scalable and modular solutions
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 4 / 52
62
Distributed Model Predictive control for energymanagement in buildings
1 MPC for energy management in buildings
2 Zone Model Predictive Control
3 Distributed Model Predictive Control
4 Conclusion
60
Distributed Model Predictive control for energymanagement in buildings
1 MPC for energy management in buildings
2 Zone Model Predictive Control
3 Distributed Model Predictive Control
4 Conclusion
MPC for energy management in buildings
Energy management in buildingsAn introduction
Energy criterionComfort indicator
Find the best way to achieve comfort
given constraints on inputs
Ensure comfort by mainting
outputs in a given set
Inputs
Optimum
Crit.Outputs
time
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 7 / 52
MPC for energy management in buildings Rule-based control
Conventional control in buildingsRule-based control
Rule-based control
Rule1: if condition[params] then action[params]Rule2: if condition[params] then action[params]...
Many issues
Coherence of the process of decisionParameters tuning ?complex situations ?
To sum up ...
Difficult to generalizeMust be fully adapted for a given scenarioDifficult to handle economical objectivesDifficult to ensure coherence of the decisionExtremely simple to implement on BEMS !
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 8 / 52
MPC for energy management in buildings Rule-based control
Conventional control in buildingsRule-based control
Rule-based control
Rule1: if condition[params] then action[params]Rule2: if condition[params] then action[params]...
Many issues
Coherence of the process of decisionParameters tuning ?complex situations ?
To sum up ...
Difficult to generalizeMust be fully adapted for a given scenarioDifficult to handle economical objectivesDifficult to ensure coherence of the decisionExtremely simple to implement on BEMS !
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 8 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Compute the Optimal Trajectory
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Apply the first part
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Compute the Optimal Trajectory
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Apply the first part
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Compute the Optimal Trajectory
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Apply the first part
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Compute the Optimal Trajectory
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Apply the first part
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetAvoid Obstacles
Closed loop trajectory
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (I)An intuitive concept...
A simplistic example
minimize path length and avoid obstacles
Initial state
TargetInitial obstacles positions
Closed loop trajectory
First Optimal Trajectory
Final obstacles positions
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 9 / 52
MPC for energy management in buildings Model Predictive control
Model Predictive Control (II)Receding Horizon Principle
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 10 / 52
MPC for energy management in buildings Model Predictive control
Why Model Predictive Control in buildings?
Thermal inertiaCoupled dynamicsConstraints (comfort, actuators, power consumption, etc.)Multi-source: several power sources (thermal, electrical, etc.)Economic objectives (varying energy tariffs)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 11 / 52
MPC for energy management in buildings Model Predictive control
MPC for building Energy managementThe ingredients ...
Model
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 12 / 52
MPC for energy management in buildings Model Predictive control
MPC for building Energy managementThe ingredients ...
Model Predictions
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 12 / 52
MPC for energy management in buildings Model Predictive control
MPC for building Energy managementThe ingredients ...
Model Predictions Objective
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 12 / 52
MPC for energy management in buildings Model Predictive control
MPC for building Energy managementThe ingredients ...
Model Predictions Objective
Optimization Problem
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 12 / 52
MPC for energy management in buildings Model Predictive control
MPC for building Energy managementThe ingredients ...
Model Predictions Objective Solver
Optimization Problem
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 12 / 52
MPC for energy management in buildings Model Predictive control
MPC for building Energy managementThe ingredients ...
Model Predictions Objective Solver
Optimization Problem
*Optimal solution
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 12 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
Grid
Gas
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
GridSupply
Storage and
transformation
Gas
Local prod.
Energy layer
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
GridSupply
Storage and
transformation
Gas
Local prod.
Energy layer
Energy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Energy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Zone controllerEnergy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Energy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
MPC for energy management in buildings Model Predictive control
Building control layersDecomposition approach
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Information
exchange
Energy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 13 / 52
60
Distributed Model Predictive control for energymanagement in buildings
1 MPC for energy management in buildings
2 Zone Model Predictive ControlZone modelingThe control problemSimulation and real-time implementationYearly simulationRoombox implementation
3 Distributed Model Predictive Control
4 Conclusion
Zone Model Predictive Control Zone modeling
Zone Model Predictive Controlzone presentation
Objective
(a) Control comfort parameters (temperature, CO2 level, lighting),(b) Minimize operational costs (energy, invoice).
∞
Fan coil unit
Ventilation
Lighting
Shutter
Occupation + internal gains
Outdoor conditions
A typical zone representation
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 15 / 52
Zone Model Predictive Control Zone modeling
Zone Model Predictive Controlzone presentation
Objective
(a) Control comfort parameters (temperature, CO2 level, lighting),(b) Minimize operational costs (energy, invoice).
