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STATE OF CALIFORNIA Edmund G. Brown, Governor

PUBLIC UTILITIES COMMISSION505 VAN NESS AVENUESAN FRANCISCO, CA94102-3298

California Public Utilities CommissionEnergy Efficiency Evaluation Measurement and Verification

Work Order Cover Page

CPUC Contract Number: 09PS5863BPrime Contractor: KEMA, Inc.Work Order Number: KEMA006, AMENDMENT 4Work Order Project Title: Energy Efficiency Potential and Goals Study AssistanceED Work Order Project Manager: Dina MackinED Contract Manager: Carmen BestContractor Project Manager: Fred CoitoWork Order Budget: $3,926,648 (initial budget of $1,903,171, plus $2,023,477 added in this

amendment)Term: Effective Date through December 31, 2012

Purpose: This amendment adds additional scope and budget to continue data gathering and analysis for the potentials, goals, and targets study.

This Work Order does not authorize work that is beyond the scope of the work authorized by the underlying prime contract, may not expand the budget of the underlying prime contract, nor can this Work Order change, amend or modify any of the substantive terms and/or conditions of the underlying prime contract nor add any new substantive terms and/or conditions to the underlying prime contract.

The actual costs to complete an approved Work Order shall not exceed the amount authorized in this Work Order. If, in the performance of the work, the Contractor determines that the actual costs are likely to exceed the estimated costs, the Contractor shall immediately notify the Energy Division Contract Manager and Project Manager. Upon such notification, the Energy Division Contract Manager will:

(1) Amend the Work Order scope to accomplish the work within estimated costs; or

(2) Amend the Work Order budget to accomplish the existing scope; or

(3) Terminate the Work Order.

Any expenses incurred by the Contractor that have not been authorized shall be borne by the Contractor. No amendments to this Work Order shall be made for work undertaken without the specific approval of the Energy Division Contract Manager.

Approvals:

08-03-2012 08-07-2012______________________________________________ _______________________________________________ED Contract Manager Date Contractor Project Manager Date

document.docx

STATE OF CALIFORNIA Edmund G. Brown, Governor

PUBLIC UTILITIES COMMISSION505 VAN NESS AVENUESAN FRANCISCO, CA94102-3298

California Public Utilities CommissionEnergy Efficiency Evaluation Measurement and Verification

Work Order Cover Page

document.docx

Contents

Scope..............................................................................................................2

Ongoing Support During the Potentials-Goals Study....................................3

Update / Integration Plan...............................................................................3

Database Architecture...................................................................................4

Update 2011 Model Components...................................................................6

Expansion of Agricultural/ Industrial/ Mining/ Street Lighting Sector (AIMS) Potential.....................................................................................................................13

Develop Technical and Economic Potential.................................................15

Market Potential Module Update.................................................................16

Policy Support..............................................................................................23

Model Integration and Reporting................................................................25

Market Transformation: Connecting Market Adoption Curves...................26

Potential, Goals, and Targets Study Page 3

ScopeThis work order amendment is to update the potential, goals, and targets project initiated in 2011 under ED Work Order KEMA006 as part of CPUC Contract Number 09PS5863B. Additional funds are also added to the KEMA support task to cover the extended work.

The following project activities are presented in this amendment: Ongoing Support During the Potentials-Goals Study (carryover from previous WO006) Update/ Integration Plan (new) Database Architecture (new) Update 2011 Model Components (new) Expansion of AIMS Potential (new) Develop Technical and Economic Potential (new) Market Potential Module Update (new) Policy Support (new) Model Integrating and Reporting (new) Market Transformation: Connecting Market Adoption Curves (new)

Ongoing Support During the Potentials-Goals StudyBudget for this task is increased by $25,000 to cover KEMA’s additional support work during the next phase of this project.

Update / Integration PlanThis new task is to develop the plan for updating the potentials, goals, and targets study.

Approach

WO006 initially provided a market potential model that informed the 2013-2014 ED DSM portfolio goals and also conceptualized a framework for establishing portfolio goals that more accurately reflect California’s legislative environment and strategic planning framework. This work order amendment has the project title ‘2013 potential, Goals, and Targets Update’ (or ‘2013 PGT’) and is a continuation of the same contract but seeks to accomplish several new objectives;

1. Align the measure list more completely with DEER and the frozen ex-ante database of DMQC vetted work papers. This includes the potential to model by climate zone and res and commercial building types.

2. Expand and refine estimate of commercial usage based behavioral potential beyond building operator training to include commissioning (Cx, Rx, RCx, MBCx). These initiatives will be renamed “Operating efficiency potential”

3. Expand and refine the ET framework, including;

a. Review the database created for the statewide IOU ET program to assess how this database can be used to inform the universe of emerging technologies included in the potential model.

b. Review other California programs including Portfolio of the Future, PIER, and EPIC initiatives

c. Review other programs and emerging technology initiatives outside of California to identify potential candidates not considered in California initiatives.


4. Expand the market sectors for which potential is being estimated to more closely align with the sector definitions used in the CEC demand forecast. CEC forecast sectors not present in the previous model that will be added to the 2014 goals modeling effort include;

a. Mining

b. Street lighting

c. Wastewater treatment Energy efficiency for water infrastructure (this is not EI for end use)

5. Refine previous estimates of potential for the industrial and agricultural markets.

6. Key revisions to the data structure supporting the modeling effort include;

a. The database structure being developed for the update will follow a ‘linked database’ structure. Linked data is a database design method and code structure that allows published dataset to be more thoroughly analyzed while reducing updates costs.

b. Measures inputs will available online either through a linked spreadsheet or online database

7. Finalize approach to modeling the SP as first conceptualized in the 2011 study

8. Incorporate financing into market potential model.

9. Options

a. Include a water / energy nexus = energy intensity (EI). Includes scoping discussion on policy implications with CPUC, including PIP review and define how to develop framework to assess potential. Budget undetermined. Lori Park as potential subcontractor.

Database Architecture

The Navigant team recognizes the importance of developing a robust measure input database that achieves the following objectives:

Well documented and easy to navigate

Uses most up to date standardized measure inputs

Is easily updateable and has a robust structure

To meet these objectives, Navigant will develop a Web- based Measure Input (WeMI) database. This database will replace the current Measure Input Characterization Sheet structure (MICS). The proposed database will enable users to update and review measure input parameters in real time. WeMI database will also enable dynamic linking with existing sources of measure parameters such as the Standard Program Tracking Database (SPTdb) and the DEER database. Linking to standardized sources will insure that the measure parameters that inform the potential study are consistent with the measure parameters being used by the IOUs and third-party evaluators.

The deliverable for this task is a secure online database that will be made available to the CPUC and other stakeholders.

Database Structure and Design

Navigant will follow best database design best practices while designing the WeMI database for this project. At the core, the WeMI database will bring together data on measure parameters necessary to calculate market potential estimates, sector wide properties such as building stock, IOU specific inputs(such as avoided cost) and other necessary inputs to calculate market potential. Additionally, the database will have the ability to update measure input parameters as and when source data (such as DEER) is updated.

Approach

Explained below are considerations Navigant will take into account while designing the WeMI database:


Relational Database: Navigant will design a relational database for the CPUC. Relational databases are dynamic solutions that link tables together through primary keys; as opposed to a flat file. Relational database are dynamic whereas flat file data storage is static; this dynamic quality allows relational database users to easily update, query and summarize data as data and requirements evolve, thus making the data within even more valuable. All system outputs can be exported to commonly used formats such as .CSV, Excel, and Access.

Primary Keys: Primary keys uniquely identify each record in any well-designed relational data system. Primary keys should be persistent, such that the keys do not vary over time, which can result in numerous data issues. The proposed data system outputs/results will be traceable to input parameters used by the data models via persistent primary keys, ensuring complete transparency. The primary keys will also make allow the WeMI to map PGT study inputs to the current SPTdb and the DEER database.

Quality Assurance: Navigant has developed both computational software and staff protocols for the quality control and review of all analytical work. Navigant has an extensive library of code designed to clean data, generate summary statistics, and ensure all evaluation products are rigorously verified for quality.

