2018 Indiana Commercial & Industrial Portfolio EM&V Report · 2018 Indiana Commercial & Industrial Portfolio . EM&V Report . Volume I of II Prepared for: Indiana Michigan Power

2018 Indiana Commercial & Industrial Portfolio EM&V Report

Volume I of II

Prepared for: Indiana Michigan Power

April 2019

Prepared by:

ADM Associates, Inc. 3239 Ramos Circle

Sacramento, CA 95827 916.363.8383

Indiana Michigan Power Company

Table of Contents 1. Introduction .......................................................................................................................................... 1

1.1. Impact Evaluation Findings ........................................................................................................................ 1

1.2. Evaluation Findings and Recommendations ............................................................................................... 2

1.3. Cost Effectiveness Evaluation Findings ..................................................................................................... 5

1.4. Organization of Report ............................................................................................................................... 5

2. Work Prescriptive ................................................................................................................................. 6

2.1. Program Description ................................................................................................................................... 6

2.2. Estimation of Ex Post Gross Savings ......................................................................................................... 6

2.3. Estimation of Ex Post Net Savings ........................................................................................................... 15

2.4. Process Evaluation .................................................................................................................................... 20

2.5. Conclusions and Recommendations ......................................................................................................... 32

3. Work Custom ..................................................................................................................................... 34

3.1. Program Description ................................................................................................................................. 34

3.2. Estimation of Ex Post Gross Savings ....................................................................................................... 34




4. Work Direct Install ............................................................................................................................. 42






5. Work Energy Management ................................................................................................................ 56






6. Cost Effectiveness Evaluation ............................................................................................................ 60


Table of Tables

Table 1-1 Savings-Related Terminology ........................................................................................ 1

Table 1-2 Components of Impact Evaluation Accounted for in Savings Variables ....................... 2

Table 1-3 Summary of Energy Savings – PY2018 ......................................................................... 2

Table 1-4 Summary of Peak Demand Impacts – PY2018 .............................................................. 2

Table 1-5 Summary of PY2018 Benefit-Cost Ratios ..................................................................... 5

Table 2-1 Population Statistics Used for Sample Design ............................................................... 7

Table 2-2 Typical Methods to Determine Savings for Prescriptive Measures ............................. 10

Table 2-3 Ex Ante kWh and Ex Post kWh of Sampled Projects by Stratum ............................... 13

Table 2-4 Project Level Ex Ante kWh and Ex Post kWh ............................................................. 14

Table 2-5 Ex Post Annual Gross kWh .......................................................................................... 15

Table 2-6 Ex Post Peak kW Reduction ......................................................................................... 15

Table 2-7 Free Ridership Scoring ................................................................................................. 18

Table 2-8 Ex Post Net kWh Savings............................................................................................. 20

Table 2-9 Ex Post Net kW Demand Reduction ............................................................................ 20

Table 2-10 Summary of Data Collection Activities for the Work Prescriptive Program ............. 21

Table 2-11 Summary of 2018 Project Incentive Payments .......................................................... 21

Table 2-12 Summary of Savings by Measure Type ...................................................................... 22

Table 2-13 Respondents’ Job Tile or Role ................................................................................... 24

Table 2-14 Financial Methods used to Evaluation EE Improvements ......................................... 25

Table 2-15 Program Awareness .................................................................................................... 26

Table 2-16 Program Feedback and Suggested Changes ............................................................... 28

Table 2-17 2017 and 2018 Company Policies and Procedures Comparison ................................ 30

Table 2-18 2017 and 2018 Clarity of Information on Completing Application Comparison ...... 31

Table 2-19 2017 and 2018 Satisfaction with Program Overall Comparison ................................ 32

Table 3-1 Population Statistics Used for Sample Design ............................................................. 35

Table 3-2 Typical Methods to Determine Savings for Work Custom .......................................... 36





Table 3-6 Ex Post Peak kW .......................................................................................................... 39



Table 4-1 Population Statistics Used for Sample Design ............................................................. 43

Table 4-2 Typical Methods to Determine Savings for Work Direct Install Measures ................. 44




Table 4-6 Ex Post Peak kW .......................................................................................................... 46



Table 4-9 Summary of Data Collection for the Work Direct Install Program .............................. 47

Table 4-10 Summary of 2018 Project Incentive Payments .......................................................... 47

Table 4-11 Summary of Savings by Measure Type ...................................................................... 48

Table 4-12 Respondents’ Job Tile or Role ................................................................................... 49

Table 4-13 Sources of Program Awareness .................................................................................. 50

Table 4-14 Project Cost Compared to Expectations ..................................................................... 51

Table 4-15 2017 and 2018 Company Policies and Procedures Comparison ................................ 52

Table 4-16 2017 and 2018 Program Participants Concerns Comparison ..................................... 53

Table 5-1 Summary of kWh Savings ............................................................................................ 58


Table 6-1 Summary of Benefits and Costs Included in each Cost Effectiveness Test ................. 60

Table 6-2 Work Prescriptive Program Cost Test Inputs and Results ........................................... 61

Table 6-3 Work Custom Program Cost Test Inputs and Results .................................................. 61

Table 6-4 Work Direct Install Program Cost Test Inputs and Results ......................................... 61

Table 6-5 Public Efficient Street Lighting Program ..................................................................... 62


Table of Figures

Figure 2-1 Summary of Program Savings by Completion Date ................................................... 21

Figure 2-2 Internal Policies for Making Decisions about Energy Efficiency ............................... 25

Figure 2-3 Acceptability of Program Application ........................................................................ 27

Figure 2-4 Satisfaction with Program Staff .................................................................................. 28

Figure 2-5 Program Satisfaction ................................................................................................... 30

Figure 2-6 2017 and 2018 Program Awareness Comparisons...................................................... 31

Figure 2-7 2017 and 2018 Any Interactions with Program Staff Comparison ............................. 32

Figure 4-1 Summary of Program Savings by Completion Date ................................................... 47

Figure 4-2 Internal Policies for Making Decisions about Energy Efficiency .............................. 49

Figure 4-3 Experience with Contractor ......................................................................................... 50

Figure 4-4 Program Satisfaction ................................................................................................... 52

Figure 4-5 2017 and 2018 Program Awareness Comparisons...................................................... 53

Figure 4-6 2017 and 2018 Wait Time for Equipment to be Installed After the Onsite Assessment Comparison ........................................................................................................................... 54


1. Introduction Under contract with the Indiana Michigan Power (I&M), ADM Associates, Inc., (ADM) performed evaluation, measurement and verification (EM&V) activities that confirmed the energy savings (kWh) and demand reduction (kW) realized through the energy efficiency programs that I&M implemented in Indiana during the during January 2018 through December 2018 (PY2018).

This chapter provides a summary of evaluation findings for the C&I program portfolio and presents information regarding the organization of the report.

1.1. Impact Evaluation Findings

The savings variables presented in this evaluation report are defined in Table 1-1.

Table 1-1 Savings-Related Terminology

Variable Definition

kWh Savings Goal kWh Savings Goal is the energy savings goal cited in the applicable portfolio plan.

Ex Ante Gross kWh Savings Ex Ante Gross kWh Savings are the annual energy savings reported by I&M and are typically obtained from I&M’s DSM/EE Program Scorecard documents.

Gross Audited kWh Savings Gross Audited kWh Savings are determined by reviewing tracking data presenting for any errors, and adjusting Ex Ante Gross kWh Savings accordingly.

Gross Verified kWh Savings

Gross Verified kWh Savings are determined by applying an installation rate to the Gross Audited kWh Savings.1 The installation rate is defined as the ratio of units that were installed (verified) to the number of units reported (claimed).

Ex Post Gross kWh Savings Ex Post Gross kWh Savings are the realized annual gross kWh savings reflecting all adjustments made by ADM, without accounting for free ridership or spillover.

Ex Post Net kWh Savings Ex Post Net kWh Savings are equal to Ex Post Gross kWh Savings, adjusted to account for free ridership and spillover.

Ex Post Net Lifetime kWh Savings

Ex Post Net Lifetime kWh Savings is the Ex Post Net kWh Savings occurring over the course of the applicable measure effective useful life (EUL).

Gross Realization Rate Gross Realization Rate is equal to Ex Post Gross kWh Savings divided by Ex Ante Gross kWh Savings.

Net-to-Gross Ratio Net-to-Gross Ratio is equal to Ex Post Net kWh Savings divided by Ex Post Gross kWh Savings.

1 Gross Verified energy impacts will be equal to Gross Audited energy impacts for the Work Prescriptive, Work Custom, Work Direct Install, and Public Efficient Street Lighting as these programs employ a sampling approach for estimating energy savings and do not have an installation rate.


Based on the definitions presented in Table 1-1, Table 1-2 presents a summary of the components of the impact evaluation that are accounted for in savings variables presented in this report.

Table 1-2 Components of Impact Evaluation Accounted for in Savings Variables

Category Tracking

Data Review

In-Service Rates

Ex Post Gross

Analysis

Net-to-Gross

Analysis Gross Audited ✓ Gross Verified ✓ ✓ Ex Post Gross ✓ ✓ ✓ Ex Post Net ✓ ✓ ✓ ✓

ADM performed EM&V activities for each of the 5 C&I programs offered by I&M during PY2018. Total C&I portfolio ex post gross energy savings are 72,392,547 kWh, while ex post net energy savings are 64,588,182 kWh, as shown in Table 1-3.

Table 1-3 Summary of Energy Savings – PY2018

Program

Ex Ante Annual

kWh Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

Ex Post Net kWh

Savings

Net-to-Gross Ratio

Lifetime Net Ex Post kWh

Savings

Work Prescriptive 47,580,784 47,580,784 47,580,784 49,040,793 103% 42,007,475 86% 629,859,550 Work Custom 25,433,823 25,433,823 25,433,823 21,499,292 85% 20,599,910 96% 310,431,508 Work Direct Install 2,047,166 2,047,166 2,047,166 1,845,211 90% 1,973,547 107% 31,607,094 Work Energy Management 7,689 7,689 7,689 7,250 94% 7,250 100% 108,750

C&I Portfolio Total 75,069,462 75,069,462 75,069,462 72,392,547 96% 64,588,182 89% 972,006,903

Total residential portfolio ex post gross peak demand savings are 14,326.59 kW, while ex post net peak demand savings are 9,466.88 kW, as shown in Table 1-4.

Table 1-4 Summary of Peak Demand Impacts – PY2018

Program Ex Ante

Gross kW Savings

Gross Audited kW

Savings

Gross Verified

kW Savings

Ex Post Gross kW Savings

Gross Realization

Rate

Ex Post Net kW Savings

Net-to-Gross Ratio

Work Prescriptive 10,099.54 10,099.54 10,099.54 6,902.83 68% 6,134.89 89% Work Custom 3,685.01 3,685.01 3,685.01 3,020.44 82% 2,990.18 99% Work Direct Install 542.04 542.04 542.04 301.29 56% 321.81 107% Work Energy Management 0.00 0.00 0.00 0.00 N/A 0.00 N/A C&I Portfolio Total 14,326.59 14,326.59 14,326.59 10,224.56 71% 9,446.88 92%

1.2. Evaluation Findings and Recommendations

Below is a summary of the key findings of the Work Prescriptive and Work Custom process evaluation.


Conclusions:

The Work Prescriptive and the Work Custom Programs met their energy savings goal. Program ex ante savings were equal to 170% of the prescriptive savings goal and 69% of the custom savings. Staff noted that interest in prescriptive measures appears to be increasing. As in the prior year, lighting projects contributed to the majority of program savings. Staff noted that a key driver of program success is the on-the-ground effort made by program staff, which generates program savings but also benefits customers through the formation of trusted relationships.

Additional support provided to trade allies based on their feedback. The program website was modified to help customers find trade ally contact information and a video was developed to educate trade allies on how to complete the program application.

The marketing and outreach approaches were consistent with PY2017 and have been effective in helping the program reach its goals. The primary marketing activities for the past program year included: quarterly newsletters, direct mailings, case studies, updates to fact sheets, trade ally customer kickoff meetings, and traditional advertising. Survey responses indicate that most participants learn of the work Prescriptive and Work Custom programs through trade allies (40%), I&M account representatives (20%), and friends and colleagues (13%).

The participation process is working well for most participants. Eighty-nine percent of participants thought that the overall application process was acceptable, and a minority (3%) found it unacceptable. Nearly all participants (90%) stated they knew from whom to get additional assistance. Additionally, nearly all respondents who had their project work inspected by program staff stated that the inspector was courteous and efficient.

