assessment of energy and capacity savings potential in iowa · 2020. 9. 28. · terry fry, sam...

$: Assessment of Energy and Capacity Savings Potential in Iowa · 2020. 9. 28. · Terry Fry, Sam Mueller, Patrick Johanning and Angela Patnode Nexant, Inc. K:\2007 Projects\2007-111$
Iowa Utility Association – Joint Assessment Study

Final Report – Volume I

Assessment of Energy andCapacity Savings Potential inIowa

Prepared for the Iowa Utility Association

February 2008

In Collaboration with Summit Blue Consulting, Nexant, Inc., A-TECEnergy Corporation, and Britt/Makela Group

Quantec Offices720 SW Washington, Suite 400Portland, OR 97205(503) 228-2992; (503) 228-3696 faxwww.quantecllc.com

Printed onrecycled paperPrinted onrecycled paper

1722 14th St., Suite 210Boulder, CO 80302(303) 998-0102; (303) 998-1007 fax

Investigators:

Hossein Haeri, Ph.D., Scott Dimetrosky, Charles Bicknell, Collin Elliot,Tina Jayaweera, Ph.D., Eli Morris, Tony Larson, Aquila Velonis,

Allen Lee, Ph.D., Eric Flora, Ross Notebaart, Nathan Vellinga, Dan Groshans,Meghan Lee, Sara Wist, Rick Ogle and Ken Seiden

Quantec, LLC.

Kevin Cooney, Randy Gunn, Mary Klos, Daniel Klos, Adam Knickelbein,Beth Baker, Rachel Freeman and Roger Hill

Summit Blue Consulting

Terry Fry, Sam Mueller, Patrick Johanning and Angela PatnodeNexant, Inc.

K:\2007 Projects \2007-111 (IUA) IUA State-Wide Savings Potential\Reports and Presentations\Final Report\IUA FinalReport_010807.doc

Iowa Utility Association – Joint Assessment Study i

Table of Contents: Volume I

Acknowledgements............................................................................................................ ix

Executive Summary .............................................................................................1Overview..............................................................................................................................1Methodology ........................................................................................................................2Planning Considerations ......................................................................................................8

1. Introduction ................................................................................................1Project Scope and Objectives...............................................................................................1Definition of Resource Potentials ........................................................................................2General Approach to Estimating Resource Potentials .........................................................3Considering the Role of Uncertainty ...................................................................................4Organization of this Report................................................................................................10

2. Data Collection .........................................................................................11Primary Data Collection ....................................................................................................11Utility Database Mining.....................................................................................................14Secondary Data ..................................................................................................................14Summary of Data Collection for High Priority Measures .................................................14Business Segment Data......................................................................................................17Additional Utility Data for Potential Analysis...................................................................17

3. Energy Efficiency .....................................................................................19Scope of Analysis ..............................................................................................................19Methodology......................................................................................................................20Summary of Resource Potential – Electricity....................................................................21Summary of Resource Potential – Natural Gas .................................................................23Detailed Resource Potential ...............................................................................................24

4. Demand Response...................................................................................39Scope of Analysis ..............................................................................................................39Estimate Demand Response Resource Potentials ..............................................................40Summary of Demand Response Resource Potential..........................................................43Resource Costs and Supply Curves ...................................................................................44Resource Acquisition Schedule .........................................................................................47Demand Response Resource Results by Program Option .................................................47

Iowa Utility Association – Joint Assessment Study ii

5. Renewable Resources .............................................................................79Scope..................................................................................................................................79Methodology......................................................................................................................80Summary of Findings.........................................................................................................80Biomass Energy .................................................................................................................83Clean Energy......................................................................................................................88Passive Efficiency Resources ..........................................................................................102Emission Reductions........................................................................................................105

Iowa Utility Association – Joint Assessment Study iii

Tables and Figures

Executive Summary .........................................................................................E-1Table 1. Technical and Economic Electric Energy-Efficiency Potential (GWh in2018) by Utility................................................................................................................E-3Table 2. Technical and Economic Electric Energy-Efficiency Potential (GWh in2018) by Sector (Alliant and MidAmerican) ...................................................................E-4Table 3. Technical and Economic Gas Energy-Efficiency Potential (Thousanddecatherms in 2018) by Utility ........................................................................................E-4Table 4. Technical and Economic Gas Energy-Efficiency Potential (Thousanddecatherms in 2018) by Sector (Alliant, Aquila, and MidAmerican)..............................E-5Table 5. Alliant Energy Technical and Market Potential (MW in 2018) .......................E-5Table 6. MidAmerican Energy Technical and Market Potential (MW in 2018) .............E-5Table 7. Levelized Costs and Market Potential (MW in 2018) .......................................E-6Table 8. Market Potential for DG Renewable Resources (2018) ....................................E-7Table 9. Economic Potential for Passive Renewable Resources by Fuel (2018) ............E-7Figure 1. Cumulative Supply Curve for Dispersed Generation RenewableResources (2018) .............................................................................................................E-7