Data Security: Any data transferred will be transferred securely using an Accellion appliance (FIPS 140-2 Level 2 certified) hosted on a secure perimeter network, although some ad hoc transfers may occur as the project progresses. Data transfers through the Accellion appliance are automatically deleted after a maximum of seven days. All Navigant laptops are PGP whole-disk encrypted (FIPS 140-2 Level 1 certified), and users are trained to encrypt DVD/CD media using PGP file encryption (FIPS 140-2 Level 1 certified).

Navigant will consistently archive all data in a standardized and well-documented fashion while maintaining data security. Navigant has been through several SAS 70 compliance audits, and understands the importance of implementing adequate controls and safeguards when hosting and/or processing sensitive data.

Update-ability: Navigant also understands that model input parameters may change over time and, therefore, the system will be built to make input updates seamless. The data system will be designed such that timely updates can be made by Navigant staff as data sources change over time (such as DEER).

Develop Measure Input and Measure QC Structure

Parallel to developing the architecture of the WeMI, Navigant will also develop a measure input development and measure input quality control (QC) structure. Navigant recognizes that documentation of measure sources, ease of accessibility of assumptions and thorough quality control are necessary to develop measure inputs that are defensible. To meet this end Navigant will develop secure web based measure input and measure review tools.

Approach

The WeMI database will have a user friendly online graphical user interface (GUI). This GUI will allow the user to navigate select a particular measure; the user will then be able to enter measure parameter inputs and document data sources. Measure reviewers will be able to access and QC measure input parameters along with the documentation for these parameters. An export function will enable the analysis team to export measure input parameters in an Excel format.

The following tasks will be conducted by Navigant to develop the WeMI GUI:

Measure Input and Measure Review Format Development.

The Navigant team will develop web based measure input pages; this will be the GUI for the WeMI database. The measure input page will allow the user to enter measure input parameter data efficiently and allow for effective documentation. For e.g., measure input staff will only have to enter measure cost once for all applicable building types. An additional feature of the GUI will be the ability to upload reports and/or analysis sheets used to determine a particular measure input parameter to WeMI.

The WeMI structure will also expedite the measure input process by dynamically linking to extracts from DEER and the SPTdb. The user will not have to manually enter any data that is contained in the DEER database or the SPTdb.


Measure input parameters will be available for QC as soon as measure data is entered. Reviewers will be able to download any analysis worksheets or third-party report that were used to calculate measure input parameters.

Measure Output Format Development.

Navigant and other approved users of the WeMI will have the ability to export measure input parameter data in an Excel based format. This measure input parameter export will be a direct input into the Analytica model being constructed for calculating Technical, Economic and Market Potential.

System Testing.

Navigant will test the system to make sure that it is ready for third party review before posting it online. The system will be tested by uploading select measure and DEER data to WeMI.

Revise Residential and Commercial Non- Emerging Technology (ET) Measure List

Navigant recognizes that in order to get a true vision of the market potential available in the IOU territories being analyzed, it is necessary to develop a measure list that includes measures in existing utility portfolio along with measures expected to play a part in the utility portfolio in the future. In order to do so Navigant will integrate the current list of DEER measures, all utility developed work-papers and other measures that are being currently analyzed by the utilities.

Approach

Navigant will conduct the following tasks to expand the current residential and commercial measure list:

» Integrate DEER. The Navigant team intends to include every measure in DEER in finalized measure list. Navigant will work with the DEER team to understand the DEER database structure and how it can be applied to the Potential Goals and Targets Project. A clear understanding of the DEER structure will enable the Navigant team to dynamically link the DEER database to WeMI database. Dynamic linking to the DEER database will allow seamless WeMI update when the DEER database is updated. This will also eliminate the need to manually look up DEER data and input into Navigant’s potential model structure, thus reducing the possibility of error and time spent.

» Integrate Non-DEER SPTdb Measures. The Navigant team is well versed with the SPTdb. Key members of the Navigant team helped construct the SPTdb. Navigant will work with the current SPTdb team to integrate non-DEER measures with frozen ex-ante values into the WeMI database.

» Integrate Work Paper and Custom Measures. Navigant will work with the CPUC, the IOUs and other stakeholders to identify and characterize measures that are not a part of DEER or the current SPTdb. Navigant will identify utility work papers where available for these measures to obtain input parameter data. For measures with no current work papers, the Navigant team will work with the IOUs to identify/ conduct custom analysis for these measures.

Update 2011 Model Components

Expansion of Behavioral Research

Energy can be saved in two ways, (1) improving the inherent equipment efficiency and (2) improving the operating efficiency of a process. Behavior-based programs are programs that provide only information – not material or financial incentives – to participants in order to achieve energy savings. Energy saving outcomes of these programs include both changes in equipment usage (operating efficiency, e.g. turning off a light) and equipment purchase and installation ( improving equipment efficiency, e.g. buying high efficiency light bulbs). These programs can be tailored to all sectors and may or may not target specific customer segments.

A variety of behavior-based energy efficiency programs have demonstrated savings across sectors, target audiences, and end-uses. Examples include home energy audits, home energy reports, media campaigns, grade-school programs, and building operator certification courses. A much broader range of program designs is possible.


There is much uncertainty in the savings potential of behavior-based programs. The case of home energy report programs illustrates this uncertainty. Pilot programs have used experimental design and large sample sizes to identify precise net program impacts. If these programs were scalable to the majority of customers and savings were persistent, behavior-energy savings would be comparable in impact to all incentive-based activities combined. However, no programs have been implemented on this scale and it would take several years of utility pilots and program scaling before the limits of scalability and savings persistence were well understood and quantifiable.

In Track 1 of Navigant’s Potentials, Goals, and Targets (PGT) project for the CPUC, Navigant estimated the savings potential from some types of behavior programs. In keeping with the data-driven approach of the rest of the study, Navigant estimated savings potential based on existing programs for which impact evaluations had provided estimates of savings. The identified programs were home energy reports, home energy audits, and building operator certification courses. Navigant identified several key areas of uncertainty in their estimates, including the following:

The full range of types of programs possible

Feasible ramping rates of annual participation of existing programs

Effectiveness of reparticipation

Disaggregation of impacts between usage and equipment based activities

Persistence of equipment savings, given that some potential is acceleration of otherwise occurring savings and some equipment savings is from customers that would not otherwise achieve these savings.

This study is to 1) identify approaches to estimating the savings potential from behavior-based initiatives more thoroughly and precisely than in the PGT report, and 2) to identify the research and data needs associated with these approaches.

Approach

Under this study, Navigant will identify a comprehensive approach to modeling the potential from behavior-based initiatives. The approach will begin by estimating the technical potential for behavior-based energy savings in each sector based on review of secondary literature and original analysis as needed. Navigant will then scale down the technical potential based on limiting factors and implementation practicalities, as determined through review of impact evaluations and discussions with program implementers, evaluators and other industry experts.

The work will contain the following tasks:

» Review of broad behavior-based potential studies. Potential studies such as Laitner et al. (2009)1 will be reviewed to identify the broad range of sectors and outcomes addressable through behavior-based programming.

» Development of a potential savings estimate methodology. Navigant will design a high-level methodology that could be used to provide estimates of achievable potential by starting with technical potentials identified in Task 1 and use program experience and evaluation findings to scale this potential down to achievable levels.

» Review of current and planned EM&V efforts and barriers. Navigant will interview staff at the CPUC ED, California IOUs, and other EM&V and research entities across the United States. Navigant would leverage their broad contact base of clients and collaborators to arrange these discussions. Literature review would also be used.

» Data gap analysis. Navigant will identify and prioritize data gaps for the methodology specified in Task 2. This task will include estimates of the uncertainties in savings potential estimates associated with these gaps.

» Description of research activities that could be conducted to address data gaps – Research activities may include program and evaluation design recommendations and additional research activities.

1Laitner, John A. “Skip”, Karen Ehrhardt-Martinez, and Vanessa McKinney, 2009. “Examining the scale of the Behaviour Energy Efficiency Continuum”, European Council for an Energy Efficient Economy 2009 Summer Study.


» Reporting and Presentation – Navigant will provide interim findings to the CPUC ED and DAWG at key stages of the work. Navigant will provide a draft and final report and presentation to the CPUC ED and DAWG to conclude this project.

