1

Utility Savings EstimationWEBINAR 1: METHODOLOGY AND ASSUMPTIONS

Olga Livingston, Rosemarie Bartlett, Doug ElliottPacific Northwest National Laboratory

PNNL-SA-95757

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

2

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

3

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 interfaceWebinar 3: Present the review version of the Utility Savings Estimator.

4

Methodology Overview

5

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) yearApply savings estimates for code-to-code changesDetermine applicable floor space subject to code

Adjust nominal energy savings by:code adoption statuscode 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. 

Methodology Overview (cont.)

6

Energy Savings from Code to Code

Reference Year

Applicable Floor Space Accelerated

Adoption Increased

Compliance

Baseline Adoption

Baseline Compliance

Nominal Savings

Alternative Scenario

Base Case

ProjectedSavings

Base Case Savings

Impact from Increased

Compliance

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

7

Energy Savings from Code to Code

Reference Year

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. 8

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

9

Code AdoptionTracked 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

10

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 statesutilities and regional energy efficiency organizations (REEOs) running programs across statesconstruction 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 versionscurrent 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 (>2045)

11

Code Adoption (cont.)

Adoption Categories

1. 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.

2. 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.

3. 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.”

4. 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.

5. 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.

12

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 RateArkansas, 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.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.

13

Code Compliance

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

14

Full code-to-code savings

Partialcode-to-code savings

70%

30%

15

Code Compliance (cont.)

Compliant buildings

Non-compliant buildings

Weighted compliance,

(energy terms)

IECC - XXXXCompliance in legal terms 30% 70%Compliance in energy terms (fraction) 1.00 0.20 44%

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

16

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

Code Compliance (cont.)

Initially compliant buildings

Initially non-

compliant buildings

Weighted compliance,

initial (energy terms)

Compliant buildings after 10 years

Non-compliant buildings after 10 years

Weighted compliance,

after 10 years

(energy terms)

IECC - XXXX

Compliance in legal terms 30% 70% 50% 50%Compliance in energy terms (fraction) 1.00 0.20 44% 1.00 0.20 60%

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

Initially compliant buildings

Initially non-compliant buildings

Weighted compliance,

initial (energy terms)

Compliant buildings after 10 years

Non-compliant buildings after 10 years

Weighted compliance,

after 10 years

(energy terms)

IECC - XXXXBase Case

Compliance in legal terms 30% 70% 50% 50%Compliance in energy terms (fraction) 1.00 0.20 44% 1.00 0.20 60%

Alternative ScenarioCompliance in legal terms 65% 35% 80% 20%Compliance in energy terms (fraction) 1.00 0.50 83% 1.00 0.50 90%

17

Sensitivity study for various aspects of compliance:

Code Compliance (cont.)

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

18

Methodology Summary

19

Estimate nominal energy savings:Select base (or reference) yearApply savings estimates for code-to-code changesDetermine applicable floor space subject to code

Adjust nominal energy savings by:code adoption statuscode 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

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 (>2045).

20

Results Provided by the Estimator

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.

21

Contact Information

Rosemarie Bartlett – webinar registration and logisticsrosemarie.bartlett@pnnl.gov

Olga Livingston – overall feedback, assumptions, estimation algorithm, tool interfaceolga.livingston@pnnl.gov

Doug Elliott – floor space projections, tool interfacedouglas.elliott@pnnl.gov

22

Future Webinars

As a follow-up to today’s webinar, each attendee will be sent a set of assumption tables.

Your feedback is greatly appreciated.

JulyWebinar 2: Introduce and discuss the proposed interface.

AugustWebinar 3: Present the review version of the utility savings

estimator.

All participants in this webinar will be sent invitations to the next webinars.

23