1. Carnegie Mellon University Solar PV and Energy Storage for Commercial & Industrial Customers Shelly Hagerman, Paulina Jaramillo, Granger Morgan, Jay Whitacre May 23, 2016 CEDM Annual Meeting 1

2. Carnegie Mellon University In previous work… 2 We showed that the only residential customers at socket parity today without subsidies are in Hawaii.

3. Carnegie Mellon University Here we ask: “are commercial and industrial customers at socket parity?” Different rate tariff structures Economies of scale in solar PV pricing Ability to finance Potential Adjustments – Load shifting – Change in tariff 3 Source: NJR Clean Energy Ventures Source: Titan Solar Construction Source: Public Power Solutions Source: SbS Services Chapter 3 >> Motivation

4. Carnegie Mellon University Research Questions Are commercial and industrial (C&I) customers at socket parity? What are the key factors that affect socket parity? Case study: Simulated and measured load and solar PV data in North Carolina 4Chapter 3 >> Research Questions

5. Carnegie Mellon University Primary Components of C&I Tariffs Customer Charge – Fixed, part of monthly minimum bill Demand Charges – Capacity based ($/kW) Flat, tiered, seasonal Energy Charges – Flat, tiered, hours of use ($/kWh) 5Chapter 3 >> Methods

6. Carnegie Mellon University SIMULATED DATA Results 6

7. Carnegie Mellon University DOE Commercial Reference Building Profiles Hourly simulated data – Uses TMY data (same weather data as solar output model) 16 building types (2,500 sf – 500,000 sf) 16 climate zones 7Chapter 3 >> Methods

8. Carnegie Mellon University Not currently at socket parity 8Chapter 3 >> Results

9. Carnegie Mellon University SunShot target  Socket parity 9

10. Carnegie Mellon University MEASURED DATA Results: Key Indicators 10

11. Carnegie Mellon University Lower load factors are more favorable 11

12. Carnegie Mellon University Economics improve when peak demand occurs during day 12

13. Carnegie Mellon University Not at socket parity for most C&I customers (in NC case study) Socket parity if SunShot cost targets are met Economics improve with lower load factors and when load peaks during the day  May be economic opportunity for load shifting 13Chapter 3 >> Conclusions

14. Carnegie Mellon University ENERGY STORAGE Black-box analysis 14

15. Carnegie Mellon University Research Questions When is a “black box” energy storage device viable? Are high-power or high-energy batteries best suited for this application? What scenarios would need to occur for any of these technologies to be economically viable? 15Chapter 4 >> Research Questions

16. Carnegie Mellon University Black-box Storage Assumptions ParameterValue Usable CapacityVaried parametrically Charge Energy Efficiency90% Available Discharge EnergyVaries (based on Ragone analysis) Capacity CostHigh power: $600/kWh; High energy: $300/kWh Max Charge RateHigh power: 1 x usable capacity; high energy: 0.5 x usable capacity 16

17. Carnegie Mellon University Black-box Algorithm 17 24-Hour Forecast Beginning of Day Routine Set initial monthly peak as average annual demand During Day Routine Charge, Discharge, Idle Reduce peak demand, charge during off-peak times

18. Carnegie Mellon University Black-box Algorithm 18 24-Hour Forecast Beginning of Day Routine Set initial monthly peak as average annual demand min max (Net Load – Battery Discharge) s.t. Discharged Energy ≤ Usable Capacity

19. Carnegie Mellon University Economic Metrics Levelized benefit of electricity (LBOE) Levelized cost of stored electricity (LCOSE) 19

20. Carnegie Mellon University Black-box behavior during peak day 20 Monthly peak demand

21. Carnegie Mellon University Lower capacity, high-energy battery best suited 21

22. Carnegie Mellon University Next Steps Expand analysis to multiple loads – Standard and TOU tariffs Implement programmable algorithms 22

23. Carnegie Mellon University Acknowledgements This work was supported by funds from: EPP GAANN of U.S. Department of Education Department of Energy under Awards DE-OE0000300 and DE-OE0000204 Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation and Carnegie Mellon University (SES- 0949710) Carnegie Mellon Electricity Industry Center (CEIC) Electric Power Research Institute Results and conclusions are the sole responsibility of the authors and may not represent the views of the funding sources. 23

24. Carnegie Mellon University Thank you! 24

25. Carnegie Mellon University Model Structure 25Chapter 3 >> Methods Installation Cost Applicable Tariff Solar Data Required Inputs Parameters Output Net Present Value (NPV) Discount Rate Financing (rate, term) Operation and Maintenance System Size Load Data Note: Excess generation not valued for base analyses (no NEM)

26. Carnegie Mellon University NPV Calculation 26 Where, DS is the annual demand savings ES is the annual energy savings PMT is the amortized annual loan payment TotalOpEx includes annual O&M costs, inverter replacement costs, insurance payments, and property tax Tax reductions include the depreciation of the PV system (using the 5-year MACRS) and all costs included within TotalOpEx d is the nominal discount rate Chapter 3 >> Methods

27. Carnegie Mellon University Breakdown of Savings: Demand vs. Energy 27

28. Carnegie Mellon University Black-box Algorithm 28 During Day Routine Every 15-min Charge Storage Discharge Storage Sufficient storage capacity? Optimally Discharge Optimally Discharge, New Monthly Peak Charge, Discharge, Idle Check Net Load Does net load exceed existing peak? YesNo YesNo Can charge at max rate? YesNo Charge at max rate Idle or charge at lower rate