Toward autonomous photovoltaic building energy management :
Modeling and control
of electrochemical batteries
Dr. Hoang-Anh Dang
HaUI Institute of Technology
This research is under supervision of Prof. Benoit Delinchant and
Prof. Frederic Wurtz in Grenoble Electrical Engineering Laboratory
Context
• Smart Building has to use less energy and can be optimally
controlled by occupant
• Two main strategies of energy management
• Reduce energy consumption and develop renewable sources
• Optimize power supply that depends on production, distribution and storage
• Importance of electrical storage management
High energy consumption
23/06/2015
Hoang-Anh DANG
Their battery could be used
for energy management
2
PREDIS Smart Building « Monitoring et
Habitat Intelligent » (MHI)
Local
GTC
Shed
Local
CTA
Office room
Computer
room
Lobby
Induction motor & variable speed drive
PREDIS MHI
EP RECH
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Temperature
Wattmeter and
sensor
controllable switch
Thematic researches :
• Metering and characterization
• Management and design
• User behavior
My research
direction in
G2Elab
3
Summary
Context
PREDIS MHI
Electrical management in PREDIS MHI
Battery modelling
PREDIS MHI case study application
Conclusions and Perspectives
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Electrical management in PREDIS MHI
• General objectives
• Autonomous renewable resources
• Control the energy profile consumption by using electrical storage
• Case study of PREDIS Computer room
Laptop 15
Inverter
DC/AC
Solar panels
Laptop 2
G2Elab electrical grid
Power system
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Disposition of laptops
Hoang-Anh DANG
Laptop 1
Electrical distribution system of computer room
5
Necessary researches
• Capitalize models for system management
• Prevision of photovoltaic production (estimated from the weather forecast)
• Prevision of PC load (estimated from timetables)
• Prevision of electric prices (given by electricity distributors)
• Prevision of storage capacity (calculated from the battery model ?)
• Model electrochemical batteries
• Develop the storage management algorithm
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Summary
Context
PREDIS MHI
Electrical management in PREDIS MHI
Battery modelling
PREDIS MHI case study application
Conclusions and Perspectives
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Battery modelling problematic
RV
should be
replaced by
Charge capacity of
a classic model
VOC
IB
VB
Charger float voltage
of a classic model
Charge current of
a classic model
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Electrochemical battery modelling
• Functional specification
Parameters, Initial States
Psp
BATTERY
Pb
Psp : Power set point
SOC
Pb: Output battery power
SOH
Joule losses
SOC : State Of Charge
Pdischarge_available
Pcharge_available
SOH : State Of Health
Pdischarge_available : Available discharge power
Pcharge_available : Available charge power
• Shepherd‘s hypothesis – discharge mode (IB > 0)
Nominal zone
Polarization zone
VB  V0  RI I B  K
VB, IB : Voltage and current
Polarization
zone
RI : Internal resistance
Exponential zone
Qmax
I B  A e B (QQmax )
Q
Qmax
Qmax  Q
Charge mode (IB < 0)
K, A, B : Voltage factors
t
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Qmax : Maximal capacity Q  Q0  c  I B dt : Instantaneous charge
0
9
Shepherd’s model :
calculation from the power set point Psp
• Calculate SOC and SOH
PB  VB  I B  I B2  I B  Psp
Determinate the battery current IB
Resolve
SOC 
Q
 I B dt
 100% NC : Cycle durability
 100 % and SOH  SOH 0 
2  N C  Qmax_ initial
Qmax
• Functional constraints
A case study of battery simulation (Ni-Cd, 1,2 V, 4200 mAh, SOC0=50%)
Pdischarge_available
0.5
100
Puissance calculée
Puissance de consigne
SOC calculé
Etat de charge (%)
