Real-time Optimization and Simulation for Integrated Power

Systems

Jing SunNaval Arch. and Marine Eng.

University of Michigan

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 ShipsConclusions

Real-time Adaptive Control Engineering Lab

Simulation andValidation

Model Development

Control designOptimization

DataHardware info

Requirements

Constraints

Transientprofiles

Hardware/configurationrecommendations

Subsystemspecifications

Sensitivity analysis results

Control strategy

Control-oriented “grey-box” model

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Our Applications

Real‐timeSimulation

Power management of Integrated Power Systems

Engine and powertraincontrol

Combined fuel cell and gas turbine cycle systems and CHP

Vessel control and dynamic positioning

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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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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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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 ShipsConclusions

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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www.defenseindustrydaily.com

www.techjournal.org

www.americanhistory.si.edu

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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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 ShipsConclusions

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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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 requirements12

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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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 ShipsConclusions

Case Study: IPS for All-Electric ShipsMain Subsystems:1. PGM: Power Generation Module

• PGM1:Gas turbine• PGM2:Fuel cell

2. EPM: Electric Propulsion Module3. 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 sources2. Reconfigurable power flow path

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Case Study: IPS for All-Electric Ships

System representation of a shipboard        integrated power system

DC Hybrid Power System (DHPS)1. Multiple power sources2. Multiple power converters3. Energy storage bank

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Test-bed Setup

• Power converter1. 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

Explore Time-scale SeparationMain Subsystems:PGM: Power Generation Module

PGM1:Gas turbinePGM2:Fuel cell

(~seconds-minutes)EPM: Electric Propulsion Module

(~ms - seconds)ZEDS:

Power Conversion Module(s – ms)

Vital loadsNon-vital loads

DC STBD bus

DC Port Bus

Zone1PCM1

NV loadPCM3

Vital loadPCM2

NV loadPCM6

Vital loadPCM5

MEPM

MEPM

Zone2PCM1

PCM1

NV loadPCM3

Vital loadPCM2

NV loadPCM6

Vital loadPCM5

PGM2PCM-4

PCM1

PCM-4

AC 4160V/60HZ

DC 600V

PORT 1100VDC

STBD 900VDC

PORT 900VDC

PGM: power generation modulePCM: power conversion moduleEPM: electric propulsion module

PGM1

STBD 1100VDC

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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

Cos

t J

Time required to solve for u

L1

L2

L3Opt

Opt

Performance loss due to non real-time

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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

Zone1PCM1

NV loadPCM3

Vital loadPCM2

NV loadPCM6

Vital loadPCM5

MEPM

MEPM

Zone2PCM1

PCM1

NV loadPCM3

Vital loadPCM2

NV loadPCM6

Vital loadPCM5

PGM2PCM-4

PCM1

PCM-4

AC 4160V/60HZ

DC 600V

PORT 1100VDC

STBD 900VDC

PORT 900VDC

PGM: power generation modulePCM: power conversion moduleEPM: electric propulsion module

PGM1

STBD 1100VDC

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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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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 ShipsConclusions

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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