1. DISPUTES & INVESTIGATIONS ECONOMICS FINANCIAL ADVISORY MANAGEMENT CONSULTING Early Lessons Learned from DOE-EPRI Framework Experience Melissa Chan MA DPU Grid Modernization Working Group December 17, 2012

2. Overview of the DOE Smart Grid Investment Grant Program Page 2 99 $3.4 B 2015 recipients in federal assistance program ends ($7.9 billion investment) AssetsBenefits Assets are divided into four categories: Advanced Metering Infrastructure (AMI) Customer Systems Distribution Transmission Benefits are divided into four categories: Economic Benefits Reliability Benefits Environmental Benefits Energy Security Benefits Our analysis involves linking installed assets to monetized benefits.

3. Four Primary Analysis Focus Areas I will discuss our experience collecting utility data on cost and performance to analyze impacts in these areas for the DOE Smart Grid Investment Grant Program. Customer Response to Time-based Rates, Information Feedback and Customer Systems Advanced Metering Infrastructure Pricing Programs and Customer Devices (smart thermostats and in-home displays) Direct Load Control Operations and Maintenance Savings from Advanced Metering Meter Reading Service changes Outage management Operations and Maintenance Savings from Distribution Automation Automated and remote operations Operational Efficiency Distribution System Reliability Feeder switching Monitoring and health sensors Energy Efficiency in Distribution Systems Voltage optimization Conservation voltage reduction Line losses 3

4. Application of the EPRI-DOE framework » The EPRI-DOE framework is not a predictive model › Develop an experimental design for smart grid asset deployment › Establish a good performance baseline for analysis › Set up methods to collect quality data for analysis » Data collected by the U.S. DOE Smart Grid Investment Grant program may be used to inform future decisions › Expected timeframe for data reports is at the program end, 2015 › Preliminary reports that summarize some utility data will be available on an annual basis Page 4

5. Smart Grid Impact on Customer Response » Customer response to time-based rates, information feedback, and customer systems (smart thermostats and in-home displays) › Collect hourly electricity usage data aggregated from the utility’s meter data management system (MDMS) › Example analyses ‒ Hour-by-hour customer demand changes due to time-based rate ‒ Monthly conservation achieved due to information feedback › Example challenges ‒ Defining appropriate treatment and control groups and “tagging” the specific customers in the (MDMS) ‒ Tying impact to benefit, such as understanding how change in peak customer demand for one utility affects peak generation plant dispatch » Lessons learned › Experimental design is needed to understand outcomes of customer pilot programs Page 5

6. Smart Grid Impact on Utility Operations and Maintenance » Utility changes in operations and maintenance costs due to automated meter reading, enhanced meter functionality, and distribution automation › Compare service costs and activities with and without technology › Example analyses ‒ Savings due to automated feeder switching ‒ Truck rolls avoided due to changes in meter operations ‒ Savings due automated meter reading › Challenges ‒ Collecting granular cost data if the utility has not segregated costs (e.g., all equipment failures are tracked but not categorized by type) ‒ Collecting granular activity data if the utility does not track activity by type (e.g., feeder switching is not tracked separately from other distribution operations costs) » Lessons learned › Create cost and activity tracking systems to meet desired data collection needs › Baseline can be difficult to estimate if bookkeeping is not categorized according to the analysis need Page 6

7. Smart Grid Impact on System Reliability » Improvements in system reliability due to smart grid assets › Understand how meter outage management systems, automated sectionalizing reclosers or other feeder automating technologies › Example analyses ‒ Changes in reliability indices (SAIFI, SAIDI, CAIDI) ‒ Changes in restoration time and number of customers affected by a major event › Challenges ‒ Collecting feeder specific reliability indices in the case that distribution automation technology is not installed system wide ‒ Understanding historical variance in reliability indices ‒ Estimating status quo restoration for major events » Lessons learned › Utilities may need to set up data management to collect reliability data for specific feeders › Major event response is easier explained qualitatively than quantitatively › Baseline can be difficult to estimate given considerable variation in reliability indices, which can be due to location of outages or frequency of tree trimming Page 7

8. Smart Grid Impact on Distribution Efficiency » Changes in distribution efficiency – losses, power factor, and peak distribution load due to voltage and VAR control › Compare service costs with and without technology (automated capacitor banks, voltage regulators, load tap changers) › Example analyses ‒ Analyze hourly real and reactive loads for feeder groups that receive new equipment ‒ Estimate changes in losses before and after new equipment is installed › Challenges ‒ Defining appropriate treatment and control feeder groups or designing a statistical analysis of treatment feeder group hourly load data ‒ Normalizing loss or power factor data for comparison with post-test loss or power factor data » Lessons learned › Average power factor and average losses are not useful metrics, power factor and losses during the distribution peak are most useful › Analysis of treatment feeder group hourly load data can lend insight into capacitor operations and optimization Page 8

9. Some early lessons from the Smart Grid Investment Grant program » Experimental design is critical, particularly for customer facing programs » Understanding operations and maintenance cost savings may be difficult to understand because a utility may need to change its activity and cost tracking to estimate changes due to technology » Treatment and control group data collection can be difficult because a utility may need to change its data management in order to specifically collect data for customers receiving customer facing programs » Isolating the affect of automation equipment on specific substations or circuits can be challenging because a utility may need change how it tracks equipment failures or maintenance, restoration, and operations activities » Not all impacts can be quantified, some may have to be discussed qualitatively Page 9

10. Contact information » Melissa Chan » Navigant Consulting » 781-270-8386 » Melissa.Chan@navigant.com Page 10