Participants are satisfied with the program. Ninety-six percent of participants were satisfied with the program overall. Rates of dissatisfaction were low for most aspects of the program. The aspects of the program that the largest share of participants reported dissatisfaction with were the amount of time to get the rebate (3% were dissatisfied) and the steps to get through the program (3% were dissatisfied), and the quality of the installation (3% were dissatisfied).

Recommendations:

Consider modifying application to collect information to develop more accurate estimates of kWh savings and kW reductions. Program energy saving estimates did not incorporate heating-cooling interactive factors (HCIF), which impacted project realization rates. Staff should consider adding a field to the measure table to indicate if the installation is in an exterior or interior space and requiring that the space heating type be selected to incorporate these factors. Alternatively, if staff prefer to minimize the application burden on participants, staff should assume a distribution of space heating types to estimate HCIFs.


Consider modifying application to incorporate coincidence factor. The realization rate for kW was 68% for the Work Prescriptive Program due to the ex ante savings calculation not applying a coincidence factor. The Work Custom program, which relies much more heavily on custom engineering calculations for ex ante, had a realization rate of 82% due to coincidence factors being incorporated into savings for many projects.

In order to minimize the discrepancy between ex ante and ex post savings, consider modifying the application to incorporate baseline wattages that account for the Energy Independence and Security Act (EISA) of 2007 for lighting like T12s and A-Line lamps.

Staff indicated that the ongoing success of the programs will require moving beyond lighting retrofits to encouraging adoption of non-lighting measures. To support this transition, staff should consider developing additional marketing materials that address non-lighting measures such as measure specific fact sheets. Additionally, staff should consider offering additional training to trade allies and customers that focuses on non-lighting improvements.

Consider polling trade allies through an email blast or the newsletter to get a sense of the types of training that may be of greatest interest to them to increase attendance at future webinars.

To aid the future enhancement of training offerings, consider instituting a formal survey process to gain feedback on the webinars and other training provided.

Below is a summary of the key findings of the Work Direct Install Program evaluation.

Conclusions:

The Work Direct Install program performed well and met its savings goal. Ex ante savings were equal to 107% of the savings goal.

There is an interest in recruiting additional trade allies, and non-lighting trade allies. A challenge noted by staff is that most of the contractors are lighting contractors and most of the small business measures are lighting. Staff would like to develop a structure where contractors can provide referrals for non-lighting measures in the process of completing lighting projects.

All survey respondents were satisfied with the program overall. All respondents were also satisfied with the quality of the installation and the type of equipment installed. One customer was dissatisfied with the proposal received from their contractor.

Recommendations:

Develop a program design or process that increases measure diversity. Staffs’ suggestion to develop a process to allow lighting contractors to make referrals for non-lighting measures may help increase measure diversity. Another approach ADM has seen is to use a third-party onsite auditor to inspect the facilities and to then schedule work with approved contractors for the measure type. In the program ADM evaluated that used this


approach, 47% of program savings resulted from non-lighting measures. However, this design likely comes at a cost of higher program administration costs.

1.3. Cost Effectiveness Evaluation Findings

The following cost effectiveness tests were performed for the programs: Total Resource Cost (TRC) test, Utility Cost Test, Participant Cost Test (PCT), and Ratepayer Impact Measure (RIM) test. A test score above one signifies that, from the perspective of the test, the program benefits were greater than the program costs. The test results for each program are presented in Table 1-5.

Table 1-5 Summary of PY2018 Benefit-Cost Ratios

Program Utility Cost Test

Total Resource Cost Test

Ratepayer Impact

Measure

Participant Cost Test

Work Prescriptive 8.13 2.86 0.72 3.13

Work Custom 5.47 2.63 0.74 3.08

Work Direct Install 3.73 2.06 0.67 2.80 Public Efficient Street Lighting Program

0.00 0.00 0.00 N/A

1.4. Organization of Report

This report is divided into two volumes that provide information on the impact, process, and cost effectiveness evaluation of the Indiana Michigan Power portfolio of C&I programs implemented in Indiana during the 2018 program year. Volume I is organized as follows:

Chapter 2: Work Prescriptive

Chapter 3: Work Custom

Chapter 4: Work Direct Install

Chapter 5: Work Energy Management

Chapter 6: Cost Effectiveness Evaluation

See report Volume II for chapters that present reports of site-level gross energy impacts, survey instruments and tabulated survey response information.


2. Work Prescriptive This chapter presents the results of both the impact and process evaluations of the Work Prescriptive Program that Indiana Michigan Power (I&M) offered to its non-residential customers during the period of January 2018 through December 2018.

The objectives of the evaluation were to:

Establish a pre-approval review procedure

Assess gross and net energy (kWh) savings and peak demand (kW) reductions resulting from participation in the program during the program year;

Document sources of program awareness among participants;

Identify barriers to participation;

Determine if there are significant differences between and among participant groups;

Assess satisfaction among participating customers and trade allies;

Document and assess quality assurance and control procedures; and

Provide recommendations for program improvement as appropriate.

2.1. Program Description

The Work Prescriptive Program provides incentives for commercial and industrial customers, inclusive of for-profit, non-profit, and public sector entities. Incentives are provided on a per unit of equipment basis.

Categories of eligible measures for this program included:

Efficient lighting and controls; Commercial kitchen equipment; HVAC systems and motors; and Compressed air nozzles.

Incentives are capped at $150,000 per site and $300,000 per company.

2.2. Estimation of Ex Post Gross Savings

Methodology for Estimating Ex Post Gross Energy Savings

2.2.1.1. Sampling Plan

ADM selected a sample of Work Prescription Program projects for which ADM performed measurement and verification (M&V) and calculated gross realized kWh savings and kW demand reductions.


A stratified sampling approach was used to develop the M&V sample. A stratified sampling approach allowed for a given statistical precision and confidence level target to be met with a smaller sample size than would have been allowed by simple random sampling. Strata boundaries were based on ex ante kWh energy savings. ADM selected a sample with a sufficient number of sample units to facilitate estimation of program ex post kWh energy savings with 10% statistical precision at a 90% confidence level.

Completed program projects accumulated over time as the program is implemented, and sample selection likely occurred at multiple points in time. The timing of sample selection was contingent upon the timing of the completion of projects during the program year.

The table below shows the number of projects, ex ante gross kWh energy savings, and sampling statistics, by stratum, of the program sample.

Table 2-1 Population Statistics Used for Sample Design

Stratum 5 Stratum 4 Stratum 3 Stratum 2 Stratum 1 Totals

Strata boundaries (kWh) < 50,000 50,000 – 150,000

150,000 – 400,000

400,000 – 1,500,000 > 1,500,000

Number of projects 600 143 54 15 1 813 Total Ex Ante Annual kWh 10,585,464 12,098,442 12,694,172 9,711,187 2,491,519 47,580,784 Average kWh Savings 17,642 84,604 235,077 647,412 2,491,519 3,476,256 Std. dev. of kWh savings 13,068 26,456 70,706 235,159 - 345,389 Coefficient of variation 0.74 0.31 0.30 0.36 - Final design sample 32 7 6 5 1 51

2.2.1.2. Review of Documentation

I&M’s program implementation contractor provided documentation for the sampled energy efficiency projects undertaken at customer facilities. The first step in the evaluation effort was to review this documentation and other program materials that were relevant to the evaluation effort.

For each sampled project, the available documentation (e.g., audit reports, savings calculation work papers, etc.) for each rebated measure was reviewed, with attention given to the calculation procedures and documentation for savings estimates. Documentation that was reviewed for all sampled projects includes program forms, reports, billing system data, weather data, and any other potentially useful data. Each application was reviewed to determine whether the following types of information had been provided:

Documentation for the equipment changed, including (1) descriptions, (2) schematics, (3) performance data, and (4) other supporting information

Documentation for the new equipment installed, including (1) descriptions, (2) schematics, (3) performance data, and (4) other supporting information

Information about the savings calculation methodology, including (1) what methodology was used, (2) specifications of assumptions and sources for these specifications, and (3) correctness of calculations.


Following this review, ADM developed recommended quality assurance/quality control (QA/QC) and due diligence procedures for program implementation contractors with the goal of minimizing the variance between ex ante and ex post energy impacts to the extent feasible and practical. Recommended QA/QC protocols included:

Procedures for program implementation contractors to use to document measure installation and facilitate post implementation verification;

Recommendations for changes to data collection forms and tracking data in those cases where data necessary to estimate energy savings in accordance with the Indiana TRM was not being collected; and

Procedures for determining baseline energy use.

In addition to the above activities, ADM completed a review of program tracking data. The purpose of the review was to assess the sufficiency of the tracking data for supporting program implementation and evaluation. To this end, ADM reviewed the program data to verify that the following fields were tracked, that the fields were populated (i.e., the data were not missing), and that the values were reasonable.

Unique customer identifier, such as customer account number;

Customer specific such as contact name and information, building type;

Project milestone dates such as application submission date, application approval, incentive payment (where applicable);

Measure specific information such as:

o type of measure;

o specific measure;

o ex ante measure kWh energy savings and peak kW reductions;

o measure attributes necessary to estimate measure savings (where applicable);

o unique measure identifier (e.g., numeric or alpha-numeric code);

o unit serial number (where applicable);

o incremental costs / project costs

Vendor/Contractor business name, contact name and information (where applicable);

Incentive amounts; and

Application status.

ADM presented recommendations relating to program-level QA/QC, inspection and due diligence procedures in ad hoc reports to I&M, or as part of the regular quarterly updates on impact and process evaluation findings.


2.2.1.3. On-Site Data Collection

On-site visits were used to collect data that were used in calculating project energy impacts. The visits to the sites of sampled projects were used to collect primary data on the measures implemented under the program. I&M energy efficiency staff was notified prior to ADM initiating customer contact.

During an on-site visit, the engineering staff accomplished three major tasks:

First, they verified the implementation status of all measures for which customers received incentives. They verified that the energy efficiency measures were indeed installed, that they were installed correctly and that they still functioned properly.

Second, they collected the physical data, when necessary, needed to analyze the energy savings that have been realized from the installed improvements and measures. Data was collected using a form that was prepared specifically for the project in question after an in-house review of the project file.

Third, they interviewed the contact personnel at a facility to obtain additional information on the installed system to complement the data collected from other sources.

Monitoring was conducted to gather additional information on the operating hours of the installed measures. Monitoring was conducted at sites for which ADM staff members determined that monitored data was necessary to minimize uncertainty associated with savings calculation of energy impacts. Monitoring was not considered necessary for sites for which other data sources and methods will support estimation of energy impacts with relatively less uncertainty.

Under certain circumstances, site visits were not necessary for M&V sample projects, if primary project data were otherwise available with which measure implementation could be verified and energy impacts could be quantified.

2.2.1.4. Procedures for Estimating Measure-Level Gross Energy Savings

The method ADM employed to determine gross savings impacts was dependent upon the types of measures being analyzed. Categories of measures included the following:

Lighting and lighting controls

Motors and VFDs

Kitchen equipment

Refrigeration

ADM typically employed a specific set of methods to determine project gross savings that was dependent upon the type of measure being analyzed. These methods are summarized in Table 2-2.


Table 2-2 Typical Methods to Determine Savings for Prescriptive Measures Type of Measure Method to Determine Savings

Lighting

Referenced data on wattages of newly-installed measures, hours-of-use data obtained from field monitoring, with baseline data informed by applicable standards or pre-existing equipment characteristics. Indiana TRM energy savings algorithm variable inputs.

Motors and VFDs

Indiana TRM. eQUEST model uses DOE-2 as its analytical engine for estimating HVAC loads and calibrated with site-level billing data to establish a benchmark.

Refrigeration

Indiana TRM; doors, controls and operation parameters verified on-site. Simulation utilizing DEER prototypical models used for refrigerated case door retrofits. Engineering analysis referencing Energy Star equations and variables.

Commercial Kitchen Equipment Indiana TRM inputs

Activities specified in Table 2-2 produced verified gross savings calculations for each sampled project.

A kWh energy savings gross realization rate and a peak kW reduction gross realization rate was calculated for each site that was part of the M&V sample. Sites with relatively high or low gross realization rates were analyzed to determine the reasons for the discrepancy between ex ante and ex post energy savings. The site-level gross impact analysis results for each M&V sample site is presented in Volume II of the report.

The following discussion describes the basic procedures used for estimating savings from various measure types.