» Project Management – The project manager for this study will communicate regularly with the PGT project management, ED project management, and the Navigant team to ensure that the Navigant teams’ activities are appropriate the ED’s objectives.

Expansion of Emerging Technologies

The 2011 potential study showed decreasing incremental potential from conventional energy efficiency technologies (HIMs, MOIs, and Secondary measures combined); this decrease was partially offset by an increase in potential from emerging technologies (ET) particularly in the commercial sector. The 2011 study improved over the 2008 study in the number of emerging technologies considered; the 2011 study considered 22 unique types of emerging technologies. This study will expand the consideration of emerging technologies to better understand their contribution to future savings.

Navigant will revisit ETs already considered as well as add additional ETs. ET’s are an evolving field with constant improvement of cost, reliability, and sometimes savings. Navigant will review ET’s included in the 2011 study to update cost, savings, and other inputs with any new data or understanding that has become available since the issue of the May 2012 final report. Navigant will also research and include a large scope of additional emerging technologies in the updated potential and goals modeling.

Approach

Navigant will conduct this work in five tasks:

Identify Additional High Potential Emerging Technologies

Navigant will develop a list of high potential emerging technologies that are not currently included in the model. We will start by reviewing the original list of approximately 100 ETs that appears in Appendix K of the May 2012 report. Navigant will supplement this list with additional promising ETs from the following sources:

Navigant’s internal databases of ETs

CPUC Emerging Technology Program Database

Party comments from the draft April 2012 Track 1 report, May 2012 final report, and CPUC decision that specifically mention emerging technologies

Additionally, Navigant will solicit input from the IOUs to determine any additional emerging technologies that should be considered. IOUs will be engaged via virtual meetings to openly discuss their “wish list” for ETs that should be considered. Navigant will request any documentation (including draft workpapers) the IOUs can provide to substantiate their interest in a particular technology.

Qualitatively Screen Emerging Technologies

After identifying the list of additional ETs for inclusion, Navigant will qualitatively screen (using metrics as illustrated in Table 1) the list to rank the measures from high to low priority.


Table 1. Illustrative Emerging Technology Scoring Matrix

Technology Assessment Scorecard

Technology Characteristic

Weight 1 2 3 4 5

Percent Energy Savings

2 Low Medium High

Market Risk 1 (High Risk)

Requires new/changed business model

Start-up, or small manufacturer

Significant changes to infrastructure

Requires training of contractors Consumer acceptance barriers exist.

Long payback (e.g., >10 years)

(Low Risk)

Trained contractors Established business models Already in U.S. Market Manufacturer committed to

commercialization Short payback period (e.g., <2

years)

Technical Risk

1 High Risk: Prototype in first field tests

Low volume manufacturer

Limited experience

New product (in any market) with broad commercial appeal

Proven technology in different application or different region

Low Risk: Proven technology in target application

Utility Ability to Impact Outcome

1 Private sector will be successful without utility involvement.

Utility is unlikely to be critical to adoption.

Utility is likely to accelerate adoption.

Utility is very important in accelerating adoption.

Utility is essential for catalyzing market.

Non-energy Benefits*

1 Few or none non-energy benefits

Some modest non-energy benefit likely

Significant benefits, difficult to quantify/not well understood

1 or 2 quantified, well-documented

Extensive, quantifiable, well-documented

Navigant will assign a “Risk” score to each ET based on the Market Risk and Technical Risk rankings from Table 5. The Risk score range from 100% (high risk, high uncertainty of market adoption and savings) to 0% (low risk, low uncertainty of market adoption and savings) and will be used to model savings.2 The Risk score will decrement the maximum

2 For example, Navigant expects LED lighting to be a low risk technology as it is well developed and commercially available. However, Navigant expects Indirect Evaporative Cooling to be a higher risk technology given its limited commercial availability and limited implementation examples.


population willing to adopt an ET. As a result, the model is will appropriately simulate greater savings from low risk ETs compared to high risk ETs.

Characterize Emerging Technologies

Navigant will characterize the emerging technologies in preparation for inclusion in the model. Measure characterization will seek data and assumptions for the following: energy savings, incremental cost, measure life, current density, maximum density, willingness and awareness. Additionally, Navigant will quantify a “Risk” score for each ET (as discussed in Task 2). Navigant will follow a format similar to Appendix K in the May 2012 report to report ET measure data.

Navigant expects measures costs to vary over time as technology and production improvement decrease the cost of ETs. For example, in the 2011 study, Navigant modeled a decreasing cost for LEDs over the study period. Depending on the selected additional ETs, Navigant may characterize cost reductions over time.

To obtain measure data, Navigant will first request any available work papers, case studies, engineering studies, and draft analyses and data from the IOUs. In cases utility data is not available (which is expected for the majority of measures) Navigant will seek additional sources including manufacturer case studies, third-party case studies, DOE and CEC data, and Navigant’s engineering expertise.

We expect Low Risk ETs will have several source of data upon which we can rely to characterize the measure; however, this will not be the case for High Risk ETs. For High Risk, Navigant will make its best estimate of reasonable measure inputs. While these inputs will have significant uncertainty, the Risk factor for these technologies will diminish their contribution to overall savings. Therefore, overall savings will continue to have high certainty.

High Risk ETs were not included in the May 2012 model and report. By including High Risk ETs in this update, Navigant presents a more complete picture of ET potential. However, it’s important to note:

Under this study, Navigant will spend more time characterizing Low Risk ETs compared to High Risk ETs

The overall measure input characterization sheet for ETs will only be partially sourced. Low Risk ETs will most likely have reliable data sources; however High Risk ETs will consist of Navigant estimates based on professional judgment.

High Risk ETs need additional study; as additional data and understanding of High Risk ETs becomes available, inputs can be updated.

The ET measure input characterization sheet will be a “living document” and should be updated in future years

ET’s included in the 2011 study were those deemed to be the most promising (Low Risk). These ET’s had readily available data and had been studied by multiple utilities and organization in recent history. Thus data collection and measure characterization in the 2011 study was a relatively simple task. However, Navigant expects the next group of additional ETs to be less understood. These additional technologies will have limited publically available data and case studies. As a result, Navigant expects the marginal effort and cost of characterizing these additional ETs to be higher compared to the original effort required by Track 1.

Model Additional Emerging Technologies

Additional ETs will be added to the PGT Analytica model to forecast savings potential. Although the model was built to easily add measures, the nature of emerging technologies requires additional modeling considerations. Navigant expects to encounter the following modeling issues:

New ET measures may compete with existing utility measures. Competing measures require additional inputs and modeling consideration.

As ETs compete with HIMs, MOIs, and Secondary measures, its possible ETs could remove some savings from the conventional measure category shifting it to the ET measure category.

New ETs may be impacted by upcoming codes and standards. If so, code impact vectors will need to be developed.


Emerging technologies should not simply shift savings away from conventional measures (via competition) but should increase overall portfolio savings potential. If a significant number of new ETs compete with conventional measures, Navigant will reexamine its competition modeling to ensure it is still a robust methodology to model emerging technologies.

Project Management and Reporting

The project manager for this study will communicate regularly with the PGT project management, ED project management, and the Navigant team to ensure that the Navigant team’s activities are in line with the ED’s objectives. Time spent on this task may include preparing for and facilitating virtual meetings with IOU representatives to solicit insight and data regarding additional emerging technologies.

Additionally, Navigant will produce a memo summarizing the work including the methodology, inputs, results, and a comparison to previous results.

Update Codes & Standards and Plug Load Potential

The 2011 potential study included savings from codes and standards (C&S) attributable to the California IOUs. These savings were obtained using a similar methodology as used by the CPUC evaluation of utility C&S advocacy programs during the 2006-08 evaluation cycle. Savings attributable to IOUs (“Net” C&S savings) discounted the total savings by several factors: compliance rate, naturally occurring market adoption (NOMAD), and utility attribution factors.

Sources for the input data included California Energy Commission (CEC) analysis of Title 20 and Title 24 codes and US DOE analysis of federal standards. Due to the unique nature of each code or standard, the study team relied on CEC and DOE analysis results for total savings. Reviewing the individual input assumptions for each code or standard was not possible in the timeline and budget given.

