Vehicle Supported Military Microgrids - Oakland...

Automotive Research Center

ARC Conference Case Study 3 | 24 May 2011 Ann Arbor MI

Vehicle Supported Military MicrogridsDesign Scheduling and Regulation for a

ARC Conference Case Study 3 | 24 May 2011, Ann Arbor, MI

Design, Scheduling, and Regulation for a Forward Operating Base

Case Study Team

F l P P l b (PI) T l E l I Hi k H i P AFaculty: Panos Papalambros (PI), Tulga Ersal, Ian Hiskens, Huei Peng, Anna Stefanopoulou, Jeffrey L. Stein

Students/Post‐docs: Changsun Ahn, Abigail Mechtenberg, Diane Peters, John Whitefoot

TARDEC: Erik Kallio, Paul Makar, Jillian McDonaldIndustry: Peter Lilienthal (HOMER Energy)

Motivation 2

Fort Drum

Fort Devens

S lf id AFBSelfridge AFB

Wright‐Patterson AFBFort Hamilton

Reducing dependency on fossil fuel

Increasing use of renewable energy resources on grid

Increasing security, autonomy, and

robustness of military g yand national grids

Motivation 3

Fort Drum

Fort Devens

Selfridge AFB

Wright‐Patterson AFB Fort Hamilton

Motivation: Flexibility 4

Army energy requirements driven by mission requirements

67%Combat Vehicles 10%

37%

Peacetime Contingency Operations

3%

16%

5%3%

6%

67%Combat Aircraft

Tactical Vehicles

Generators

Non Tactical Vehicles

19%

11%

22%

3%

37%

Non‐Tactical Vehicles

Facilities

22%

Generators Generators

“U.S. Army Energy Security and Sustainability: Vital to National Defense,” Torchbearer National Security Report, April 2011

Vehicle Supported Military Microgrid Example 5

• Installation of microgrid at Schofield Barracks, tied to critical infrastructure

• Photovoltaic arrayPhotovoltaic array

• Dedicated electric vehicle charging

• Grid connected

• Conventional generator for extendedConventional generator for extended backup power

• Advanced stationary energy storage

• Load managementg

• AC architecture• Microgrid officially, fully operational as of March 2011of March 2011

• Data collection and operational support for one year

Contingency Basing Structure to Enable Wide Area Security and Combined Arms Maneuver

Any Base Variant

Super FOB

VariantNETWORK

Forward Operating Base

6000 Soldiers

Combat Outpost

Patrol Base

2000 Soldiers

Patrol Base

“At or near the tactical edge, over 300 Soldiers

g70% of fuel used by our military forces can be for power generation” [DARPA BAA‐11‐53]50 Soldiers

Case Study: Vehicle Supported FOB 7

Goal: Developing an integrated tool for designing vehicle supported FOBpp

Design & Operating points,d b

RegulationDesign & Scheduling

disturbance sizes

Battery & APU

Centralized controlLong time‐scale

Decentralized controlShort time‐scale

power constraints

V2G Mi idRegulationLiterature 8

V2G Microgrid

R d d

Adequacy ofexisting capacity

Regulation issues[1, 2]

Reduced Emissions[8, 10, 12, 16]

PEV controlfor regulation

[33, 34, 35]

g p y[3]

Ancillary services[4, 5, 9, 14, 15, 17]

Droop control[1, 27‐31]

Economics[4, 9, 15, 17,36]

Control [18‐23]

Inverter control[32]

TODAY’S CASE STUDY

Impact on Renewable Po er

Microgrid modeling, controls, and practical implementation

[38]

MicrogridDesign

PEV [40,41]V2G

Technological Power[5, 10, 12, 14, 15]

[ 0,4 ]

[42] Optimal dispatching of distributed energy resources

[39]

gDesignAspects[37]

References available upon requestDesign&Scheduling

FOB Simulations[43]

FOB with Solar + JLTVs with export power 9

APUBattery Pack

APU

~50 soldiers120 kW peak po er Joint Light Tactical Vehicle with on‐board120 kW peak power67 kW average power$6‐8/gal fully‐burdened

