Economics of storage local meeting july 8 2009
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ECONOMICS OF STORAGE LoCal Meeting - July 8, 2009. Presented by Mike He and Prabal Dutta. Lots of Storage Technologies. Pumped Water. LiSulphur. NiMH. SMES. Supercap. NiCad. Flywheel. EEStor. Li+. Thermal. Compressed Air. LiPoly. LiSulphur. Why Store Energy?. Peak-to-Average

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ECONOMICS OF STORAGE LoCal Meeting - July 8, 2009

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ECONOMICS OF STORAGELoCal Meeting - July 8, 2009

Presented by Mike He and Prabal Dutta


Lots of Storage Technologies

Pumped Water

LiSulphur

NiMH

SMES

Supercap

NiCad

Flywheel

EEStor

Li+

Thermal

Compressed Air

LiPoly

LiSulphur


Why Store Energy?

  • Peak-to-Average

    • Reduce electricity rate by shaving peak load

  • Match Supply and Demand

    • When supply/demand is inelastic or intermittent

  • Economic Arbitrage

    • When price of electricity varies substantially and

    • An efficient market exists to buy and sell real energy


Peak-to-Average

  • When electricity cost is set by peak power draw

    • Peak-shaving yields big dividends

    • Benefits accrue at all times, not just at peak load times

  • Load shift if possible

  • Generate electricity locally if feasible economically

  • LoCal

    • Buy electricity when local demand is low

    • Convert and store electricity for later use

    • Convert and use electricity when highest local demand


Match Supply and Demand

When supply/demand is inelastic or intermittent

Solar

S/D well-matched for typical industrial loads; storage overkill

S/D poor-match for early morning or evening residential loads

Wind

S/D matching is variable, TBD

Statistical multiplexing plays a role in smoothing out

LoCal

Store when supply is high but demand is low

Use when supply is low but demand is high


Economic Arbitrage

When a sufficient wholesale price difference exists

Buy electricity when price is low

Convert and store electricity for later sale

Convert and sell electricity when price is high


IESO: A Concrete Analysis

http://www.iemo.com/imoweb/marketdata/marketToday.asp ($1 CAD = $.86 USD)


Ontario IESO (July 7, 2009)

  • Wholesale electricity price ($CAD/MWh)

    • Min:$3.52

    • Avg:$20.99

    • Max:$42.32

    • Range:$38.80

  • Average hourly demand

    • Min:15,000 MW

    • Avg:17,162 MW

    • Max:19,570 MW

    • Range:5,070 MW


Ontario IESO (July 7, 2009)


Ontario IESO (July 7, 2009)

?


Ontario IESO (July 7, 2009)


Storage Economics

  • To be marginally viable, must satisfy:

    • CostPUE / PriceDeltaPUE < CycleLife

    • CostPUE / PriceDeltaPUE => cycles needed to profit

  • Where

    • CostPUE is Cost per unit of energy storage

      • Li+ (e.g. $300/kWh)

      • Pumped Hydro ($10-$45/kWh)

    • PriceDeltaPUE is max(price) - min(price) per unit energy

    • CycleLife is number of cycles of storage technology


Storage Economics

  • IESO Case Study on July 7, 2009

    • PriceDeltaPUE = $38.80

  • (CostPUE / PriceDeltaPUE / CycleLife) ?<? 1

  • TechCostPUEPDPUECycleLifeLHS

  • Li+$300/kWh$.0388/kWh12006.44

  • Pump. Hydro$45/kWh$.0388/kWhInf (need 1160)<1


Energy Density


Cost


Lifetime vs Efficiency


Per-cycle Cost


Summary Table


Sample Load Profiles


Sample Load Profiles


Marginal revenue potential drops quickly and varies

Mild Spring Weekend Day

Warm Summer Weekday


Storage power density matters:Small window to buy cheap

Mild Spring Weekend Day

Warm Summer Weekday

1x

Power Density

2x


Buy-low/sell-high cycle rateis limited: must hold for a while


Unexpected arbitrage cancreate opportunistic profits

http://www.iemo.com/imoweb/marketdata/marketToday.asp ($1 CAD = $.86 USD)


Must disentangle residential, commercial, industrial, and night life/party loads

Ontario

Night Life


Limitations

  • Assumes Time-of-Use pricing

  • Assumes zero price-elasticity

    • For marginal profit potential analysis

    • Likely reality: price sensitivity high at peak load times

  • Garbage-in/Garbage-out

    • Wholesale prices and load profiles are average values

    • No distinction between $USD and $CAD

  • Only a marginal viability analysis

    • Average-case viability much lower


Takeaways

  • Storage still has a long way to go for economic viability in financial arbitrage

  • Peak to average case may be more economically viable

  • Cost, power density, efficiency, cycle life are important factors

  • Need a metric for: Capacity X CycleLife

  • 100W lightbulb for a day = Raising a car ~1km


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