∞
Fan coil unit
Ventilation
Lighting
Shutter
Occupation + internal gains
Outdoor conditions
Variables Description Unit
uw FCU valve opening [−]
uf FCU fan speed [−]
uh Elec. heating control [−]
uv Ventilation control [−]
ul Lighting control [−]
{uib}i=1,...,Nf
Blind ctrl facade i [−]
Tw Inlet FCU water temp. [oC]
Tex Outdoor temperature [oC]
Tadj Adjacent zones temp. [oC]
{φig}i=1,...,Nf
Global irr. flux facade i [ Wm2 ]
Occ Number of occupants [−]
Cex Outdoor CO2 level [ppm]
T Indoor air temperature [oC]
C Indoor CO2 level [ppm]
L Indoor illuminance [Lux]
Description of Input/Output andexogenous variables
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 15 / 52
Zone Model Predictive Control Zone modeling
Zone Modelingelectrical analogy
Thermal model
Internal wall
External wall
Internal wall
window
Air duct
Zone air
T
Tex
uv…
Internal gains + equipment
ub
c0 · ub · φg
c2 · φg
T 1adjc1 · (1− ub) · φg
1− ub
Shaded part
Ground
Voltage generator
Current generator
Capacitance
Resistor
Variable resistor (depending on the parameter p)p
Constantci
TNadj
adj
Damper
Heat transfer phenomena are essentially linear,Varying resistors depending on controlled inputs make the systembilinear.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 16 / 52
Zone Model Predictive Control Zone modeling
Zone Modelingelectrical analogy
CO2 accumulation model���������� �� ������������� �������Heat transfer phenomena are essentially linear,Varying resistors depending on controlled inputs make the systembilinear.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 16 / 52
Zone Model Predictive Control Zone modeling
Zone Modelingelectrical analogy
Indoor illuminance model��������������� ������ ����������� ������Heat transfer phenomena are essentially linear,Varying resistors depending on controlled inputs make the systembilinear.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 16 / 52
Zone Model Predictive Control Zone modeling
Zone ModelBilinear state-space representation
Zone model - bilinear system
{x+ = A · x +
[B(y ,w)
]· u + F ·w
y = C · x + [D(w)] · u
x state, y output, w disturbance, u input.The matrices [B(y ,w)] and [D(w)] are affine in their arguments.
Simulator form
yk := Z(uk ,wk , xk )
boldfaced vectors are predicted profiles (e.g.uk := [uT
k ,uTk+1,u
Tk+N−1]T ).
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 17 / 52
Zone Model Predictive Control Zone modeling
Zone ModelBilinear state-space representation
Zone model - bilinear system
{x+ = A · x +
[B(y ,w)
]· u + F ·w
y = C · x + [D(w)] · u
x state, y output, w disturbance, u input.The matrices [B(y ,w)] and [D(w)] are affine in their arguments.
Simulator form
yk := Z(uk ,wk , xk )
boldfaced vectors are predicted profiles (e.g.uk := [uT
k ,uTk+1,u
Tk+N−1]T ).
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 17 / 52
Zone Model Predictive Control Zone modeling
Zone ModelBilinear state-space representation
Zone model - bilinear system
{x+ = A · x +
[B(y ,w)
]· u + F ·w
y = C · x + [D(w)] · u
x state, y output, w disturbance, u input.The matrices [B(y ,w)] and [D(w)] are affine in their arguments.
Simulator form
yk := Z(uk ,wk , xk )
boldfaced vectors are predicted profiles (e.g.uk := [uT
k ,uTk+1,u
Tk+N−1]T ).
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 17 / 52
Zone Model Predictive Control The control problem
The control problemproblem description
Energy criterionComfort indicator
Find the best way to achieve comfort
given constraints on inputs
Ensure comfort by mainting
outputs in a given set
Inputs
Optimum
Crit.Outputs
time
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 18 / 52
Zone Model Predictive Control The control problem
The control problemThe comfort indicator
Comfort is only required during presenceComfort constraints are relaxed to ensure feasibility of theproblem
y
y
y
Occ
Time
Time
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 19 / 52
Zone Model Predictive Control The control problem
The control problemThe comfort indicator
Comfort is only required during presenceComfort constraints are relaxed to ensure feasibility of theproblem
ρ1
y
ρ0
y
δy δy
ρ0 < ρ1
JC(y)
Comfort region
y
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 19 / 52
Zone Model Predictive Control The control problem
The control problemMathematical formulation
NMPC-related optimization problem
Minimizeu∈U
J := JE(p) + JC(y) (1)
where:the boldfaced vectors stand for predicted profiles (e.g.y := [yT
k , . . . , yTk+N−1]T ),
p ∈ Rnp is the power consumption,JC is the discomfort criterion.JE is the energy criterion.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 20 / 52
Zone Model Predictive Control The control problem
The control problemproblem resolution
Optimization problem - explicit form:
u(s)k ←
NLPk : Minimizeuk ,δ0,δ1,δd ,yk
