Smart Grid: Carbon and Economic Implications for Colorado April 29, 2010

1. Rebecca Johnson, Ph.D. PUC Smart Grid Policy Specialist E-mail: rebecca.johnson@dora.state.co.us Smart Grid: Carbon and Economic Implications for ColoradoApril 29, 2010

2. Presentation Overview • Results from national studies on the energy and CO2 impacts of smart grid • Colorado smart grid case study • Evaluation of Colorado-specific changes in CO2 and levelized cost under a variety of smart grid scenarios • Key policy implications

3. Results from National Studies on the Energy and CO2 Impacts of Smart Grid

4. Sources of Savings - EPRI Source: Electric Power Research Institute. “The Green Grid: Energy Savings and Carbon Emissions Reductions Enabled by a Smart Grid”. 2008

5. Sources of Savings - PNNL Source: Pacific Northwest National Laboratory. “The Smart Grid: An Estimation of the Energy and CO2 Benefits”. 2010

6. Sources of Savings – Brattle Group Source: The Brattle Group. “How Green is the Smart Grid?”. 2009

7. Why it is Important to Understand Smart Grid Implications at the State Level • National-to-state and state-to-state electricity fuel mixes vary dramatically. • Changes in CO2 due to changes in the electricity infrastructure are fuel mix dependent and are therefore state specific. • Electricity policy is largely developed at the state level. Source: EIA 2006 Electricity Profiles

8. Colorado Smart Grid Case Study • Quantified Colorado-specific changes in CO2 and levelized cost under a variety of smart grid scenarios. • Modeled all generating units in the state plus Laramie River Station in Wyoming (coal unit owned by Tri-State) • Evaluated smart grid enabled: • demand response • large scale wind integration • energy efficiency • plug-in hybrid electric vehicle (PHEV) integration

9. Research Design:Experimental Variables • Degrees of Grid Intelligence • Demand Response (Demand Flattening) • Wind Generation • Energy Efficiency (Demand Destruction) • Plug-in Hybrid Electric Vehicles (PHEVs)

10. Experimental Variables:Degrees of Grid Intelligence • Conventional Grid • Business-as-usual operation. • Intermediate Grid (non-dynamic load shaping) • Time-of-use pricing, enhanced consumer information, and programmable appliances shift demand from peak to off-peak. • Demand curve is flattened in a predictable way, but system does not have the ability to dynamically shape demand to match supply. • Advanced Grid (dynamic load shaping) • Dynamic demand shaping via real-time pricing, enhanced consumer information, price-responsive programmable appliances, and direct load control. • System dynamically matches supply and demand using all generating options, storage, and demand response. • Managed PHEV load follows renewable generation.

11. Experimental Variables:Demand Response Intermediate Grid (non-dynamic load shaping) • Time-of-use pricing, enhanced consumer information, and programmable appliances shift demand from peak to off-peak. • Demand curve is flattened in a predictable way, but system does not have the ability to dynamically shape demand to match supply. • Advanced Grid • (dynamic load shaping) • Dynamic demand shaping via real-time pricing, enhanced consumer information, price-responsive programmable appliances, and direct load control. • System dynamically matches supply and demand using all generating options, storage, and demand response. • Managed PHEV load follows renewable generation.

12. Results: Demand Response • Without wind, perfect ability to flatten load increases CO2 by 1% and decreases levelized costs by 0.2%. • More relevant to municipalities and rural electric associations than to PSCo. • With 20% wind, demand response reduces wind integration costs by up to $18 million per year. Smart grid contributes <1% of total CO2 reductions. • With 50% wind, demand response reduces wind integration costs by up to $226 million per year. Smart grid contributes up to 9% of total CO2 reductions.

13. Experimental Variables:Wind Integration • Smart grid supports wind integration by aligning demand with renewable generation.

14. Results: Wind Integration • Smart grid reduces wind integration costs by reducing curtailment. • Curtailment expense is calculated as levelized cost plus foregone production tax credit ($86.50 per MWh).

15. Experimental Variables:Energy Efficiency Source: Ventyx Consulting

16. Sources of Energy Efficiency Modeled 5% and 15% energy efficiency improvements • Consumer demand reductions – highly uncertain • Feedback • 4% to 12% (Neenan & Robinson, 2009; PNNL, 2010) • Time-based pricing • 4% (King & Delurey, 2005) • Reductions in Transmission and Distribution Losses – relatively certain • 2.4% (Xcel Energy, 2008)

17. Results: Energy Efficiency Impacts on CO2 and Levelized Costs

18. Experimental Variables:Plug-in Hybrid Electric Vehicles Sources: Ventyx Consulting, General Motors

19. Results: PHEVs • A ‘typical’ PHEV in Colorado would emit 48% less CO2 than an internal combustion vehicle. • Very high penetrations of PHEVs would rarely overwhelm system generating capabilities. • However, highly problematic from the distribution level perspective (7/1 CIM). • Managed charging is critical.

20. CO2 Summary

21. Levelized Cost Summary

22. CO2 and Levelized Cost Reductions Are Not Aligned

23. Comparison of Results

24. Policy Implications:Energy Efficiency • Problem: • The traditional utility business model is a disincentive to efficiency. • Potential State-Level Policy Solutions: • Alternate Business Models • Shared Savings • Bonus Return on Equity • Virtual Power Plant • Performance-Based Renewable Energy and Energy Efficiency Targets

25. Policy Implications:Wind Integration • Smart grid’s wind integration benefits require consumer adoption. • If consumers don’t adjust their behavior in response to smart grid, the technology will become an expensive mechanism to marginally improve electric utility operational efficiency. • Consumer-centric mechanisms to promote adoption. • Outreach and education • Time-based pricing • Incentives and rebates • Privacy and data security assurance • Data ownership clarity

26. Upcoming Commissioner Informational Meetings • June 7th, 9:00 am to 11:00 am • Topic: Electricity use feedback and customer behavior • Speakers: • Dr. Ahmad Faruqui, The Brattle Group • Dr. Karen Ehrhardt-Martinez, formally with NRRI, now consulting with her own firm, Human Dimensions Research Associates • Nancy Brockway, former NH PUC Commissioner and current consultant on consumer and low income issues • July 1st, 9:00 am to 11:00 am • Topic: Smart grid’s role in emerging markets • Speakers: • Peter Fox-Penner, the Brattle Group • Emerging markets overview • Paul Denholm, the National Renewable Energy Laboratory • Plug-in hybrid electric vehicles impact on the electric grid • August , date and time tbd • Technical aspects of smart grid • Communications platforms • IT infrastructure • Interoperability standards

27. Questions? E-mail: rebecca.johnson@dora.state.co.us

28. Acknowledgements • Research supported by CU’s Renewable and Sustainable Energy Institute (RASEI) • Data provided by Ventyx Consulting • Research guidance from the National Renewable Energy Laboratory (NREL), Ventyx Consulting, and Xcel Energy