Utility Savings Estimation

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Utility Savings Estimation

• Webinar 1 Methodology and Assumptions

• Olga Livingston, Rosemarie Bartlett, Doug Elliott
• Pacific Northwest National Laboratory
• PNNL-SA-95757

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Utility Savings Estimator

• The objective is to develop a generic tool that
• estimates potential energy savings from increased
  compliance with energy codes
• utilizes well-understood definitions of
  compliance
• provides easily understood results that can be
  compared across several utilities or several
  segments within utility coverage area or
  aggregated to the national level

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Utility Savings Estimator

• Generic tool will contain defaults for
  code-to-code savings, commercial and residential
  floor space forecasts and projected code adoption
  
• Utilities can overwrite defaults in the generic
  computational algorithm with their own
  utility-specific assumptions, where applicable
  (for example, floor space growth in a particular
  segment of the utility coverage area)
• Estimation for commercial and residential
  buildings follows one methodology, but the
  computation is implemented in separate files

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Webinar Objectives

• Webinar 1 objectives
• Introduce computational methodology.
• Introduce definitions of compliance used in the
  tool.
• Introduce and discuss adoption and compliance
  assumptions, including implicit adoption and
  effects of learning on compliance.
• Obtain feedback on methodology and assumptions
  from participants.
• Webinar 2 Introduce and discuss the proposed
  interface
• Webinar 3 Present the review version of the
  Utility Savings Estimator.

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Methodology Overview

• The basic methodology is the same as that used
  for the U.S. Department of Energy Building Energy
  Codes Program (BECP) to assess national
  benefits.1
• Estimate nominal energy savings
• Select base (or reference) year
• Apply savings estimates for code-to-code changes
• Determine applicable floor space subject to code
• Adjust nominal energy savings by
• code adoption status
• code compliance levels
• Analyze multiple code compliance scenarios
• Aggregate residential and commercial savings
• 1. Belzer DB, SC McDonald, and MA Halverson. 
  2010.  A Retrospective Analysis of Commercial
  Building Energy Codes 1990 2008. PNNL-19887.
  Pacific Northwest National Laboratory, Richland,
  WA. 

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Methodology Overview (cont.)
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Default Inputs

• 2010 is suggested as the reference year
• Code-to-code savings are grouped by end use and
  fuel
• Pacific Northwest National Laboratory conducted
  extensive simulations to compare code-to-code
  savings for various versions of residential and
  commercial energy codes
• Simulation results are the same set as utilized
  in the U.S. DOE determination process, as well as
  in state-by-state cost-effectiveness analysis
• Savings for future code editions will be
  developed as part of the U.S. DOE Building Energy
  Codes Program effort

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Default Inputs Floor Space

• The estimator is pre-populated with residential
  and commercial construction projections by state.
• Residential construction permit data by county
  and place is available from the United States
  Census Bureau.
• Lagged permit data to convert permits to
  completions for historical, state-level data.
• Applied AEO (Annual Energy Outlook from EIA)
  growth forecast to state-level Census data to
  obtain projections of households by state.
• Used time series of AEO and RECS-based average
  floor space per household to convert households
  to floor space.
• Scaled up for additions.

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Default Inputs Floor Space (cont.)

• Commercial construction information is available
  from McGraw-Hill Construction Dodge data
• Lagged construction data to obtain historical,
  state-level time series
• Applied AEO growth forecast to state-level data
  to obtain projections of floor space by state
  (new construction and additions)
• State shares based on multi-year average to avoid
  short-term distortions due to economic downturn
• Alterations incorporated via a state-level
  scalar, based on multi-year ratio of new
  construction and additions to alterations

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Code Adoption

• Tracked historical code adoption and effective
  code data by state
• Performed analysis of jurisdictional adoption for
  home-rule states, which included contacting code
  officials at the municipal and county levels to
  verify the energy code versions in effect
• Divided the states into five adoption categories
  based on historical adoption patterns, their
  respective regulatory review cycles and recent
  legislative activity related to energy codes

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Code Adoption (cont.)

• Code adoption scenarios also consider implicit
  adoption when states do not explicitly adopt an
  energy code, but building practices are
  nevertheless changing under influence from within
  the state or surrounding states
• utilities and regional energy efficiency
  organizations (REEOs) running programs across
  states
• construction contractors and architect firms with
  operations in multiple states
• Implicit code adoption is assumed to occur within
  10 years of the code version year
• Future code adoption is projected based on
  observed differences in
• historical adoption lags across different code
  versions
• current code cycles across various states
• If interested in analyzing only a particular
  code version, overwrite future adoption
• for the rest of the code versions with a year
  outside of analysis period (gt2045)

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Adoption Categories

• States with Criteria Exceeding MEC/IECC. These
  states have historically developed their own
  codes, which are generally more advanced than the
  most recent MEC/IECC standards.
• States with Rapid Adoption Rate. Many of the
  states in this category have regularly reviewed
  and adopted energy codes. Many of the states have
  begun to follow a systematic 3-year review
  cycle and some have passed legislation that
  mandates regular adoption.
• States with Medium Adoption Rate. These states
  have demonstrated a willingness to adopt a
  state-level energy code, but their review cycle
  and adoption activities often lag behind the
  rapid adopters.
• States with Slower Adoption Rate. These states
  are judged to have taken little action from 1990
  to 2010 to adopt a new or update an existing
  energy code applying to buildings without
  substantial DOE support.
• States without a Statewide Energy Code. Some
  states still have not adopted a mandatory
  state-level code. In terms of the analytical
  approach, these states reflect no direct,
  historical impact from the BECP however, due to
  additional assistance from the American Recovery
  and Reinvestment Act of 2009 (ARRA 2009) and
  assistance from the BECP, most of these states
  are expected to adopt the IECC or an equivalent
  model energy code within five years.

