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Evaluating storage technologies for solar and wind energy Jessika - - PowerPoint PPT Presentation

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Evaluating storage technologies for solar and wind energy Jessika E. Trancik MIT Institute for Data, Systems, and Society March 5, 2017 Andlinger Center Highlight Seminar Series Princeton University 7000 600 a b 6000 500 Cumulative GW P

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Evaluating storage technologies for solar and wind energy

Jessika E. Trancik MIT Institute for Data, Systems, and Society March 5, 2017 Andlinger Center Highlight Seminar Series Princeton University

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1200 1000 800 600 400 200

Cumulative GWP Wind

2035 2030 2025 2020 2015 2010 2005 2000

Actual IEA 2006 IEA 2008 IEA 2009 IEA 2010 IEA 2011 IEA 2012 IEA 2013 IEA 2014

600 500 400 300 200 100

Cumulative GWP Nuclear

2035 2030 2025 2020 2015 2010 2005 2000

Actual IEA 2006 IEA 2008 IEA 2009 IEA 2010 IEA 2011 IEA 2012 IEA 2013 IEA 2014

7000 6000 5000 4000 3000 2000 1000

Cumulative GWP Fossil

2035 2030 2025 2020 2015 2010 2005 2000

Actual IEA 2006 IEA 2008 IEA 2009 IEA 2010 IEA 2011 IEA 2012 IEA 2013 IEA 2014

600 500 400 300 200 100

Cumulative GWP Solar (PV + CSP)

2035 2030 2025 2020 2015 2010 2005 2000

Actual IEA 2014 IEA 2013 IEA 2012 IEA 2011 IEA 2010 IEA 2009 IEA 2008 IEA 2006 EIA 2013 EIA 2011 EIA 2010

a b c d

Fossil Nuclear Wind Solar

Trancik, Brown, Jean, Kavlak, Klemun, Edwards, McNerney, Miotti, Mueller, Needell, Technical Report, 2015

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SLIDE 3

World Max Central Min LCOE [$/MWh]

Coal (world) Coal + CCS (world) Coal + Ctax (world) Coal (USA) Coal (China) Coal (Australia) Coal (UK) Coal + CCS (USA)

500 400 300 200 100

Windonshore (worl) Windonshore (USA) Windonshore (China, India) Windonshore (Europe) Windonshore (Africa)

Region Max Central Min

NGCC (world) NGCC + CCS (world) NGCC + Ctax (world) NGCC (USA) NGCC (Australia) NGCC (UK) NGCC (Japan) NGCC + CCS (USA) PVutility (world) PV (USA) PV (South America) PV (Middle East) PV (Africa) PV (Europe) PV (China, India)

Coal Natural gas Wind Solar (PV)

Trancik, Brown, Jean, Kavlak, Klemun, Edwards, McNerney, Miotti, Mueller, Needell, Technical Report, 2015

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SLIDE 4

Modeling energy systems to accelerate low-carbon technology development

performance targets performance trends technology design

time performance

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SLIDE 5

Fundamental insight + tools to inform decisions:

  • engineers
  • private investors
  • policy makers (R&D, regulations)
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SLIDE 6

Research areas

  • Determinants of the rate of technological improvement
  • Adoption potential of technologies evaluated against energy

demand dynamics

  • Emissions impacts of energy technologies evaluated against

climate targets

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SLIDE 7

Research areas

  • Determinants of the rate of technological improvement
  • Adoption potential of technologies evaluated against energy

demand dynamics

  • Emissions impacts of energy technologies evaluated against

climate targets

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SLIDE 8
  • Example 1: Evaluate stationary storage cost structures against

electricity demand, prices and resource availability

  • Example 2: Evaluate mobile battery specific energy against personal

vehicle travel patterns

How much improvement needed in energy storage technologies?

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SLIDE 9

Role of storage technologies for renewable energy

  • Wind, solar resources are intermittent
  • Storage can be used to:
  • Match renewables supply to demand
  • Increase renewable plant revenue

Bob West

For background see:

  • D. Rastler, EPRI, Dec. 2010;
  • E. Hittinger, J.F. Whitacre, J. Apt, J. Power Sources, 206, 2012
  • S. Sundararagavan, E. Baker, Solar Energy, 2012
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SLIDE 10
  • Wind, solar resources are intermittent
  • Storage can be used to:
  • Match renewables supply to demand
  • Increase renewable plant revenue

Bob West

For background see:

  • D. Rastler, EPRI, Dec. 2010;
  • E. Hittinger, J.F. Whitacre, J. Apt, J. Power Sources, 206, 2012
  • S. Sundararagavan, E. Baker, Solar Energy, 2012

Role of storage technologies for renewable energy

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SLIDE 11

Evaluating storage techs for solar and wind energy

  • How to compare diverse storage technologies on a single scale?