Analyzing Savings from Lighting Measures: Lighting measures examined included retrofits of existing fixtures, lamps and/or ballasts with energy efficient fixtures, lamps or LED lamps/drivers. These types of measures reduce demand, while not affecting operating hours. Participants often completed retrofit projects in combination with the installation of lighting control measures, such as motion sensors or daylight controls. Controls reduce the operating hours and/or current passing thorough the connected fixture or group of fixtures.

Analyzing the savings from such lighting measures required data for retrofitted fixtures on (1) baseline wattages and post-retrofit wattages and (2) hours of operation before and after the retrofit. Hours of operation were typically determined based on metered data collected after measure installation for a sample of fixtures.

Data collected determined the average operating hours for retrofitted fixtures by using light intensity loggers where lighting efficiency measures were installed. Usage areas are areas within a facility with comparable average lighting operating hours. For industrial customers, common


usage areas included production, warehouse, and office areas. To facilitate analysis of lighting hours, usage areas were associated with each lighting logger.

Annual energy savings for each sampled fixture/lamp was determined by the following formula:

Annual Energy Savings = kWhbaseline - kWhafter

The input values for this formula were determined through the following steps:

Results from the monitored sample calculated the average operating hours of the metered lights in each period for every unique building type/usage area.

Factoring the average operating hours by the baseline and post-installation average demand for each usage area calculated the kWh energy consumption and peak period kW demand of each usage area.

The annual baseline kWh energy consumption was the sum of the baseline kWh energy consumption of all of the usage areas. Similarly, the post-retrofit kWh energy consumption was the sum of the post-retrofit kWh energy consumption of all of the usage areas. The calculated energy savings were the difference between baseline and post-installation energy consumption.

To account for the impact of implemented lighting measures on HVAC operation, in calculating lighting energy savings and peak demand reductions, ADM applied the HVAC interactive effects multipliers for commercial buildings found in secondary sources.

Energy savings for lighting were determined by one of two methods. With sufficient monitoring data, applying an algorithm to time series monitoring data to estimate the lighting operating hours prior to implementation of lighting controls. For each monitored hour during which there was any lighting use, survey data was applied to determine the behavior with the absence of lighting controls. This survey asked questions by usage area for the manual lighting control behavior both within the workday, and also at the end of the workday.

Analyzing Savings from Motors and VFDs: A variable-frequency drive (VFD) is an electronic device that controls the speed of a motor by varying the magnitude of the voltage, current, or frequency of the electric power supplied to the motor. The factors that make a motor load a suitable application for a VFD are (1) variable speed requirements and (2) high annual operating hours. The interplay of these two factors are summarized by information on the motor's duty cycle; which essentially show the percentage of time during the year that the motor operates at different speeds. The duty cycle should show good variability in speed requirements, with the motor operating at reduced speed a high percentage of the time.

Potential energy savings from the use of VFDs are usually most significant with variable-torque loads, which were estimated to account for 50% to 60% of total motor energy use in the non-residential sectors. Energy saving VFDs may be found on fans, centrifugal pumps, centrifugal blowers, and other centrifugal loads, most usually where the duty cycle of the process provided a wide range of speeds of operation.


When tractable, building simulation software is ADMs preferred approach, as it allowed calculation of secondary energy impacts which quantify a measure’s impacts on other building systems. Building simulation software also enabled ADM to more accurately account for the interactive effects that multiple measures have on one another when installed in the same facility. Each simulation produced estimates of HVAC energy and demand usage under different assumptions about equipment and/or construction conditions.

In cases in which DOE-2 simulation is inappropriate because data were not available to properly calibrate a simulation model and engineering analysis provides more accurate M&V results, engineering spreadsheet models were developed referencing a secondary literature source (i.e. Indiana TRM) and primary data, including data collected on-site.

Analyzing Savings from Refrigeration Equipment: Analysis of savings from refrigeration was inherently project-specific. Depending on the specificity of processes, TRM inputs may have been appropriate. Otherwise, because analyzing the processes through simulations was generally not feasible, reliance was made on engineering analysis of the process affected by the improvements.

Results of Ex Post Gross Savings Estimations

The kWh gross realization rate is the ratio of sampled measure ex post gross kWh energy savings to sampled measure ex ante kWh energy savings. The kW gross realization rate is the ratio of sampled measure ex post gross kW demand savings to sampled measure ex ante kW demand savings. Since a stratified sampling approach was employed for this program, stratum-level kWh and kW gross realization rates were developed for each sampling stratum.

Program-level gross ex post gross kWh energy savings are calculated as follows:

The ex-ante kWh energy savings of non-sampled measures are factored by the applicable stratum-level kWh gross realization rates to calculate ex post gross kWh energy savings for non-sampled measures.

The ex post gross kWh energy savings of all sampled measures and all non-sampled measures are summed.

Program-level gross ex post gross kW demand savings are calculated as follows:

The ex-ante kW demand savings of non-sampled measures are factored by the applicable stratum-level kW gross realization rates to calculate ex post gross kW savings for non-sampled measures.

The ex post gross kW demand savings of all sampled measures and all non-sampled measures are summed.

2.2.2.1. Ex Post Gross kWh Savings

The realized gross kWh savings of the Work Prescriptive Program for the sampled projects are summarized in Table 2-3.


Table 2-3 Ex Ante kWh and Ex Post kWh of Sampled Projects by Stratum

Stratum Ex Ante

Gross kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

5 548,613 633,971 116% 4 687,151 695,797 101% 3 1,435,646 1,427,568 99% 2 3,480,015 3,384,061 97% 1 2,491,519 2,491,519 100%

Total 8,642,944 8,632,916 100%

Project-level realization rates are displayed in Table 2-4 below.


Table 2-4 Project Level Ex Ante kWh and Ex Post kWh

Project ID Ex Ante

Gross kWh Savings

Ex Post Gross kWh

Savings

Project Gross Realization

Rate 5333 39,100 58,031 148% 5154 19,774 16,172 82% 4608 33,656 38,232 114% 5127 548,796 621,786 113% 5069 174,981 317,206 181% 4366 92,450 84,214 91% 5073 9,792 4,834 49% 5006 19,584 14,263 73% 4991 67,080 67,080 100% 4862 67,080 67,080 100% 4861 26,703 26,703 100% 4734 4,896 18,104 370% 4707 3,672 4,515 123% 4636 15,912 4,834 30% 5042 1,714 1,892 110% 4964 48,829 47,047 96% 4912 16,564 5,841 35% 4693 35,838 43,317 121% 5023 5,032 3,421 68% 4648 36,082 31,600 88% 5022 37,520 38,879 104% 4973 31,900 31,601 99% 4895 148,847 176,239 118% 4818 7,946 7,946 100% 4624 264,443 395,182 149% 3613 690,440 583,711 85% 4420 3,973 3,973 100% 4324 584,292 584,292 100% 5241 5,676 4,662 82% 5240 3,590 5,230 146% 5078 6,745 6,067 90% 5077 2,208 4,834 219% 4999 1,920 2,918 152% 4694 7,579 11,535 152% 5134 118,260 118,260 100% 4738 161,184 95,030 59% 4708 161,184 80,248 50% 5534 118,260 47,902 41% 4881 20,467 24,480 120% 5028 17,544 23,773 136% 5025 340,927 302,817 89% 4922 42,381 86,339 204% 4485 9,163 11,102 121% 5546 9,313 5,080 55% 5482 5,922 2,276 38% 5402 445,875 426,179 96% 5401 332,927 237,086 71% 5167 75,174 135,022 180% 4665 17,618 44,467 252% 5415 2,491,519 2,491,519 100% 5131 1,210,612 1,168,092 96%

All Non-Sample Projects 38,937,840 40,407,878 104%

Total 47,580,784 49,040,793 103%


The ex post annual gross kWh savings for the Work Prescriptive Program during the period January 2018 through December 2018 are presented in Table 2-5.

Table 2-5 Ex Post Annual Gross kWh

Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

47,580,784 47,580,784 47,580,784 49,040,793 103%

2.2.2.2. Ex Post Gross kW Reductions

The ex post peak kW reduction for the Work Prescriptive Program during the period January 2018 through December 2018 are presented in Table 2-6.

Table 2-6 Ex Post Peak kW Reduction

Ex Ante Gross kW Savings

Gross Audited

kW Savings

Gross Verified

kW Savings

Ex Post Gross

kW Savings

Gross Realization

Rate

10,099.54 10,099.54 10,099.54 6,902.83 68%

2.3. Estimation of Ex Post Net Savings

Methodology for Estimating Ex Post Net Energy Savings

The net savings analysis was used to determine what part of the gross energy savings achieved by program participants could be attributed to the effects of the program. The net savings attributed to program participants are the gross savings less free ridership, plus spillover.

2.3.1.1. Methodology for Estimating Free Ridership

Information collected through a survey of a sample of program participants was used for the net-to-gross analysis.

All survey response data were systematically reviewed by a researcher who was familiar with the program, the individual project, and the social science theory underlying the decision maker survey instrument. As part of this review, the researcher determined whether the available information justified modifying the free ridership score calculated in accordance with the algorithm outlined below. The free ridership score calculated in accordance with the algorithm outlined below could be revised in instances in which there were significant inconsistencies between responses provided by the decision maker or in cases in which the responses were invalidated by other information regarding the project. As part of this review, the researcher can communicate with the decision maker to attempt to resolve any apparent inconsistency. In some cases in which the decision maker responded inconsistently, the researcher may drop the sample point. Information on any modifications to the free ridership score along with associated rationale and references to supporting data is be presented in the EM&V reporting.


Several factors were considered in the determination of the presence of free ridership. These included:

Financial ability to afford the installed measure without a program rebate;

Plans and intentions of the firm to install a measure even without support from the program;

Influence that the program had on the decision to install a measure; and

A firm’s previous experience with a measure installed under the program.

To assess these factors, program participants were asked a series of questions about the decision to implement the program project. Based on their responses, respondents were assigned a free ridership score used to estimate the extent of project free ridership.

Several criteria were used to determine what portion of a customer’s savings for a particular project was attributed to free ridership. The first criterion was based on the response to the following two questions:

Would you have been financially able to install the equipment or measures without the financial incentive from the [Program Name]?

To confirm, your organization would NOT have allocated the funds to complete a similar energy saving project if the program incentive was not available. Is that correct?

If a customer answers “No” to the first question and “Yes, that is correct” to the second, a free ridership score of 0 is assigned to the project. That is, if a customer required financial assistance from the program to undertake a project, then that customer is not deemed a free rider.

For decision makers that indicate that they were able to undertake energy efficiency projects without financial assistance from the program, three factors are analyzed to determine what percentage of savings that are attributable to free ridership. The three factors are:

Plans and intentions of firm to install a measure even without support from the program;

Influence that the program had on the decision to install a measure; and

A firm’s previous experience with a measure installed under the program.

For each of these factors, rules were applied to develop binary variables indicating whether or not a participant’s behavior shows free ridership. These rules make use of answers to questions on the decision maker survey questionnaire.

The first factor required determining if a participant’s intention was to install an energy efficiency measure even without the program. The answers to a combination of several questions were used with a set of rules to determine whether a participant’s behavior indicates likely free ridership. Two binary variables account for customer plans and intentions: one, based on a more restrictive set of criteria that may describe a high likelihood of free ridership, and a second, based on a less restrictive set of criteria that could describe a relatively lower likelihood of free ridership.

The first, more restrictive criteria indicated customer plans and intentions that likely signify free ridership are as follows:


The respondent answers “yes” to the following two questions: “Did you have plans to install the measure before participating in the program?” and “Would you completed the [MEASURE] project even if you had not participated in the program?”

The respondent answers “definitely would have installed” to the following question: “If the financial incentive from the [PROGRAM] had not been available, how likely is it that you would have installed [MEASURE] anyway?”

The respondent answers “did not affect timing of purchase and installation” to the following question: “How did the availability of information and financial incentives through the [PROGRAM] affect the timing of your purchase and installation of [MEASURE]?”

The respondent answers “no, the program did not affect level of efficiency that we chose for equipment” in response to the following question: “Did you purchase and install the [MEASURE] earlier than you otherwise would have without the program?”

The second, less restrictive criteria indicated customer plans and intentions that likely signify free ridership are as follows:

The respondent answers “yes” to the following two questions: “Did you have plans to install the [MEASURE] before participating in the program?” and “Would you completed the [MEASURE] project even if you had not participated in the program?”

