The design, execution, and outcomes of efficiency programs...

Unintended consequences: The design, execution, and outcomes of

efficiency programs in California

Sam Borgeson Energy and Resource Group UC Berkeley [email protected]

Talk summary

1. The difference between saving energy and the meaning/practice of efficiency programs

2. How structure leads to outcomes in EE programs

– i.e. What’s with all the light bulbs?

3. Opportunities for future/deeper savings suggested by a broader view of energy savings

Sam Borgeson: Unintended consequences 2

This is meant to stimulate discussion, not to smear EE programs.

Energy = power x duration


duration

Energy

Types of energy savings


name characteristics example (based on dominant effect)

efficiency Same/better service, less energy

higher COP AC unit

substitution* substitution of different, equally acceptable service

radiant cooling?

imperfect substitution* substitution of different, tolerable service

fan substituted for AC

conservation reduced service that is tolerated

raising cooling setpoint enough to be noticed

waste elimination unnoticed service reduction

overnight shutdown of lights

* Note that the desirability of substitutes is subjective, so different people tend to categorize the similar measures differently.

CA EE program savings (2006-2008)


Source: CEC 2006-2008 program evaluation

90% of savings from 14 measures

Interior screw lighting Refrigerant Charge and Airflow

Linear fluorescent High bay fluorescent lighting

Recycle refrigerator Refrigeration Door gasket

Outdoor CFL Fixture Night light

Refrigeration strip curtain Lighting – other

CFL Fixture Linear fluorescent delamping

On-site Audit Rooftop or split system

“In the 2006‐2008 portfolio fourteen measure groups were responsible for over 90% of the reported statewide electricity [savings]” 8 of 14 are lighting:

6 Sam Borgeson: Unintended consequences

That’s a lot of lighting: over 100M bulbs since 2006

• Lighting is ubiquitous, cheap, and predictable

• We back that up with good rebates

• What’s wrong with that?


Organizational interactions


CPUC (+CEC)

DecoupledUtilities

Consulting firms

Customers

Statewide planning, evaluation, and research

•Accountable to public •Set rules for EE programs •Review and approve programs •Determine compliance and payments

evaluation measurement verification

•Plan and implement measures subject to CPUC approval •Performance determined by CPUC via program rules (i.e. E3 calculator; DEER) •Paid for estimated kWh/therms saved

•Fund programs via utility bills •Receive individual and social benefits •Not to be financially harmed (i.e. only “cost effective” measures)

Schematic design of CA’s EE programs with decoupled utilities

Design and implement measures

Tensions inherent in this structure • Organizational incentives shape program design,

implementation and evaluation – Program rules must navigate adversarial positions and

questions of financial motivation for claims – Program administration requires countable savings (i.e. CPUC

needs numbers for oversight)

• BUT while efficiency savings are often visible, they cannot be measured with objectivity and precision – “program evaluation cannot precisely and accurately

determine the counterfactual” – Carl Brown at ECEEE 2009 – To achieve countable savings, plausible but subjective

assumptions pervade programs and their evaluation*


* See for example, CIEE’s CPUC commissioned “Behavioral assumptions in energy efficiency …” series of papers by Lutzenhiser, Moezzi, Woods, and Sullivan

Database for Energy Efficiency Resources (DEER)

• Sets the rules for energy savings, cost, and durability for measures by vintage, building type, and climate zone to expedite verification

• Ex. Lighting:


Residential Commercial

CFL (24 permutations) CFL (12 permutations)

Linear Fluorescent (92 permutations)

Exit Lighting (6 permutations)

Other Lighting (21 permutations)

Ubiquitous?

Energy end use percentages by building type for US buildings. Data from CBECS 2003 (EIA 2006)


Predictable?

• What would have happened otherwise? – Counterfactual adoption quantified with net to gross ratio (NTG)

• How long will they last? – Effective useful life quantified with (EUL)

• How much money will customers save? – Assumptions about time of use and variable costs of power


“The E3 [spreadsheet] calculator is the official CPUC EE program cost‐effectiveness tool used to calculate utility energy savings … for … programs and portfolios”

Trouble with EUL • “California DEER Study uses a 9.4-year lamp life …

[our results show] lamps have an observed life of 4.8 to 5.5 years…the current DEER approach of CFL measure life might overestimate the actual life by at least 44%.”*

• DEER was changed to 6.3, but, claiming they played by the rules, utilities were paid using 9.4 year life

• 2 of 5 commissioners voted against the final 2006-2008 cycle payments, saying they rewarded utilities "for subpar performance”


*Source: “Welcome to the Dark Side: The Effect of Switching on CFL Measure Life” 2008 ACEEE Summer Study

Systemic problems revealed by evaluators

The approved 2006-2008 Energy Division savings evaluation budget was almost $120 million.