1. Introduction ................................................................................................1Figure 2. General Methodology for Assessment of Demand-Side ResourcePotential ...............................................................................................................................4

2. Data Collection .........................................................................................11Table 10. Residential Primary Data Collection Efforts .....................................................12Table 11. Non-Residential Primary Data Collection Efforts .............................................13Table 12. Summary of Data Sources for Residential Sector Measures .............................14Table 13. Summary of Data Sources for Non-Residential Sector Measures .....................16

3. Energy Efficiency .....................................................................................19Table 14. Energy-Efficiency Measure Counts (Base-Case Scenario) ...............................19Table 15. Technical and Economic Electric Energy-Efficiency Potential (GWh in2018) by Utility..................................................................................................................21Table 16. Technical and Economic Electric Energy-Efficiency Potential (GWh in2018) by Sector (Alliant and MidAmerican) .....................................................................22Table 17. Technical and Economic Energy-Efficiency Potential (GWh in 2018)by Sector and Resource Type ............................................................................................22Table 18. Technical and Economic Gas Energy-Efficiency Potential (Thousanddecatherms in 2018) by Utility ..........................................................................................23

Iowa Utility Association – Joint Assessment Study iv

Table 19. Technical and Economic Gas Energy-Efficiency Potential (Thousanddecatherms in 2018) by Sector (Alliant, Aquila, and MidAmerican)................................23Table 20. Technical and Economic Gas Energy-Efficiency Potential (Thousanddecatherms in 2018) by Sector and Resource Type...........................................................24Table 21. Residential Sector Electric Energy-Efficiency Potential by Utility (GWhin 2018) ..............................................................................................................................24Figure 3. Residential Sector Electric Economic Potential by Segment .............................25Table 22. Residential Sector Electric Energy-Efficiency Potential by End Use(GWh in 2018) ...................................................................................................................26Figure 4. Residential Sector Electric Economic Potential by End Use .............................26Table 23. Residential Sector Gas Energy-Efficiency Potential by Utility(Thousand decatherms in 2018).........................................................................................27Figure 5. Residential Sector Gas Economic Potential by Segment ...................................28Table 24. Residential Sector Gas Energy-Efficiency Potential by End Use(Thousand decatherms in 2018).........................................................................................28Figure 6. Residential Sector Gas Economic Potential by Segment ...................................29Table 25. Commercial Sector Energy-Efficiency Potential by State (GWh in2018) ..................................................................................................................................29Figure 7. Commercial Sector Economic Potential by Segment.........................................30Table 26. Commercial Sector Electric Energy-Efficiency Potential by End Use(GWh in 2018) ...................................................................................................................30Figure 8. Commercial Sector Economic Potential by End Use .........................................31Table 27. Commercial Sector Gas Energy-Efficiency Potential by Utility(Thousand decatherms in 2018).........................................................................................32Table 28. Commercial Sector Gas Energy-Efficiency Potential by End Use(Thousand decatherms in 2018).........................................................................................32Figure 9. Commercial Sector Gas Economic Potential by End Use..................................33Table 29. Industrial Sector Energy-Efficiency Potential by State (GWh in 2018)............34Figure 10. Industrial Sector Economic Potential by Segment ...........................................34Table 30. Industrial Sector Electric Energy-Efficiency Potential by End Use(GWh in 2018) ...................................................................................................................35Figure 11. Industrial Sector Electric Economic Potential by End Use ..............................35Table 31. Industrial Sector Gas Energy-Efficiency Potential by Utility (Thousanddecatherms in 2018)...........................................................................................................36Figure 12. Industrial Sector Gas Economic Potential by Segment....................................37Table 32. Industrial Sector Gas Energy-Efficiency Potential by End Use(Thousand decatherms in 2018).........................................................................................37Figure 13. Industrial Sector Gas Economic Potential by End Use ....................................38

4. Demand Response...................................................................................39Figure 14. Schematic Overview of Demand Response Assessment Methodology ...........41