Jk (uk ,yk
(s)
) (2a)
Subject To :
[Φ(yk
(s)
,wk )] · uk + δ−0 + δ−1 ≥ yk−Ψxk − Ξwk (2b)
[Φ(yk
(s)
,wk )] · uk − δ+0 − δ+1 ≤ yk −Ψxk − Ξwk (2c)D · uk − δ+d + δ−d = a (2d)0 ≤ uk ≤ 1 (2e)
δ0 ≥ 0 , δd ≥ 0 , 0 ≤ δ1 ≤[δyδy
](2f)
Nonlinear optimization problem due the product terms u · yUpdate the output trajectory y(s)
k by simulating the NL system:
y(s+1)k = Z(u(s)
k ,wk , xk )
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 21 / 52
Zone Model Predictive Control The control problem
The control problemproblem resolution
Optimization problem - explicit form:
u(s)k ←
NLPk : Minimizeuk ,δ0,δ1,δd ,yk
Jk (uk ,yk
(s)
) (2a)
Subject To :
[Φ(yk
(s)
,wk )] · uk + δ−0 + δ−1 ≥ yk−Ψxk − Ξwk (2b)
[Φ(yk
(s)
,wk )] · uk − δ+0 − δ+1 ≤ yk −Ψxk − Ξwk (2c)D · uk − δ+d + δ−d = a (2d)0 ≤ uk ≤ 1 (2e)
δ0 ≥ 0 , δd ≥ 0 , 0 ≤ δ1 ≤[δyδy
](2f)
Nonlinear optimization problem due the product terms u · y
Update the output trajectory y(s)k by simulating the NL system:
y(s+1)k = Z(u(s)
k ,wk , xk )
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 21 / 52
Zone Model Predictive Control The control problem
The control problemproblem resolution
Optimization problem - explicit form:
u(s)k ←
LP(s)k : Minimize
uk ,δ0,δ1,δd ,�yk
Jk (uk ,yk(s)) (2a)
Subject To :
[Φ(yk(s),wk )] · uk + δ−0 + δ−1 ≥ y
k−Ψxk − Ξwk (2b)
[Φ(yk(s),wk )] · uk − δ+0 − δ+1 ≤ yk −Ψxk − Ξwk (2c)
D · uk − δ+d + δ−d = a (2d)0 ≤ uk ≤ 1 (2e)
δ0 ≥ 0 , δd ≥ 0 , 0 ≤ δ1 ≤[δyδy
](2f)
Nonlinear optimization problem due the product terms u · yUpdate the output trajectory y(s)
k by simulating the NL system:
y(s+1)k = Z(u(s)
k ,wk , xk )
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 21 / 52
Zone Model Predictive Control The control problem
The control problemproblem resolution
Optimization problem - explicit form:
u(s)k ← LP(s)
k : Minimizeuk ,δ0,δ1,δd
Jk (uk ,yk(s)) (2a)
Subject To :
[Φ(yk(s),wk )] · uk + δ−0 + δ−1 ≥ y
k−Ψxk − Ξwk (2b)
[Φ(yk(s),wk )] · uk − δ+0 − δ+1 ≤ yk −Ψxk − Ξwk (2c)
D · uk − δ+d + δ−d = a (2d)0 ≤ uk ≤ 1 (2e)
δ0 ≥ 0 , δd ≥ 0 , 0 ≤ δ1 ≤[δyδy
](2f)
Nonlinear optimization problem due the product terms u · y
Update the output trajectory y(s)k by simulating the NL system:
y(s+1)k = Z(u(s)
k ,wk , xk )
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 21 / 52
Zone Model Predictive Control The control problem
The control problemproblem resolution
Optimization problem - explicit form:
u(s)k ← LP(s)
k : Minimizeuk ,δ0,δ1,δd
Jk (uk ,yk(s)) (2a)
Subject To :
[Φ(yk(s),wk )] · uk + δ−0 + δ−1 ≥ y
k−Ψxk − Ξwk (2b)
[Φ(yk(s),wk )] · uk − δ+0 − δ+1 ≤ yk −Ψxk − Ξwk (2c)
D · uk − δ+d + δ−d = a (2d)0 ≤ uk ≤ 1 (2e)
δ0 ≥ 0 , δd ≥ 0 , 0 ≤ δ1 ≤[δyδy
](2f)
Nonlinear optimization problem due the product terms u · y
Update the output trajectory y(s)k by simulating the NL system:
y(s+1)k = Z(u(s)
k ,wk , xk )
Fixed-point algorithm: y(s)k
LP−→ u(s)k
SIM−→ y(s+1)k
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 21 / 52
Zone Model Predictive Control The control problem
Convergence analysis
No formal convergence proof of the algorithm is provided,Run the algorithm starting from 100 random (unrealistic) initialguesses.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 22 / 52
Zone Model Predictive Control Simulation and real-time implementation
Computational burden
Computation time for N = 720, Nparu = 20, Npar
y = 20 (Intelr Xeonr @ 2.67GHz, 3.48 Go RAM - ILOG CPLEX 12.1 for LPs)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 23 / 52
Zone Model Predictive Control Yearly simulation
The case studysmall business building
Typical french small business building built in 2006, 20 zones, 540 [m2],Electrical heater,Local dampers for ventilation control,Automated blinds,Location Trappes (near Paris),Modeled using the SIMBAD toolbox.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 24 / 52
Zone Model Predictive Control Yearly simulation
MPC integration in SIMBADConfiguration step
1 Build the structure of the building(.xml),
2 Identify the dynamical models ofeach zone,
3 Generate automatically C codeable for zones and energy layerrepresentations,
4 Instantiate MPCs for the wholebuilding (observers, powersestimators, available forecast,occupancy schedule, availableequipments, etc.)
−→ need for efficient code toperform a yearly simulation
use of C code when appropriate
vectorized m-code
logical matrix indexation
Simbad
.mdl
Off-line Identification
.xml
Energy layer and
zones models
Code generation
Simulator
configuration
Structural
description
Coordinator
MPC1
Energy
layer.m
MPC1MPC1
.cpp
Zones MPC’s and
Coordinator instantiation
.xml
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 25 / 52
Zone Model Predictive Control Yearly simulation
MPC integration in SIMBADExample: 20 zones building:
≈70 inputs / ≈60 outputs / ≈160states
Simulation
Refreshing period: 5 min.