Joint Light Tactical Vehicle with on‐board auxiliary power unit and battery storage • 30 kW export power

k h bPower supplied by Solar Panels and Power‐Export JLTVs

cost of fuelRadial network

• 2 ‐ 20 kWh Li‐ion battery

Design & Operating points,disturbance sizes

Regulationg

Schedulingdisturbance sizes

Battery & APUpower constraints



power constraints

Design + Scheduling Goal 11

Operational Constraints

Optimization Component Sizes and

Military Requirements

Minimizing Fuel and Capital Cost

SchedulingRequirements

Uncertainties

Design + Scheduling: FOB with Solar + JLTVs 12

1) Solar Panel Peak Power

2) Number of JLTVs2) Number of JLTVs3) Battery pack size(Export power fixed: 30 kW)

Solar Irradiance (W/m2)

Solar Radiance Inputs and Error 13

Solar SupplyHourly solar irradiance taken from NASA d f K b l

Solar Irradiance (W/m )24

NASA data for area near Kabul.

Design and Scheduling optimization used variable but ou

r of Day

optimization used variable, but deterministic solar input.

Ho

Solar Error ModelingDay of Year

1

gAn error variable created for non‐linear model predictive control algorithm of the solar panel output power.

APU Model 14

Non‐Linear APU ModelSurrogate model constructed for generator from Cummins dieselgenerator from Cummins diesel generator data (50 generators in data set)

Efficiency

Model Inputs and OutputsAPU efficiency calculated as a function of APU rated power and ppercent load

APU

Model Inputs: Power Load w/ Error 15

Known

2kW/soldier2kW/soldier

Power Load Profile

(Part 1) Diurnal power load modeled with radial basis functions based on 5 key parameters.

(Part 2) Seasonal power load modeled with average daily temperature sine function to model change in HVAC needs based on published ECUs power load based on temperature.

Diurnal Power Load 16

Parameters

Adding Seasonal Variations 17

80) Summer

High

60

70

tion

(oF Air Conditioning

HeatSummer

Comfort Level (65/75)

40

50

re V

aria

t SummerLowHeat Only

Winter

20

30

mpe

ratu

r

High

WinterLow

0 5 10 15 200

10Tem

50‐FOB = 4*ECUs .

0 5 10 15 20

Time of Day (hr)Used our Temperature model and HVAC model from N. C. McCaskey, “Renewable Energy Systems for Forward Operating Bases: A Simulations‐Based Optimization Approach ” Colorado State University, 2010

Total Power Load (kW)

20100

110

Operational Power Load (kW)

20 60

65

70

Diurnal Yearly Load without Error

s du

ring

the

Day

10

15

70

80

90

s du

ring

the

Day

10

15

40

45

50

55

Hou

rs

5

10

50

60

70

Hou

rs

5

10

25

30

35

Total Power Load w/ Error 1 (kW)

130

365 Days of Year

50 100 150 200 250 300 350

40

4-ECU: HVACs (kW)

365 Days of Year

50 100 150 200 250 300 350

20

the

Day

15

20

100

110

120

he D

ay

15

20

40

45

Seasonal Variations from HVACfor Yearly Power Load without ErrorOperational + Seasonal Variationsfor Yearly Power Load without Error

Hou

rs d

urin

g t

10

60

70

80

90

Hou

rs d

urin

g th

1030

35

Power Load used in this Case Study.

365 Days of Year

50 100 150 200 250 300 350

5

40

50

60

365 Days of Year

50 100 150 200 250 300 350

5

20

25

Sample FOB Case to Compare 19

Example of CONUS FOB Demand Data with UM Interpretation 20

• Data collected in support of NetZero+ JCTD at Ft. Irwin

Mathematical Framework – MG Hubs 21

Hub‐based energy network [Geidl]

diesel gas

electricity electricity

+ ‐

H2H1

diesel gas

solar H3

Model Inputs L: load at hubModel InputsIndividual hub layoutsComponent sub‐modelsPower loads vs. time

L: load at hubc: energy conversion factorP: energy input to hubs: energy storage efficiency

Solar supply : change in energy storageE

Geidl, M. and G. Andersson, “A modeling and optimization approach for multiple energy carrier power flow,” in Proc. IEEE Power Eng. Soc. PowerTech, St. Petersburg, 2005, Russian Federation.