Iowa Utility Association – Joint Assessment Study v

Table 33. Alliant Energy Technical and Market Potential (MW in 2018) .......................43Table 34. MidAmerican Energy Technical and Market Potential (MW in 2018) .............44Table 35. Levelized Costs and Market Potential (MW in 2018) .......................................45Figure 15. Alliant Energy Territory Supply Curve (Cumulative MW in 2018) ...............46Figure 16. MidAmerican Energy Territory Supply Curve (Cumulative MW in2018) ..................................................................................................................................46Table 36. Residential DLC Air-Conditioning: Technical and Market Potential(MW in 2018) ....................................................................................................................48Table 37. Assumptions for DLC Residential Air-Conditioning Potential .........................50Table 38. DLC Air-Conditioning and Water Heating: Technical and MarketPotential (MW in 2018) ....................................................................................................51Table 39. Assumptions for DLC Residential Air-Conditioning and Water HeatingPotential .............................................................................................................................52Table 40. DLC Air-Conditioning: Technical and Market Potential (MW in 2018) .........53Table 41. Assumptions for DLC Small Commercial Air-Conditioning Potential.............54Table 42. DLC Large Commercial: Technical and Market Potential (MW in2018) ..................................................................................................................................55Table 43. Assumptions for DLC Large Commercial Potential..........................................56Table 44. Thermal Energy Storage: Technical and Market Potential (MW in2018) ..................................................................................................................................58Table 45. Assumptions for TES Potential..........................................................................59Table 46. Interruptible Program: Technical and Market Potential (MW in 2018) ...........61Table 47. Assumptions for Interruptible C&I Potential.....................................................62Table 48. Demand Buyback: Technical and Market Potential (MW in 2018) ..................64Table 49. Assumptions for DBB Potential ........................................................................65Table 50. Time of Use Rates: Technical and Market Potential (MW in 2018) .................67Table 51. Assumptions for Residential TOU Potential .....................................................68Table 52. Residential CPP : Technical and Market Potential (MW in 2018)...................71Table 53. Assumptions for Residential CPP Potential.......................................................72Table 54. C&I CPP: Technical and Market Potential (MW in 2018)................................73Table 55. Assumptions Used for C&I CPP .......................................................................74Table 56. RTP: Technical and Market Potential (MW in 2018)........................................76Table 57. Assumptions for C&I RTP ................................................................................77

5. Renewable Resources .............................................................................79Table 58. Installed DG Renewable Capacity by Resource (2006) ....................................81Table 59. Technical Potential for DG Renewable Resources (2018) ................................81Table 60. Technical Potential for Passive Renewable Resources (2018)..........................82Table 61. Market Potential for DG Renewable Resources (2018) ....................................82Table 62. Economic Potential for Passive Renewable Resources by Fuel (2018) ............83

Iowa Utility Association – Joint Assessment Study vi

Figure 17. Cumulative Supply Curve for Dispersed Generation RenewableResources (2018) ...............................................................................................................83Table 63. Biomass Energy Prototypical Generating Units ................................................85Table 64. Costs for Technologies Considered (2007 Dollars)...........................................86Table 65. Biomass Energy Technical Potential (GWh in 2018) by ResourceCategory.............................................................................................................................87Table 66. Biomass Energy Market Potential (GWh) by Sector in 2018............................88Table 67. Costs, Measure Life and Capacity Factor for Clean Energy Resources ............89Table 68. Technical Potential of Clean Energy Resources by Technology (GWhin 2018) ..............................................................................................................................92Figure 18. PV Potential Methodology ...............................................................................93Table 69. Potential Hydro Sites .........................................................................................96Table 70. Technical Potential by Technology Class (GWh in 2018) ................................96Figure 19. Estimated Average Annual Wind Speeds for Iowa..........................................98Table 71. Clean Energy Market Potential (GWh) by Sector in 2018 ..............................100Figure 20. Clean Energy Average Monthly Market Potential (2018) .............................100Table 72. Passive Efficiency Potentials by Sector (GWh in 2018) .................................102Table 73. Passive Efficiency Potentials by Sector (1000 DTh in 2018)..........................103Table 74. Levelized Costs of Passive Efficiency Measures in Residential Sector ..........103Figure 21. Residential Sector Passive Renewable Resources: Economic ElectricPotential by End Use........................................................................................................104Table 75. Levelized Costs of Passive Efficiency Measures in Commercial Sector ........104Figure 22. Commercial Sector Passive Renewable Resources: Economic ElectricPotential by End Use........................................................................................................105Figure 23. Commercial Sector Passive Renewable Resources: Economic GasPotential by End Use........................................................................................................105Table 76. Estimated Emissions Savings Potential ...........................................................106

Iowa Utility Association – Joint Assessment Study vii

Table of Contents: Volume II

Appendix A: Energy Efficiency Measure Descriptions

Appendix B: Customer Surveys

B-1 Summary of Survey Results

B-2 Survey Instruments

Appendix C: Supplemental Material – Energy Efficiency

Appendix D: Supplemental Material – Demand Response

Appendix E: Supplemental Material – Renewables

Appendix F: Building Simulations

Appendix G: Attribution of Energy Savings: An Assessment of the Net-to-Gross Ratio Issue

Appendix H: Bibliography of Specific Conservation Potentials Assessment and Best PracticesStudies