≈ 2,102,400 optimizationproblems (600-900 d.v × 1000con.) solved during the wholeyear simulation.
Simulation time ≈ 18 [h]
−→ need for efficient code toperform a yearly simulation
use of C code when appropriate
vectorized m-code
logical matrix indexation
Simbad
.mdl
Off-line Identification
.xml
Energy layer and
zones models
Code generation
Simulator
configuration
Structural
description
Coordinator
MPC1
Energy
layer.m
MPC1MPC1
.cpp
Zones MPC’s and
Coordinator instantiation
.xml
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 25 / 52
Zone Model Predictive Control Yearly simulation
MPC integration in SIMBADExample: 20 zones building:
≈70 inputs / ≈60 outputs / ≈160states
Simulation
Refreshing period: 5 min.
≈ 2,102,400 optimizationproblems (600-900 d.v × 1000con.) solved during the wholeyear simulation.
Simulation time ≈ 18 [h]
−→ need for efficient code toperform a yearly simulation
use of C code when appropriate
vectorized m-code
logical matrix indexation
Simbad
.mdl
Off-line Identification
.xml
Energy layer and
zones models
Code generation
Simulator
configuration
Structural
description
Coordinator
MPC1
Energy
layer.m
MPC1MPC1
.cpp
Zones MPC’s and
Coordinator instantiation
.xml
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 25 / 52
Zone Model Predictive Control Yearly simulation
Simulation results (I)Zone MPC illustration- an office
48 [h] simulation - office # 1
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 26 / 52
Zone Model Predictive Control Yearly simulation
Simulation results (II)Yearly simulation results
1 Perfectly known forecast (α = 1)2 Errors on forecast (α = 0)3 Rule-based control
Energy cons. [kWh/m2/year] GTC [%] TCV [k·OC·h]Rule based? 142 91.6 322MPC (α = 1) 119 (−16%) 91.8 295MPC (α = 0) 122 (−14%) 88.1 310
Energy consumption / Comfort - Rule-based vs. MPC
?: more advanced RB control strategy (≈-50% compared to current practice)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 27 / 52
Zone Model Predictive Control Yearly simulation
Simulation results (II)Yearly simulation results
1 Perfectly known forecast (α = 1)2 Errors on forecast (α = 0)3 Rule-based control
Energy cons. [kWh/m2/year] GTC [%] TCV [k·OC·h]Rule based? 142 91.6 322MPC (α = 1) 119 (−16%) 91.8 295MPC (α = 0) 122 (−14%) 88.1 310
Energy consumption / Comfort - Rule-based vs. MPC
?: more advanced RB control strategy (≈-50% compared to current practice)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 27 / 52
Zone Model Predictive Control Yearly simulation
Simulation results (II)Yearly simulation results
1 Perfectly known forecast (α = 1)2 Errors on forecast (α = 0)3 Rule-based control
Energy cons. [kWh/m2/year] GTC [%] TCV [k·OC·h]Rule based? 142 91.6 322MPC (α = 1) 119 (−16%) 91.8 295MPC (α = 0) 122 (−14%) 88.1 310
Energy consumption / Comfort - Rule-based vs. MPC
?: more advanced RB control strategy (≈-50% compared to current practice)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 27 / 52
Zone Model Predictive Control Other features
Other features (I)Handling fan coil units
The FCU model is a static nonlinear heat emission characteristic:
φth(uw ,uf , T , Tw ) = (Tw − T ) · φN(uw ,uf )
Thermal emission characteristic
Heating coil
(Valve)
(Zone air temp.)
Return water
TwTair
uw
uf
uh
φth(Tw, Tair, uf , uw)
(Electrical heating coil )
(Fan)
(Hot water supply)
Adapt the algorithm to handle FCUs and preserve the LP formulation
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 28 / 52
Zone Model Predictive Control Other features
Other features (I)Handling fan coil units
The FCU model is a static nonlinear heat emission characteristic:
φth(uw ,uf , T , Tw ) = (Tw − T ) · φN(uw ,uf )
PWA approx.
Heating coil
(Valve)
(Zone air temp.)
