A Multi Scale Design and Control Framework for …conferences.ict.illinois.edu › RESINworkshop2011 › project...Prof. Hosam K. Fathy ME Penn State Prof. Carl Simon MMPEI/Math U

NSF EFRI Grant: Dynamically Coupled Sustainable and Resilient Infrastructures

PI Prof. Jeffrey L. Stein ME U of M

Co‐PI Prof. Zoran Filipi ME ClemsonProf. Greg Keoleian SNRE U of MProf. Huei Peng ME U of MProf. Mariesa Crow EE Missouri U. of Sci.

& Tech.

Particip.‐Invest. Prof. Duncan Callaway Energy Resources Group UC BerkeleyProf. Hosam K. Fathy ME Penn StateProf. Carl Simon MMPEI/Math U of MProf. Jing Sun Naval U of MProf. Ian Hiskens EE U of M

A Multi‐Scale Design and Control Framework for Dynamically Coupled Sustainable and Resilient

Infrastructures, with Application to Vehicle‐to‐Grid Integration


Power Infrastructure

Stochastic Resources and Loads

Renewable Resources

Exhaustible Resources

Mobility/Energy Demands

Power Generation

Storage & Distribution Transportation Infrastructure

PHEVs

Chiao‐Ting LiPh.D. Student

Jarod KellyResearch Scientist


Environmental assessment of plug‐in hybrid electric vehicles using naturalistic drive cycles

and travel pattern information

3

Jarod C. Kelly

From presentation at 6th International Conference on Industrial Ecology byBrandon M. Marshall, Jarod C. Kelly, Gregory A. Keoleian,

Tae‐Kyung Lee, Zoran Filipi


Understanding sustainability

• Sustainable energy definition from United Nations Development Programme (2000)– energy produced and used in ways that

support human development over the long term, in all its social, economic, and environmental dimensions.

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Some sustainability indicators

Source: Based on World Bank (2000), op. cit., p. 39 and IEA Energy Statistics Division.; Keoleian Univ. Michigan

• Environmental Indicators• Greenhouse gases (GHG)

• Per unit emissions of GHG expressed in CO2 equivalents • Local emissions / criteria pollutants

• Deposits of SO2 per kilometre

• Energy Supply Indicators• Reliability

• % of time that source is available• Import dependency• Energy diversification

• Sum of squares of shares of different sources in effective energy consumption

• Economic Indicators• Average subsidy per effective unit of energy• Consumption

• Social Indicators• Affordability• Education• Health

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Life cycle analysis

Well to Tank

Tank to Wheels

Well to Tank

• Well-to-wheel analyses– total fuel cycle for feedstocks – powertrain efficiency

• Full life cycle assessment– well-to-wheel analysis– vehicle production

Source: Argonne National Lab; Keoleian Univ. Michigan

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• Evaluate the sustainability performance of PHEVs in

Michigan using two different evaluation methods.

• Characterize sustainability performance using fuel‐cycle

energy and emissions quantifications.

Goal

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PHEVNDC

Based on energy consumption curves generated with

naturalistic drive cycles

PHEV energy consumption model comparison

PHEVAVG

Based on an average of vehicle efficiencies from HEV/PHEV literature

32 mpg; 0.274 kWh/mile

Naturalistic drive cycles Average consumption rates

Image: 2011 Chevrolet Volt, Courtesy General Motors

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One week of PHEV charging from the Michigan grid

PHEV charging from the Michigan (2009) electrical grid: electricity consumption from the PHEVNDC model shows a 12.6% increase over

electricity consumption from the PHEVAVG model

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Total Fuel Cycle (TFC) energy per mileTotal fuel cycle energy• Includes all life cycle energy

used to drive the vehicle, from mining, processing and transporting fuels to vehicle propulsion

Allocation methods•Average (AA):

Portion of every power plant attributed to PHEVs based on proportion of PHEV load to total load•Marginal (MA):

Only the energy from added plants dispatched to provide power for vehicle charging are assigned to PHEVs

CS: charge sustaining mode, engine onlyCD: charge depleting mode, battery only

All light duty conventional vehicles (CV) in Michigan, 2010

Midsize PHEV based on 2009 Michigan grid

Midsize PHEV based on 2020 western

states grid

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Greenhouse gas emissions

• The PHEV environmental assessment for Michigan* tracks three greenhouse gases (GHGs): Carbon Dioxide (CO2), Methane (CH4), and Nitrous Oxide (N2O), and use IPCC 4th Assessment Report to calculate

mass of CO2e = mCO2 + 25 * mCH4 + 298* mN2O *(Keoleian et al, 2010)

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Criteria pollutant emissions

Five other air pollutants defined as criteria pollutants are tracked by the PHEV environmental assessment in Michigan*

•Nitrogen Oxides (NOX )•Carbon Monoxide (CO)•Sulfur Dioxide (SOX)•Volatile Organic Compounds (VOC)•Particulate Matter (PM10).

