Real-time Optimization and Simulation for Integrated Power Systems

1. Real-time Optimization and
Simulation for Integrated Power
Systems
Jing Sun
Naval Arch. and Marine Eng.
University of Michigan

2. Presentation outline
 Introduction: RACE‐Lab at University of
Michigan
 Integrated Power Systems
 IPS Power Management, Real‐time
Optimization and Simulation
 A Case Study: IPS for All‐Electric Ships
 Conclusions

3. Real-time Adaptive Control Engineering Lab
Simulation and
Validation
Model
Development
Control design
Optimization
Data
Hardware info
Requirements
Constraints
Transient
profiles
Hardware/configuration
recommendations
Subsystem
specifications
Sensitivity analysis
results
Control strategy
Control-oriented
“grey-box” model
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4. Our Applications
Real‐time
Simulation
 Power management of
Integrated Power Systems
 Engine and powertrain
control
 Combined fuel cell and gas
turbine cycle systems and
CHP
 Vessel control and dynamic
positioning
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5. Active Projects
 Energy Management and Configuration
Optimization for All‐Electric Ships
(ONR/NEEC)
 Integrated Fuel Cell and Fuel Reforming
Systems Dynamic Analysis and Control
Design (TARDEC/ARC, NSF)
 Vehicle Electrification (DoE/CERC)
 Control and Optimization of Advanced
Automotive Powertrains (Ford, Toyota)
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6. Lab Facilities
• An 8‐node Opal‐RT real‐time simulator
• A DC hybrid power system
• Combined cycle SOFC/GT hardware
simulation bench (in progress)
• A fully instrumented model ship
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7. Presentation outline
 Introduction: RACE‐Lab at University of
Michigan
 Integrated Power Systems
 IPS Power Management, Real‐time
Optimization and Simulation
 A Case Study: IPS for All‐Electric Ships
 Conclusions

8. Integrated Power Systems
 Power systems that combine multiple
power sources/loads through synergetic
integration
 Examples of integrated power systems
– Hybrid vehicles
– All‐electric ships
– SOFC/GT system
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9. Characteristics of IPS
 Multiple and heterogeneous power/heat plants involved
 High efficiency and (intended for) self‐sustaining
 Close thermal, chemical, mechanical and electrical
couplings
 More complex and challenging tasks for control,
optimization and integration
– High efficiency  system often operates on or close to the
boundary of admissible state and input sets
– Mobile requirements  require fast load following capability
and sufficient power reserve and safety margin
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10. Presentation outline
 Introduction: RACE‐Lab at University of
Michigan
 Integrated Power Systems
 IPS Power Management, Real‐time
Optimization and Simulation
 A Case Study: IPS for All‐Electric Ships
 Conclusions

11. Power Management for IPS
 Coordinate multiple, heterogeneous power plants
(including energy storage devices)
 Manage transient operations
 Assure safe operation in case of component and
subsystem failure
 Facilitate effective system reconfiguration
 Achieve optimal performance in terms of power quality
and system operation efficiency
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12. Optimization in Power Management of IPS
 Optimization: A natural formalism for power
management of IPS
– Assure optimal performance during normal operation
– Guarantee effective reconfiguration in case of failures
– Enforce hard and soft constraints
 Challenges:
– Computationally intensive (nonlinear dynamics, long
time horizon, mixed form of models)
– Real‐time performance requirements
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13. Our Approach
 Algorithm development
– Integrated perturbation analysis and sequential
quadratic programming (IPA‐SQP) to speed up
optimization
– Sensitivity function approach to explore multiple
time‐scales in IPS
– Incremental reference governor to reduce problem
complexity
 Algorithm evaluation and validation
– RT‐Lab for algorithm development and evaluation
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14. Presentation outline
 Introduction: RACE‐Lab at University of
Michigan
 Integrated Power Systems
 IPS Power Management, Real‐time
Optimization and Simulation
 A Case Study: IPS for All‐Electric Ships
 Conclusions