While conducting the potential study, several C&S related items were identified for further investigation. Additionally, the original scope of work for the goals study included several additional aspects of C&S analysis beyond what was presented in the potential study. This study describes Navigant’s plan to address these items:

Integrate the C&S model into the Analytica model

Review inputs

o Review and better document NOMAD assumptions

o Review building stock and baseline measure population assumptions embedded in the C&S results

Incorporate C&S programs in to portfolio cost effectiveness calculations

The energy efficiency potential from plug load devices was also identified as an area of further investigation. The study team notes that the majority of plug load savings potential resides in codes and standards; therefore, the study team will pay special attention to this end use during the C&S review.

Approach

Navigant will conduct this work in four tasks:

Integration of C&S Model into Analytica

The C&S results were developed by a separate spreadsheet model given the short timeline of Track 1. In this task, the algorithms of the spreadsheet model will be replicated in Analytica to recreate the model’s output. Navigant modelers will work with HMG modelers to translate the methodology and inputs. Replicating the model into Analytica will provide more transparency and provides an opportunity to conduct scenario analysis on C&S savings. Scenario analysis is already planned as part of broader goals study scope.


Review C&S Inputs

Once the C&S model is translated to Analytica, they study team will further investigate model inputs to refine the savings potential.

Review NOMAD Assumptions

NOMAD factors were obtained from the CPUC 06-08 evaluations which only examined 2005 Title 20 and Title 24 codes. NOMAD factors for all other C&S’s in the Track 1 study were estimated using CEC CASE studies or leveraging the 2005 evaluation data for similar end uses. The study team will review NOMAD assumptions as they have a large impact on IOU net C&S savings. This review will include:

Documenting the methodology to develop NOMAD from the 06-08 evaluation

Identifying and documenting the basis of NOMAD estimates for unevaluated C&S’s

Discussing the uncertainty in NOMAD estimates and (if data allows) conducting sensitivity tests on NOMAD inputs.

Additional NOMAD data may become available as part of the CPUC 2010-12 evaluation cycle. The study team will incorporate any additional data (draft or final) that becomes available. The current timeline on the EM&V cycle is to deliver draft results in January 2013.

Review Baseline Measure Population Assumptions

CEC analysis that provides input to the Track 1 C&S model is based on older housing projections made prior to the 2008 recession. Utilities commented these data should be reviewed as it could lead to an overstatement of Title 24 savings potential. The study team looked into this issue and documented the known discrepancies in the May 2012 report. As part of this task, the study team will review the discrepancies and incorporate any changes as needed.

Similarly, Title 20 and federal standards savings are based on statewide or national equipment sales data embedded in CEC and DOE analysis. These embedded assumptions may differ from the population and measure density data used to estimate utility rebate program potential. Navigant will assess the discrepancy of savings potential for key measures; in this process Navigant may adjust values for select C&S as needed.

Review Treatment of Plug Load Measures

Stakeholders commented the energy savings potential from plug load efficiency is not well understood and that the goals study should revisit the end use. Navigant expects a significant portion of plug load efficiency savings to appear in the form of C&S savings. C&S’s set broad reaching performance standards for appliances such as requiring low passive power draw. The May 2012 report contained multiple C&S that affected plug loads including:

Battery chargers

Computers (Desktops, Notebooks)

External Power Supplies

Microwave Ovens

Televisions

Residential Dehumidifiers

The study team will review these C&S in more detail to ensure they appropriately capture plug load potential. If they do not, additional measures may be added.


Incorporating C&S in Cost Effectiveness Test Results

IOU claim energy savings from a portion of C&S savings through C&S advocacy programs. The savings contribute towards the IOU’s overall savings goals and should be considered when calculating IOU energy efficiency program cost effectiveness. The study team previously examined several options to include these savings (and their associated costs); however, given other priorities at the time, the issue was not resolved.

Typically both program administrative costs and equipment costs are included in IOU portfolio cost effectiveness tests. While IOU program administrative costs are well understood, several perspectives on C&S equipment costs have been presented to the study team:

1. Customers see higher costs with more efficient technology. Increasing minimum efficiencies will increase the cost of “base” equipment to the customer. Current data on the cost of various higher efficiency levels can be used to quantify the future cost of code-complaint equipment.

2. No incremental cost associated with the new code or standard because it is the new baseline. By definition of “baseline,” the incremental cost of the baseline equipment is set to zero and increased efficiency levels beyond the baseline are compared to the baseline.

3. Costs decline due to economies of scale. Manufacturers retool production and invest in R&D. The resulting research and economies of scale decrease the cost of code compliant equipment compared to its cost in the absence of code.

In this task, the study team will review these options and solicit input from CPUC staff on the appropriate path. Depending on the selected option, equipment cost data collection may be required.

Project Management and Reporting

The project manager for this study will communicate regularly with the PGT project management, ED project management, and the Navigant team to ensure that the Navigant team’s activities are in line with the ED’s objectives. Time spent on this task may include preparing for and facilitating meetings with CPUC staff, program evaluators, CEC C&S staff, and other stakeholders to solicit insight and data.

Additionally, Navigant will produce a memo summarizing the work including the methodology, inputs, results, and a comparison to previous results.

Expansion of Agricultural/ Industrial/ Mining/ Street Lighting Sector (AIMS) Potential

The AIMS sectors consume approximately 20 % of statewide IOU territory energy consumption3. The Updates Study (Update to the Potential Goals and Targets – 2012, Track 1) modeled the industrial and agricultural sectors and estimated energy savings potential for the Industrial Sector and the Agricultural sector. Minimal research was conducted to ascertain the inputs used to model the Agricultural4 and Industrial5 sectors. Due to time and budget constraints, thorough research into these sectors was not possible. The mining sector potential, in particular, has not been addressed in previous Potentials, Goals, and Targets studies.

AIMS programs are designed to provide energy/demand savings primarily through changes in equipment (i.e., retrofit and/or replace on burnout) and operating patterns6. Although, programs implemented within these sectors have been

3http://ecdms.energy.ca.gov/elecbyutil.aspx 4 High level research was conducted to estimate maximum technically feasible savings as percentage of end use. This research was limited to secondary, online data collection.5 The inputs for the industrial sector were considered a “pass- through” from the previous study. According to the final Track 1 report, the vintage of the data used for the industrial sector dates back to the 1980s. e.g., Economic Analysis of California Cotton Ginning Technology, California Agriculture, November-December 19886 Navigant will ensure that there is no overlap between savings due to operational/ behavior change and equipment change.


http://ecdms.energy.ca.gov/elecbyutil.aspx

successful in establishing both participant interest and energy/demand savings, there is higher penetration of energy efficient equipment in the residential and commercial sectors as compared to the other three sectors.

Additionally, programs tailored to the agricultural, industrial, and mining sectors differ from their residential and commercial counterparts due to the unique application of incented technologies, along with an increased emphasis on productivity and equipment reliability over energy usage reduction. Experience has also shown that customers within these sectors are often exposed to different economic and financial drivers which may influence adoption trends.

Collectively, these factors serve as the impetus for expanding the savings potential methodology and assumptions from agricultural and industrial initiatives, and developing an estimate of energy savings potential in the mining sector. Specifically, the main tasks that Navigant proposes to conduct to draft a work-plan for estimating the potential for these three sectors are:

Market and Energy Characterization

Develop Measure List and Measure Inputs

Market Characterization

In this task, the Navigant team will characterize the markets for the AIMS sectors to better understand the following:

Energy consumption profile for these sectors.

Near and long term energy consumption forecasts for these sectors.

Baseline equipment currently in use in these sectors

Current and planned energy efficiency efforts in these sectors.

Approach

Navigant will characterize the AIMS markets through a combination of secondary research and market actor/ expert interviews. Specifically Navigant will conduct the following tasks:

Secondary Research: Navigant will first develop a list of resources that provide insight into the energy consumption profile of these sectors. This will be done through online research and by initiating dialogue with market experts. Through this dialogue, the Navigant team will also seek guidance on additional resources available to characterize the AIMS market.

Preliminary Analysis: Navigant will conduct preliminary analysis of the data collected via secondary research to form a preliminary view of the market.