*(Keoleian et al, 2010)

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Summary

• Evaluated environmental impacts of PHEVs in Michigan using two approaches

• Find that even using a more aggressive (and realistic) energy consumption characterization, PHEVs outperform conventional vehicles in total fuel cycle energy and GHG emissions

• PHEVs increase emissions of SOx, NOx and particulate matter• Primarily due to contribution from coal‐based electricity

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Drive cycles

The Environmental Protection Agency (EPA) developed federal driving schedules

• Speed versus time curves originally used for emissions certification testing of conventional vehicles

• Widely accepted analysis approach in determining fuel economy

• Not necessarily representative of actual driving behavior

• EPA continues to adjust and combine standard test cycles in an effort to achieve real‐world driving characteristics

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Naturalistic drive cycles

Engineers at the University of Michigan developed synthetic naturalistic drive cycles*

•Characterized from a database of actual driving generated in Field Operational Tests in Southeast Michigan

•Procedure utilizes Markov chains to generate synthetic drive cycles statistically matched to dynamics of real‐world driving

•Used to predict energy usage as a function of trip length and reproducible for arbitrary driving distances

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*(Filipi, et al, 2009)


Predicting PHEV energy consumption

Previous approach:

• Examine driving distance distribution from travel survey*

• Choose PHEV all‐electric range (Example: PHEV30 travels 30 miles on battery power only)

• Split travel survey data into battery miles and gasoline miles based on all‐electric range

• Use estimated fuel economy (mpg), and electric efficiency (kWh/mile) to determine energy consumption of fleet

All electric range = 30 miles

45% of fleet miles are battery powered,

55% are gasoline powered*(EPRI, 2001, 2007; Samaras, et al, 2007; Elgowainy, et al, 2010)

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VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

New approach:

• Examine individual vehicle‐trips in the travel survey*

• Apply a naturalistic drive cycle to each trip based on distance• Calculate gasoline and battery usage from energy consumption curves

Predicting PHEV energy consumption

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VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

VehicleID

Trip Distance

1 15

1 10

2 30

2 37

3 12

3 4

3 16

*(Keoleian et al, 2010) 20 400

Trip distance


Sustainability & Reliability of Electricity Grid

with Plug‐In Electric Vehicle Control

Chiao‐Ting Li, Huei Peng, Jing Sun

University of Michigan

NSF EFRI Grant: Dynamically Coupled Sustainable and Resilient Infrastructures 19

Control Integration on Electricity Grid

• Synergy exists between– The controllable plugplug‐‐in vehicle chargingin vehicle charging– The renewable but intermittent wind energywind energy

• Appropriate system control can exploit the synergy to– Improve sustainability– Retain reliability

• Metrics for sustainability and reliability across both the transportation and electricity sector on a common base: cost


– Three distributions:• Plug‐in time• Plug‐off time• Battery state of charge (SOC)

– Data source: UMTRI & NHTS

Modeling Efforts• The plug‐in vehicle (PEV) fleet

– These distributions help to • Quantify the additional load imposed by PEVs

• Quantify the leverage power (control authority) granted by PEVs


=

=

=

=

Modeling Efforts• The electricity gridConventional Grid (Reference Case)Conventional Grid (Reference Case) Grid with IntegrationGrid with Integration

– No renewables– Uncoordinated PEV charging

– Wind energy is included– Controlled PEV charging

GridGrid


=

=

Controller Structure & Realization

The realization tells• Wind energy utilization• Non‐renewable generation utilization• Load magnitude• Grid frequency deviation …

Planning (Scheduling)

Realization (Dispatch)


Sustainability & Reliability Metrics• Sustainability:

– Reduction of fossil fuel use in transportation sector– Penetration of renewables in electricity sector

• Reliability:– Retain the same LOLP (loss of load probability) in electricity sector– We measure how much grid reserve can be retired while retaining

the same LOLP

• Furthermore, the improvement is converted into cost reduction/savingcost reduction/saving– We count dollar saved only in the end‐use phase

(exclude mining, fuel transporting, plant installation etc.)


Sustainability & Reliability Metrics

• PEVs act as the intermediary to bridge the transportation and electricity sector• S & R measurement, eventually, shows up as cost reduction in both sectors• This assessment can be a planning tool for investors or policy makers to set

penetration targets in both sectors

Electricity GridElectricity GridTransportationTransportation

PEV

ICE

PEV

ICE

PEV

Existin

g Load

(25% PEV penetration) (10% wind energy penetration)

Sustaina

bility

Reliability

Sustaina

bility


Summary• PEVs act as the intermediaryintermediary to bridge the transportation and

electricity sectors, and enables the control integration• Models were developed to capture major dynamicsmajor dynamics on the grid,

with which we test the control integration• We assess sustainability and reliability across two sectors on a

common base: costcost Transportation Electricity Grid• Fossil fuel use • Renewable penetration

• Loss of load probability

• Cost reduction

• There are still things that can be included into the assessment

Transportation Electricity Grid• Emissions

• Energy diversity• More capable of enduring disturbances/break downs

Documents

A Multi Scale Design and Control Framework for …conferences.ict.illinois.edu › RESINworkshop2011 › project...Prof. Hosam K. Fathy ME Penn State Prof. Carl Simon MMPEI/Math U