Non-Residential Network Computer Power Management

UES Measure Update

Regional Technical ForumJuly 16, 2013

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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).

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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

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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:

STATEBASESTATE

STATEBASE HoursPowerUEC ,

Power State Duty Cycle, as %Base Measure

Unplugged 5% 5%Off 48% 71%Sleep 2% 2%Idle 42% 19%Active 3% 3%Total 100% 100%

highest power state

lowest power state

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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:

STATEBASESTATE

STATEBASE HoursPowerUEC ,

Power State Duty Cycle, as %Base Measure

Unplugged 5% 5%Off 48% 71%Sleep 2% 2%Idle 42% 19%Active 3% 3%Total 100% 100%

highest power state

lowest power state

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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):

STATE

STATEBASESTATEBASE HoursPowerMpDTUEC ,

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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

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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

STATEBASESTATE

STATEBASE HoursPowerUEC ,

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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

STATEBASESTATE

STATEBASE HoursPowerUEC ,

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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

STATEBASESTATE

STATEBASE HoursPowerUEC ,

1 See workbook for analysis.

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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)

STATEBASESTATE

STATEBASE HoursPowerUEC ,

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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

STATEBASESTATE

STATEBASE HoursPowerUEC ,

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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)

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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

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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.11 Based on conversations with Breanna Chance (Tacoma Power) and Joe Schmutzler (PSE)..

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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 types1

• 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 measure1 Based on conversation with Breanna Chance, Tacoma Power.

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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.”