Braff, Mueller, Trancik, Nature Climate Change 2016

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Moving beyond lists of attributes...

Castillo and Gayme, 2014

Storage technologies. L/A battery Li-ion battery NaS battery VRB flow battery Energy storage capacity (kW h) 6100 610 6100 20–50 Typical power output (MW) 1–100 0.1–5 5 0.01–10 Energy density (W h/L) 50–80 200–500 150–250 16–33 Power density (W/L) 10–400 Discharge duration Hours Minutes–hours Hours 2–8 h Charge duration Hours Minutes–hours Hours 2–8 h Response time <Seconds Seconds Milliseconds <Seconds Lifetime (years) 3–10 10–15 15 5–20+ Lifetime (cycles) 500–800 2000–3000 4000–40,000 1500–15,000 Roundtrip efficiency (%) 70–90% 85–95% 80–90% 70–85% Capital cost per discharge ($/kW) $300–$800 $400–$1000 $1000–$2000 $l200–$2000 Capital cost per capacity ($/kW h) $150–$500 $500–$1500 $125–$250 $350–$800 Power quality p p Transient stability p Ancillary services Regulation p p p Spinning reserves p p p p Voltage control p p p

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SLIDE 13
  • How to compare diverse storage technologies on a single scale?
  • At what costs do storage technologies add value to renewables?
  • How do current devices compare to these targets?
  • How to optimally improve future storage technologies?

Braff, Mueller, Trancik, Nature Climate Change 2016

Evaluating storage techs for solar and wind energy

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SLIDE 14

Consider three locations, two energy resources

  • Consider wind and solar at three sites:
  • Barnstable, MA
  • McCamey, TX
  • Palm Springs, CA
  • Datasets:
  • Hourly real-time electricity pricing (ISONE, ERCOT, CAISO)
  • Hourly generation of solar and wind plants
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SLIDE 15

Manage storage to maximize revenue

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Manage storage to maximize revenue

Rtotal = max(

N

X

t=0

P(t)(xgeneration(t) + xdischarge(t) − xcharge(t)/η)) subject to:

revenue electricity price wind, solar resource

− subject to: 0 ≤ xdischarge ≤ ˙ Emax 0 ≤ xcharge ≤ min(ηxgeneration(t), η ˙ Emax) 0 ≤

N

X

t=0

(xcharge(t) − xdischarge(t)) ≤ h ˙ Emax.

power capacity constraint energy capacity constraint

{ {

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SLIDE 17

Managing storage to maximize revenue

50 100 1 2 3 1 Days 1 2 50 100 Summer 1 2 3 1 Days 1 2 50 100 Fall 1 1 2

  • Electricity

price Solar and wind plant output (no storage) Solar and wind plant output (with storage)

Braff, Mueller, Trancik, Nature Climate Change 2016

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SLIDE 18

2 4 0.5 1 MW/MW Installed 0.5 1 1.5 2 100 200

Spring

2 4 0.5 1 Days MW/MW Installed 0.5 1 1.5 2 50 100 2 4 0.5 1 Days MW/MW Installed 0.5 1 1.5 2 50 100 2 4 0.5 1 0.5 1 1.5 2 100 200

Summer

2 4 0.5 1 Days 0.5 1 1.5 2 50 100 2 4 0.5 1 Days 0.5 1 1.5 2 50 100 2 4 0.5 1 0.5 1 1.5 2 100 200

Fall

2 4 0.5 1 Days 0.5 1 1.5 2 50 100 2 4 0.5 1 Days 0.5 1 1.5 2 50 100 2 4 0.5 1 0.5 1 1.5 2 100 200 $/MWh

Winter

2 4 0.5 1 Days 0.5 1 1.5 2 50 100 $/MWh 2 4 0.5 1 Days 0.5 1 1.5 2 50 100 $/MWh Solar Gen Wind Gen Solar Out Wind Out Price