Either the respondent answers “definitely would have installed” or “probably would have installed” to the following question: “If the financial incentive from the [PROGRAM] had not been available, how likely is it that you would have installed [MEASURE] anyway?”

Either the respondent answers “did not affect timing of purchase and installation” to the question: “Did you purchase and install the [MEASURE] earlier than you otherwise would have without the program?” or the respondent indicates that that while program information and financial incentives did affect the timing of equipment purchase and installation, in the absence of the program they would have purchased and installed the equipment within the next two years.

The respondent answers “no, the program did not affect level of efficiency that we chose for equipment” in response to the following question: “Did you choose equipment that was more energy efficient than you would have chosen because of the program?”

The second factor required determining if a customer reports that a recommendation from a program representative or past experience with the program was influential in the decision to install a particular piece of equipment or measure.

The criterion indicating that program influence may signify a lower likelihood of free ridership is that either of the following conditions is true:

The respondent answers “very important” to the following question: “How important was previous experience with the [Program Name] in making your decision to install [Equipment/Measure]?


The respondent answers “yes” to the following question: “Did a representative of the [Program Name] recommend that you install [Equipment/Measure]?”

The third factor required determining if a participant in the program indicates that he or she had previously installed an energy efficiency measure similar to one that they installed under the program without an energy efficiency program incentive during the last three years. A participant indicating that he or she had installed a similar measure is considered to have a likelihood of free ridership.

The criteria indicating that previous experience could signify a higher likelihood of free ridership are as follows:

The respondent answers “yes” to the following question: “Before participating in the [Program Name], had you installed any equipment or measure similar to [Rebated Equipment/Measure] at your facility?”

The respondent answers “yes, purchased energy efficient equipment but did not apply for financial incentive.” to the following question: “Has your organization purchased any energy efficient equipment in the last three years for which you did not apply for a financial incentive through the [Program Name?”

The four sets of rules just described were used to construct four different indicator variables that address free ridership behavior. For each customer, a free ridership value was assigned based on the combination of variables. With the four indicator variables, there were 11 applicable combinations for assigning free ridership scores for each respondent, depending on the combination of answers to the questions creating the indicator variables. Table 2-7 shows these values.

Table 2-7 Free Ridership Scoring

Indicator Variables

Free Ridership Score

Had Plans and Intentions to Install

Measure without [Program Name]?

(Definition 1)

Had Plans and Intentions to Install

Measure without [Program Name]?

(Definition 2)

[Program Name] had influence on

Decision to Install Measure?

Had Previous Experience with

Measure?

Y N/A Y Y 100% Y N/A N N 100% Y N/A N Y 100% Y N/A Y N 67% N Y N Y 67% N N N Y 33% N Y N N 33% N Y Y N 0% N N N N 0% N N Y N 0% N N Y Y 0%


2.3.1.2. Methodology for Estimating Spillover

Program participants could implement additional energy saving measures without receiving a program incentive because of their participation in the program. The energy savings resulting from these additional measures constitute program participant spillover effects.

To assess participant spillover savings, survey respondents were asked whether or not they implemented any additional energy saving measures for which they did not receive a program incentive. Respondents were also asked to provide information on the measures implemented for use in estimating the associated energy savings.

To determine if the savings from the reported measures were attributable to the program, survey respondents were asked questions about the degree to which their experience with the program influenced them to implement the measures and the likelihood of implementing the measures in the absence of the program. Specifically, respondents were asked the following questions:

SO1: How important was your experience with the [PROGRAM_NAME] in your decision to install this lighting equipment?

SO2: If you had NOT participated in the [PROGRAM_NAME], how likely is it that your organization would still have installed this lighting equipment?

The responses to these questions were used to develop a spillover score as follows:

Spillover = Average(SO1, 10 – SO2)

Savings from measures associated with a spillover score of 7 or greater were considered attributable to the program.

All survey response data were systematically reviewed by a researcher who was familiar with the portfolio and the social science theory underlying the survey instrument. As part of this review, the researcher could determine whether the available information justifies modifying the spillover score calculated in accordance with the algorithm outlined below. The spillover score calculated in accordance with the algorithm outlined above could be revised in instances in which there were significant apparent inconsistencies between responses provided by the decision maker or in cases in which the responses were apparently invalidated by other information regarding the measure(s). Additionally, responses may be dropped in cases where respondents do not report sufficient information to estimate the savings associated with the measure implemented.

2.3.1.3. Methodology for Estimating Non-Participant Spillover

Non-participant spillover was estimated using data collected through the PY2017 non-participant survey. To estimate PY2018 nonparticipant spillover, the PY2017 non-participant spillover ratio was applied to the 2018 customer annual kWh usage. That is, total non-participant spillover (NPSO) savings for the portfolio was estimated as follows:

NPSO = (2017 Survey Spillover / 2017 Survey Usage) * 2018 Population Usage

Where,


Survey Spillover = Total kWh associated with 2017 spillover measures

Survey Usage = Total 12-month kWh usage for the 2017 survey sample

Population Usage = Total 12-month kWh usage for the 2018 population

The total non-participant spillover was allocated to the program in proportion to its share of the 2018 expenditures.

Non-participant peak demand spillover impacts were estimated by multiplying the non-participant kWh savings by the ratio of the program ex post gross kW savings to ex post gross kWh savings.

Results of Ex Post Net Energy Savings Estimation

2.3.2.1. Ex Post Net kWh Savings

Table 2-8 summarizes the net ex post kWh savings of the Work Prescriptive Program. The annual net savings totaled 42,007,475 kWh. The net-to-gross ratio is 86%.

Table 2-8 Ex Post Net kWh Savings

Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

Ex Post Net kWh Savings

Net-to-Gross Ratio

Ex Post Net Lifetime

kWh Savings

47,580,784 47,580,784 47,580,784 49,040,793 103% 42,007,475 86% 629,859,550

2.3.2.2. Ex Post Net kW Demand Reductions

Table 2-9 summarizes the net ex post kW demand reduction of the Work Prescriptive Program. The net demand reduction equaled 6,134.89 kW. The net-to-gross ratio is 89%.

Table 2-9 Ex Post Net kW Demand Reduction

Ex Ante Gross kW Savings

Gross Audited

kW Savings

Gross Verified

kW Savings

Ex Post Gross

kW Savings

Gross Realization

Rate


Net-to-Gross Ratio

10,099.54 10,099.54 10,099.54 6,902.83 68% 6,134.89 89%

2.4. Process Evaluation

ADM completed a limited process evaluation of the PY2018 program. The limited process evaluation focused on notable program changes and developments, findings from a review of the program database, and findings from a survey of participating customers.

Data were collected from multiple data sources to support the process evaluation of the program. The data collection activities and the sample sizes are presented in Table 2-10.


Table 2-10 Summary of Data Collection Activities for the Work Prescriptive Program Data Collection Activity Mode of Data Collection Sample Size

Participant survey Email Staff interviews Telephone in-depth interviews 3

Process Evaluation Findings

Because of the similarity in implementation procedures and program operations, process evaluation findings for the Work Prescriptive and Work Custom are presented together in this section.

2.4.1.1. Summary of Program Participation Data

The weekly and cumulative energy savings of the Work Prescriptive and Custom Programs are shown by completion date in Figure 2-1. As shown, savings accrued fairly consistently with some accelerated activity during the June to November period.

Figure 2-1 Summary of Program Savings by Completion Date

Table 2-11 summarizes the incentive payments made to sites through the Work Prescriptive and Custom programs. As shown, 1% of sites reached the incentive cap in the Work Custom Program and none reached the cap in the Work Prescriptive Program.

Table 2-11 Summary of 2018 Project Incentive Payments

Program Number of Sites

Average Incentive Payment

Minimum Incentive Payment

Maximum Incentive Payment

Percent of Sites Reaching

Incentive Cap Custom 184 $10,064 $2 $162,249 1% Prescriptive 521 $3,164 $5 $53,475 0%


Lighting accounted for most of the measures installed through the programs (see Table 2-12). In total, lighting was implemented through 779 projects and resulted in over 52 million in ex ante kWh savings. New construction accounted for the second largest amount of ex ante savings with 6,461,255 kWh of savings expected from that measure.

Table 2-12 Summary of Savings by Measure Type

Measure Type Ex Ante kWh Savings

Number of Projects with Measure Type

Lighting 52,855,615 779 New Construction 6,461,255 41 Compressed Air 5,439,216 22 Motors 2,599,519 14 HVAC 1,582,109 21 Refrigeration 1,450,099 52 Process 1,441,332 12 Motor 928,573 34 Kitchen 168,627 10 Others 77,179 2 Cooking 11,083 1

2.4.1.2. Program Operations Perspective

ADM evaluators interviewed two senior energy efficiency consumer program coordinators at I&M and a program manager from Lockheed Martin to gain insights in the Work Prescriptive Program for 2018.

Summary of Process Evaluation Findings

Below is a summary of the key findings of the process evaluation.

Both the Work Prescriptive and Custom Programs met their savings goals in PY2018. Ex ante savings were equal to 69% of the adjusted Work Custom savings goal and 170% of the adjusted Work Prescriptive savings goals. Staff noted a trend in increasing savings resulting from prescriptive measures relative to custom project savings, which staff noted was especially relevant in the Indiana territory that saw an increase in prescriptive projects in the first part of the year. The ease of completing the project application was cited as a potential factor in the increase.

Lighting projects and savings were again a major contributor to meeting these savings targets, particularly for the Work Prescriptive program. Neither I&M nor Lockheed Martin anticipate that “slowing down.”

Staff indicated some of the largest energy savings came from compressed air projects for the Work Custom program in 2018.

Staff emphasized that the “boots on the ground” is a key driver of program success. The variety of forms of customer assistance provided at customer sites not only drives the


program to exceeding its goals but has other customer benefits as well. Those visits help I&M develop relationships and become trusted energy advisors, which keeps them involved in the program in future years.

Few program design and operations changes were made in PY2018. The programs largely ran as they had in PY2017. Staff noted that one custom lighting measure was added as a prescriptive measure. Staff reported that there were no significant changes to the operational procedures of the programs.

Trade ally feedback was solicited and lead to program enhancements. The implementation contractor completed a survey of trade allies in the third quarter of 2018. Based on the results, staff created a tutorial video to help trade allies with how to complete applications. Trade allies also suggested to make it easier for customers to find their information on the Electric Ideas website. Staff updated the website to reflect the request. I&M staff emphasized the importance of valuing the trade ally network and continuing expansion of it. Currently the program has 131 trade allies.

There were not any significant changes to the marketing and/or outreach approach. The primary marketing activities for the past program year included: quarterly newsletters, direct mailings, case studies, updates to fact sheets, trade ally customer kickoff meetings, and traditional advertising. Additionally, staff reported that there was a video developed to explain the rebate opportunities.

Efforts are progress to improve targeting of outreach and marketing. I&M staff is currently working with a variety of data sources to develop a fuller picture of their customer base and the degree to which they have participated in the programs in the past.

No major changes were made to data management or reporting. Overall, Lockheed Martin’s management of the program tracking remained consistent with prior years. One change that was made was that Lockheed Martin now reports outreach activities and project completions into I&M’s tracking system (EECP). Additionally, the program now develops monthly and quarterly forecast reports and presents this information to I&M senior management.

There have not been any significant changes to the QA/QC procedures in 2018. The QC practices include pre and post inspections on projects that are over $10,000, projects over $30,000 require monitoring, and 10% random inspections of projects under $10,000.

2.4.1.3. Participant Survey Findings

ADM completed a survey of program participants to collect information regarding their experience with the Work Custom & Prescriptive Program. The survey was administered online. A total of 113 customers completed the survey.

Respondents’ Roles and Policies for Energy Efficiency

Customers provided responses to several questions about their role and internal policies for making decisions about energy efficiency. Twenty-nine percent of respondents identified themselves as a facilities manager, 16% as a proprietor/owner, and 11% identified themselves as some other facilities manager/maintenance position. Table 2-13 displays the full distribution of responses.


Table 2-13 Respondents’ Job Tile or Role

Job Title or Role Percent (n = 112)

Facilities Manager 29% Proprietor/Owner 16% Other facilities management/maintenance position 11% Manager 8% Other* 6% Chief Financial Officer 5% Other financial/administrative position 5% President/CEO 5% Energy Manager 4% Church staff 3% Director 3% Administrative staff 2% Vice President of Operations 2% Plant engineer 1% Superintendent 1%

*Other job titles or roles were: Utilities Supervisor, Contractor, Business Manager, Township Trustee, Energy and Sustainability Specialist, and Fire Chief

Program participants were asked about their organizations’ energy efficiency policies and staff. As seen in Figure 2-2, 47% of survey respondents indicated their organization has a person responsible for managing energy use, less than half (41%) stated that they have a specific policy requiring that energy efficiency be considered when purchasing equipment, and 28% indicated they have defined energy savings goals. Carbon reduction goals were less common, with 19% indicating that their organization has a carbon reduction goal.