Return water
TwTair
uw
uf
uh
φth(Tw, Tair, uf , uw)
(Electrical heating coil )
(Fan)
(Hot water supply)
Adapt the algorithm to handle FCUs and preserve the LP formulationM. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 28 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
1 Preheat the first day during off-peak hours,2 Optimal start the second day during on-peak hours,
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
Sensitivity of the solution to the ratio between high and low energyprice periods (βp)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
Heater half dimensioned + another zone (more inertia)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
Heater half dimensioned
+ another zone (more inertia)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
Heater half dimensioned
+ another zone (more inertia)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
Heater half dimensioned + another zone (more inertia)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Other features
Other features (II)variable energy prices
The optimal behavior is linked to the dynamical characteristics ofeach zone
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 29 / 52
Zone Model Predictive Control Roombox implementation
Roombox implementation
Roombox
Main features:Power output: lighting, shuttersand blinds, HVACNetwork connection to BMSEthernet port for local PCInputs for switches andwindow contacts 24 VccOutput protection (SCprotection, overload ...)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 30 / 52
Zone Model Predictive Control Roombox implementation
Roombox implementation
Roombox
Main features:Power output: lighting, shuttersand blinds, HVACNetwork connection to BMSEthernet port for local PCInputs for switches andwindow contacts 24 VccOutput protection (SCprotection, overload ...)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 30 / 52
Zone Model Predictive Control Roombox implementation
Roombox implementation
Roombox
ObjectiveImplement the MPC algorithm onthe Roombox→
To study the real-timeimplementationTo identify the main relatedissues
Validation→ Virtual signals sent via theethernet port (measures andforecast)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 30 / 52
Zone Model Predictive Control Roombox implementation
Roombox implementation
.h
.cpp
.cpp
.m
ecplise®
.b Code compilationCode translation
human
Matlab®
C/C ++
binary
C/C ++
MPC
Solver
(LP)
.cppMatrix
library
Roombox
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 30 / 52
Zone Model Predictive Control Roombox implementation
Roombox implementation
Roombox
ConditionsPrediction horizon 12 h.sampling period 2 min.zone: nu = 6, ny = 3GLPK (GNU MILP solver)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 30 / 52
Zone Model Predictive Control Roombox implementation
Roombox implementation
Roombox
Results
≈ 6 [s] / iteration
8.2 % of memory usage
Able to run more than one threadof the algo. on one Roombox
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 30 / 52
60
Distributed Model Predictive control for energymanagement in buildings
1 MPC for energy management in buildings
2 Zone Model Predictive Control
3 Distributed Model Predictive ControlProblem presentationDistributed MPC design
4 Conclusion
Distributed Model Predictive Control Problem presentation
Building control layersDecomposition approach
Objective
Coordinate the energy layer and the zone layer→ Manage resourcecoupling constraints
Energy layer →energy supply,storage andtransformation
Zone layer →consumeenergy toprovide comfort
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Zone controllerEnergy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 32 / 52
Distributed Model Predictive Control Problem presentation
Building control layersDecomposition approach
Objective
Coordinate the energy layer and the zone layer→ Manage resourcecoupling constraints
Energy layer →energy supply,storage andtransformation
Zone layer →consumeenergy toprovide comfort
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Energy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 32 / 52
Distributed Model Predictive Control Problem presentation
Building control layersDecomposition approach
Objective
Coordinate the energy layer and the zone layer→ Manage resourcecoupling constraints
Energy layer →energy supply,storage andtransformation
Zone layer →consumeenergy toprovide comfort
Heat
Storage
PumpBoilerElectrical storage
∞ ∈
∋ △
GridSupply
Storage and
transformation
Gas
Local prod.
Zone layer
Energy layer
Information
exchange
Energy cons. /
ensure comfort
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 32 / 52
Distributed Model Predictive Control Problem presentation
Handling coupling resource constraints
∞ ∈
∋ △
Grid
Coordinator
Communication
Local MPC
control
Power
limitations
Energy
pricesElectrical storage
Objectives:Power limitation constraint on the whole building cons.
p+b +
∑`∈Z
p` ≤ Pg
Manage the storage capability (elec. battery)M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 33 / 52
Distributed Model Predictive Control Problem presentation
Handling coupling resource constraints
∞ ∈
∋ △
Grid
Coordinator
Communication
Local MPC
control
Power
limitations
Energy
pricesElectrical storage
Objectives:Power limitation constraint on the whole building cons.
p+b +
∑`∈Z
p` ≤ Pg
Manage the storage capability (elec. battery)M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 33 / 52
Distributed Model Predictive Control Problem presentation
Handling coupling resource constraints
∞ ∈
∋ △
Grid
Coordinator
Communication
Local MPC
control
Power
limitations
Energy
pricesElectrical storage
Objectives:Power limitation constraint on the whole building cons.
p+b +
∑`∈Z
p` ≤ Pg
Manage the storage capability (elec. battery)M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 33 / 52
Distributed Model Predictive Control Distributed MPC design
Zone Predictive controllerSlight modifications ...
Each zone controller controls localvariables
while meeting localconstraints on resources:
MPC`
(r`)
: Minimizez`≤z`≤z`
L` · z`
Subject To:
A` · z` ≤ b`
A′` · z` ≤ r`
One gets:
(J`,g`)← MPC`(r`)
J` := J`(r`) : optimal valueg` := g`(r`) : sub-gradient at r`
yℓuℓ
MPCℓ
Forecast
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 34 / 52
Distributed Model Predictive Control Distributed MPC design
Zone Predictive controllerSlight modifications ...
Each zone controller controls localvariables while meeting localconstraints on resources:
MPC`(r`) : Minimizez`≤z`≤z`
L` · z`
Subject To:
A` · z` ≤ b`
A′` · z` ≤ r`
One gets:
(J`,g`)← MPC`(r`)
J` := J`(r`) : optimal valueg` := g`(r`) : sub-gradient at r`
yℓuℓ
MPCℓ
Forecast
rℓ
rℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 34 / 52
Distributed Model Predictive Control Distributed MPC design
Zone Predictive controllerSlight modifications ...