Texas Mass California

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SLIDE 19

MA Solar

0.01 0.02 0.03 0.04

Probability Density

0MWh/MWgen 4MWh/MWgen 16MWh/MWgen

20 40 60 80 100 120 0.01 0.02 0.03 0.04

Probability Density Price ($/MWh) MA Wind

MA solar MA wind

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SLIDE 20

Balancing the cost and benefit of storage

  • Value of energy storage
  • Storage system sized to maximize chi

χ = Rtotal CRF(Cgen + ˙ Emax(Cpower

storage + hCenergy storage))

.

annualization factor annual revenue storage power storage cost wind, solar cost hours

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SLIDE 21

1 1 . 6 2.0 1 . 8 Texas Power Cost ($/kW) $1/W 50 100 150

  • Power Cost ($/kW)
  • 2.4

max

Wind

profitability threshold

  • 0.4

0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 150

profitability threshold

  • Energy Cost

($/kWh) 50 100 150

Storage Energy Capacity Cost ($/kWh) Wind Capacity Cost: $1/W Location: McCamey, Texas Storage Power Capacity Cost ($/kW)

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SLIDE 22

1 1.6 2.0 1 . 8 Texas Power Cost ($/kW) $1/W 50 100 150

  • 0.4

0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 0.9 $2/W $3/W Massachusetts Power Cost ($/kW) 50 100 150 1.5 1 . 6 Energy Cost ($/kWh) California Power Cost ($/kW) 50 100 150 50 100 150 8 1 Energy Cost ($/kWh) 50 100 150 0.6 Energy Cost ($/kWh) 50 100 150 1.0 1.1 1.5 1.6 0.8 0.9 1 . 7 . 8 . 9 1 . 0.7 0.8 0.9 0.6 0.7 . 6 . 7 . 8

  • 2.4

max

Wind

profitability threshold Braff, Mueller, Trancik, Nature Climate Change 2016

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SLIDE 23

Storage technologies compared to value-adding cost thresholds

Braff, Mueller, Trancik, Nature Climate Change 2016

Energy Cost ($/kWh) 200 400 600 800 1000 Power Cost ($/kW) 200 400 600 800 1000 1200

$3/W $2/W $1/W $0.5/W Lead-acid Ni/Cd Na/S CAES Li-Ion Zn/Br V-redox PHS

Energy Cost ($/kWh) 1000 2000 Power Cost ($/kW) 1000 2000 3000 4000 Energy Cost ($/kWh) 1000 2000 3000 Power Cost ($/kW) 2000 4000 6000 Texas Wind PHS CAES Lead-acid Lead-acid Na/S CAES PHS V-redox Zn/Br Ni/Cd Li-Ion

PHS: pumped hydro storage CAES: compressed air energy storage

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SLIDE 24
  • Storage today can add value to wind and solar in some locations
  • Cost improvement needed for wide-spread profitability
  • Optimal cost improvement trajectories relatively location invariant
  • Cost targets can inform industry and government tech strategies

Braff, Mueller, Trancik, Nature Climate Change 2016

Evaluating storage techs for solar and wind energy

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SLIDE 25
  • Example 1: Evaluate stationary storage cost structures against

electricity demand, prices and resource availability

  • Example 2: Evaluate mobile batteries against personal vehicle travel

patterns

How much improvement needed in energy storage technologies?

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SLIDE 26

Cost and emissions of vehicle powertrains (see carboncounter.com)

Miotti, Supran, Kim, Trancik, Environmental Science & Technology 2016; carboncounter.com

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SLIDE 27

Cost and emissions of vehicle powertrains (see carboncounter.com)

Miotti, Supran, Kim, Trancik, Environmental Science & Technology 2016; carboncounter.com

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SLIDE 28
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SLIDE 29
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SLIDE 30

How do mobile batteries measure up to energy demand?