Figure 2-2 Internal Policies for Making Decisions about Energy Efficiency

Survey respondents provided feedback on the financial method businesses use to evaluate energy efficiency improvements – 57% said they use simple payback, 48% indicated they use lifecycle cost, 33% use initial cost, and 19% use internal rate of return. Table 2-14 summarizes the results.

Table 2-14 Financial Methods used to Evaluation EE Improvements

Financial Method Percent (n = 23)

Initial Cost 33% Simple payback 57% Internal rate of return 19% Life cycle cost 48% We don’t use any of these 5%

Program Awareness and Experiences Participants provided feedback regarding the various ways they learned about the program; Table 2-15 summarizes their feedback. The most common source of program awareness mentioned by 40% of survey respondents were program trade allies/contractors/equipment vendors/energy consultants. Twenty percent of respondents first heard about the program from I&M account representative, and 13% from friends or colleagues.


Table 2-15 Program Awareness

Program Awareness Percent (n = 112)

From a Trade Ally/contractor/equipment vendor/ energy consultant 40% From an Indiana Michigan Power Account Representative 20% Friends or colleagues 13% Some other way 13% From Indiana Michigan’s website 6% From a program representative / Lockheed Martin 4% At an event/trade show 2% Received an informational brochure 2% From a search engine (Google, Yahoo, Bing) 1%

Most survey respondents, 64%, indicated that they were involved in completing the application followed by 34% who indicated a contractor, 24% who indicated an equipment vendor, 15% who indicated another member of their company, and 2% who indicated a designer or architect. Those who completed the application themselves provided feedback regarding their experience with the application using a scale from 1 (not at all clear) to 5 (completely clear). The majority, 89%, of respondents indicated that the information on how to complete the application was completely or somewhat clear.

Respondents also rated how acceptable various aspects of the application were; Figure 2-3 displays the feedback. Most survey respondents found all aspects of the application process to be either somewhat or completely acceptable.

Ninety percent of respondents said they knew who to go to for assistance if necessary.


Figure 2-3 Acceptability of Program Application

Regarding the incentive amount compared to expectations, 72% of respondents believed it was about what they expected, 16% thought it was somewhat less, 6% thought it was somewhat more, 3% thought it was much more and 3% thought it was much less.

Experience with Inspector

Per the program guidelines, projects are subject to inspection once installation is complete. Fifty-eight percent of survey respondents indicated that a program representative inspected the work done through the program. Ninety-two percent of the respondents that receive a post-installation inspection thought the inspector was courteous and 90% indicated the inspector was efficient.

Program Satisfaction

Survey respondents provided feedback regarding their overall satisfaction with various aspects of the program including program staff, the online energy tool, the equipment installed, the rebate, the program overall, and I&M as their electric service provider.

Less than half of the respondents (32%) indicated they had interactions with program staff at some point during the project. As shown in Figure 2-4, the majority of which were very or somewhat satisfied with how thoroughly staff addressed their questions/concerns (97%) and how long it took for staff to address their questions/concerns (94%).


Figure 2-4 Satisfaction with Program Staff

Respondents rated their satisfaction with various aspects of the program; Figure 2-5 displays the results. Respondents reported being very or somewhat satisfied with the equipment that was installed (98%) and the quality of the installation (90%). Respondents were also either being very or somewhat satisfied with the steps it took to get through the program (90%), the amount of time it took to receive their incentive (89%), and the range of equipment that qualifies for incentives (87%). Overall 96% of survey respondents were either very satisfied or mostly satisfied with their program overall. Survey participants were provided an opportunity to give feedback on any changes they would like to see the program. Table 2-16 provides a summary of their responses.

Table 2-16 Program Feedback and Suggested Changes

Response Count Example comments

Shorten the application form

and/or reduce paperwork

9

All the paperwork.

We do many purchases throughout the year for LED upgrades. Hate to spend the time to fill out a form for a $4 incentive and it becomes complicated to fill out the form for multiple upgrades and incentives. Would be great if myself or someone in my staff could get training and then access to a portal which allowed quick and easy turn in incentives and then I&M would just pay that out quarterly or yearly.

Reduce the number of pages of the application.

Not make the application so lengthy and easier to fill out for those who doesn't speak the electrical language.

I would simplify the application process.

Not have to gather receipts for equipment and materials used and attach to email Increase incentive

amounts 3 Increase the incentives


Response Count Example comments

Higher incentive more incentive dollars per fixture Faster approval time 1 Quicker approval time

Improve savings estimates and/or rebate estimates

3

To be honest about LED lighting savings per month. Very misleading. Would not have spent money if I&M and contractor were honest about savings with LED lighting switch over.

The amount I was told I would get back at the start was about double what I got back at the finish.

Make sure the amount quoted is close to the actual incentive amount

Project process issues 5

We did our project in multiple stages with different lighting vendors based on existing relationships. One vendor completely owned the project for us and with the other I had to do 100% of the application work. Knowing the technical specs was the most difficult. Perhaps the install community should be better educated so their sales folks/estimators are ALWAYS willing to take on the application process.

Because it was a public school project, it went through the architect bid process. It was very difficult to get individual pricing and invoices from the electrical contractor. It took many emails/calls. Because of the nature of the contractor pricing by bid, they did not really want to share the cost per fixture.

response on rebate & HVAC upgrade needs an I&M rep

Not do year to year. Do it by project start to finish. I did not have time to get the other 400+ lights installed before my paperwork needed to be completed. I have to reapply now.

Include an energy audit before and after to compare the installation and long-term savings benefit.

Additional equipment eligible

for incentives 2

Included new welding machines discount

This is a one for one program. I wish the incentive would have included 1 to 2. We converted 8' T8 fluorescent fixtures (59 watts) to 4' T8 LED's (34 watts) which reduced our wattage by 9 watts more per 8' vs. going to 8' LED's (43 watts).

Other 1 The lighting fixture industry is consistently improving their products and we would like to have an independent source for accreditation of these new products.


Figure 2-5 Program Satisfaction

Similarly, 91% of survey respondents were either very or somewhat satisfied with I&M as their electric service provider. Yearly Trends

ADM compared 2018 survey results to those in 2017 to observe any differences from year to year. Key findings are presented in this section. The increases or decreases are presented as the difference in the percentage of customers providing a response in 2018 as compared to 2017. (i.e., an increase or decrease in percentage points from 2017 to 2018).

There was a decrease in the share of customers who had policies and procedures supportive of energy efficiency in place from 2017 to 2018 (see Table 2-17).

Table 2-17 2017 and 2018 Company Policies and Procedures Comparison

Response

Percentage by

Response (2017)

Percentage by

Response (2018)

Point Percentage Difference

A person or persons responsible for monitoring or managing energy usage 56% 47% -9

Defined energy savings goals 37% 28% -9 A specific policy requiring that energy efficiency be considered when purchasing equipment 44% 41% -3

Carbon reduction goals 22% 19% -3

Figure 2-6 summarizes the differences between 2017 and 2018 and how survey respondents learned of the program. Trade allies accounted for the largest increase from 2017 and 2018 for the


source of program awareness (12 percentage point increase), followed by I&M Power Account Representatives.

Figure 2-6 2017 and 2018 Program Awareness Comparisons

There was an overall increase in survey respondents who indicated the information on completing an application was somewhat or completely clear from 2017 to 2018 (see Table 2-18).

Table 2-18 2017 and 2018 Clarity of Information on Completing Application Comparison

Response

Percentage by

Response (2017)

Percentage by

Response (2018)


1 - Not at all clear 0% 0% 0% 2 7% 3% -4% 3 10% 8% -2% 4 52% 53% 1% 5 - Completely clear 31% 36% 5%

There was a 20 point decrease from 2017 to 2018 in the percentage of survey respondents who stated they had interactions with program staff during the course of their project (see Figure 2-7).


Figure 2-7 2017 and 2018 Any Interactions with Program Staff Comparison

Overall, there was a five-point increase among survey respondents who indicated they were somewhat or very satisfied with the program overall between the two program years (see Table 2-19).

Table 2-19 2017 and 2018 Satisfaction with Program Overall Comparison

Response

Percentage by

Response (2017)

Percentage by

Response (2018)


1 - Very dissatisfied 1% 1% 0% 2 2% 0% -2% 3 6% 3% -3% 4 27% 36% 9% 5 - Very satisfied 65% 60% -5%

2.5. Conclusions and Recommendations

Conclusions

The Work Prescriptive and the Work Custom Programs met their energy savings goal. Program ex ante savings were equal to 170% of the prescriptive savings goal and 69% of the custom savings. Staff noted that interest in prescriptive measures appears to be increasing. As in the prior year, lighting projects contributed to the majority of program savings. Staff noted that a key driver of program success is the on-the-ground effort made by program staff, which generates program savings but also benefits customers through the formation of trusted relationships.

Additional support provided to trade allies based on their feedback. The program website was modified to help customers find trade ally contact information and a video was developed to educate trade allies on how to complete the program application.

The marketing and outreach approaches were consistent with PY2017 and have been effective in helping the program reach its goals. The primary marketing activities for the past program year included: quarterly newsletters, direct mailings, case studies, updates to


fact sheets, trade ally customer kickoff meetings, and traditional advertising. Survey responses indicate that most participants learn of the work Prescriptive and Work Custom programs through trade allies (40%), I&M account representatives (20%), and friends and colleagues (13%).

The participation process is working well for most participants. Eighty-nine percent of participants thought that the overall application process was acceptable, and a minority (3%) found it unacceptable. Nearly all participants (90%) stated they knew from whom to get additional assistance. Additionally, nearly all respondents who had their project work inspected by program staff stated that the inspector was courteous and efficient.

Participants are satisfied with the program. Ninety-six percent of participants were satisfied with the program overall. Rates of dissatisfaction were low for most aspects of the program. The aspects of the program that the largest share of participants reported dissatisfaction with were the amount of time to get the rebate (3% were dissatisfied) and the steps to get through the program (3% were dissatisfied), and the quality of the installation (3% were dissatisfied).

Recommendations

Consider modifying application to collect information to develop more accurate estimates of kWh savings and kW reductions. Program energy saving estimates did not incorporate heating-cooling interactive factors (HCIF), which impacted project realization rates. Staff should consider adding a field to the measure table to indicate if the installation is in an exterior or interior space and requiring that the space heating type be selected to incorporate these factors. Alternatively, if staff prefer to minimize the application burden on participants, staff should assume a distribution of space heating types to estimate HCIFs.

Consider modifying application to incorporate coincidence factor. The realization rate for kW was 68% for the Work Prescriptive Program due to the ex ante savings calculation not applying a coincidence factor. The Work Custom program, which relies much more heavily on custom engineering calculations for ex ante, had a realization rate of 82% due to coincidence factors being incorporated into savings for many projects.

In order to minimize the discrepancy between ex ante and ex post savings, consider modifying the application to incorporate baseline wattages that account for the Energy Independence and Security Act (EISA) of 2007 for lighting like T12s and A-Line lamps.


3. Work Custom This chapter presents the results of both the impact and process evaluations of the Work Custom Program that Indiana Michigan Power (I&M) offered to its non-residential customers during the period of January 2018 through December 2018.

The objectives of the evaluation are to:

Establish a pre-approval review procedure

Assess gross and net energy (kWh) savings and peak demand (kW) reductions resulting from participation in the program during the program year;


Characterize levels of awareness among non-participants and identify barriers to participation;

Determine if there are significant differences between and among participant groups;

Assess satisfaction among participating customers and trade allies;




The Work Custom Program targets commercial and industrial accounts and provides incentives to implement efficiency measures not covered by the prescriptive program. The program provides an incentive of $0.06 per kWh saved. The program measures include non-prescriptive lighting and HVAC, and refrigeration measures, compressed air measures, industrial process improvements, and retro-commissioning.

Incentives are capped at $150,000 per site and $300,000 per company.




The sampling approach for the Work Custom Program was the same as the approach described in section 2.2.1.1 of this document on page 6.





