1. Non-Residential Network Computer Power Management UES Measure Update Regional Technical Forum July 16, 2013

2. Measure Overview Installation of a centralized energy management system that controls when desktop computers and monitors plugged into a network power down to lower power states. Currently 9 measure applications by building and HVAC type: – K-12 school, large office, small office – Electric heat, heat pump, gas heat The majority of uptake has been with schools where savings tend to be greatest (due to idle computers). 2

3. Measure History May 2010 – Measure passed by RTF as provisional with agreement that more data collection would be needed for proven UES May/June 2011 – Measure brought back to the RTF – Incorporated regional data for this measure reported by Puget Sound Energy (PSE) and Avista in 2011 – RTF passed measure as proven and active – Sunset date set at ~18 months due to concerns of rapid changes in technology and market practices 3

4. Measure Savings Desktop unit energy consumption (UEC) is based on 1) power draw at various states of operation and 2) time spent in those states: Savings are achieved by shifting hours from high power states to low power states: 4 Power State Duty Cycle, as % BaseMeasure Unplugged5% Off48%71% Sleep2% Idle42%19% Active3% Total100% highest power state lowest power state

5. Measure Savings Desktop unit energy consumption (UEC) is based on 1) power draw at various states of operation and 2) time spent in those states: Savings are achieved by shifting hours from high power states to low power states: 5 Power State Duty Cycle, as % BaseMeasure Unplugged5% Off48%71% Sleep2% Idle42%19% Active3% Total100% highest power state lowest power state

6. Measure Savings (continued) Monitor unit energy consumption (UEC) is calculated the same way, with the only difference being an additional multiplier for Monitors per DeskTop (MpDT): 6

7. Summary of Actions Taken to Update UES Ported previous measure workbook into new template Cleaned up and organized workbook Updated costs with sources Investigated how savings may have changed since RTF measure adoption in 2011 As part of this process, spoke with a number of individuals who are familiar with this measure: – Joe Schmutzler, PSE – Tom Lienhard, Avista – Ted Brown, Seattle City Light – Breanna Chance, Tacoma 7

8. How have savings changed since 2011? Equation of computer energy consumption shows that savings could be impacted in two main ways: – Changes in computer/monitor power (at various states) – Changes in hours spent at each power state 8

9. How have savings changed since 2011? Power could have changed since 2011 by: – More or less efficient stock of computer equipment – Changes in Energy Star saturation 9

10. How have savings changed since 2011? Stock of computers and monitors updated using current Energy Star lists Energy Star desktop penetration updated from 15% to 17% based on 2011 Energy Star sales data; Energy Star monitor penetration updated to 43% 1 10 1 See workbook for analysis.workbook

11. How have savings changed since 2011? Hours spent in each state could have changed since 2011 by: – General changes in usage patterns (e.g. schools using computers more or less in class than before) – Changes in the customer base adopting this measure (e.g. adoption by schools slowing down but offices ramping up) – Changes in users’ power management behavior (e.g. users manually powering down machines after use or at night) – Changes in power management features of computer operating systems (e.g. Windows 7/8) 11

12. How have savings changed since 2011? Changes in usage patterns or user behavior would be difficult to know without more recent metered studies. Changes in operating system saturation (e.g. Windows XP vs. Vista vs. Windows 7/8) unknown Even if operating system mix is known, this is not necessarily indicative of levels of enabled power management. Evidence of school adoption decreasing (due to saturation) and other commercial businesses increasing 12

13. In Summary Parameters updated: – List of current Energy Star compliant machines Desktops Monitors – Saturation of Energy Star machines Desktops Monitors Parameters not updated (due to lack of data) – Baseline and measure case duty cycles for desktops and monitors – Average monitors per desktop (currently using 1.2 based on a 2008 study) 13

14. In Summary (continued) What would need to be done in order to update remaining parameters? – Collect pre- and post-network consumption data of more recent installations (in particular, operating systems since Windows XP) – Consumption data should indicate: Time spent in various states (active, idle, sleep, off) pre and post Average power use at these states – Data collected on computer network characteristics: Desktop and monitor models (low power units vs. high-wattage “workstations”) Operating system Monitors per desktop 14

15. Is this market nearly transformed? February 2011 PSE evaluation found that 6 out of 16 (38%) program non-participants had installed power management software without a rebate. Factors other than energy savings pulling this measure into the market (ability to track computer users) Measure cost (~$6 to $10 per computer) relative to savings is low barrier to adoption for some customers. A couple of NW utilities considering phasing out prescriptive rebate over the next year or two (PSE may switch to custom analysis) According to PSE and Tacoma, schools and government may be heavily saturated, but other commercial businesses may be relatively untapped for this measure. 1 15 1 Based on conversations with Breanna Chance (Tacoma Power) and Joe Schmutzler (PSE)..

16. Staff Proposal Restrict UES to schools and move it to small-saver given that: – Data used in original analysis is most reflective of schools – Evidence that much of school potential has been achieved – Baseline technology in schools may be less likely to change as rapidly as other commercial building types 1 Recommend programs use their own custom analyses for other commercial building types given that: – Less is known of usage patterns outside of schools – Average equipment power of higher tech commercial businesses could be significantly different from schools – Still unclear how advances in software have impacted baseline A proven UES could be developed for other commercial building types if more recent data could show: – Updated duty cycle and power assumptions for different commercial buildings – Equipment models, operating systems, and monitors per computer for networks adopting this measure 16 1 Based on conversation with Breanna Chance, Tacoma Power.

17. Decision “I _______ move to approve the updates to the non-res network computer power management workbook, restrict the UES measure to schools, and move to small saver; set the measure status to “Active”; and change the sunset date to January 31, 2015.” 17