Puissance (% Pnom)
1
0
Pcharge_available
-0.5
80
60
40
20
SOCmin = 2%
0
-1
0
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2
4
6
8
Temps (s)
Hoang-Anh DANG
10
12
14
0
4
x 10
2
4
6
8
Temps (s)
10
12
14
4
x 10
10
Model parameters
• Identified from measurement or/and in catalogue datasheet
• Estimated from existed tools or/and experimental publications
Lead – acid
Li – ion
Ni – Cd
Ni – Mh
Full charge voltage (Vfull)
1,0888Vnom
1,164Vnom
1,1442Vnom
1,178Vnom
Discharge current (Inom)
0,2Qrat
0,43478Qrat
0,2Qrat
0,2Qrat
Internal resistance (RI)
Vnom
Qnom  100
Maximal capacity (Qmax)
1,05Qnom
Extracted capacity at
nominal voltage (Qnom)
0,31028Qrat
0,90435Qrat
0,96136Qrat
0,96154Qrat
Exponential voltage (Vexp)
1,0181Vnom
1,0804Vnom
1,0671Vnom
1,0847Vnom
Extracted capacity at the end
of exponential zone (Qexp)
0,003333Qrat
0,04913Qrat
0,27955Qrat
0,2Qrat
Table of typical battery operation points
(Battery model/Matlab Simulink) (Tremblay & Dessaint, 2009)
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Validation
Validation in charge mode
(DELL PRECISION, Li-ion, 11,1 V, 85 Wh, SOC0=5%)
Psp = 19,5V × 6,7A = 130,65 W
80
Puissance mesurée
Puissance calculée
70
Puissance de charge (W)
60
50
40
30
Real end of
charge time
20
Estimated end of
charge time
10
0
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0
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1000
2000
3000
4000
Temps (s)
5000
6000
7000
Error = 7%
8000
12
Summary
Context
PREDIS MHI
Electrical management in PREDIS MHI
Battery modelling
PREDIS MHI case study application
Conclusions and Perspectives
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Hoang-Anh DANG
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Electrical management system
Solar power forecast
Photovoltaic power (W)
600
500
400
Wattmeter 15
300
200
100
0
Laptop 15
0
4
8
12
16
Time (h)
20
24
RF Zigbee protocol
USB
Inverter
DC/AC
Xbee
Commander
communication
server
TCP/IP
module
Wattmeter 2
Solar panels
Laptop 2
RF receiver and
transmitter
RF X10 protocol
RF
transmitter
Wattmeter 1
Electricity prices forecast
0.09
0.08
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Total power
consumption
forecast
0.07
0.06
0.05
0.04
0.03
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Laptop 1
0
4
8
12
16
Time (h)
20
24
600
Total power consumption (W)
Power system
Electrical price (€/Kwh)
0.1
500
400
300
200
100
0
0
4
8
12
16
Time (h)
20
24
14
Electrical management strategy :
predictive et real-time control
• Predictive control : batteries are pre-charged during chosen time
intervals
• Objective : buy the electricity at lowest price moment
• The charge plan is generated, from charge starting time and charge duration (are
calculated by using the battery model)
• Real-time control : batteries are charged/discharged by ON/OFF
controllable switches
• Objective : maximize de la solar power production
Puissance PV
Consommation totale
Predictive
control
phase
and ensure the uninterrupted power consumption
• Case of SOC ≤ SOCmin : charge mode is mandatory
Real-time
control
phase
• Case SOC > SOCmin : charge mode is sorted
70%
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Reactive
point
15%
0
4
8
12
16
20
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Simulation : Inputs
Puissance photovoltaïque (W)
Puissance totale (W)
600
500
400
300
200
100
0
0
5
10
15
Temps (h)
20
600
500
400
300
200
100
0
0
5
10
15
Temps (h)
Total power consumption
20
Solar power
Prix d'électricité (€/KWh)
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
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0
5
10
15
Temps (h)
Electricity prices
20
16
Simulation : Results
800
PC1
PC3
PC4
60
PC5
40
PC6
20
600
Puissance (W)
PC2
80
Etat de charge (%)
Puissance de consommation totale
Puissance photovoltaïque
700
100
500
400
300
PC7
200
PC8
100