We’re paying a lot to learn that our programs aren’t working as well as planned (and it’s getting worse)


EISA 2007


Light bulb performance standards

Energy Independence and Security Act of 2007 implementation schedule


Kicking the habit


“Utilities will begin to phase traditional mass market CFL bulb promotions and giveaways out of program portfolios and shift focus toward new lighting technologies and other innovative programs” - CPUC on 2010-2012 plans

Lighting elements comprise 54% of total planned GWh savings for 2010-2012 cycle

Savings categories

Equipment upgrade

Equipment repair

Service timing change

Service setpoint change

↓service intensity

Substitution of services

Service shutdown

Energy savings types


Require control

Most lighting

Targeting operational efficiency: Controls and diagnostics

“An extensive quantity of evidence from case studies indicates that building systems often do not operate as intended and suffer from faults…” “Placed in the context of commercial buildings, the faults account for between 2% and 11% of all energy consumed by commercial buildings.” “Three faults, ‘HVAC Left on When Space Unoccupied,’ ‘Lights Left on When Space Unoccupied,’ and ‘Duct Leakage,’ appear to account for about two‐thirds of the total energy impact of the key faults.” Sam Borgeson: Unintended consequences 19

Source: ‘Energy Impact of Commercial Building Controls and Performance Diagnostics’ prepared by TIAX for DoE, 2005

TIAX on public vs. private data

“The data to address the aforementioned gaps likely exist, but not in the public literature. Energy Service Companies (ESCOs) and utilities may have collected proprietary information to understand the cost-benefit relationship of different energy saving measures...”

– TIAX for DoE, 2005


Have they?

Targeting operational energy: Commissioning


• Paying attention to how your building is operating and fixing problems you find

• Bad news: we aren’t even getting elementary installation, operating practices, and maintenance right

• Good news: it is very easy to find savings in uncommissioned buildings! – Savings are consistent and feature attractive ROIs.

Commissioning: measures implemented


Data Source: Mills (2009)

Operations/ Control

Tweaks / Maintenance

New Design / Equipment

Retro-commissioning: Simple payback


Source (Mills 2009)

• $0.30/sqft • Median energy

savings of 16% • Median simple

payback of 1.1 yr • No kidding!

6 months

1 month

5 years

1 year

Conclusions • Much of the dominance of lighting efficiency

programs can be related to the organizational structure and incentives faced by the regulators and utilities

• Because they determine $$$, a great deal of public funding is spent on “measuring” counterfactuals

• There is large scale and affordable EE potential for diagnostics and operational controls

• Pursuing these will require reform of the existing incentives armed with theories of organizational behavior


Questions? Ideas? Sam Borgeson Energy and Resources Group UC Berkeley

[email protected]

25 The Efficiency Resource: By Sam Borgeson

CPUC behavior studies


What’s different this time?

• Emergence of digital controls over the past 30 years

• Software based controls cheaper and increasingly ubiquitous (flexibility of control)

• Computing power and algorithms for more sophisticated analysis of data

• Growing awareness that delivered efficiency goes well beyond technology alone


Commissioning

Almost no buildings adapt well. They’re designed not to adapt; also budgeted and financed not to, constructed not to, administered not to, maintained not to, regulated and taxed not to, even remodeled not to. But all buildings adapt anyway, however poorly, because the usages in and around them are changing constantly. Stewart Brand “How Buildings Learn”


End uses by building type

Energy end use percentages by building type for US buildings. Data from CBECS 2003 (EIA 2006)


Building energy use is highly variable

• Many factors contribute to building operating strategies and power demand – Building type/purpose – Site/weather – Construction materials – Major equipment – Controls – Occupancy – Behavior


Source: CPUC Energy Division Staff Review of Utility 2009-2011 Energy Efficiency Portfolio Filings,


Reported vs. actual costs and benefits

TRC = Total Resource Cost: NPV of system benefits / costs to participants + administrators PAC = Program Administrator Cost: NPV of system benefits / cost to administrators


Commissioning investment


• Driven by information

• Process, not an event or product

Source: (Mills 2004)

Energy envelope


Can we explain time varying demand from buildings well enough to inform EE?

W W

S S

W = winter break S = summer break

Date

Building load curve metrics


min, aka base (kW)

max (kW)

rang

e (k

W)

high duration (hrs)

Visualizing load


12AM 3AM 6AM 9AM 12PM 3PM 6PM 9PM 12AM200

220

240

260

280

300

320

340Wurster load curve

kW

time of day

Wurster color coded load

2AM 4AM 6AM 8AM 10AM 12AM 2PM 4PM 6PM 8PM 10PM 12PM

220 240 260 280 300 320

Building load “heat map”


Wurster Hall (kW)

period (15 minute)

day

coun

t

20 40 60 80

50

100

150

200

250

300

350

400

450

500

150

200

250

300

350

400

450

Fan schedule change

Winter break

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The design, execution, and outcomes of efficiency programs...

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