Strata boundaries (kWh) < 80,000 80,000 – 200,000

200,000 – 500,000 500,000 – 900,000 > 900,000

Number of projects 116 26 28 10 4 184 Total Ex Ante Annual kWh 3,158,388 3,326,380 8,322,100 6,468,146 4,158,809 25,433,823 Average kWh Savings 27,227 127,938 297,218 646,815 1,039,702 2,138,900 Std. dev. of kWh savings 20,804 30,419 70,190 128,250 - 249,663 Coefficient of variation 0.76 0.24 0.24 0.20 - Final design sample 5 4 5 4 2 20


The process for reviewing program M&V and due diligence procedures for the Work Custom Program was the same as the approach described in See section 2.2.1.2 of this document on page 7.


The data collection procedure for the Work Custom Program was the same as the approach described in section 2.2.1.3 of this document on page 9.



Lighting

HVAC

Compressed Air System

Refrigeration

Process Improvements

Building Controls

Retro-Commissioning

ADM typically employed a specific set of methods to determine project gross savings that was dependent upon the type of measure being analyzed. These methods are summarized in Table 3-2.


Table 3-2 Typical Methods to Determine Savings for Work Custom Type of Measure Method to Determine Savings

Lighting Based on data on wattages before and after installation of measures and hours-of-use data from field monitoring; Indiana TRM based inputs

HVAC (including packaged units, chillers, cooling towers, controls/EMS)

eQUEST model used DOE-2 as its analytical engine estimating HVAC loads and calibrated with site-level billing data.

Compressed Air Systems Engineering analysis, with monitored data on power and schedule of operation

Refrigeration

Indiana TRM; doors, controls and operation parameters verified on-site. Simulation utilizing DEER prototypical models used for refrigerated case door retrofits

Process Improvements Engineering analysis, with monitored data on power and schedule of operation


Measure-level energy savings gross realization rates were calculated for each site for which on-site data collection and energy impact analysis was performed. Sites with relatively high or low gross realization rates were analyzed to determine the reasons for the discrepancy between ex ante and ex post energy savings. The site-level gross impact analysis results for each M&V sample site was presented in Volume II of the report.

The following sections describe the basic procedures used for estimating energy savings of various types of measure.

Analyzing Savings from Lighting Measures: The procedure for the estimation of energy savings for sampled lighting measures for the Work Custom Program is the same as the approach described in “Plan for Analyzing Savings from Lighting Measures” in section 2.2.1.4 on page 9.

Analyzing Savings from HVAC Measures: Savings estimated for HVAC measures installed at a facility was derived by using the energy usage estimates developed through eQUEST simulations and engineering calculations. The HVAC simulations also allowed calculation of the primary and secondary effects of lighting measures on energy usage. Each simulation produced estimates of HVAC energy and demand usage to be expected under different assumptions about equipment and/or construction conditions. There were cases in which eQUEST simulations were inappropriate because data was not available to properly calibrate a simulation model, and engineering analysis provides more accurate M&V results.

For the analysis of HVAC measures, typically, energy savings calculation relied upon monitoring data and other data collected during on-site visits. Using this data, ADM prepared estimates of the energy savings for the energy efficient equipment and measures installed in each of the participant facilities. Engineering staff developed independent estimates of the savings through engineering


calculations or through simulations with energy analysis models. By using energy simulations for the analysis, the energy use associated with the end use affected by the measure(s) being analyzed was quantified. With these quantities in hand, it was a simple matter of determining what the energy use would have been without the measure(s).

Before making the analytical runs for each site with sampled project HVAC measures, engineering staff prepared a model calibration run. This was a base case simulation to ensure that the energy use estimates from the simulations were reconciled against actual data on the building's energy use. This run was based on the information collected in an on-site visit pertaining to types of equipment, their efficiencies and capacities, and their operating profiles. Current operating schedules were used for this simulation, as were local (TMY) weather data covering the study period. The model calibration run was made using actual weather data for a time period corresponding to the available billing data for the site.

The goal of the model calibration effort was to have the results of the eQUEST simulation come within approximately 10% of the patterns and magnitude of the energy use observed in the billing data history. In some cases, it was not possible to achieve this calibration goal because of idiosyncrasies of particular facilities (e.g., multiple buildings, discontinuous occupancy patterns, etc.).

Once the analysis model had been calibrated for a particular facility, ADM performed three steps in calculating estimates of energy savings for HVAC measures installed or to be installed at the facility.

First, an analysis of energy use at a facility under the assumption that the energy efficiency measures were not installed was performed.

Second, energy use at the facility with all conditions the same but with the energy efficiency measures now installed was analyzed.

Third, the results of the analyses from the preceding steps are compared to determine the energy savings attributable to the energy efficiency measure.

Analyzing Savings from Compressed Air Measures: Measures to improve the efficiency of a compressed air system include the reduction of air leaks, resizing of compressors, installing more efficient compressors, improved controls, or a complete system redesign. Savings from such measures were evaluated through engineering analysis of compressor performance curves, supported by data collected through short-term metering.

ADM field staff obtained nameplate information for the pre-retrofit equipment either from the project file or during the on-site survey. Performance curve data was obtained from the Compressed Air Gas Institute (CAGI). Engineering staff then conducted an engineering analysis of the performance characteristics of the pre-retrofit equipment. During the on-site survey, field staff inspected the as-built system equipment, take pressure and load readings, and interviewed the system operator to identify seasonal variations in load. Potential interactions with other compressors were assessed and it was verified that the rebated compressor is being operated as intended.


When appropriate, short-term measurements were performed to reduce the uncertainty in defining the load on the as-built system. These measurements were taken either with a multi-channel logger, which record true power for several compressors, with current loggers, which provide average amperage values, or with motor loggers to record operating hours. The appropriate metering equipment was selected by considering variability in load and the cost of conducting the monitoring.

ADM used engineering calculations to calculate the annual energy savings due to the compressed air measures. This was facilitated through the use of CAGI efficiency curves allowing for the calculation of the CFM output of a given compressor based on monitoring data. Using the assumption that the CFM demand of the facility remained the same for the baseline and as-built compressors, CAGI curves can then were used to determine the kW demand of the preexisting compressor. This data was then extrapolated to the entire year and normalized to production data when appropriate. Project energy savings were calculated by subtracting the as-built from the baseline energy consumption.

Analyzing Savings from Refrigeration and Process Improvements: Analysis of savings from refrigeration and process improvements were inherently project-specific. Depending on the specificity of processes, TRM inputs could have be appropriate. Otherwise, since analyzing the processes through simulations was generally not feasible; reliance was made on engineering analysis of the process affected by the improvements. Major factors in ADM’s engineering analysis of process savings were operating schedules and load factors.

Results of Ex Post Gross Savings Estimation


The realized gross kWh savings of the Work Custom Program for the sampled projects are summarized in Table 3-3.


Stratum Ex Ante

Gross kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

5 5 141,756 133,207 4 4 479,305 286,411 3 3 1,328,869 1,008,324 2 2 2,454,364 2,390,526 1 1 1,987,278 1,877,510

Total Total 6,391,572 5,695,978




Project ID Ex Ante

Gross kWh Savings

Ex Post Gross kWh

Savings

Project Gross

Realization Rate

5154 2,488 3,034 122% 5327 115,310 35,985 31% 4366 581,528 460,713 79% 5073 173,098 98,439 57% 5006 347,528 210,907 61% 4734 89,212 50,678 57% 4707 63,913 46,793 73% 4636 308,877 225,201 73% 4686 213,555 215,457 101% 4626 232,942 219,401 94% 4420 101,685 101,308 100% 3260 994,308 956,956 96% 3997 13,750 12,518 91% 5028 225,967 137,359 61% 4922 720,480 684,454 95% 4485 593,398 681,027 115% 3168 992,970 920,554 93% 4992 12,600 12,600 100% 5234 49,005 58,262 119% 4665 558,958 564,333 101%

All Non-Sample Projects

19,042,251 15,803,314 83%

Total 25,433,823 21,499,292 85%

The ex post annual gross kWh savings for the Work Custom Program during the period January 2018 through December 2018 are presented in Table 3-5.


Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

25,433,823 25,433,823 25,433,823 21,499,292 85%


The ex post peak kW reduction for the Work Custom Program during the period January 2018 through December 2018 are presented in Table 3-6.

Table 3-6 Ex Post Peak kW

Ex Ante Gross

kW Savings

Gross Audited

kW Savings

Gross Verified

kW Savings

Ex Post Gross

kW Savings

Gross Realization

Rate

3,685.01 3,685.01 3,685.01 3,020.44 82%




The procedure for the estimation of program-level kWh energy savings and program-level kW demand reductions was the same as the approach described in section 2.3.1 of this document on page 15.



Table 3-7 summarizes the net ex post kWh savings of the Work Custom Program. The annual net savings totaled 20,599,910 kWh. The net-to-gross ratio is 96%.


Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate


Net-to-Gross Ratio


kWh Savings

25,433,823 25,433,823 25,433,823 21,499,292 85% 20,599,910 96% 310,431,508


Table 3-8 summarizes the net ex post kW demand reduction of the Work Custom Program. The net demand reduction equaled 2,990.18 kW. The net-to-gross ratio is 99%.


Ex Ante Gross

kW Savings

Gross Audited

kW Savings

Gross Verified

kW Savings

Ex Post Gross

kW Savings

Gross Realization

Rate


Net-to-Gross Ratio

3,685.01 3,685.01 3,685.01 3,020.44 82% 2,990.18 99%


Methods and findings related to the process evaluation of the Work Custom Program are presented in the Work Prescriptive Chapter in section 2.4 on page 21.


Conclusions

The summary of conclusions for the Work Custom Program are presented in Section 2.5.1 on page 32.


Recommendations

The summary of recommendations for the Work Custom Program are presented in Section on page 32.


4. Work Direct Install This chapter presents the results of both the impact and process evaluations of the Work Direct Install Program that Indiana Michigan Power (I&M) offered to its non-residential customers during the period of January 2018 through December 2018.

The objectives of the evaluation are to:

Assess gross and net energy (kWh) savings and peak demand (kW) reductions that resulted from participation in the program during the program year;


Characterize levels of awareness among non-participants and identify barriers to participation;

Determine if there were significant differences between and among participant groups;

Assess satisfaction among customers and trade allies that participated;




The Work Direct Install Program targets energy efficiency improvements in small commercial/retail establishments, food service facilities and grocery store/supermarkets with demand of less than 150 kW by providing onsite energy assessments and incentives for energy efficient lighting and refrigeration equipment. The program measures are installed by a program qualified trade ally.

Work Direct Install incentives are designed to cover up to 70% of the installed measure cost and are payable to the trade ally or the customer. Incentives are provided on a per unit of equipment basis. Participating customers may also receive Work Prescriptive or Work Custom incentives.

Incentives are capped at $3,000 per site and $21,000 per company, across all programs.


The procedure for the estimation of program-level gross kWh energy savings and gross kW demand reductions for the Work Direct Install Program is the same as the approach described in section 2.2 of this document on page 6.



The sampling approach for the Work Direct Install Program is the same as the approach described in section 2.2.1 of this document on page 6.





Strata boundaries (kWh) < 6,000 6,000 – 12,000 12,000 – 25,000 25,000 – 35,000 > 35,000 Number of projects 64 50 34 16 8 172 Total Ex Ante Annual kWh 212,051 446,576 563,053 450,860 374,626 2,047,166 Average kWh Savings 3,313 8,932 16,560 28,179 46,828 103,812 Std. dev. of kWh savings 1,631 1,757 3,754 2,583 8,424 18,149 Coefficient of variation 0.49 0.20 0.23 0.09 0.18 Final design sample 3 3 4 3 2 15


The process for reviewing program M&V and due diligence procedures for the Work Direct Install Program is the same as the approach described in section 2.2.1.2 of this document on page 7.


The data collection procedure for the Work Direct Install Program is the same as the approach described in section 2.2.1.3 of this document on page 9.



Lighting and lighting controls

Motors and VFDs

Kitchen equipment

Refrigeration

ADM typically employed a specific set of methods that determined project gross savings that were dependent upon the type of measure being analyzed. These methods are summarized in Table 4-2.


Table 4-2 Typical Methods to Determine Savings for Work Direct Install Measures Type of Measure Method to Determine Savings

Lighting Based on data on wattages before and after installation of measures and hours-of-use data from field monitoring; Indiana TRM based inputs

Refrigeration

Indiana TRM; doors, controls and operation parameters verified on-site. Simulation utilizing DEER prototypical models used for refrigerated case door retrofits

Motors and Controls Measurements of power and run-time obtained through monitoring.