Temperature Drive cycles Electric Vehicle Characteristics Realized Range

Vehicle model

Demographics/ Built Environment Vehicle Range Requirements

Demand model

Household Travel Needs

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SLIDE 31

TripEnergy Model

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Demand Model

Limited information

  • n a specific trip

TripEnergy Matching Trips with known energy requirements Energy Distribution

NHTS: GPS surveys + vehicle model

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SLIDE 33

Vehicle Model

Braking losses

Kinetic energy

Powertrain losses Auxiliary losses

Battery

Charging losses Auxiliary

a b

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SLIDE 34

Ftr(v) = a+bv + cv2 + (1 + q)mdv dt

Rotational Inertia Mass Drag Coefficients

Etr = Z

Ftr(t)>0

Ftr(t)v(t) dt

Tractive Energy Calculation

Time (s)

200 400 600 800 1000 1200 1400

Speed (mi/hr)

20 40 60 80

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SLIDE 35

Vehicle Model

ηaux ηdrive

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Drive Efficiency Calculation

36

ηdrive(fbrake) = η∗

pt

1 − η∗

ptfbrakeη∗ r

ηpt = 0.908

ηr = 0.849

Solve system of equations with EPA results Test Result: Expression for Efficiency:

ηdrive ≈ f(drive cycle)

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SLIDE 37

Trip Average Velocity

20 40 60 80

Eta Drive

0.5 1 1.5

Actual Predicted EPA trips

Trip Average Velocity

20 40 60 80

Eta Drive

0.1 0.2 0.3 0.4 0.5 0.6

Actual Predicted EPA trips

Drive Efficiency Validation

BEV ICEV

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SLIDE 38

Energy Intensity (kWh/mi)

100 200 300 400 500 600 700 800

Time (s)

20 40 60 80

Speed (mi/hr)

Based&on&driving&pa.erns& across&all&U.S.&ci4es&and& millions&of&drivers….&

100 101 102

Vehicle Day Energy (kWh)

0.02 0.04 0.06 0.08 0.1

Portion of Days

D A P G S P

87 61

R i c h m

  • n

d

Vehicle Capacity Current Vehicle Capacity

!vehicle(days!(%!covered!by!Nissan!Leaf)! gasoline!! displaced!(%)!

~90%%of%vehicles%can$be$replaced$ by$a$low.cost$electric$vehicle$on$ an$average$day,$even$if$only$ nigh7me$charging$is$available.$ $ This$number$is$remarkably$ similar$across$diverse$ci=es,$from$ Houston$to$New$York.$

Needell, McNerney, Chang, Trancik, Nature Energy 2016

Batteries evaluated against U.S. driving patterns

87%

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SLIDE 39

Diminishing returns to battery improvement

20 40 60 80 100 120

Battery Capacity (kWh)

0% 20% 40% 60% 80% 100%

GSP

Current Vehicle Capacity ARPA-E Target Capacity

Gasoline substitution potential Battery capacity (kWh), constant mass

Needell, McNerney, Chang, Trancik, Nature Energy 2016

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Predicting electric vehicle range

  • Range is not constant—73 miles on average but with a distance of 58

miles, a 5% chance of running out of charge

  • Range does not increase linearly with battery capacity

Current Capacity

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SLIDE 41
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SLIDE 42
  • Example 1: Evaluate stationary storage cost structures against

electricity demand, prices and resource availability

  • Example 2: Evaluate mobile battery specific energy against personal

vehicle travel patterns

How much improvement needed in energy storage technologies and materials?

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SLIDE 43

Conclusions and discussion

  • Energy storage development next ~15 years critical for renewables

growth and climate change mitigation

  • Some storage technologies becoming profitable for renewables in

several locations, but further development needed

  • 87% of US personal vehicle-day energy needs met with today’s

batteries w/out recharging, but other powertrains needed to enable widespread electric vehicle adoption

  • Energy storage materials and device development targets can be

quantified by examining patterns of energy demand

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SLIDE 44
  • Energy storage development next ~15 years critical for renewables

growth and climate change mitigation

  • Some storage technologies becoming profitable for renewables in

several locations, but further development needed

  • 87% of US personal vehicle-day energy needs met with today’s

batteries w/out recharging, but other powertrains needed to enable widespread electric vehicle adoption

  • Energy storage materials and device development targets can be

quantified by examining patterns of energy demand

Conclusions and discussion

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SLIDE 45

Magdalena Klemun, Michael Chang, Gonçalo Pereira, Joshua Mueller, Fabian Riether, Marco Miotti, Mandira Roy Morgan Edwards, Zach Needell, Jessika Trancik, James McNerney, Göksin Kavlak, Victor Ocana