1. 1 Developing an Investment Strategy with the Smart Grid Investment Model TM Jerry Jackson, Ph.D., Leader and Research Director Smart Grid Research Consortium, 37 N. Orange Ave, Suite 500 Orlando, FL 32801 Jjackson@smartgridresearchconsortium.org 979-204-7821Jjackson@smartgridresearchconsortium.org Smart Grid Research Consortium Conference & Workshop Rosen Shingle Creek, Orlando, Florida October 20-21, 2011

2. 2 Smart Grid “Best Practices” Smart Grid 1.0: ?  2010 oAMI/smart meters oCustomer pricing/engagement focus Smart Grid 2.0: 2010  oDistributed communication,intelligence and control throughout the distribution system  Substation, feeders  Equipment in businesses and homes oIntegration of distributed resources oQuestions concerning customer program impact persistence oData & data analytics reflect new challenges oSmart grid options as part of a comprehensive, integrated strategy

3. 3 Expanding the Scope of SG Options Complicates Investment Decisions

4. 4 What Does This Mean for Utility Investment Analysis? The good news oAMI/smart meter costs/benefits more well defined oGrowing number of DA “use cases” oRecognition that SG investments are complicated Challenges oCustomer DM program benefits may decline over time oComparing metering/customer programs/DA applications is difficult oEach utility is unique oCost/benefit calculations are no longer simple

5. 5 Consider a Simplified SG Investment Analysis Question Utility considering AMI/smart meter, demand management and DA investments Which,if any, investments should be undertaken now

6. 6 Options? AMI/Smart Meters Communications/Software/Hardware Customer Demand Mgmnt Communications/Software/Hardware Distribution Automation Communications/Software/Hardware Distribution Automation Communications/Software/Hardware AMI/Smart Meters Communications/Software/Hardware Customer Demand Mgmnt Communications/Software/Hardware Distribution Automation Communications/Software/Hardware ? ? ?

7. 7 Consortium’s SGIM considers oCosts and benefits of technologies/applications oUnique utility/utility customer characteristics oUtility monthly hourly loads and SG load impacts  Avoided power purchase costs Conduct “what-if” scenario analysis oQuantitative model framework Quantitative Financial Investment Analysis Provides Strategy Insights

8. 8 Example Coop Analysis Illustration “Representative Coop” o~100,000 customers, 0.65 system load factor; $0.05/kWh, $12/kW summer; $0.02-0.03/kWh, $6-8/kW spring/fall/winter oResidential: 70% customers, 60% kWh coincident peak kW

9. 9 SGIM Cost/Benefits Summary

10. 10 No Customer Programs Replacing EM Meters With AMI Typically Provides Attractive Returns

11. 11 However, More Typical Coops With AMR Systems May Have Difficulty Justifying AMI PLC with remote connect/disconnect

12. 12 Customer Programs Can Improve AMR  AMI Returns 20 % PCT/pricing + 30% cust engagement (5% AC/SH savings)

13. 13 Consider DA Impact of Conservation Voltage Regulation (Reduction), CVR CVR Advantages oNo customer participation required oOptions: manual adjustments  full automation oUtility & customer savings Source: RWBeck VVC Objective: Maintain acceptable voltage under all loading conditions Source: EPRI Source: Distribution Efficiency Initiative Study

14. 14 $50k/substation; 1% voltage reduction How Does CVR Stack Up as a 1 st Step ?

15. 15 $250k/substation; 4% voltage reduction More Extensive Conservation Voltage Regulation Saves Even More

16. 16 Additional 1% voltage reduction using meters for EOL voltage Without customer programs in this scenario CVR Savings Can Justify a DA/AMI Initiative at the AMR Utility Even W/O Cust Programs

17. 17 Conclusions CVR turns out to be a better primary option in this situation than oAMI based on reasonably well-defined cost/benefits oCustomer demand management programs based on impacts and concerns over impact persistence Advanced meters can be included in a CVR strategy providing EOL measuring/monitoring in addition to traditional benefits Results depend heavily on customer end-use hourly loads and avoided costs Results can be expected to vary considerably from utility to utility depending on infrastructure and customer characteristics

18. 18 Consortium’s Smart Grid Investment Model Provides More “Real-World Detail”

19. 19 Along With More Insightful Graphical Results Presentations

20. 20 Smart Grid Investment Model Supports SG Investment Strategy Development Detailed characterization of utility infrastructure and customer characteristics Forecasting customer class-end use hourly loads and program impacts Incorporation of all SG technology/program impacts Ability to conduct alternative “what-if” scenarios