PC9
0
5
10
15
1.5
COM
0
PC10
20
5
PC13
PC14
0.5
PC15
0
5
10
15
20
Monthly cost (€)
Ratio
Classic case study
6,4
10,67
PREDIS case study
0,6
1
Electricity cost comparison
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Puissance (W)
Temps (h)
Battery states of charge et control states of controllable switches
10
15
Temps (h)
20
Comparison between the total power
consumption (at electrical outlet) and the
solar power production
PC12
1
0
0
PC11
700
0.1
600
0.09
500
0.08
400
0.07
300
0.06
200
0.05
100
0.04
0
0.03
-100
0.02
-200
0
5
10
15
Temps (h)
20
Prix d'électricité (€/KWh)
0
0.01
Exchanged power with the power system 17
Real system application : Inputs
Prediction for 22/05/2013
500
350
400
250
Puissance (W)
Puissance (W)
300
200
150
100
200
100
50
0
300
0
300
600
900
1200
1500
0
0
300
600
900
Temps (minutes)
Temps (minutes)
1200
1500
Solar power forecast
Power consumption forecast
Prix d'électricité (€/MWh)
100
80
60
40
20
0
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Prix moyen pondéré à la baisse
Prix moyen pondéré à la hausse
0
2
4
6
8
10 12 14 16
Temps (heures)
Electricity prices forecast
18
20
22
24
18
Real system application: Results
SOC (%)
80
60
40
600
PC1
PC2
PC3
PC4
PC5
PC6
PC7
PC8
PC9
PC10
500
400
300
200
100
0
20
1.5
0
500
1000
1500
Temps (minutes)
0.5
0
0
500
1000
1500
Temps (minutes)
Battery states of charge et control states of
controllable switches
Monthly cost (€)
Ratio
Classic case study
5,8
11,6
PREDIS case study
0,5
1
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Electricity
Hoang-Anh
DANGcost comparison
1500
Puissance consommée
120
Puissance photovoltaïque
Prix réel d'électricité
700
1
500
1000
Temps (minutes)
Real total power consomption
800
Puissance (W)
Commutation
0
0
600
100
500
80
400
60
300
40
200
20
100
0
0
500
1000
Temps (minutes)
Prix d'électricité (€/MWh)
100
700
Puissance (W)
Measurement in 22/05/2013
0
1500
Comparison between the total power consumption (at
electrical outlet) and the solar power production 19
Summary
Context
PREDIS MHI
Electrical management in PREDIS MHI
Battery modelling
PREDIS MHI case study application
Conclusions and Perspectives
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Hoang-Anh DANG
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Conclusions
• The battery model developed is simple enough to be
implemented only from the typical characteristics of the
battery and its nominal variables,
• It remains sufficiently realistic regarding the evaluation of
battery power, which depends on the state of charge,
• This model has been validated and used to test different
strategies for energy management in PREDIS platform in
order to maximize the photovoltaic autonomous.
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Perspectives
Portable electrical
devices management
V2H management
Application in
Vietnam situation ?
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Project: Micro Smart Grid Development
and Application for Building Energy
Management
Weather station
Load bank
PV panels 15 kWp
Energy manager
ALR
AC Grid
Modbus
Distribution
cabinet
PLCs
Energy controller
Transducer
elevator
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Battery energy
Hoang-Anh
DANG
storage
station
Lab
HVAC
Real Loads – USTH building
Classroom
23
Next seminars ?
Energy consumption in one year
• PREDIS Building Simulation
Température ambiante de la salle informatique
Température (°C)
30
• PREDIS thermal management
Modèle global
COMFIE Pléiades
25
20
15
0
1
2
3
4
5
6
7
Temps (mois)
8
9
10
11
12
Température ambiante de l'espace bureaux
Température (°C)
30
Hoang-Anh DANG
CO2
regulation
25
20
15
23/06/2015
Modèle global
COMFIE Pléiades
0
1
2
3
4
5
6
7
Temps (mois)
8
9
temperature
regulation
10
11
12
24
Thank you for your attention
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