Design Optimization Framework 22

Optimal design of a microgrid requires balancing capital costs of equipment with expected operation costs

requires solving a nested optimal scheduling problem for each designrequires solving a nested optimal scheduling problem for each designcould also consider other costs (e.g., transportation, maintenance)

Optimal Design ProblemOptimal Design Problem

Optimal Scheduling Problem

minimize: capital + operation costs

variables: component sizes Optimal Scheduling Problemvariables: component sizes

constraints: ‐ operational limitsb k ’

minimize: operation costs (e.g., over 1 year)

‐ backup req’ts‐ reliability‐ volume/footprint limits‐ silent watch req’ts

y )

variables: energy generation / storage scheduling

q‐ other military req’ts constraints: operational limits

A $7/ ll f ll b d d t f f l

Initial Results: Design + Scheduling 23

89 kW Solar Panel(~4,100 ft2 rigid PV or ~14,400 ft2 of flexible PV)

Assumes $7/gallon fully‐burdened cost of fuel

( 4,100 ft rigid PV or 14,400 ft of flexible PV)

Plug‐in JLTVs: 4

Battery Capacity: 2.1 kWh

Added Weight: 35 kg

PV panel + JLTV w/ export power( li d) $9 00

“Business as Usual” – Two generators( li d) $ 00 Cap Cost (annualized): $91,500

Fuel Use (annual): 38,000 gallonsChange in Fuel Use: ‐29%

Cap Cost (annualized): $4,400Fuel Use (annual): 53,600 gallons

Example Optimal Scheduling Result 24

Optimal scheduling on a time horizon results in strategic energy storage use

GEN

BATT

LOAD

Optimal Scheduling Result for One Day Power Flow

Design & Operating points,disturbance sizesg



T i di b l d i d

Vulnerable Operating State w/ Disturbance 26

Transient power disturbances can cause power loss and equipment damage

Regulation problem can show design limitations and suggest improvements

50100Total Power LoadSolar

Drop in Solar Power of 50 kW

30

40

60

80

er (kW)

kW)

SolarGeneratorBattery

10

20

20

40

tery Pow

e

Power (k

10

0

20

0 Batt

‐10‐20

1 3 5 7 9 11 13 15 17 19 21 23

Hour of Day


Regulationg




What is the regulation problem? 28

Voltage regulationKeep length constant

Frequency regulationKeep rotational speed constant

Significance of regulation 29

60 Hz60 Hz60 Hz

Frequency Variation Voltage Variation

LOW HIGH

Speed

LOW HIGH

Speed

LOW HIGH

Speed

110V

LOW HIGH

110V

LOW HIGH

110V

LOW HIGH

110V

LOW HIGH

110V

LOW HIGH

110V

LOW HIGH

Speed

Temperature

Efficiency

Power

Speed

Temperature

Efficiency

Power

Speed

Temperature

Efficiency

Power

Voltage increases.Light is getting bright.Voltage exceeds the limitation.Light bulb burns out.Devices may get harmed.

Voltage decreases.Light is getting dark (brown out).Current to the devices increases.

Voltage exceeds the limitation.Current to the devices increases too much.

Power

May burn out earlier than designed.

Power

Impedance goes down.Current goes up and cooling goes down.May burn out earlier than designed.

Power

Frequency and power balance 30

Power Generation = Power DemandPower Generation > Power DemandPower Generation < Power Demand

60Hz

60.5Hz

60Hz

60.5Hz

60Hz

60.5Hz

59.5Hz59.5Hz59.5Hz

PowerGeneration

PowerDemand

PowerGeneration

PowerDemand

PowerGeneration

PowerDemand

Frequency regulation framework 31

Demand fluctuation

Disturbance

PV power drop

Disturbance

Demand fluctuation

Disturbance

PV power drop

Disturbance

Demand fluctuation

Disturbance

t

Pdemandd

t

PPV

t

Pdemandd d

t

PPV

t

Pdemandd d

Performance Goal

Battery PackAPU Battery PackAPU

• Frequency: 60Hz ± 0.5Hz• Voltage: 110V± 5%

Fast ActuatorLimited Energy Capacity

p2

Battery PackSlow Actuator

Unlimited Energy Capacity

p1

APUFast Actuator

Limited Energy Capacity

p2

Battery PackSlow Actuator

Unlimited Energy Capacity

p1

APU

tt

p1

tt

p1

Simple vs. full model 32

Simple Modelfor Control Design

Full Modelfor Validationg

LinearFocus on frequency

NonlinearFocus on frequency and voltageFocus on frequency Focus on frequency and voltage

