TECHNICAL MEMORANDUM



### TO: Carla Frisch, U

. S

. Department of Energy, Office of Energy Policy and Systems Analysis (DOE

- EPSA)

FROM: Ian M. Hoffman and Ling Jin, Lawrence Berkeley National Laboratory (LBNL)

DATE: May 15 , 201 5

CC: Lisa C. Schwartz and Charles A. Goldman

SUBJECT: Energy Efficien cy Portfolio and Program Lifetimes : Temporal Distribution of Electricity Savings

Summary

In a peer

- reviewed brief funded by DOE

- EPSA (Hoffman et al. 2015 a ) , w e describe and report energy

s avings life times 1 obtained from regulatory report s filed by administrators of electricity efficiency programs for customers of investor

- owned utilities. DOE

- EPSA requested additional information on th e lifetime and magnitude of electricity savings from these programs. W e employ ed a statistical cluster analysis to identify logical cohorts of program savings lifetimes . We then quantif ied the energy saved within each lifetime cohort or tier for the peri od 2009 to 2013. The results thus represent the magnitude and timing of aggregated energy savings for program administrators in 25 states 2 for the most recent period for which data are available.

We find that efficiency programs can be segregated by savi ngs lifetimes into reasonably discrete ranges or tiers (based on the types of measures or other efficiency actions )

. Reported gross a nnual savings 3 and lifetime savings can be summed for each of these discrete tier s as an estimate of the magnitude of savin gs over time

. These tiers and their associated savings thus show the temporal distribution of the reported energy impacts of efficiency programs in more than half of states where substantial efficiency programs are funded by customers of investor

- owned utilities

.

1 Savings lifetime is the number of years of annualized savings that a given energy efficiency measure, program or portfolio (a collection of programs targeting multiple sectors) is expected to deliver savings. A measure lifetime is defined as the mean numb er of years that the measure is in place, operable and delivering energy savings. Program, market sector and portfolio lifetimes are averages of the lifetimes of the measures installed for that program, market sector or portfolio.

2 States from which savin gs lifetimes could be obtained include AR, AZ, CA, FL, HI, IA, ID, IL, IN, MA, MD, ME, MN, NC, NM, NY, OH, OK, OR, PA, RI, SC, UT, VT, and WA

. 3 Annual savings, also known as annual incremental savings, are the energy savings resulting from a given measure, program

or portfolio in the first year after installation . First

- year savings are typically annualized in most states

— i.e., assumed to constit ute a full year of energy savings regardless of when during the year the efficiency action was taken. Lifetime savings are annual savings summed over each year of the estimated lifetime of a given measure, program, market sector or program portfolio.Temporal Distribution of Electricity Savings Carla Frisch, DOE EPSA Page 2 of 7 

### Background

I n 2012, LBNL beg an collecting detailed data on energy efficiency programs funded by utility
customers and reported in regulatory filings by the administrators of those programs . The LBNL D emand

- Side Management (DSM) Program Database include s program

- level spending, annual and lifetime net and gross energy savings by fuel
, customer participation rates , total costs
, cost breakdowns
, and, where available, average savings lifetimes of the measu res installed or actions elicited by the programs. We characterize programs on multiple levels

— by market sector, program implementation type and technology

— according to a typology of more than 60 program types. LBNL developed the program typology through o ur review of hundreds of regulatory filings in 3 4 states (Hoffman et al. 2013). Characterizing programs in a detailed fashion, with specified applications of reasonably defined sets of measures, allows better definition of savings lifetimes. Sorting by pro gram type enables aggregation of program data across states and benchmarking of program results for individual program administrators and states

.

L BNL researchers draw upon the database to calculate the cost of saving electricity through various types of efficiency programs (Billingsley et al. 2014, Hoffman et al. 2015 b ) , among other uses . The lifetime of efficie ncy measure 4 savings for each program

— how many years
, on average , the savings last

— is a n essential value in quantifying energy savings and avoi ded emissions, as well as assessing the resource potential and cost

- effectiveness of saving energy through efficiency programs.

LBNL distributed a draft technical brief for external technical peer review in December 2014 on the state of knowledge of energy savings lifetimes (Hoffman et al. 2015 a )

. In the b rief , we reported rang es, medians and averages of program savings lifetimes from the LBNL DSM program database during the period 2009 to 2013

.

DOE

- EPSA requested more detail on the distribution of energy savings over the lifetimes of energy efficiency programs. Following is a description of our analysis.



### Approach LBNL collects program savings lifetimes reported directly by the energy efficiency program administrator, where available . A s of March 2015 , the LBNL Program Impacts database include d
~ 6,000 program years of data

. 5 However , program savings lifetimes are obtainable 6 for only
~ 1,600 program

4 M e asures in the LBNL DSM program database include devices, equipment, processes and behaviors or actions promot ed, incentivized or installed as part of utility customer

- funded efficiency programs . Efficiency measures through other activities, such as buildin g energy codes and end

- use equipment standards , are not included

. 5 LBNL counts the data points in its DSM program database in terms of program years

. For example, a program administered and operated over three years would generate three program years of d ata.