Expert Interviews and Feedback: The Navigant team will seek to interview market experts including (but not limited to) the CEC, IOU representative for each of these sectors. Through these interviews, the Navigant team will seek feedback on its market view, and seek industry expert’s top-down level view of the markets being analyzed.

The deliverable of the market characterization task will be a memorandum and a spreadsheet (one for each sector) detailing Navigant’s market characterization analysis.

Develop Measure List and Measure Inputs

Development of a robust measure list and accurate measure list is necessary to develop an accurate technical and economic potential view of the California market. As a part of this effort Navigant will ensure that the measure list that they develop is indicative of current and planned energy efficiency efforts in those sectors and also represents un-tapped potential that exists in those sectors.

Approach

To develop a comprehensive measure list and associated measure inputs for the AIMS sectors Navigant will conduct the following tasks:


Define Measure List. Navigant will utilize the results of the market characterization to develop a draft measure list for each of the AIMS sectors. This draft measure list aim to meet the following criteria:

Comprehensiveness of current industry specific energy efficiency measures

Industry-specific end use / emerging technologies that are not currently captured in the potential model

Third Party Feedback and Finalize Measure List. Navigant will release the draft measure list for review to the CPUC, IOUs, and other interested parties for comments. To insure the development of a comprehensive and forward looking measure list, it is necessary to obtain expert feedback. The Navigant team will work with the CPUC to develop a streamlined process for obtaining constructive feedback from the IOUs and the CPUC. This feedback will be used to develop a finalized measure list that will inform Technical, Economic and Market Potential view of the AIMS sectors.

Develop Measure Inputs. The Navigant team will develop measure parameter inputs necessary to calculate the technical, economic and market potential view of the AIMS sectors. To complete this task, Navigant will rely on secondary research and expert interviews. The measure parameter inputs that the Navigant team will work to develop include:

Measure energy and demand savings

Measure cost

Measure effective useful life (EUL)

Measure Net to Gross (NTG)

Measure applicability

Baseline and energy efficient measure density

Codes & Standards vectors

It should be noted that the measure inputs will not be limited the parameters described here; as the project progresses, Navigant will formalize the list of measure specific input parameters.

Develop Technical and Economic Potential

Align Baseline Energy Consumption with CEC Forecasts

Approach

A calibrated baseline is a necessary first step to obtain a reasonable energy savings potential estimate. Navigant will work with the finalized measure list of each sector, the input measure density parameters to calculate a sector level view of energy consumption. This bottom up calculation of energy consumption will be calibrated against CEC forecast for each sector.

The Navigant team will work with the CEC to identify sector level energy consumption forecasts for all six sectors being analyzed. The Navigant team will then understand the composition of the annual energy consumption for each sector; while doing so, Navigant will utilize the results of the Market Characterization exercise conducted for the AIMS sectors. Navigant will work with the CEC and the IOUs to understand the energy consumption composition by end use for residential and commercial sectors as well.

Calculate Technical and Economic Potential

Approach


Navigant’s “bottom-up” approach uses the input data to calculate Technical, Economic, and Market Potentials. Calculating the estimates of Technical and Economic Potential is relatively straightforward: the estimates are the product of available building stocks, technology densities, and measure impacts.

For Technical Potential, it is assumed that all measures can be implemented in all available applications at the same time. Technical potential changes by small amounts over time to reflect changes in the amount of building stocks over time caused by new construction.

Economic Potential is the subset of Technical Potential that includes only the efficient technologies that pass the TRC screen. However, the measures included in Economic Potential can be modified by the user to include some measures that do not pass the TRC, but are included within a utility’s portfolio or measures that do pass the TRC test.

The treatment of mutually exclusive measures differs when calculating Technical vs. Economic Potential. Mutually exclusive measures are a set of available technologies (such as several residential hot water measures including energy-efficient tanks, heat pump water heaters, tankless water heaters, and solar water heat) that serve the same function. However, only one of them can be installed and care must be taken to not double-count potential, but also to identify which measures or what share of each measure should be part of the calculations. The Navigant team will identify which of these competing, mutually exclusive technologies offers the most energy savings and uses only the savings from this specific measure to estimate technical potential.

Unlike Technical Potential, Economic Potential recognizes that not all potential comes from the most efficient option. For mutually exclusive measures that pass the TRC or TRC screen, measure applicability represents each measure’s share of the available application. The measure applicability share by mutually exclusive measure represents a weighted share based on each measure’s TRC value. Equal TRC values would mean equal applicability shares among the measures. The greater the delta in TRC between measures, the greater the applicability for the measure with the larger TRC value.

Interactive effects are also treated differently between Technical and Economic Potential. For Technical Potential, interactive effects are proportionately spread among the competing technologies. For Economic Potential, the interactive effects can be smaller as only measures that pass the TRC screen are included. Interactive effects among only the measures that pass TRC are proportionately spread.

Thus, for some measures screened by the TRC, per measure impacts may be greater for the measure included in Economic Potential compared to the measures included in Technical Potential. The Technical and Economic Potential will be calculated using Analytica.

Market Potential Module Update

The 2012-13 PGT effort seeks to update and enhance the market potential module in order to support the goal-setting effort for the IOU energy efficiency portfolios for 2015 and beyond. This effort will leverage the work already completed on the market potential module during the 2011-12 PGT effort. This effort seeks to strengthen the analytics behind the market potential calculation, to enhance transparency, and to finalize the frameworks necessary to develop goals for the IOUs.

Update and Incorporate Approach to Financing

Energy efficiency financing programs are designed to increase investment in energy efficiency upgrades through assisting in the up-front cost to the customer. Programs utilize different methods of financing energy efficiency upgrades including on-bill repayment, property assessed clean energy (PACE), low-interest loans, and energy efficiency mortgages. Historically, many financing programs have been part of larger energy efficiency funding programs providing financing in addition to rebates for energy efficiency products.

During Navigant’s initial work on including financing in the 2011-12 PGT effort, the team sought to identify any energy efficiency financing programs that had completed an impact evaluation that had attributed savings specifically to the financing program. The team’s primary and secondary research found that multiple parties have completed briefs and white papers on financing mechanisms for energy efficiency used by a variety of programs across the Unites States.


However, the information in these papers to date was too narrow in scope to support attribution of savings to financing programs; the existing data is limited to loan amounts, number of participants, interest rates, and program budgets. 7

At the time of the research, impact evaluations had not been completed that clearly attribute savings directly to the financing portion of the energy efficiency program. It is difficult to attribute savings to energy efficiency financing programs alone because the programs often operate in conjunction with rebates or other financing incentives.

In lieu of historical savings estimates for financing programs, Navigant estimated the savings potential from two types of financing programs for the 2011-12 PGT effort: off-bill repayment and on-bill repayment. However, there is much uncertainty in the savings potential of energy efficiency financing programs. Navigant estimated savings potential based on a study of consumers’ interest in financing incentives versus price reductions in the automobile industry. This approach was used due to the lack of data in the energy efficiency industry for financing programs. Navigant identified several key areas of uncertainty in their estimates, including the following:

The effect on demand/willingness from a change in interest rate

The effect on demand/willingness from on-bill versus off-bill repayment

The range of demand/willingness for financing

The subsidized and unsubsidized interest rate

This study is to 1) identify approaches to estimating the savings potential from energy efficiency financing programs more thoroughly and precisely than in the current PGT model, and 2) identify the research and data needs associated with these approaches.

Approach

Under this study, Navigant will identify a comprehensive approach to modeling the potential from energy efficiency financing programs. The existing model incorporates financing options by using starting point values for the relationship between quantity demanded and change in the interest rate; the point values originate in research conducted by the automobile sector. The team is not aware of a similar study in the energy efficiency sector in California or in other states. Additional research could provide additional background and data for these values in California’s energy efficiency sector.

The work will include the following tasks:

Review the most current literature and research on energy efficiency financing programs – Navigant will review the literature that has been published since January 2012 on energy efficiency financing programs. These types of programs were under discussion at the CPUC and were also being reviewed by the Environmental Defense Fund and Lawrence Berkeley National Laboratory8. Navigant will complete informal interviews with these organizations to understand the latest developments in this space.

Enhancement of a potential savings methodology – From the findings in Task 1, Navigant will assess the current approach to financing in the model and, assuming it is warranted, will design a high-level methodology that could be used to provide estimates of the potential from energy efficiency financing programs.