A kWh energy savings gross realization rate and a peak kW reduction gross realization rate was calculated for each site that was part of the M&V sample. Sites with relatively high or low gross realization rates were analyzed to determine the reasons for the discrepancy between expected and realized energy savings. The site-level gross impact analysis results for each M&V sample site is presented in the annual program EM&V report.

The following discussion describes the basic procedures used for estimating savings from various measure types.

Analyzing Savings from Lighting Measures: The procedure for the estimation of energy savings for sampled lighting measures for the Work Direct Install Program is the same as the approach described in “Plan for Analyzing Savings from Lighting Measures” in section 2.2.1.4.

Analyzing Savings from Refrigeration, Motors, and Controls measures: Analysis of savings from refrigeration measures was inherently project-specific. Because of the specificity of processes, analyzing the processes through simulations was generally not feasible. Rather, reliance was made on engineering analysis of the process affected by the improvements. For many measures, the Indiana TRM was referenced along with project-specific information.



The realized gross kWh savings of the Work Direct Install Program for the sampled projects are summarized in Table 4-3.



Stratum Ex Ante

Gross kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

5 5 12,821 12,370 4 4 28,832 25,402 3 3 59,441 41,916 2 2 95,411 98,308 1 1 104,966 108,032

Total Total 301,471 286,028



Project ID

Ex Ante Gross kWh

Savings

Ex Post Gross kWh

Savings

Project Gross

Realization Rate

5009 43,800 70,189 160% 5022 29,181 31,620 108% 4849 31,796 32,254 101% 4818 34,434 34,434 100% 4778 12,168 8,141 67% 5036 11,986 10,065 84% 4789 4,423 6,394 145% 5393 3,106 2,382 77% 4760 14,448 14,447 100% 5679 7,224 7,223 100% 5546 20,058 10,073 50% 5482 12,767 9,255 72% 5686 61,166 37,843 62% 5669 5,292 3,594 68% 5639 9,622 8,114 84%

All Non-Sample Projects

1,745,695 1,559,183 89%

Total 2,047,166 1,845,211 90%

The ex post annual gross kWh savings for the Work Direct Install Program during the period January 2018 through December 2018 are presented in Table 4-5.


Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

2,047,166 2,047,166 2,047,166 1,845,211 90%



The ex post peak kW reduction for the Work Direct Install Program during the period January 2018 through December 2018 are presented in Table 4-6.

Table 4-6 Ex Post Peak kW

Ex Ante Gross

kW Savings

Gross Audited

kW Savings

Gross Verified

kW Savings

Ex Post Gross

kW Savings

Gross Realization

Rate

542.04 542.04 542.04 301.29 56%



The procedure for the estimation of program-level kWh energy savings and program-level kW demand reductions was the same as the approach described in section 2.3.1 of this document on page 15.



Table 4-7 summarizes the net ex post kWh savings of the Work Direct Install Program. The annual net savings totaled 1,973,547 kWh. The net-to-gross ratio is 107%.


Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate


Net-to-Gross Ratio

Ex Post Net

Lifetime kWh

Savings

2,047,166 2,047,166 2,047,166 1,845,211 90% 1,973,547 107% 31,607,094


Table 4-8 summarizes the net ex post kW demand reduction of the Work Direct Install Program. The net demand reduction equaled 321.81 kW. The net-to-gross ratio is 107%.


Ex Ante Gross

kW Savings

Gross Audited

kW Savings

Gross Verified

kW Savings

Ex Post Gross

kW Savings

Gross Realization

Rate


Net-to-Gross Ratio

542.04 542.04 542.04 301.29 56% 321.81 107%



ADM completed a limited process evaluation of the PY2018 program. The limited process evaluation focused on notable program changes and developments, findings from a review of the program database, and findings from a survey of participating customers.

Data were collected from multiple data sources to support the process evaluation of the program. The data collection activities and the expected sample sizes are presented in Table 4-9.

Table 4-9 Summary of Data Collection for the Work Direct Install Program

Data Collection Activity Mode of Data Collection Sample Size Participant survey Online 20 Staff interviews Telephone in-depth interviews 3

Process Evaluation Findings

4.4.1.1. Summary of Program Participation Data

Figure 4-1 summarizes the weekly and cumulative savings during the program year. As shown, projects were completed at a fairly steady rate during the year after late-February.

Figure 4-1 Summary of Program Savings by Completion Date

A summary of incentive payments made is shown in Table 4-10. As shown, 5% of projects reached the incentive cap for the Work Direct Install Program. Customers that reach the incentive cap in this program may seek incentives through the Work Prescriptive Program.

Table 4-10 Summary of 2018 Project Incentive Payments

Program Number of Sites

Average Incentive Payment

Minimum Incentive Payment

Maximum Incentive Payment

Percent of Sites Reaching

Incentive Cap SBDI 158 $1,241 $50 $4,154 5%


Lighting measures accounted for the largest share of program savings. One hundred sixty lighting projects were completed and resulted in estimated ex ante savings of 1,945,877 kWh.

Table 4-11 Summary of Savings by Measure Type

Measure Type

Ex Ante kWh Savings

Number of Projects with Measure Type

Lighting 1,945,877 160 Refrigeration 101,289 19 Total 2,047,166 179

4.4.1.2. Program Operations Perspective

The summary of the program operations perspective presented in Section 2.4.1 was inclusive of findings related to the Work Direct Install Program. Below is a summary of the key findings of the process evaluation which are specific to the Work Direct Install program for 2018.


There is an interest in recruiting additional trade allies. I&M staff expressed the desire to recruit new trade allies for the program. There is a subset of trade allies within the network that are SBDI approved, who are high performing and focused on small business. Customers are required to work with one of these approved trade allies and cannot install themselves. Program staff indicated the Work Direct Install program relies on this subset of trade allies to market the program. A challenge noted is that most of the contractors are lighting contractors and most of the small business measures are lighting. Staff would like to develop a structure where contractors can provide referrals for non-lighting measures in the process of completing lighting projects.

4.4.1.3. Participant Survey Findings

ADM completed a survey of program participants to collect information regarding their experience with the Work Direct Install Program. The survey was administered online. A total of 20 customers completed the survey.

Respondents’ Roles and Policies for Energy Efficiency

Respondents provided feedback regarding their roles within their organizations. Thirty-five percent of survey respondents identified themselves as the president or CEO, 25% as managers, and 10% as proprietor or owner. The full distribution of responses is summarized below in Table 4-12.


Table 4-12 Respondents’ Job Tile or Role

Job Tile or Role Count Percent (n = 20)

President/CEO 7 35% Manager 5 25% Proprietor/Owner 2 10% Facilities Manager 1 5% Other facilities management/maintenance position 1 5% Church staff 1 5% Trustee 1 5% Procurement specialist 1 5% Financial officer 1 5%

Program participants were asked about their organizations’ energy efficiency policies and staffing. As seen in Figure 4-2, less than half of the survey respondents reported having any type of internal policies in place for making decisions regarding investments in energy efficiency. Forty-five percent of respondents indicated their organization has a person responsible for managing energy use and 28% have a specific policy requiring that energy efficiency be considered when purchasing equipment. Defined energy savings goals and carbon reduction goals were less common. The results suggest that few customers have organizational resources or policies guiding decision making related to energy use and conservation, which underscores the importance of program incentives and technical support to this customer segment.

Figure 4-2 Internal Policies for Making Decisions about Energy Efficiency

Program Awareness

Survey respondents were asked to identify how they learned about the program incentives. Fifty percent of respondents became aware of the program through a program trade ally or contractor, 35% learned of the program through word-of-mouth via a friend or colleague, 10% learned of the


program through some other source, and 5% from an event or trade show. Table 4-13 summarizes the full distribution of responses.

Table 4-13 Sources of Program Awareness

Program Awareness Count Percent (n = 20)

From a Trade Ally/contractor/equipment vendor/ energy consultant 10 50% Friends or colleagues 7 35% Some other way 2 10% At an event/trade show 1 5%

Respondents provided feedback regarding their first impressions of the program and if they had concerns about participating. Eighty-nine percent of respondents indicated that it was an easy decision and 11% had some concerns.

Experience with the Contractor and Project Costs

Survey respondents rated the degree to which they agreed or disagreed with various statements about their contractor on a scale of 1 (completely disagree) to 5 (completely agree). Figure 4-3 displays the results. Overall, survey participants were satisfied with the trade ally they worked with to complete their projects.

Figure 4-3 Experience with Contractor

Respondents provided feedback regarding how long they waited for equipment to be installed after their contractor performed the onsite assessment. Most customers (45%) indicated they waited 3 – 4 weeks, 25% of customers indicated they waited 1 – 2 weeks, 15% indicated they waited 5-6 weeks, 10% waited less than 1 week, and 5% waited more than 6 weeks. In terms of the project


cost, 70% of survey respondents indicated it was about what they expected, 15% said it was much less, 10% said it was much more, and 5% said it was much more.

Table 4-14 Project Cost Compared to Expectations

Program Awareness Count Percent (n = 20)

It was much less 3 15% It was somewhat less 0 0% It was about the amount expected 14 70% It was somewhat more 2 10% It was much more 1 5%

Additional and Previous Equipment Purchases

Participants provided feedback regarding other energy efficient equipment purchases their organizations made in the last 3 years. Twenty-two percent of participants indicated they have made other purchases but had not applied for incentives, 22% had purchased equipment but applied for the incentive and 56% had not purchased any energy efficient equipment. Regarding similar measure types their organizations had installed at the same location, 32% of respondents indicated they had implemented similar equipment or measures prior to when they participated in the program.

Satisfaction

Survey respondents provided feedback regarding their satisfaction with program staff, the overall program, and I&M as their electric service provider.

Eighty-eight percent of survey respondents (n = 15) indicated a program representative inspected the work done through the project. Among those whose work was inspected, all either somewhat or completely agreed that the inspector was courteous and efficient. None of the survey participants indicated they had any interactions with program staff.

Participants also rated their satisfaction with various aspects of the program, using a scale from 1 (very dissatisfied) to 5 (very satisfied) (see Figure 4-4). Overall most participants were somewhat or very satisfied with every aspect of the Work Direct Install Program. Feedback suggests respondents were very satisfied with the equipment and the program overall, and slightly less satisfied with the amount of time between the audit and the installation. One customer was very dissatisfied with the proposal they received from their contractor. The respondent stated that the proposal stated a higher rebate than what they received. Most survey respondents (90%) reported being somewhat or very satisfied with I&M as their electric service provider.


Figure 4-4 Program Satisfaction

Yearly Trends

ADM compared 2018 survey results to those in 2017 to observe any differences from year to year. Key findings are presented in this section. The increases or decreases are presented as the difference in the percentage of customers providing a response in 2018 as compared to 2017. (i.e., an increase or decrease in percentage points from 2017 to 2018).

There was a 12 percentage-point increase among survey respondents who indicated there was a person/persons responsible for monitoring or managing energy usage and a three point decrease among those who stated they had carbon reduction goals (see Table 4-15).

Table 4-15 2017 and 2018 Company Policies and Procedures Comparison

Response

Percentage by

Response (2017)

Percentage by

Response (2018)


A person or persons responsible for monitoring or managing energy usage 33% 45% 12%

Defined energy savings goals 14% 15% 1% A specific policy requiring that energy efficiency be considered when purchasing equipment 24% 25% 1%

Carbon reduction goals 14% 11% -3%

Trade allies accounted for the largest increase from 2017 and 2018 for the source of program awareness (12 percentage point increase), followed by friends or colleagues (see Figure 2-1Figure


4-5). This finding may suggest that trade allies are actively increasing their efforts to improve awareness about the program.

Figure 4-5 2017 and 2018 Program Awareness Comparisons

There was an increase among survey respondents who stated it was an easy decision to make about participating in the program when they were first approached (see Table 4-16). This finding could suggest that when trade allies’ approach potential participants about the program, they are increasingly providing them with adequate information and are able to answer customer questions.

Table 4-16 2017 and 2018 Program Participants Concerns Comparison

Response

Percentage by

Response (2017)

Percentage by

Response (2018)


I had some concerns 16% 11% -5% It was an easy decision 84% 89% 5%

Figure 4-6 summarizes the how long program participants had to wait for the equipment to be installed after the onsite assessment was performed. There was a 23 point increase among those who had to wait 3 – 4 weeks and a 25 point decrease among those who stated 1 – 2 weeks.