Appendix I: Benchmarking and Best Practices Study

Iowa Utility Association – Joint Assessment Study ix

Acknowledgements

This study required compilation of a large amount of data from various sources, includingseveral departments from Alliant, Aquila, and MidAmerican. It would be difficult to overstatethe importance of the contributions made by staff at each of the utilities, all of whom madeexceptional efforts to meet the comprehensive data requests necessary to complete this study in atimely manner. We are especially indebted to Tom Balster, Bob Holmes, Sarah Else HarveyDorn, Dorothy Landt, Lisa Pucelik, Julie Blackwell, and Gilbert Nunez from Alliant Energy;Matt Daunis and Nora Hard from Aquila; David J. McCammant, Rick Leuthauser, Judy Moore,John O’Roake, and Richard Walker from MidAmerican Energy.

We acknowledge with gratitude the guidance and insight provide by the staff of the Iowa Officeof Consumer Advocate (OCA), including Jennifer Easler, Joe Murphy, Khosrow Khojasteh, andother members of the OCA Team who worked closely with us and supplied valuable feedback onour approach and methodology, ensuring that the study would meet the expectations of allstakeholders.

We are especially grateful to Jack Clark of the Iowa Utility Association for coordinating thislarge and complex effort. His hard work, attention to detail, and facilitation skills allowed us tocomplete this study on time and on budget.

Iowa Utility Association – Joint Assessment Study ES-1

Executive Summary

Overview

Quantec, LLC, in collaboration with Nexant, Inc., Summit Blue Consulting, A-TEC EnergyCorporation, and the Britt/Makela Group, was retained by the Iowa Utility Association toconduct an assessment of technical and economic opportunities for electric and gas energy-efficiency and renewable resources in the service territories of the Association’s three investor-owned utility members, namely Alliant Energy Corporation (Alliant), Aquila, Inc. (Aquila), andMidAmerican Energy Company (MidAmerican).1

Chapter 35 of the 1999 Iowa Administrative Code (199 IAC 35) sets forth the Iowa Utility Board(IUB) rules to implement legislation enacted in 1990 and modified in 1996 requiring the Utilitiesto provide energy-efficiency programs for their customers. The current rules with respect to thisassessment became effective on February 17, 1999.

The goals of this project, as specified in the request for proposals (January 31, 2007), included:

1. Conduct primary market research to collect data on energy-efficiency measuresincluding, but not limited to, current saturations and market adoption trends, and otherkey inputs for the technical assessment.

2. Develop estimates of “technical” and “economic” potentials for electric energy-efficiencyand peak capacity reduction, natural gas energy efficiency, and select renewableresources2 for all major end uses in various customer sectors (including the low-incomesegment or the residential class) by construction vintage for each of the utilities.3

3. Investigate the implications of certain provisions of the Federal Energy Policy Act of2005 (EPACT), particularly raising equipment efficiency standards and implementationof demand response programs.

4. Review best practices for the delivery and verification of savings from the deployment oftechnical resources identified in this study to inform future program planning design andevaluation, including the low-income programs.

5. Assess the trends in new construction energy code compliance in the residential sectorand determine the effects of non-compliance on energy savings potentials.

This study addressed each of these objectives. Specifically, the Quantec team conducted asubstantial primary data collection effort in order to provide both utility-specific and Iowa-

1 Atmos Energy Corporation received a waiver from the Iowa Utilities Board (IUB) not to participate in thestudy.

2 Non-renewable dispersed generation (e.g. Combined Heat & Power) was considered outside the scope of thisstudy.

3 The assessment of economic potential was added to the scope of work when the contract was awarded to theQuantec Team in May 2007.


specific inputs for the technical and economic potential estimates. In addition, the 2005 EPACTprovisions were incorporated into the potential estimates, as known changes in federal standardseffectively raised the baseline efficiency standards and, in some cases, lowered the potentialestimates. The best practices research included a benchmarking study to compare utility impactsvs. spending, plus focused on the various approaches for incorporating freeridership andspillover effects into net-to-gross estimates, with a recommendation for how these effects shouldbe examined in Iowa. Finally, Volume III contains the results of the code compliance study. Thisstand-alone study was initiated in late 2007 in order to allow a large enough inventory of homesthat were required to meet the 2006 IECC, which was not fully enforced in Iowa until April2007.

Methodology

This general methodology is best described as a combination “top-down/bottom-up” approach.The top-down methodology component begins with the most current utility load forecasts,decomposes them into their constituent customer sector, customer segment, and end-usecomponents. The bottom-up component considers the potential technical impacts of variousdemand-side and supplemental resource technologies, measures, and practices on each end use,which are then estimated based on engineering calculations, taking into account fuel shares,current market saturations, technical feasibility, and costs. These unique impacts are aggregatedto produce estimates of resource potential at the end-use, customer sector, and service territorylevels. In many ways, the approach is analogous to generating two alternative load forecasts atthe end-use level (one with and one without DSM and supplemental resources) and calculatingresource potential as the difference between the two forecasts.