Data gap analysis – Navigant will identify and prioritize gaps in the data needed for the methodology specified in Task 2. This task will include estimates of the uncertainties associated with these gaps.

Description of research activities that could be conducted to address data gaps – Research activities could include surveys with California consumers and interviews with the CPUC and banks or other entities offering energy efficiency financing products.

7 Examples include Brown, Matthew, “State Energy Efficiency Policies Options and Lessons Learned: Paying for Energy Upgrades Through Utility Bills,” Alliance to Save Energy, Brown, Matthew, “State Energy Efficiency Policies Options and Lessons Learned: Energy Efficiency Loan Programs,” Alliance to Save Energy and The Cadmus Group, “On-Bill Finance for the Small Business Market,” February 25, 2011.8 Navigant discussed energy efficiency financing with Mark Zimring at LBL during the Task 2 model development.


Develop relevant model structure. After receiving approval for the approach to quantifying savings from financing, Navigant will incorporate the approach into the model structure. It is anticipated that some data gaps will not be resolved in time to develop final market potential or goals; in this event, Navigant will use best estimates as placeholders until the data are available.

Reporting and presentation – Navigant will provide interim findings to the CPUC ED and DAWG at key stages of the work. Navigant will provide draft and final reports and a presentation to the CPUC ED and DAWG to conclude this project.

Project management – The project manager for this study will communicate regularly with the PGT project management, ED project management, and the Navigant team to ensure that the Navigant team’s activities are in line with the ED’s objectives.

Enhance Analysis of Strategic Plan (Including Zero Net Energy Buildings) and AB 758

The Strategic Plan sets a statewide roadmap for 2009 to 2020 and beyond to reduce energy use and maximize clean energy sources. CPUC spearheaded the development of the Strategic Plan and first released and adopted it in September 2008 with the support from the Governor’s Office, the California Energy Commission (CEC), the California Air Resource Board (CARB), the State’s utilities, local government, and others. It contains detailed goals targeted at different economic sectors, addressing a cross section of technologies, and employing various approaches to reaching the market.

To date, the Navigant team employed a three-step process to analyze the Strategic Plan and to estimate the energy savings associated with priority goals. Together, these three steps provide information about the structure of the Strategic Plan, the relative priority of the Strategic Plan goals, the energy savings associated with the priority Strategic Plan goals, and the metrics that CPUC may consider in assessing progress towards the Strategic Plan goals. The four steps included the following:

Step 1: Create a Strategic Plan Assessment Database.

Step 2: Identify the Most Influential Strategies.

Step 3: Model Savings from Priority Strategic Plan Goals.

It is anticipated that the scope of Navigant’s 2011-12 efforts to model the Strategic Plan may no longer reflect CPUC priorities. Navigant took a focused approach to modeling six Strategic Plan initiatives due to time and budget limitations. CPUC’s priorities may have shifted since CPUC signed off on those six initiatives in August 2011, and it is possible that additional initiatives may create quantifiable savings that were not modeled for the 2011-12 PGT effort. Further, the 2011-12 effort omitted in-depth analysis of agricultural and industrial measures because of the limited data available to support that analysis; the addition of the AIMS study in Section 8 provides additional context for considering those initiatives. Finally, Navigant’s approach to modeling the six initiatives may need updating to reflect a new approach to calculating market potential, as seen in Section 8.

In addition, AB 758 created the Comprehensive Energy Efficiency Program for Existing Buildings. AB 758 requires the CEC to develop and implement a comprehensive program to achieve greater energy savings in existing residential and nonresidential buildings through energy assessments, benchmarking, building energy use ratings and labels, energy efficiency financing, public outreach and education, green workforce training, and more.

The 2011-12 PGT modeling effort did not include savings from AB 758 because it did not meet the criteria for inclusion in the modeling effort. The energy savings goals were not clear, and there were no clear plans to track any savings accomplishments from AB 758 in the future. While number of programs were expected to produce energy savings, they were all either funded by ARRA (which expired prior to the scope of the 2011-12 study), were accounted for elsewhere, or were not yet developed enough to allow for savings estimates.

This document outlines a plan to update the model to reflect changing CPUC priorities, new data, and changes to the calculation of market potential as relates to the Strategic Plan and AB 758. Navigant will re-engage with CPUC, CEC, and other stakeholders to determine how priorities related to the Strategic Plan and AB 758 have shifted. Based on the results of those discussions, Navigant will leverage its existing database and ranking of Strategic Plan initiatives and


research into AB 758 to identify other potential priority initiatives for modeling. Then, Navigant will incorporate those additional priority initiatives into the model and update the approach to modeling the market potential for these efforts as needed.

Zero Net Energy (ZNE) buildings will be a focus of the 2012-13 PGT analysis of the Strategic Plan. ZNE buildings were not included in the 2011-12 PGT modeling effort, but CPUC indicates that there will be additional focus on these efforts in the future. Since the Strategic Plan includes strategies for ZNE in both the residential and commercial sector, Navigant suggests that the energy efficiency aspects of ZNE be included in this update to the Strategic Plan analysis.

Approach

This study will build on the analysis of the Strategic Plan conducted for the 2011-12 PGT effort. It will update the Strategic Plan initiatives modeled to reflect changing priorities, including ZNE buildings. This study will include the following tasks:

Review priorities with stakeholders. Navigant will engage with relevant stakeholders, including CPUC, CEC, the IOUs, and other organizations as needed, to better understand how the Strategic Plan and AB 758 are used today. Navigant will investigate current and anticipated future priorities to inform the selection of additional initiatives for modeling. The goal of this engagement is to connect the modeling approach with the way that the Strategic Plan and AB 758 are used. Navigant will include a special focus on the energy efficiency aspects of ZNE, as CPUC indicates that this is of particular interest going forward.

Select additional initiatives for modeling. Navigant will work with CPUC to select additional Strategic Plan initiatives for modeling as appropriate based on conversations with stakeholders in Task 1. Navigant will work with CPUC and CEC to determine the best approach to incorporating AB 758. Navigant will leverage its existing database of Strategic Plan initiatives, including the existing ranking criteria that it includes. Navigant’s budget anticipates that another three to four initiatives will be added to the existing six.

Model/develop inputs. Navigant will adjust its approach to modeling the Strategic Plan initiatives and AB 758 once they have been selected in Task 2. During the 2012-13 PGT effort, Navigant found that it is more efficient to develop the approach once the team has concrete examples of what needs to be modeled. Using the updated approach, Navigant will model the Strategic Plan initiatives and AB 758 and develop necessary inputs. Where possible, Navigant will leverage parallel efforts, such as those currently undertaken by CPUC and the IOUs related to ZNE.



Update Market Potential Methodology and Module

The market potential results of the PGT study will most directly inform the goals for the IOU energy efficiency programs. Market potential estimates adoption rates and the corresponding energy savings that could actually be achieved during program periods. Market potential builds on technical and economic potential, which set maximum adoptions levels based on end use saturation and economic feasibility respectively, by considering the timing of adoptions and other factors that may result in demand elasticity. Market potential results incorporate an assessment of how decision makers in each sector are expected to respond to market factors and dynamics.

In the previous study, market potential was calculated using two different approaches:

For measures included in the standard IOU programs, the model calculates market potential as a function of two factors (previously referred to as willingness and awareness) that establish the maximum market potential, and the adoption rates are interpolated to achieve that market potential within a specified number of years for a specified curvature.


For emerging technologies and measures that are created by other policy drivers (e.g., Strategic Plan initiatives or Legislative Initiatives), the model employs the Bass diffusion9 approach. Bass diffusion is a widely accepted methodology that has been used to forecast adoption and market dynamics in a wide range of industries, as well as to retrospectively explain the diffusion of historic technologies and products.10,11,12

The original proposal for the 2011-12 Goals and Targets project anticipated that all market potential estimates would rely on the Bass diffusion approach. However, a shift in the project schedule limited the project team’s ability to fully implement the Bass approach. Instead the previous study leveraged existing EERAM model infrastructure based on a Willingness & Awareness framework when the timeline for goals results was pushed up by several months. This framework served the purpose of quickly estimating market potential, but the acceptance of the results could be enhanced by using a more transparent and widely applied approach.