Each zone controller controls localvariables while meeting localconstraints on resources:
MPC`(r`) : Minimizez`≤z`≤z`
L` · z`
Subject To:
A` · z` ≤ b`
A′` · z` ≤ r`
One gets:
(J`,g`)← MPC`(r`)
J` := J`(r`) : optimal valueg` := g`(r`) : sub-gradient at r`
yℓuℓ
MPCℓJℓ(rℓ) gℓ(rℓ)
Forecast
rℓ
rℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 34 / 52
Distributed Model Predictive Control Distributed MPC design
Zone Predictive controllerSlight modifications ...
Each zone controller controls localvariables while meeting localconstraints on resources:
MPC`(r`) : Minimizez`≤z`≤z`
L` · z`
Subject To:
A` · z` ≤ b`
A′` · z` ≤ r`
One gets:
(J`,g`)← MPC`(r`)
J` := J`(r`) : optimal valueg` := g`(r`) : sub-gradient at r`
yℓuℓ
MPCℓ
rℓ
Jℓ(rℓ)
Coordinator
Forecast
rℓ
gℓ(rℓ)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 34 / 52
Distributed Model Predictive Control Distributed MPC design
The coordination layer
At the coordination layer, the problem is the following:How to affect optimally resource profiles r := {r`}`∈Z to minimize thetotal cost function ?
→ Solve the master problem:
Minimizeze,r
[ Le · ze︸ ︷︷ ︸Energy layer cost fct.
+∑`∈Z
J`(r`)︸ ︷︷ ︸Zone cost fct.
] S.t. C(r, ze) ≤ be︸ ︷︷ ︸Global constraints
Zone nz
CoordinatorEnergy layer
pnzp1
bpb unz ynz
MPC1 MPCnz
y1u1
Zone layer
Zone 1Batterypg
rnz gnz (rnz)Jnz(rnz)r1 g1(r1)J1(r1)
problem:→ J` are not available !→ built-up approximations of J` → Bundle method
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 35 / 52
Distributed Model Predictive Control Distributed MPC design
The coordination layer
At the coordination layer, the problem is the following:How to affect optimally resource profiles r := {r`}`∈Z to minimize thetotal cost function ?
→ Solve the master problem:
Minimizeze,r
[ Le · ze︸ ︷︷ ︸Energy layer cost fct.
+∑`∈Z
J`(r`)︸ ︷︷ ︸Zone cost fct.
] S.t. C(r, ze) ≤ be︸ ︷︷ ︸Global constraints
problem:→ J` are not available !→ built-up approximations of J` → Bundle method
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 35 / 52
Distributed Model Predictive Control Distributed MPC design
The coordination layer
At the coordination layer, the problem is the following:How to affect optimally resource profiles r := {r`}`∈Z to minimize thetotal cost function ?
→ Solve the master problem:
Minimizeze,r
[ Le · ze︸ ︷︷ ︸Energy layer cost fct.
+∑`∈Z
J`(r`)︸ ︷︷ ︸Zone cost fct.
] S.t. C(r, ze) ≤ be︸ ︷︷ ︸Global constraints
problem:→ J` are not available !
→ built-up approximations of J` → Bundle method
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 35 / 52
Distributed Model Predictive Control Distributed MPC design
The coordination layer
At the coordination layer, the problem is the following:How to affect optimally resource profiles r := {r`}`∈Z to minimize thetotal cost function ?
→ Solve the master problem:
Minimizeze,r
[ Le · ze︸ ︷︷ ︸Energy layer cost fct.
+∑`∈Z
J`(r`)︸ ︷︷ ︸Zone cost fct.
] S.t. C(r, ze) ≤ be︸ ︷︷ ︸Global constraints
problem:→ J` are not available !→ built-up approximations of J` → Bundle method
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 35 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle method
1 The coordinator affects localresources
2 Each zones gives:The value of the cost function J`(r`)A sub-gradient g`(r`) (sensitivity)
yℓuℓ
MPCℓJℓ(rℓ) gℓ(rℓ)
Forecast
rℓ
rℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 36 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
Unknown at the coordination layer
?