6 Either directly reported by the program administrator or derived from lifetime savings reported by the program administrator.Temporal Distribution of Electricity Savings Carla Frisch, DOE EPSA Page 3 of 7 years (27% of the total sample) . T he analysis described in this brief is based upon this subset of 1
, 600 program years. 7 More than 50 utilities and other energy efficiency program administrators in 25 states contributed to those data through their regulatory filings, statewide databases and other sources.

For the current analysis, LBNL calculated the savings

- weighted average lifetime for each of the program types identified in our data collection. We then performed a cluster analysis to help determine logical divisions into cohorts or tiers of savings lifetimes within the program data. C luster analysis is a commonly used statistical technique for grouping objects or values based on their similarity to one another versus other objects or values.

A number of algorithms can be used to define c lusters . The nature of the data and the interre lationships among the data points guide the selection of the algorithm. The defining quality for our clusters is the relationship between : 1) the lifetime for each program year and 2) the averages for each program type

. I n essence, we group ed the 1,600 pro gram

- year data points by their proximity to the average for each type of energy efficiency program

.

For our cluster analysis , we chose one of the most common methods, known as k

- means (Anderberg 1973)

. The variable k is the number of clusters. The method starts with assignment of each data point to a cluster so as to minimize the distance of cluster members from th e ce nter of the cluster , which is designated randomly from the start . The algorithm then readjusts the centers and cluster membership
to achieve the least squared Euclidean distance within each cluster and summed across all clusters. In essence, the method seeks to minimize differences within each cluster and maximize differences among the clusters. In our case, programs within each cluster would therefore have similar savings lifetimes and program types.

The algorithm iterates until it converges on a loca l optimum. Because the local optimum may not result in a global optimum or produce stable, repeatable clusters , we randomly seeded the selection of the initial cluster centers, resulting in 50 random starts of the search for a global minimum in the distanc es of the members within each cluster from its center. We specified a number of clusters ranging from three to seven.

We reviewed the cluster results for coherence in the vertical dimension

— i.e., the degree to which each cluster appeared well defined acr oss a certain range of years, with minimal overlap in lifetimes. Based upon this criteri on , the program savings lifetimes grouped most cleanly into four clusters.

7 We elected not to use the entire dataset of 6,000 program years because savings lifetimes for about 70% of the data are d eveloped based upon the average savings lifetimes for each type of program, as reported or derived for the subset of 1,600+ program years. Using the larger dataset , with lifetime savings based upon averages, would have skewed the range of savings lifetimes in the cluster analysis toward the average for each program type and thus also biased the estimated annual and lifetime savings for each tier of program savings lifetimes

.Temporal Distribution of Electricity Savings Carla Frisch, DOE EPSA Page 4 of 7 We summed the annual and lifetime energy savings for each program year in each cluster and

c alculated each cluster’s share of savings relative to total annual and lifetime savings for all program years with reported or calculated savings lifetimes.



### Results

With the ~1
, 600 program years sorted among four clusters or tiers of savings lifetimes, the energy savings for each tier of program lifetimes can be depicted as a share of the total savings for the dataset (Figures 1 and 2)

.

Figure 1 . Annual electricity savin gs for each tier of program savings lifetimes, as a percentage of the total annual saving s of all programs in the dataset

For annual energy savings across the 1,600 efficiency program year s for which savings lifetimes can be obtained
, about 6 7% of the annual savings comes from programs defined by measures lasting 11 years or less (Figure 1)

. This is consistent with the savings

- weighted average lifetime of 10.2 years across all 1,600 program years

. Programs defined by measures lasting 19 years or more account f or about 2% of overall annual energy savings in our dataset

. If we consider the lifetime savings of programs in our portfolio, then about 53% of the savings comes from programs defined by measures lasting 1 1 years or less (Figure 2). 35% 32% 31%

2%

0% 5% 10% 15% 20% 25% 30% 35% 40%

1 to 8 years, n=270 9 to 11 years, n=547 12 to 18 years, n=660 19+ years, n=130Percent of Total Portfolio AnnualSavings

Program Savings Lifetimes (years)Temporal Distribution of Electricity Savings Carla Frisch, DOE EPSA Page 5 of 7 Figure 2 . Percentage of total lifetime savings in the dataset for each tier of program savings lifetimes
The duration of the annual and lifetime savings in our national portfolio of programs can be depict ed in stepwise fashion, ratcheting down as the energy savings from each tier of programs comes to an end (Figure 3 ).