Navigant suggests updating the approach used in the previous study to improve the transparency and acceptability of model results. Navigant proposes to apply the Bass diffusion approach consistently across all measures to calculate market potential. Bass diffusion is a reliable, yet flexible methodology that can incorporate a variety of factors and market dynamics such as leading-edge adoption, word of mouth, advertising and promotion, price elasticity, and other factors that influence end use purchase decisions. It is one of the most widely referenced and influential approaches in management science.13

Approach

Under this study, Navigant will update the model structure and inputs to incorporate the Bass diffusion approach as the underlying framework for estimating market potential for all measures. In addition to developing the appropriate model structure, Navigant will use publicly-available inputs (with appropriate citations when available) to model the adoption of each technology or measure included in the model.

This study also allocates budget for other high-level model structural changes that are anticipated as part of the 2012-13 update project. Navigant anticipates that additional modeling needs will arise to integrate the various components of the model described in this document, as well as to complete other tasks that may arise during the course of the project. Navigant will allocate a portion of the budget under this study to accommodate any additional efforts.

The work will include the following tasks:

Identify needed structural changes to accommodate the Bass diffusion approach. The project team expects that structural changes will be needed for several reasons: (1) to update the calculation behind the market potential for IOU program measures, (2) to align the market potential calculation with the updated categorization of measures (i.e., highlighted in Section 6 above), and (3) to integrate market potential calculations for IOU program savings with the calculations for savings estimates from other policy drivers (i.e., Strategic Plan, Legislative initiatives).

Incorporate needed structural changes to accommodate the Bass diffusion model. After developing a list of needs, the project team will modify the model to incorporate those changes.

Research input values needed to operationalize new model structure. The Bass diffusion model relies on two key inputs for each type of technology: the coefficient of innovation and the coefficient of imitation. Since the Bass diffusion model has been so widely applied, there are many publicly-available sources of values for these inputs for different types of technologies.14 Navigant plans to use these values that are already publicly available to leverage existing

9 Bass, Frank . "A new product growth model for consumer durables". 1969. Management Science 15 (5): p215–227.10 Mahajan, Vijay; Muller, Eitan and Bass, Frank. "Diffusion of new products: Empirical generalizations and managerial uses". 1995. Marketing Science11 Mahajan, Vijay; Muller, Eitan; Wind, Yoram. “New-Product Diffusion Models”. International Series in Quantitative Market. 2000. Springer Science+Business Media, Inc.12 Sterman, John. “Business Dynamics: Systems Thinking and Modeling for a Complex World”. 2000 McGraw-Hill.13 Hopp, W.J. December 2004. “Ten Most Influential Papers of Management Science’s First Fifty Years.” Management Science: 50(12) Supplement 1763.14 It is possible that some values will not be available in time to develop final market potential or goals; in this event, Navigant will use best estimates as placeholders until the data are available.


research; developing new values for each technology individually could easily run into the millions of dollars by itself. Navigant will also calibrate the model with the results of the 2011-12 PGT potential modeling effort; Navigant will review results to ensure that they reflect expert judgment and other practical considerations that can be difficult to simulate.

Complete other model structure updates as needed. This task allows Navigant to provide the support needed to resolve other structural changes to the model that result from the existing scope of work.

Reporting and presentation. Navigant will provide interim findings to the CPUC ED and DAWG at key stages of the work. Navigant will provide draft and final reports and a presentation to the CPUC ED and DAWG to conclude this project.


Calibrating to Current and Planned Market Landscape

Calibrating the model is a critical step to ensure the accuracy and acceptance of model results This step will ensure that the analysis aligns with past program accomplishments (i.e., by calibrating to previous program evaluation results) and with historic market saturation data. It will also create consistency across related CPUC projects, including the 2011-12 PGT effort.

This step will strengthen the market potential 2012-13 PGT in two ways:

Leverage new data. The 2011-12 PGT effort calibrated results against the 2006-08 IOU energy efficiency program evaluations and against the Residential Appliance Saturation Survey (RASS) and the Commercial End Use Survey (CEUS). In addition to those sources, the results of the 2009 program evaluations are also available; the team will calibrate the results of the analysis against the 2009 program accomplishments where appropriate.

Compare to results of 2011-12 PGT effort. It will be useful to compare the results of the methodology used for this 2012-13 PGT effort (as outlined in Section 9) with those from the methodology used in the 2011-12 effort. This will provide stakeholders with some consistency and minimize concerns about a revised methodology.

Approach

Navigant will consider the results of its market potential analysis in the context of knowledge and expectations about the current and future market, respectively. This will involve the following steps:

Calibrate with existing data sources. Navigant will calibrate the results of the market potential analysis with existing sources of data. These may include the 2006-08 and 2009 IOU energy efficiency program evaluations results, the RASS and CEUS studies, and other major sources that become available by mid-November. That timing will provide the team with sufficient time to adapt the model in time to provide final results by March.

Compare the results with planned market context. Navigant will examine targets for the IOUs during the 2013-14 bridge period to ensure that the model reflects anticipated accomplishments.


Final Attribution and Goal Setting

Almost as important as understanding the total market potential is understanding the drivers behind that potential and what portion of that potential can be expected to come from which delivery mechanisms. During the PGT work in 2011-2012, the Navigant team developed a two-tier attribution framework that focuses on attributing savings among policy drivers and between the delivery mechanisms:

Among policy drivers. The first tier of analysis estimates the amount of overlap in energy savings between various policy drivers. It is used to estimate total market potential without double-counting, thus providing a more accurate understanding of the total available savings.


Between delivery mechanisms. The second tier then attributes incremental savings to one of four primary delivery mechanisms: IOUs, the California Energy Commission (CEC), the U.S. Department of Energy (DOE), or naturally occurring. This step will allow policy makers to understand the key organizations and avenues though which savings are actually delivered, including IOU programs.

The study team obtained high-level buy in from CPUC and stakeholders for this overarching framework by presenting it at several meetings with stakeholders, including DAWG meetings and check-in calls with the IOUs. These presentations focused on the framework for considering attribution rather than specifics about the methodology that would be used to attribute the savings.

The approach to assigning attribution values and specifically how to execute the attribution framework, was not finalized during the 2011-2012 PGT work. Due to other priorities, the project team refocused its efforts away from finalizing and implementing the attribution methodology. A general proposal exists, but further vetting with CPUC and potentially other stakeholders is needed before Navigant can implement any plans.

Additionally, CPUC has suggested that attribution between delivery mechanisms may be simplified. Since CPUC focuses on assigning energy efficiency goals for the IOUs, CPUC may only require attribution to be defined as IOU and non-IOU savings. The study team will review this issue with CPUC and gain closure before moving forward with the modeling efforts.

Approach

Under this study, Navigant will identify a comprehensive and stakeholder-accepted approach to assigning and modeling attribution of energy savings. Currently in the model, the team has developed the structure to calculate and assign savings from four key policy drivers (IOU programs, the Strategic Plan/ legislative initiatives, codes and standards, and naturally occurring) and to attribute savings to four different delivery mechanisms (IOU programs, codes and standards, naturally occurring, and the Strategic Plan).

This study will focus on developing, securing approval for, and implementing an attribution methodology that will result in attribution values that can be applied to savings potential. The work will include the following tasks:

Develop and secure approval for the methodology for attribution among policy drivers and between delivery mechanisms. Navigant will initially discuss options for assigning attribution with the CPUC. Pending preliminary buy-in from CPUC, Navigant will vet the proposed approach with key stakeholders, through either a DAWG meeting or IOU briefing call. Based on the feedback, Navigant will work with CPUC to develop a final methodology that receives CPUC approval.

Implement the method for assigning attribution. Navigant will implement the methodology that CPUC approves for assigning attribution. Navigant anticipates that the methodology will include some type of input from relevant parties and will collect that input using agreed-upon methods, which may include interviews or panel discussions. Some additional analysis of that input will likely contribute to the actual values needed for the model. The key output of this task will be a set of values that can be included in the model.

Update model structure and enter input values. Depending on the methodology used, Navigant may need to make some adjustments to the model structure. In that event, Navigant will make those structural changes. Then, the results of Task 2 will be input into the model and reviewed for accuracy. In the event that some data gaps are not resolved in time to develop final market potential or goals, Navigant will use best estimates as placeholders until the data are available.