Separate assessments of technical and economic potential for residential, commercial, andindustrial sectors were made for each utility, split by fuel type. Within each utility’s sector-levelassessment, the study further distinguished among customer segments or facility types and theirrespective applicable end uses. In total, the analysis assessed the technical and economicpotential for 304 unique electric and 152 unique gas energy-efficiency measures. These measuresare primarily composed of technologies that are currently available on the market. Within the 10-year span of this study, it is likely new, unanticipated, measures will gain market acceptance,changing the overall potential.

To ensure an accurate representation of the Iowa market for use in modeling the energy andcapacity savings potential, the data collection efforts included over 840 telephone and 380 on-site surveys of residential and non-residential customers, trade allies and contractors. In additionto the comprehensive primary data collection efforts, the study relied on other data sources,including utility database mining, Dun and Bradstreet business segment data, and varioussecondary sources such as studies conducted by other utilities and energy-efficiency agenciesaround the country and third-party studies conducted by private research organizations, state andfederal agencies. Finally, the study relied on a substantial amount of utility data, includingcustomer counts, electric and gas sales, system hourly load shapes, sales and demand forecasts,historical demand and efficiency achievements, and avoided costs.


Summary of Results

Energy Efficiency

Table 1 and Table 2 show 2018 baseline sales (the end of the 10-year planning horizon) andpotential by utility and sector, respectively. As shown, the results of this study indicate 9,767GWh of technically feasible electric energy-efficiency potential by 2018. Approximately6,800 GWh of these resources are cost-effective at an average levelized per-unit cost of3 cents/kWh. The identified economic potential amounts to 17% of forecast load in 2018 andover 1,500 MW of peak demand reduction.

These savings are based on forecasts of future consumption absent any utility program activities.While consumption forecasts account for the past savings each utility has acquired, the estimatedpotential is inclusive of—not in addition to—current or forecasted program savings.

Technical and economic potential are a function of baseline sales, but are roughly comparablewhen analyzing in percentage terms. Differences in technical potential as a percent of baselinesales are driven by differences in the distribution of customers by segment and other utility-specific customer characteristics. In addition to these differences, the economic potential variesdue to differences in utility avoided costs.

Table 1. Technical and Economic Electric Energy-Efficiency Potential(GWh in 2018) by Utility

UtilityBaseline

SalesTechnicalPotential

TechnicalPotential as

% ofBaseline

EconomicPotential

EconomicPotential as

% ofBaseline

Economicas % of

Technical

EconomicPotential

(MW)

AverageLevelized

Cost

Alliant 18,250 4,453 24% 3,304 18% 74% 662 $0.03

MidAmerican 21,329 5,314 25% 3,473 16% 65% 875 $0.03

Total 39,580 9,767 25% 6,777 17% 69% 1,537 $0.03

Each sector’s technical and economic potentials are given in Table 2. The residential sectorrepresents the largest portion of both the total technical and economic potentials at 51% and47%, respectively. The commercial sector is the second largest contributor to the technicalpotential, but because industrial improvements are highly cost-effective, it becomes the smallestcontributor to the economic potential at about 23% (Table 2).


Table 2. Technical and Economic Electric Energy-Efficiency Potential(GWh in 2018) by Sector (Alliant and MidAmerican)

SectorBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical

EconomicPotential

(MW)

AverageLevelized

Cost

Residential 10,819 4,937 46% 3,215 30% 65% 997 $0.04

Commercial 9,086 2,695 30% 1,563 17% 58% 270 $0.03

Industrial 19,675 2,136 11% 1,999 10% 94% 270 $0.01

Total 39,580 9,767 25% 6,777 17% 69% 1,537 $0.03

Table 3 and Table 4 show 2018 baseline sales and potential by sector and utility, respectively, fornatural gas efficiency potential. As shown, the results of this study indicate over 40,000,000decatherms of technically feasible gas energy-efficiency potential by 2018, the end of the 10-year planning horizon. Approximately 28,500,000 decatherms of these resources are cost-effective at an average levelized per-unit cost of 44 cents/therm. The identified economicpotential amount to 27% of forecast load in 2018 and over 1,500 peak day decatherms.

As with electric potential, technical, and economic potential are a function of baseline sales, butthey are roughly comparable across utilities when analyzing in percentage terms. Differences areagain driven by utility customer characteristics and avoided costs.