Figure 3 . Temporal distribution of energy savings and savings

- weighted average lifetime for each tier of program savings lifetimes 22% 31% 43%

4%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

1 to 8 years, n=270 9 to 11 years, n=547 12 to 18 years, n=660 19+ years, n=130Percent of Total Portfolio LifetimeSavingsTemporal Distribution of Electricity Savings Carla Frisch, DOE EPSA Page 6 of 7 For the 25

- state portfolio
, 100% of the savings from the initial investment in the programs last from one to eight years ( with a savings

- weighted average lifetime of 6.5 years)

. About 65% of the savings continue at least for the next one to three years (average life time of 9.6 years from the initial investment in the programs)

. Some 31% of the savings continue for another one to six years (average lifetime of 14.2 years )

. A small fraction of the savings (2%) continue s for as much as another 27 years for the longest

- l ived measures and programs (average lifetime of 21.2 years ).

Distinct types of programs can be identified within each tier of program lifetimes and portfolio savings (Table 1).

Table 1

. Prevalent types of energy efficiency programs associated with each tier of program savings lifetimes Tier of Program Savings Lifetime Prevalent Program Types Tier I <= 8 y ears Savings

- weighted average lifetime: 6.5 y ears • Res idential /Com mercial Lighting

• Res idential Appliance Recycling

• Res idential Behavioral Feedback

• Consumer Electronics

• Res idential /Com mercial HVAC Tune

Ups

• Com mercial Retro

- Commissioning

Tier II 9 to 1 1 y ears Savings

- weighted average lifetime: 9.6 y ears • Agricultural Pumps

• Residential/Commercial Appliances

• Commercial Lighting

• Small Commercial Prescriptive

• Res idential Direct

- Install Retrofits

• Multi

- Family Retrofits

• Pool Pumps

• Street Lighting

• Voltage Optimization and Transformer Upgrades Tier III 1 2 to 18 years Savings

- weigh ted average lifetime: 14.2 y ears • Data Centers

• Refrigerated Storage

• Industrial Motors

• Industrial and Agricultural Process

• Commercial HVAC

• Sm all Commercial Retrofits

• MUSH 8 Retrofits

8 Municipalities (and other government buildings) , universities, k

- 12 schools and h ospitalsTemporal Distribution of Electricity Savings Carla Frisch, DOE EPSA Page 7 of 7 • Residential Water Heaters

• Whole

- Home Retrofits

• Low Income (Shell/System) Tier IV 19+ y ears Savings

- weighted average lifetime: 21.2 y ears • Residential New Construction

• Commercial New Construction

• Residential HVAC

• Residential Insulation

• Cool Roofs

Th is work demonstrates that efficiency programs for electric utility customers can be segregated into logical tiers based on similar program savings lifetimes. An estimate of the magnitude of energy savings over time can be developed from the g ross annual and lifetime savings for each of these tiers , as reported by program administra tors. These tiers and their associated savings thus reflect the temporal distribution of the reported impacts of energy efficiency programs in 25 states

. Programs defined primarily by measures with savings lifetimes of 1 1 years or less account for 67 % of a nnual savings and 53 % of lifetime savings across the 25

- state sample.



### References Anderberg, M.R. 1973. “Cluster Analysis for Applications. Monographs and Textbooks on Probability and Mathematical Statistics” Academic Press Inc., New York.

Billingsley, M. A. , I. M. Hoffman, E. Stuart
, S. R. Schiller
, C. A. Goldman and K. Hamachi LaCommare 2014. “The Program Administrator Cost of Saved Energy for Utility Customer

- Funded Energy Efficiency Programs.” Lawrence Berkeley National Laboratory. LBNL

- 6595E. April. http://emp.lbl.gov/cost

- saved

- energy

Hoffman, I. M., M. A. Billingsley, S. R. Schiller, C. A. Goldman and E. Stuart 2013

. “Energy Efficiency Program Typology and Data Metrics: Enabling Multi

- State Analyses Thr ough the Use of Common Terminology” LBNL

- 6370E

. August. http://emp.lbl.gov/sites/all/files/lbnl

- 6370e.pdf

Hoffman, I.M., S.R. Schiller, A. Todd, C.A. Goldman, L.C. Schwartz 2015 a . “Energy Savings Lifetimes and Persistence: Practices, Issues and Data” LBNL. May. http://emp.lbl.gov/publications/energy

savings

- lifetimes

Hoffman, I

. M., G. Rybka, G. Leventis, C.A. Goldman , L. Schwartz, M.A. Billingsley and S.R. Schiller 2015 b . “The Total Cost of Saving Electricity Through Utility Customer

- Funded Energy Efficiency Programs: Estimates at the National, State, Sector and Program Level” LBNL

. April. http://emp.lbl.gov/sites/all/files/total

- cost

- of

- saved

- energy.pdf