Data gap analysis and needs assessment. Navigant will identify and prioritize gaps in the data needed for the methodology specified in Task 2. This task will include estimates of the uncertainties associated with these gaps. Navigant will identify potential research activities that would provide needed data for this effort in the future.

Develop relevant model structure. After receiving approval for the approach to attribution, Navigant will incorporate the approach into the model structure.




Policy Support

Demand Forecast Integration

The California Energy Commission (CEC) requires the results of the Updates study (Updates to the Potential Goals and Targets 2012 - Track 1) for their long term program planning and California demand forecasts. The internal workings of the CEC model and data format are different from the outputs of the EERAM Potential Model developed as a part of the Updates study.

This phase has 2 tasks

1. Inform the Long Term Program Planning process through integration of Track 1 results.

2. Address incremental uncommitted potential in the CEC (California Energy Commission) demand forecast

Navigant has been conducting work related to this task since releasing the first draft of the Track 1 results (March 2012).

Approach

Support CEC use of Track 1 Results released May 2012

a. IOU Program Results: Modify Analytica model to generate the format of savings results useful to the CEC. Modifications are only needed to the accounting of cumulative savings, core market potential algorithms will not change. Specifically, CEC needs:

i. New incremental net savings starting in 2013 out to 2022

ii. Cumulative net savings including re-participants starting in 2013.

Deliverable: Excel file of savings by sector and service territory.

b. Usage-Based Behavior Results: Generate percent savings vector from usage-based behavior analysis. Percent savings is the savings (from usage-based behavior) compared to the total end use demand (residential use)

Deliverable: Excel file of percent savings by service territory and sector.

c. C&S (Codes & Standards) Results: Provide C&S percent energy use reduction vectors to the CEC. Current C&S results calculated in Track 1 are not in a format useful to the CEC. CEC needs savings by end use and sector at the net level prior to utility attribution. CEC has already modeled all existing C&S in effect to-date. CEC needs data on future C&S (on the books) which consists of mostly federal standards. We expect multiple complications in this task around the following issues:

i. Lack of clear translation to CEC end uses

ii. Need for results by sector

iii. Previous federal analysis used national population and sales – not necessarily translatable to CEC end uses.

Deliverables:

i. Excel file of Track 1 C&S model presenting net savings prior to attribution

ii. A codes and standards model capable of modeling variations in compliance rate for CEC scenario analysis.


iii. Estimate of percent savings from select federal standards

Address Incremental Uncommitted in CEC Demand Forecast

Task 1 provided information for CEC and CPUC to integrate Market Potential results into the LTPP forecasting. The update to the study will build on a base Market Potential estimate to provide information for additional savings from additional sources including (but not limited to):

Strategic initiatives

Financing programs

Future codes and standards

We expect to coordinate with CEC and CPUC on an additional integration task once Track 2 results are complete. We are unable to determine the level of effort required at this time as Track 2 results are still in development and we have not interfaced with CEC in depth on Track 2 related issues.

Savings Decay

The California public utilities commission defines Savings Decay as the amount of savings that do not persist at the end of a measure’s life, in the absence of incentive programs. The current assumption states that only 50% of the savings will persist after a measure life (in the absence of a program). This persistence is due to market transformation. Specifically, CPUC decision 09-09-047 states that:

“. . . until EM&V results inform better metrics, utilities may apply a conservative deemed assumption that 50% of savings persist following the expiration of a given measure’s life. This reflects our expectation that our energy efficiency program efforts are in fact resulting in market transformation, changing consumption habits and preferences, while acknowledging that measure uptake in the absence of program support may not be universal.”

The 50 % decay value is an assumption made by the CPUC in the absence of better data. Although decay recovery savings do not affect the incremental new participation savings, they are a part of the cumulative savings accounting. This is as “incremental new participation savings” only represent savings from new participants. Hence, savings due to re-installation only affect cumulative potential.

The model currently breaks down savings persistence into two components, re-installation rate and re-participation rate. The re-installation rate is the percentage of participants that decide to reinstall the measure after its EUL (Effective Useful Life). Savings Decay is calculated as:

Savings Decay = 1 – (Re-Installation Rate) * (Re-Participation Rate)

As a part of this study, Navigant proposes to do the following:

Evaluate the present methodology of calculating savings decay via re-installation and re-participation rate

Understand if the value of decay differs for different measures.

Conduct primary and secondary research to better understand the value of savings decay for different measures in California

Approach

Under this study, Navigant will work with the CPUC to better define savings decay value for different measures. Navigant’s research will rely on interviewing industry experts and analyzing data collected from existing studies. Explained (and presented) below are the different tasks that Navigant will conduct as a part of this work-order.


Figure 1 Proposed Tasks to better Understand Decay

Data Collection and Preliminary Analysis. As a part of this task, Navigant will review existing literature on savings persistence and decay. The studies that Navigant will review will not be limited to studies conducted in California, to understand how savings decay is dealt with in other regions. The studies will include, but not be limited to:

“Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior” Lisa A.Skumatz, Ph.D.

“Southern California Edison NRNC Persistence Study Final Report”, RLW Analytics, 1998

“Southern California Edison 1994 Commercial CFL Manufacturers’ Rebate Persistence Study”

“Southern California Edison Residential Refrigerator Recycling Ninth Year Retention Study, 2004”

“PG&E Statewide Multi-year Billing Analysis Study: Commercial Lighting Technologies, 1998”

Through the literature review, Navigant will summarize, extract and understand available data in the studies it reviews, and produce a draft memo of its findings.

Interview Experts .The evaluation team will identify and interview industry experts to review the results of Navigant’s draft memo and to identify other sources of information that may be available to inform this study.

Final Data Analysis. Based on feedback from Task2 and the data collected as a part of Task 1, the Navigant team will conduct a final analysis to characterize savings decay for different measures in California.

Characterize Uncertainty. The final task of this phase is characterizing the uncertainty in the savings decay estimates. Along with characterizing uncertainty, Navigant will identify the actions required to reduce this uncertainty.

Final Memorandum. The deliverable will be a final memorandum that presents the results of Navigant’s research and recommends targeted primary data collection.

Model Integration and Reporting

The project team will deliver a draft report to the CPUC and KEMA by December 12, 2012. The draft report will include the following elements:

1. A description of the methodologies employed to develop the Potential Model and draft results for the technical and economic potential Analysis by the 4th quarter of 2012


2. Final potential model results and a Goals and Targets report outlining market potential for the 2015 fo4rward planning timeframe by the end of the 1st quarter of 2013

3. All model software and tools, including full documentation including appendices and all supporting documentation

Much of the report will have been previously provided to CPUC and KEMA through the interim deliverables; these will be incorporated into the report or the appendices as appropriate.

Market Transformation: Connecting Market Adoption Curves

In some measure categories, the adoption of new technologies may be affected by the adoption of predecessor technologies. For example, the adoption of LEDs and CFLs are likely connected because of increasing consumer awareness about the energy consumed by electric lighting. This study seeks to identify areas in which these connections exist and to develop preliminary estimates of the effect on market potential.

Approach

Under this study, Navigant will develop preliminary estimates of the relationships among emerging technologies and their predecessor technologies. The work will include the following tasks:

Identify relevant technology groups. The team will identify “competitor” groups of technologies whose market adoption may be interrelated. Navigant will begin by examining the list of emerging technologies and mapping those technologies to existing technologies where appropriate.

Select technology groups for further analysis. The team will determine within which technology groups interrelated market adoption patterns exist. This will involve a literature review to identify relevant examples of similar technology adoption patterns in the past. Then the team will work with CPUC to select which technology groups warrant further analysis.

Develop model structure and adoption curves. For those technology groups selected for further analysis, Navigant will develop a model structure that allows for interrelation of the technology adoption curves in the market module. Once that is complete, Navigant will define the relationships among adoption curves of relevant technologies within the technology groups.

Reporting and presentation. Navigant will provide interim findings to the CPUC ED and DAWG at key stages of the work. Navigant will provide draft and final reports and a presentation to the CPUC ED and DAWG to conclude this project.



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