Includes battery model

Battery model 33

T.K. Lee and Zoran Filipi

D. Di Domenico, A. Stefanopoulou, and G. Fiengo, "Lithium‐ion battery state of charge and critical surface charge estimation using an electrochemical model‐based extended Kalman filter," Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 132, no. 6.

based on

Power Controller Design 34

60Hz

60.5Hz

Housing59.5Hz

Office

Housing

HousingC1

C2

ControllersControllers

Requirements

Assuming 2.3C max rate

Frequency Control Result 35

60Hz

60.5Hz

Housing60Hz

60.5Hz

Housing60Hz

60.5Hz

Housing60Hz

60.5Hz

Housing60Hz

60.5Hz

Housing60Hz

60.5Hz

Housing60Hz

60.5Hz

Housing59.5Hz

Office

Housing

HousingC159.5Hz

Office

Housing

HousingC159.5Hz

Office

Housing

HousingC159.5Hz

Office

Housing

HousingC159.5Hz

Office

Housing

HousingC159.5Hz

Office

Housing

HousingC159.5Hz

Office

Housing

HousingC1

C2C2C2 .C2C2 .C2 .C2 .

RequiredBattery Power

Simple vs. full model 36


Full Modelfor Validation


Full Modelfor Validationg

LinearFocus on frequency

NonlinearFocus on frequency and voltage

g

Focus on frequency Focus on frequency and voltageIncludes battery model

Simulation with full model 37

Battery assumed constant voltage source


19kWh Battery with diffusion dynamics

No solution!

Physical explanation 39

Current

VoltageVoltage

Voltage drop limits AC power

One solution 40

……

……

…

Add more cells in parallel


38kWh Battery with diffusion dynamics


53.8kWh Battery with diffusion dynamicsy y

Evolution of battery sizing 43

53.8 kWh

19 kWh

8.4 kWh

After design and After regulation After consideringAfter design and scheduling

considerations

After regulationconsiderations

After considering battery dynamics


Regulationg


Battery & APUpower constraints



power constraints

Additi l t i t f R l ti bl t l t 53 8 kWh b tt it

Final Results: Des. + Sched. + Reg. 45

87 kW Solar Panel(~4 000 ft2 i id PV (1 9 t i t ) ~14 000 ft2 f fl ibl PV (6 6 t i t ))

Additional constraint from Regulation problem: at least 53.8 kWh battery capacity

(~4,000 ft2 rigid PV (1.9x tennis courts) or ~14,000 ft2 of flexible PV (6.6x tennis courts))

Plug‐in JLTVs: 4

Battery Capacity: 14.3 kWhAdded Weight: 240 kgAdded Weight: 240 kg

28% saving in fuel

New DesignOriginal Design

28% saving in fuelcompared to “business as usual”

New DesignCap Cost (annualized): $94,100Fuel Use (annual): 38,600 gallons

Original DesignCap Cost (annualized): $91,500Fuel Use (annual): 38,000 gallons

Summary of findings 46

• Critical coupling exists between short and long time scale system behaviortime scale system behavior

• Proposed integrated approach can take this coupling into accountcoupling into account

• Smart energy management strategies can d f lreduce fuel use

• Battery voltage fluctuations may impose additional design considerations

Next steps 47

• Develop and incorporate models for additional components (e.g., hybridized vehicles)components (e.g., hybridized vehicles)

• Develop framework for designing and analyzing reconfigurable topologyanalyzing reconfigurable topology

• Analyze interplay between prediction errors d h iand horizon

• Develop flexible and scalable controllers

• Validation of proposed methods and tools

Conclusion 48

O i d h d d l h lOur integrated methods and tools can help achieve this power and energy vision

Fort Drum

Fort Devens

Selfridge AFB

Wright‐Patterson AFB Fort Hamilton

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