Table 3. Technical and Economic Gas Energy-Efficiency Potential(Thousand decatherms in 2018) by Utility

UtilityBaseline



% of BaselineEconomic

Potential


% ofBaseline

Economicas % of

Technical

EconomicPotential (Peak

daydecatherms)

AverageLevelized

Cost

Alliant 27,484 10,600 39% 7,683 28% 72% 88,822 $0.45

Aquila 16,307 6,556 40% 4,842 30% 74% 58,990 $0.55

MidAm 61,704 23,497 38% 16,039 26% 68% 197,144 $0.40

Total 105,495 40,653 39% 28,564 27% 70% 344,855 $0.44

Each sector’s technical and economic potentials are given in Table 4. As with electric potential,the residential sector represents the largest portion of both the total technical and economicpotential (about 65% of each). Almost all the remaining potential lies in the commercial sector,with a small portion (897,000 decatherms) in industrial (Table 4).


Table 4. Technical and Economic Gas Energy-Efficiency Potential(Thousand decatherms in 2018) by Sector (Alliant, Aquila, and MidAmerican)

SectorBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical


daydecatherms)

AverageLevelized

Cost

Residential 65,968 26,532 40% 18,654 28% 70% 248,713 $0.44

Commercial 34,475 13,224 38% 9,013 26% 68% 93,784 $0.48

Industrial 5,052 897 18% 897 18% 100% 2,459 $0.07

Total 105,495 40,653 39% 28,564 27% 70% 344,955 $0.44

Demand Response

Table 5 and Table 6 report estimated resource potential for all demand response resources for theresidential, commercial, and industrial sectors for Alliant and MidAmerican. Market potential ishighest in the industrial sector due to the interruptible program. Note, however, that the analysisdoes not account for program interactions and overlap, and thus the total technical and marketpotential estimates are provided as examples only, but are not fully attainable.

Table 5. Alliant EnergyTechnical and Market Potential (MW in 2018)

Sector 2018 Sector Peak 2018 TechnicalPotential

2018 MarketPotential

MarketAcceptance as

% of 2018 SectorPeak

Residential 988 590 71 9%Commercial 970 602 70 9%Industrial 1475 1195 262 21%Total 3434 2388 403 14%Note: Individual results may not sum to total due to rounding.Note: Interactions between programs has not been taken into account.Note: DLC RES AC has been eliminated from these potential results to account for complete overlap with DLC RES AC

and water heating.

Table 6. MidAmerican EnergyTechnical and Market Potential (MW in 2018)



MarketAcceptance as


Residential 1,367 809 97 9%Commercial 964 388 38 5%Industrial 1,516 868 159 13%Total 3,846 2,065 295 10%Note: Individual results may not sum to total due to roundingNote: Interactions between programs has not been taken into account


Table 7 displays the market potential and per-unit ($/kW-year) costs by the various demandresponse programs examined as part of this study. The largest potential is for interruptible tariffsand residential direct load controls. Real-time pricing and critical peak pricing (both C&Iprograms) are estimated to be the least expensive options, with a levelized cost of $11/kW-yearfor Alliant, while critical peak pricing and demand bidding are the least expensive options forMidAmerican, with a levelized cost of $19/kW-year and $17/kW-year, respectively. Theseresults also do not account for program interactions and overlap.

Table 7. Levelized Costs and Market Potential (MW in 2018)Alliant Energy MidAmerican Energy

Levelized Cost MarketPotential (MW)

LevelizedCost ($/kW)

MarketPotential (MW)


Direct Load Control (DLC)Residential (A/C only) 48 $55 66 $56Residential (A/C and WH) 53 $62 72 $63Small Commercial (A/C) 1 $96 1 $81Medium to LargeCommercial

1 $119 1 $169

Thermal Energy Storage(TES)

1 $135 1 $150

Interruptible Tariffs 291 $45 170 $26Demand Bidding 18 $14 15 $17TOU Rates 7 $38 10 $87Critical Peak Pricing (CPP)

Residential 11 $95 15 $95C&I 11 $11 9 $19

Real-Time Pricing (RTP) 9 $11 - - - - - -

Renewable Resources

In addition to traditional energy-efficiency resources, this report includes an analysis of twoclasses of renewable resources: active (dispersed generation) and passive (energy-efficiency)resources. Active resources, loosely defined as “dispersed generation” (DG), include energy-based resources of biomass and three “clean generation” (non-combustion) resources: buildingphotovoltaics (on-site solar), small hydro, and small wind. Passive resources fall into two broadcategories: passive solar building design and renewable efficiency measures.

For DG resources, market potential represents the portion of technical potential that mightactually be installed. It should be realized that not all these resources are economic, but,nonetheless, may be installed by customers willing to accept long payback times. For passiveefficiency measures, the economic potential is provided, as determined for other energy-efficiency resources.