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
epi(Jℓ)
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
r(0)ℓ
SensitivityFunction value
epi(Jℓ)
gℓrℓ Jℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
J̌ℓ
r(0)ℓ
epi(Jℓ)
gℓrℓ Jℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
J̌ℓ
r(1)ℓ
r(0)ℓ
epi(Jℓ)
gℓrℓ Jℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
J̌ℓ
r(1)ℓ
r(0)ℓ
r(2)ℓ
epi(Jℓ)
gℓrℓ Jℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
The bundle methodCutting plane approximation
rℓ
Jℓ
J̌ℓ
r(1)ℓ
r(0)ℓ
r(2)ℓ
epi(Jℓ)
r(3)ℓ
gℓrℓ Jℓ
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 37 / 52
Distributed Model Predictive Control Distributed MPC design
Distributed MPC scheme
Process of decision is distributed among several agentsThe coordinator manages only the shared resourcesA restricted number of negotiation iterations is allowed
yℓuℓ
MPCℓ
rℓ J̌ℓ(·)
J̌ℓ(rℓ)
BMℓ
unz
Zone nz
ynz
MPCnz
rnz J̌nz(·)
J̌nz(rnz)
BMnz
Zone 1
MPC1
r1 J̌1(·)
J̌1(r1)
BM1
Jℓ(rℓ) gℓ(rℓ) gnz(rnz)Jnz (rnz)g1(r1)J1(r1)
Coordinator
Power tariff
forecast
Meteorological
service
Occupancy
forecast
Service layer
Power
limitations
Zone ℓ
SensorsActuators
rnzrℓr1
r
Energy level
SensorsActuators SensorsActuatorsSensorsActuators
rE yEuE
Access to
external services
y1u1
Master ProblemEnergy
layer
Zone
layer
Now Distribute the optimization problem solving over timeM. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 38 / 52
Distributed Model Predictive Control Distributed MPC design
Distributed MPC scheme
Process of decision is distributed among several agentsThe coordinator manages only the shared resourcesA restricted number of negotiation iterations is allowed
yℓuℓ
MPCℓ
rℓ J̌ℓ(·)
J̌ℓ(rℓ)
BMℓ
unz
Zone nz
ynz
MPCnz
rnz J̌nz(·)
J̌nz(rnz)
BMnz
Zone 1
MPC1
r1 J̌1(·)
J̌1(r1)
BM1
Jℓ(rℓ) gℓ(rℓ) gnz(rnz)Jnz (rnz)g1(r1)J1(r1)
Coordinator
Power tariff
forecast
Meteorological
service
Occupancy
forecast
Service layer
Power
limitations
Zone ℓ
SensorsActuators
rnzrℓr1
r
Energy level
SensorsActuators SensorsActuatorsSensorsActuators
rE yEuE
Access to
external services
y1u1
Master ProblemEnergy
layer
Zone
layer
Now Distribute the optimization problem solving over timeM. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 38 / 52
Distributed Model Predictive Control Distributed MPC design
Distributing the optimization over timeThe memory mechanism
The idea is simply to keep a certain part of the information(approximation) from one decision instant to next one...
rℓ
J(k−1)ℓ
...by introducing a memory factor.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 39 / 52
Distributed Model Predictive Control Distributed MPC design
Distributing the optimization over timeThe memory mechanism
The idea is simply to keep a certain part of the information(approximation) from one decision instant to next one...
J(k−1)ℓ
rℓ
J(k)ℓ
...by introducing a memory factor.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 39 / 52
Distributed Model Predictive Control Distributed MPC design
Distributing the optimization over timeThe memory mechanism
The idea is simply to keep a certain part of the information(approximation) from one decision instant to next one...
rℓ
J(k)ℓ
J̌(k,0)ℓ |mk = 0
...by introducing a memory factor.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 39 / 52
Distributed Model Predictive Control Distributed MPC design
Distributing the optimization over timeThe memory mechanism
The idea is simply to keep a certain part of the information(approximation) from one decision instant to next one...
J̌(k,0)ℓ |mk = 1
J̌(k,0)ℓ |mk = 0
Decreasing
memory factor
rℓ
J(k)ℓ
...by introducing a memory factor.M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 39 / 52
Distributed Model Predictive Control Distributed MPC design
Memory mechanismIllustration
I J(smax )` is given at decision instant k − 1
rℓ
J̌ℓ
r⋆ℓ(k−1)
J̌(smax)ℓ,k−1
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 40 / 52
Distributed Model Predictive Control Distributed MPC design
Memory mechanismIllustration
I Decrease it (memory factor m`,k)
rℓ
J̌ℓ
Initialization
r⋆ℓ(k−1)
J̌(smax)ℓ,k−1
J̌(0)ℓ,k
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 40 / 52
Distributed Model Predictive Control Distributed MPC design
Memory mechanismIllustration
I First iteration (exchange between zone layer and coordinator)
rℓ
J̌ℓ
Initialization
Iterations
r⋆ℓ(k−1)
J̌(smax)ℓ,k−1
J̌(0)ℓ,k
J̌(s=1)ℓ,k
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 40 / 52
Distributed Model Predictive Control Distributed MPC design
Memory mechanismIllustration
I Iterate (exchanges between zone layer and coordinator)
rℓ
J̌ℓ
Initialization
Iterations
r⋆ℓ(k−1)
r⋆ℓ(k)
J̌(smax)ℓ,k−1
J̌(0)ℓ,k
J̌(s=2)ℓ,k
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 40 / 52
Distributed Model Predictive Control Distributed MPC design
Memory mechanismIllustration
I One gets the latest approximation at decision instant k
rℓ
J̌ℓ
Initialization
Iterations
r⋆ℓ(k−1)
r⋆ℓ(k)
Obsolete part of the
approximation
J̌(smax)ℓ,k−1
J̌(0)ℓ,k
J̌(s=2)ℓ,k
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 40 / 52
Distributed Model Predictive Control Distributed MPC design
Memory mechanismIllustration
I And so on ...
rℓ
J̌ℓ
r⋆ℓ(k)
J̌(s=2)ℓ,k
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 40 / 52
Distributed Model Predictive Control Distributed MPC design
DMPC simulation
DMPC- 3 iterations with memory
Time [h]
Closed-loop trajectories
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 41 / 52
Distributed Model Predictive Control Distributed MPC design
Effect of the memory mechanismAchieve better solutions faster with memory !