Figure 4-6 2017 and 2018 Wait Time for Equipment to be Installed After the Onsite Assessment Comparison


Conclusions

Below is a summary of the key findings of the process evaluation.


There is an interest in recruiting additional trade allies, and non-lighting trade allies. A challenge noted by staff is that most of the contractors are lighting contractors and most of the small business measures are lighting. Staff would like to develop a structure where contractors can provide referrals for non-lighting measures in the process of completing lighting projects.

All survey respondents were satisfied with the program overall. All respondents were also satisfied with the quality of the installation and the type of equipment installed. One customer was dissatisfied with the proposal received from their contractor.

Recommendations

Develop a program design or process that increases measure diversity. Staffs’ suggestion to develop a process to allow lighting contractors to make referrals for non-lighting measures may help increase measure diversity. Another approach ADM has seen is to use a third-party onsite auditor to inspect the facilities and to then schedule work with approved contractors for the measure type. In the program ADM evaluated that used this approach, 47% of program savings resulted from non-lighting measures. However, this design likely comes at a cost of higher program administration costs.



5. Work Energy Management This chapter presents the results of both the impact and process evaluations of the Work Energy Management Program that Indiana Michigan Power (I&M) offered to its non-residential customers. Participants in 2018 enrolled in 2017. The program was not open to new enrollments in 2018.

The objectives of the evaluation were to:

Assess gross and net energy (kWh) savings and peak demand (kW) reductions resulting from participation in the program during the program year; and

Assess the program cost-effectiveness based on various economic tests.


The Work Energy Management Program deployed energy management switches, sensors, control systems, near real time communication channels, and back office control software algorithms to manage end-use loads during periods of forecasted high energy market prices. The program created both peak demand and energy savings because these assets working together allow I&M to control the time periods that equipment operates.

The program targets any end-use load capable of being remotely and automatically managed without negatively impacting customer business and process operations. Although many types of I&M commercial and industrial (C&I) customer can participate if the customer is willing and capable of meeting program requirements, certain C&I customers are a better fit for the program because their loads can be more readily managed without adversely affecting business operations.

Participants in 2018 enrolled in 2017. The program was not open to new enrollments in 2018.



5.2.1.1. Data Collection

The ex post gross savings analysis utilized interval consumption data for the participating sites along with data provided by I&M on the event periods.


To calculate the energy and demand savings of the commercial and industrial components, it was necessary to determine the baseline energy consumption during periods when the installed devices or control strategy are reducing the site’s energy consumption. This was done using the 3:10 methodology. For each event, an average baseline was developed using the three days of the prior ten, non-holiday weekdays (which were not event days). In the analysis of DR event savings, the purpose of a baseline curve is to represent what the kW profile would have been without the DR event. These baselines represent the average of previous non-event days at the site. Due to the non-


uniform nature of commercial energy consumption, the average baseline curve does not necessarily match with the DR event day curve before and after the event. To accurately calculate DR event savings, it is often necessary to normalize the baseline curve to "fit" the event curve both before and after the affects from the DR event. The baseline profile is normalized to the event day by offsetting the baseline profile to match the event day profile by averaging the hour ending prior to the start of the event.

The demand reduction was calculated (see equation below) as the difference between the normalized baseline and the event day demand during each event hour and for the two hours following the event conclusion. All interval data obtained for the sites was aggregated into hourly data for kW demand reduction analysis.

𝑘𝑘𝑊𝑊𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(ℎ𝑟𝑟=𝑠𝑠) = 𝑘𝑘𝑊𝑊𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏(ℎ𝑟𝑟=𝑠𝑠) × �𝑘𝑘𝑊𝑊1ℎ𝑟𝑟𝑟𝑟𝑟𝑟𝑏𝑏

𝑘𝑘𝑊𝑊1ℎ𝑟𝑟𝑟𝑟𝑟𝑟𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏� − 𝑘𝑘𝑊𝑊ℎ𝑟𝑟=𝑠𝑠

Where,

𝑘𝑘𝑊𝑊𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(ℎ𝑟𝑟=𝑠𝑠) = kW savings for hour i

𝑘𝑘𝑊𝑊𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏(ℎ𝑟𝑟=𝑠𝑠) = Average of 3 qualifying baseline days kW values for hour i

𝑘𝑘𝑊𝑊1ℎ𝑟𝑟𝑟𝑟𝑟𝑟𝑏𝑏 = kW value of event day one hour prior to the event without pre-cooling, two hours prior to the event with pre-cooling

𝑘𝑘𝑊𝑊1ℎ𝑟𝑟𝑟𝑟𝑟𝑟𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠𝑏𝑏 = kW value obtained from the 3 of 10 baseline curve one hour prior to the event without pre-cooling, two hours prior to the event with pre-cooling

Energy savings were calculated on a site-by-site basis. The analysis process includes the following steps:

1) Usage data was read into the R statistical software package.

2) Usage data quality was checked, and anomalies are removed, if necessary.

3) The installation, site active & baseline dates were integrated into the data to establish when participation and active controlling building equipment started.

4) Baseline power consumption during the active period was determined using the 3:10 methodology.

5) The measured active period energy consumption was then compared to the baseline consumption to determine energy and demand savings.

5.2.1.3. Calculation of Program Level Savings

Site level hourly energy use data was provided for four sites while the program was implemented at eleven sites. The energy savings (kWh) for each of the four sites was determined for all events. The graphs of the event days for each site were evaluated visually to identify any results where the


normalized baseline did not match the event data. Two sites on two events were identified that had baselines that matched the event data poorly and were removed from the analysis.

To calculate the program level savings, the kWh savings were summed across all events and all sites and compared to the site and event level tracking data-referenced values. A savings factor was calculated and applied to the tracking data-referenced savings for all sites that was provided by the implementer to determine the ex post gross savings.


Energy savings were determined for each participant with energy data by hour for each event and aggregated by date. These values were compared to the tracking data-referenced savings and a savings factor of 0.71 was determined. This resulted in an ex post annual gross kWh savings of 7,250 kWh.


The Work Energy Management program had a Gross kWh Savings of 7,250 kWh and a Gross Realization Rate of 94%.

Table 5-1 Summary of kWh Savings

Ex Ante Gross kWh Savings

Gross Audited kWh Savings

Gross Verified kWh Savings

Ex Post Gross kWh Savings

Gross Realization Rate

7,689 7,689 7,689 7,250 94%


There were no ex ante or ex post gross kW savings for the Work Energy Management Program.



The technology provided through the program is not available outside of the program and the achieved curtailments are dependent on the use of the program-supplied technology and I&M’s determination of when curtailment events occur. Ex post energy kWh and ex post kW demand reductions will be calculated through application of program net-to-gross ratios equal to 1.0.



Table 4-7 summarizes the net ex post kWh savings of the Work Energy Management Program. The annual net savings totaled 7,250 kWh. The net-to-gross ratio is 100%.



Ex Ante Gross kWh

Savings

Gross Audited

kWh Savings

Gross Verified

kWh Savings

Ex Post Gross kWh

Savings

Gross Realization

Rate

Ex Post Net kWh

Savings

Net-to-Gross Ratio


kWh Savings

7,689 7,689 7,689 7,250 94% 7,250 100% 108,750


There were no ex post net kW savings for the Work Energy Management Program.


No process evaluation was performed for the PY2018 Work Energy Management program.


6. Cost Effectiveness Evaluation The following cost effectiveness tests were performed for each program: Total Resource Cost (TRC) test, Utility Cost Test (UCT), Participant Cost Test (PCT), and Ratepayer Impact Measure (RIM) test. A score above one signifies that, from the perspective of the test, the program benefits were greater than the program costs. The benefits and costs associated with each test are defined in Table 6-1.

Table 6-1 Summary of Benefits and Costs Included in each Cost Effectiveness Test

Variable Definition PCT UCT RIM TRC

Benefit Cost Benefit Cost Benefit Cost Benefit Cost

Incentives Incentives paid to customers. ✓ ✓ ✓

Program Installation Costs

Installation costs paid by program.

✓ ✓ ✓

Bill Savings / Lost Revenue

Reduction in electricity costs faced by customers as a result of implementation of program measures. Equal to revenue lost to the utility.

✓ ✓

Avoided Energy Costs

Energy-related costs avoided by utility.

✓ ✓ ✓

Avoided Capacity Costs

Capacity-related costs avoided by utility, including T&D.

✓ ✓ ✓

Incremental Costs

Incremental costs associated with measure implementation, as compared with what would have been done in absence of program.

✓ ✓

Program Overhead Costs

Program costs other than incentive or installation costs.

✓ ✓ ✓

Table 6-2 through Table 6-5 summarize key financial benefit and cost inputs for the various tests along as well as the test results for each residential program.


Table 6-2 Work Prescriptive Program Cost Test Inputs and Results

Table 6-3 Work Custom Program Cost Test Inputs and Results

Table 6-4 Work Direct Install Program Cost Test Inputs and Results

Benefit Cost Benefit Cost Benefit Cost Benefit CostIncentives 2,458,990$ 2,458,990$ 2,458,990$ Program Installation Costs -$ -$ -$ Bill Savings (NPV) 27,047,696$ Lost Revenue (NPV) 38,860,036$ Avoided Energy Costs (NPV) 24,008,796$ 24,008,796$ 24,008,796$ Avoided Capacity Costs (NPV) 6,742,246$ 6,742,246$ 6,742,246$ Avoided T&D Costs (NPV) -$ -$ -$ Incremental Costs 9,435,500$ 9,435,500$ Program Overhead Costs 1,325,654$ 1,325,654$ 1,325,654$ Total BenefitsTotal CostsTest Score 3.13 8.13 0.72 2.86

9,435,500$ 3,784,645$ 42,644,681$ 10,761,155$ 29,506,686$ 30,751,041$ 30,751,041$ 30,751,041$

Variable PCT UCT RIM TRC

Benefit Cost Benefit Cost Benefit Cost Benefit CostIncentives 1,492,191$ 1,492,191$ 1,492,191$ Program Installation Costs -$ -$ -$ Bill Savings (NPV) 12,317,030$ Lost Revenue (NPV) 17,719,454$ Avoided Energy Costs (NPV) 11,829,509$ 11,829,509$ 11,829,509$ Avoided Capacity Costs (NPV) 3,301,837$ 3,301,837$ 3,301,837$ Avoided T&D Costs (NPV) -$ -$ -$ Incremental Costs 4,478,149$ 4,478,149$ Program Overhead Costs 1,275,848$ 1,275,848$ 1,275,848$ Total BenefitsTotal CostsTest Score 3.08 5.47 0.74 2.63

4,478,149$ 2,768,039$ 20,487,494$ 5,753,998$ 13,809,221$ 15,131,346$ 15,131,346$ 15,131,346$


Benefit Cost Benefit Cost Benefit Cost Benefit CostIncentives 196,103$ 196,103$ 196,103$ Program Installation Costs -$ -$ -$ Bill Savings (NPV) 1,301,411$ Lost Revenue (NPV) 1,906,808$ Avoided Energy Costs (NPV) 1,191,902$ 1,191,902$ 1,191,902$ Avoided Capacity Costs (NPV) 370,480$ 370,480$ 370,480$ Avoided T&D Costs (NPV) -$ -$ -$ Incremental Costs 535,408$ 535,408$ Program Overhead Costs 223,141$ 223,141$ 223,141$ Total BenefitsTotal CostsTest Score

1,497,514$ 1,562,382$ 1,562,382$ 1,562,382$


535,408$ 419,245$ 2,326,053$ 758,549$ 2.80 3.73 0.67 2.06


Table 6-5 Public Efficient Street Lighting Program

Benefit Cost Benefit Cost Benefit Cost Benefit CostIncentives -$ -$ -$ Program Installation Costs -$ -$ -$ Bill Savings (NPV) 0$ Lost Revenue (NPV) 0$ Avoided Energy Costs (NPV) 0$ 0$ 0$ Avoided Capacity Costs (NPV) -$ -$ -$ Avoided T&D Costs (NPV) -$ -$ -$ Incremental Costs -$ -$ Program Overhead Costs 4,824$ 4,824$ 4,824$ Total BenefitsTotal CostsTest Score

-$ 4,824$ 4,824$ 4,824$ N/A 0.00 0.00 0.00

0$ 0$ 0$ 0$



Documents

2018 Indiana Commercial & Industrial Portfolio EM&V Report · 2018 Indiana Commercial & Industrial Portfolio . EM&V Report . Volume I of II Prepared for: Indiana Michigan Power