The market potential for all renewable resources is shown in Table 8 for DG renewable resourcessavings and in Table 9 for passive resources. Compared to the technical potential of DGresources, this potential is significantly less due to economic considerations, low awareness oftechnologies, and other permitting or interconnection concerns (details are provided in the results


sections, below). Among the DG resources, biomass energy composes the largest percentage ofmarket potential (155 GWh), followed by small wind (103 GWh), and PV (25 GWh). Thepercentage of technical potential economic for passive efficiency resources is 97% of electric(453 GWh) and 88% of gas (644 thousand DTh).

Table 8. Market Potential for DG Renewable Resources (2018)

Resource Potential (GWh) Percent ofPotential

Biomass Energy 155 54%Building Photovoltaics 25 8%Small Hydro 7 2%Small Wind 103 36%Total 290 100%

Table 9. Economic Potential for Passive Renewable Resources by Fuel (2018)Resource Potential

Electric passive efficiency resources 453 GWhGas passive efficiency resources 643,806 Dth

Figure 1 presents the cumulative supply curve for all DG resources. Biomass Energy is brokeninto potential from Industrial Biomass (direct combustion) and Anaerobic Digesters (biogascombustion).

Figure 1. Cumulative Supply Curve for Dispersed Generation Renewable Resources (2018)

Ind Biomass

PV

Wind

HydroAnaerobicDigesters

$-

$0.10

$0.20

$0.30

$0.40

$0.50

$0.60

$0.70

0 50,000 100,000 150,000 200,000 250,000 300,000

Cummulative MWh

Lev

eliz

ed

Cos

t($/

kWh

)


Planning Considerations

Resource potential studies are important means of developing reasonably reliable estimates ofthe magnitude, costs, and timing of demand-side management resources and ,as such, serve as acritical first step in a utility’s resource planning process. The results of these studies also helpguide and inform the program development process.

These studies are also complex undertakings, requiring compilation of a large amount of datafrom multiple sources, and a number of pivotal assumptions about future technological trends,market conditions, and consumer behavior. For example, the assessment of the technicalpotential is inherently a static analysis and assumes “frozen” efficiencies for all baselinetechnologies. Estimates of economic potentials similarly depend on assumed technology costsand on determinants of the utility’s avoided costs, particularly for fuel prices.

Clearly, the emergence of new technologies and enhancements to existing ones will affect thepotentials for all types of demand-side management, and fluctuations in avoided costs willdirectly affect the expected future value of these resources. The results of this study are alsosensitive to changes in macro-economic conditions and structural changes, such as fluctuationsin energy prices, the institution of more stringent energy codes and standards, or the impositionof a carbon tax. The findings of this study, therefore, should be considered “indicative” ratherthan “conclusive.” Inevitably, much of the data used in this study will have to be updated, andmany of its assumptions will need to be revisited periodically.

Resource potential assessment objectives differ from those of program design and productdevelopment in that they seek to provide estimates of technically feasible and cost-effectiveenergy-efficiency opportunities. They are useful in understanding not only the amounts ofavailable opportunities, but the sectors and end uses where they might be concentrated. Yet, theyprovide little information or guidance as to how and by what means the identified resourcepotential might be deployed. The potential for many identified resources might be realizedthrough legislative action to institute efficiency codes and standards. Consumer education ormarket transformation initiatives can also serve as an effective means of promoting energyefficiency.

Finally, the scope of this study was limited to analyzing technical and economic potentials only.Except in the case of renewables and demand response options, no attempt was made to accountfor the effects of market barriers, which tend to impede the penetration of energy-efficiencytechnologies and programs. Once the effects of such barriers are accounted for, amounts ofrealistically achievable potential are likely to be lower than that suggested by the economicpotential.

Iowa Utility Association – Joint Assessment Study 1

1. Introduction

Quantec, LLC, in collaboration with Nexant, Inc., Summit Blue Consulting, A-TEC EnergyCorporation, and the Britt/Makela Group, was retained by the Iowa Utility Association toconduct an assessment of technical and economic opportunities for electric and gas energy-efficiency and renewable resources in the service territories of the Association’s three maininvestor-owned utility members, namely Alliant Energy Corporation (Alliant), Aquila, Inc.(Aquila), and MidAmerican Energy Company (MidAmerican).4

Chapter 35 of the 1999 Iowa Administrative Code (199 IAC 35) sets forth the Iowa Utility Board(IUB) rules to implement legislation enacted in 1990 and modified in 1996 requiring the Utilitiesto provide energy-efficiency programs for their customers. The current rules with respect to thisassessment became effective on February 17, 1999.

On September 14, 2006, the IUB Staff advised the rate-regulated utilities and interestedstakeholders that they should begin the process of initiating a new energy-efficiency plan andshould proceed to develop plans to replace their current plans that are scheduled to expire at theend of 2008. The new plans are required to include an assessment of the potential for energy andcapacity savings in Iowa. This assessment would become the foundation on which each utilitycould then develop their own tailored energy-efficiency plans that both comply with the IUBrules and adhere to each company’s goals and objectives for this activity.

