Storage Economics: IESO Case Study Analysis

1. ECONOMICS OF STORAGELoCal Meeting - July 8, 2009 Presented by Mike He and Prabal Dutta

2. Lots of Storage Technologies Pumped Water LiSulphur NiMH SMES Supercap NiCad Flywheel EEStor Li+ Thermal Compressed Air LiPoly LiSulphur

3. 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

4. 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

5. 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

6. 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

7. IESO: A Concrete Analysis http://www.iemo.com/imoweb/marketdata/marketToday.asp ($1 CAD = $.86 USD)

8. 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

9. Ontario IESO (July 7, 2009)

10. Ontario IESO (July 7, 2009) ?

11. Ontario IESO (July 7, 2009)

12. 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

13. Storage Economics • IESO Case Study on July 7, 2009 • PriceDeltaPUE = $38.80 • (CostPUE / PriceDeltaPUE / CycleLife) ?<? 1 • Tech CostPUE PDPUE CycleLife LHS • Li+ $300/kWh $.0388/kWh 1200 6.44 • Pump. Hydro $45/kWh $.0388/kWh Inf (need 1160) <1

14. Energy Density

15. Cost

16. Lifetime vs Efficiency

17. Per-cycle Cost

18. Summary Table

19. Sample Load Profiles

20. Sample Load Profiles

21. Marginal revenue potential drops quickly and varies Mild Spring Weekend Day Warm Summer Weekday

22. Storage power density matters:Small window to buy cheap Mild Spring Weekend Day Warm Summer Weekday 1x Power Density 2x

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

24. Unexpected arbitrage cancreate opportunistic profits http://www.iemo.com/imoweb/marketdata/marketToday.asp ($1 CAD = $.86 USD)

25. Must disentangle residential, commercial, industrial, and night life/party loads Ontario Night Life

26. 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

27. 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