15. Case Study: IPS for All-Electric Ships
Main Subsystems:
1. PGM: Power Generation Module
• PGM1:Gas turbine
• PGM2:Fuel cell
2. EPM: Electric Propulsion Module
3. ESM: Energy Storage Module
4. PCM: Power Conversion Module
• PCM1: DC/DC
• PCM2: DC/AC
• PCM3: DC/DC
• PCM4: AC/DC & DC/DC
• PCM5: DC/AC
• PCM6: DC/AC
Main features of IPS:
1. Redundant power sources
2. Reconfigurable power flow path
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16. Case Study: IPS for All-Electric Ships
System representation of a shipboard integrated power
system
DC Hybrid Power System (DHPS)
1. Multiple power sources
2. Multiple power converters
3. Energy storage bank
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17. Test-bed Setup
• Power converter
1. Unidirectional DC/DC (1, 2)
2. Bidirectional DC/DC (3)
• RT‐LAB with 4 targets
• Power supply (1, 2)
• Electronics load (1, 2)
• Energy storage bank 17

18. Explore Time-scale Separation
Main Subsystems:
PGM: Power Generation Module
PGM1:Gas turbine
PGM2:Fuel cell
(~seconds-minutes)
EPM: Electric Propulsion Module
(~ms - seconds)
ZEDS:
Power Conversion Module
(s – ms)
Vital loads
Non-vital loads
DC STBD bus
DC Port Bus
Zone1
PCM1
NV
load
PCM3
Vital
load
PCM2
NV
load
PCM6
Vital
load
PCM5
MEPM
MEPM
Zone2
PCM1
PCM1
NV
load
PCM3
Vital
load
PCM2
NV
load
PCM6
Vital
load
PCM5
PGM2PCM-4
PCM1
PCM-4
AC 4160V/60HZ
DC 600V
PORT 1100VDC
STBD 900VDC
PORT 900VDC
PGM: power generation module
PCM: power conversion module
EPM: electric propulsion module
PGM1
STBD 1100VDC
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19. Use RT-Lab for Algorithm Development
 Explore the trade‐off between
optimality and computational
efficiency
 Level 1: static optimization (all
dynamics are considered
infinitely fast)
 Level 2: ignore fast dynamics
 Level 3: consider both slow and
fast time dynamics
– Calculate the corrections to the
Level 2 solution
CostJ Time required to solve for u
L1
L2
L3
Opt
Opt
Performance
loss due to non
real-time
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20. Simulation and Validation
From_PM
From_Gen
From_FC
From_Loads
To_Con
To_Loads
To_FC
To_Gen
To_PM
SS_ZEDS
From_PM
From_Gen
To_Con
To_PM
To_Gen
SS_Propulsion
From_PM
From_ZEDS
To_Con
To_ZEDS
To_PM
SS_Loads
From_G/T
From_ZEDS
From_Prop
To_Con
To_PM
To_ZEDS
To_Prop
To_GT
SS_Generator
From_PM
From_Gen
To_Con
To_Gen
SS_G/T
From_PM
From_ZEDS
To_PM
To_ZEDS
To_Con
SS_Fuelcell
From_Con
From_Gen
From_FC
From_Prop
From_Loads
From_ZEDS
To_Con
To_Loads
To_ZEDS
To_GT
To_FC
To_Prop
SM_PowerManagement
From_PM
From_ZEDS
From_Loads
From_Prop
From_FC
From_G/T
From_Gen
To_PM
SC_Console
DC STBD bus
DC Port Bus
Zone1
PCM1
NV
load
PCM3
Vital
load
PCM2
NV
load
PCM6
Vital
load
PCM5
MEPM
MEPM
Zone2
PCM1
PCM1
NV
load
PCM3
Vital
load
PCM2
NV
load
PCM6
Vital
load
PCM5
PGM2PCM-4
PCM1
PCM-4
AC 4160V/60HZ
DC 600V
PORT 1100VDC
STBD 900VDC
PORT 900VDC
PGM: power generation module
PCM: power conversion module
EPM: electric propulsion module
PGM1
STBD 1100VDC
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21. Simulation and Validation
 With the time‐scale separation
– Better tracking
– The timeliness of the optimal
solution proved to be critical
Power demand
Solution with
Time scale
separation
Solution
without Time
scale
separation
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22. Presentation outline
 Introduction: RACE‐Lab at University of
Michigan
 Integrated Power Systems
 IPS Power Management, Real‐time
Optimization and Simulation
 A Case Study: IPS for All‐Electric Ships
 Conclusions

23. Conclusions
 Real‐time performance is an essential
requirement IPS system performance
 Computational Efficiency is critical for
optimization‐based power management for IPS
 Case study illustrates the utility of efficient
algorithm and computational tools in developing
effective IPS with desired real‐time performance
 RACELab has been using RT‐Lab in algorithm
development and methodology research
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