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 42 / 52
Distributed Model Predictive Control Distributed MPC design
Distributed Model Predictive ControlOther features
1 Handling shared variables2 Including local production
Electricity
Grid
Electrical storage
pg
p+b
Zone layerEnergy layer
p←g1
……
p−b
p←gnz
p←gℓ
p←b1
p←bnz
p←bℓ
Ventilation systemuv
Air duct
Tex
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 43 / 52
Distributed Model Predictive Control Distributed MPC design
Distributed Model Predictive ControlOther features
1 Handling shared variables2 Including local production
Grid
Electrical
storage
p1
p2
pnz
pg
pb
ps
Zone layerEnergy layer
… …
pℓ
Local production
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 43 / 52
60
Distributed Model Predictive control for energymanagement in buildings
1 MPC for energy management in buildings
2 Zone Model Predictive Control
3 Distributed Model Predictive Control
4 Conclusion
Conclusion
Conclusion
Summary1 Zone MPC design (Bilinear
model, MIMO)generic frameworkenergy savingsModerate computationalburdenReal-time implementation
2 Build a distributed solutionbased on local controllers
Handle global powerlimitations (multi-sources)Handle storage equipmentManage shared actuatorsDistributed-in-timeoptimization
yℓuℓ
MPCℓ
Forecast
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 45 / 52
Conclusion
Conclusion
Summary1 Zone MPC design (Bilinear
model, MIMO)generic frameworkenergy savingsModerate computationalburdenReal-time implementation
2 Build a distributed solutionbased on local controllers
Handle global powerlimitations (multi-sources)Handle storage equipmentManage shared actuatorsDistributed-in-timeoptimization
∞ ∈
∋ △
Grid
Coordinator
Communication
Local MPC
control
Power
limitations
Energy
pricesElectrical storage
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 45 / 52
Conclusion
Conclusion
BenefitsA generic and coherent frameworkModular→ scalable, maintenance concernsRepresents a good answer for smart-grid connectivity
IssuesAvailability of the model of the buildingAvailability of forecastMuch more computationally demanding
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 46 / 52
Conclusion
Conclusion
BenefitsA generic and coherent frameworkModular→ scalable, maintenance concernsRepresents a good answer for smart-grid connectivity
IssuesAvailability of the model of the buildingAvailability of forecastMuch more computationally demanding
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 46 / 52
Conclusion
For future ...
Projects1 First MPC prototype in North-Andover (USA) starting in few weeks2 Extend the current framework to manage smart districts
(building← zone, district← building): Ambassador project(Europe)
but also ...1 Deployment tools for large scale penetration2 MPC commissioning3 Code certification for large deployment
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 47 / 52
Conclusion
For future ...
Projects1 First MPC prototype in North-Andover (USA) starting in few weeks2 Extend the current framework to manage smart districts
(building← zone, district← building): Ambassador project(Europe)
but also ...1 Deployment tools for large scale penetration2 MPC commissioning3 Code certification for large deployment
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 47 / 52
Acknowledgement
Acknowledgement
http://www.homesprogramme.com
This work is part of HOMES collaborative program.
The HOMES program is funded by OSEO (http://www.oseo.fr).
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 48 / 52
Publications
Publications I
Conferences
M. Y. Lamoudi, M. Alamir, and P. Béguery. Distributed constrained modelpredictive control based on bundle method for building energymanagement. In 50th IEEE Conference on Decision and Control andEuropean Control Conference- Orlando, 2011.
M. Y. Lamoudi, M. Alamir, and P. Béguery. Unified NMPC for multi-variablecontrol in smart buildings. In IFAC 18th World Congress, Milano, Itlay,2011.
M. Y. Lamoudi, M. Alamir, and P. Béguery. Model predictive control forenergy management in buildings- part 1: zone model predictive control.In IFAC conference on Nonlinear Model Predictive Control, 2012.
M. Y. Lamoudi, M. Alamir, and P. Béguery. Model predictive control forenergy management in buildings- part 2: Distributed model predictivecontrol. In IFAC conference on Nonlinear Model Predictive Control, 2012.
M. Y. Lamoudi, P. Béguery, and M. Alamir. Use of simulation for thevalidation of a predictive control strategy. In 12th International IBPSAConference , Sydney, Australia, 2011.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 49 / 52
Publications
Publications IIP. Béguery, M. Y. Lamoudi, O. Cottet, O. Jung, N. Couillaud, andD. Destruel. Simulation of smart buildings HOMES pilot sites. In 12thInternational IBPSA Conference , Sydney, Australia, 2011.
Book chapter
M. Y. Lamoudi, M. Alamir, and P. Béguery. A distributed-in-timeNMPC-based coordination mechanism for resource sharing problems.Chapter in Distributed Model Predictive Control made easy. SpringerVerlag, 2012. (to appear)
Schneider-Electric white papers
M. Y. Lamoudi, P. Béguery, O. Nilsson and B. Leida. Model PredictiveControl - toward smarter energy management systems. White paper,Schneider-Electric, Jan. 2012.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 50 / 52
Publications
Publications III
Patents
M. Y. Lamoudi, P. Béguery, and M. Alamir. Procédé de commande pourgérer le confort d’une zone d’un bâtiment selon une approchemulticritères et installation pour la mise en œuvre du procédé, 2011.
C. Guyon, M. Y. Lamoudi and P. Béguery, Procédé et dispositif derépartition de flux d’énergie éléctrique et système électriquecomportant un tel dispositif, 2012.
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 51 / 52
Questions
Thank you for your attentionQuestions ?
M. Y. Lamoudi - Schneider-Electric/Gipsa-lab - DMPC for Energy management in buildings - 11/29/2012 52 / 52