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Adoption Assumptions by State
Adoption Category States
1. Exceeding MEC/IECC California, Oregon, and Florida
2. Rapid Adoption Rate Georgia, Maine, Massachusetts, New Hampshire, New York, North Carolina, Utah, Vermont, and Washington
3. Medium Adoption Rate Connecticut, Delaware, Iowa, Maryland, Minnesota, Montana, New Jersey, Ohio, Rhode Island, South Carolina, Virginia, and Wisconsin
4. Slow Adoption Rate Arkansas, District of Columbia, Hawaii, Idaho, Illinois, Indiana, Kansas, Kentucky, Louisiana, Michigan, Nebraska, Nevada, New Mexico, Pennsylvania, Texas, Tennessee, and West Virginia
5. No Statewide Code (b) Alabama, Alaska, Arizona,(a) Colorado,(a) Mississippi, Missouri, North Dakota, Oklahoma, South Dakota, and Wyoming
a. Although no statewide energy code exists in these home rule states, the overall adoption category is based on the level of activity at municipal and county levels. Thus, some level of energy savings from explicit code adoption occurred in these states. a. Although no statewide energy code exists in these home rule states, the overall adoption category is based on the level of activity at municipal and county levels. Thus, some level of energy savings from explicit code adoption occurred in these states.
b. Construction practices are assumed to eventually meet an older code, but with a substantial lag. The acceleration impact of the BECP program is assumed to reduce the lag. b. Construction practices are assumed to eventually meet an older code, but with a substantial lag. The acceleration impact of the BECP program is assumed to reduce the lag.
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Code Compliance

• Two aspects of energy code compliance
• compliance in legal terms, which is defined as
  meeting all of the provisions of the code
• compliance in energy terms, which accounts for
  energy savings in buildings that only partially
  meet the requirements of the new energy code

Full code-to-code savings
Partial code-to-code savings
30
70
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Code Compliance (cont.)

• Compliance is modeled as the weighted average
  compliance in energy terms is weighted by the
  compliance in legal terms

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Code Compliance (cont.)

• Time dimension of compliance
• Initial compliance vs. compliance after 10 years

Interpolate from 44 to 60 over 10 years

• Time dimension of compliance captures effects of
  utility programs targeting compliance, as well as
  learning by doing

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Code Compliance (cont.)

• Sensitivity study for various aspects of
  compliance

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Code Compliance Levers

• Increase the initial legal compliance fraction
  of the construction fully compliant with the
  energy codes
• Increase initial compliance in energy terms
  fraction of savings in buildings that only
  partially meet the code requirements
• Newer code and initial higher compliance are not
  the only levers in the model
• Model allows accounting for increased compliance
  with existing code version over time

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Methodology Summary

• Estimate nominal energy savings
• Select base (or reference) year
• Apply savings estimates for code-to-code changes
• Determine applicable floor space subject to code
• Adjust nominal energy savings by
• code adoption status
• code compliance levels
• Analyze multiple code compliance scenarios
• Aggregate residential and commercial savings
• Compute code savings scenarios by segment and
  aggregate to the utility/program coverage area

Savings from Increased Compliance Alternative
Scenario Base Case
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Results Provided by the Estimator

• Energy savings as a result of increased
  compliance (multiple aspects of compliance are
  considered), by fuel type, by year and cumulative
  over the analyzed time frame
• Emissions savings, by year and cumulative over
  the analyzed time frame
• Consumer savings, by year and cumulative over the
  analyzed time frame
• The estimator handles multiple code versions. If
  interested in analyzing compliance with a
  particular code version only, overwrite future
  adoption for the rest of the code versions with a
  year outside of analysis period (gt2045).

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Conclusions

• Utility Savings Estimator is a straightforward
  framework to analyze savings from increased
  compliance.
• Common definitions of compliance and estimation
  methodology enable comparison across different
  segments of the utility coverage area.
• Common framework and definitions also allow
  comparison of results across different players
  and programs targeting energy code compliance.
• In turn, utility-level studies based on a common
  model will provide a more sound foundation for
  national codes benefits analysis.

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Contact Information

• Rosemarie Bartlett webinar registration and
  logistics
• rosemarie.bartlett_at_pnnl.gov
• Olga Livingston overall feedback, assumptions,
  estimation algorithm, tool interface
• olga.livingston_at_pnnl.gov
• Doug Elliott floor space projections, tool
  interface
• douglas.elliott_at_pnnl.gov

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Future Webinars

• As a follow-up to todays webinar, each attendee
  will be sent a set of assumption tables.
• Your feedback is greatly appreciated.
• July
• Webinar 2 Introduce and discuss the proposed
  interface.
• August
• Webinar 3 Present the review version of the
  utility savings estimator.
• All participants in this webinar will be sent
  invitations to the next webinars.