Project Scope and Objectives

Studies of demand-side management potentials are important tools for policy analysis, utilityresource planning, and program design. As such, reasonably accurate projections of actualpotentials for these resources, as well as reliable estimates of their associated costs, are critical inguiding utilities as they design their resource acquisition programs. Demand-side managementobjectives may be met through a broad range of technology- and activity-based measures,behavior modification, and/or legislative action, such as the institution of energy-efficiencycodes and standards. Demand-side resource potential also varies depending on the utility’s loadcharacteristics, customer mix, local market conditions and climate.

The goals of this project, as specified in the request for proposals (January 31, 2007), included:

1. Conduct primary market research to collect data on energy-efficiency measuresincluding, but not limited to, current saturations and market adoption trends, and otherkey inputs for the technical assessment.

2. Develop estimates of “technical” and “economic” potentials for electric energy-efficiencyand peak capacity reduction, natural gas energy efficiency, and select renewable

4 Atmos Energy Corporation received a waiver from the Iowa Utilities Board (IUB) not to participate in thestudy.


resources for all major end uses in various customer sectors (including the low-incomesegment or the residential class) by construction vintage for each of the three utilities.5

3. Investigate the implications of certain provisions of the Federal Energy Policy Act of2005 (EPACT), particularly raising equipment efficiency standards and implementationof demand response programs.

4. Review best practices for the delivery and verification of savings from the deployment oftechnical resources identified in this study to inform future program planning design andevaluation, including the low-income programs.

5. Assess the trends in new construction energy code compliance in the residential sectorand determine the effects of non-compliance on energy savings potentials.

This study addressed each of these objectives. Specifically, the Quantec team conducted asubstantial primary data collection effort in order to provide both utility-specific and Iowa-specific inputs for the technical and economic potential estimates. In addition, the 2005 EPACTprovisions were incorporated into the potential estimates, as known changes in federal standardseffectively raised the baseline efficiency standards and, in some cases, lowered the potentialestimates. The best practices research primarily focused on the various approaches forincorporating freeridership and spillover effects into net-to-gross estimates, with arecommendation for how these effects should be examined in Iowa. Finally, the code compliancestudy examined a sample of homes to assess compliance with the 2006 International EnergyConservation Code (IECC).

Definition of Resource Potentials

Estimation of technical and economic potential in this study is based on best-practice researchmethods and analytic techniques that are both standard in the utility industry and are consistentwith the requirements of Chapter 35 of the 1999 Iowa Administrative Code. Consistent withaccepted industry standards, this study’s approach distinguishes among four definitions ofresource potential widely used in utility resource planning.

Naturally occurring conservation refers to reductions in energy use that occur due to normalmarket forces, such as technological change, energy prices, market transformation efforts, andimproved energy codes and standards. In this analysis, naturally occurring conservation isaccounted for in several ways. First, the potential associated with certain energy-efficiencymeasures assumes a natural rate of adoption. For example, the savings associated with ENERGYSTAR® appliances account for current trends in customer adoption. Second, current codes andstandards are applied in the consumption characteristics of new construction. Finally, theassessment accounts for the gradual increase in efficiency as older equipment in existingbuildings is retired and replaced by units meeting current standards. However, this assessmentdoes not forecast changes to codes and standards; rather, it treats them at a “frozen” efficiencylevel.

5 The assessment of economic potential was added to the scope of work when the contract was awarded to theQuantec Team in May 2007.


Technical potential assumes that all available DSM measures and supplemental resource optionsmay be implemented, regardless of their costs or market barriers. For energy-efficiencyresources, technical potential further falls into two classes: discretionary (retrofit) and lost-opportunity resources. It is important to recognize that the notion of technical potential is lessrelevant to resources such as capacity-focused programs and distributed generation since mostend-use loads may be subject to interruption through load curtailment or displacement by on-sitegeneration from a strictly “technical” point of view.

Economic potential represents a subset of technical potential only consisting of those measuresthat meet the cost effective criterion based on the societal test, as it is defined in Chapter 35 ofthe 1999 Iowa Administrative Code. For each measure, the test is structured as the ratio of thenet present values of the measure’s benefits and costs. Only measures with a benefit-to-cost ratioof 1.0 or greater are deemed cost effective. The methodology for cost-effectiveness calculationsand relevant benefit and cost elements are described in detail in Chapter 3 and Volume II,Appendix C.

Achievable potential is defined as the portion of economic potential that might be assumed to bereasonably achievable in the course of the planning horizon, given market barriers that mayimpede customer participation in utility programs. Achievable potential can vary sharply basedon program incentive structures, marketing efforts, energy costs, customer socio-economiccharacteristics, and other factors. This study did not analyze achievable potential.

General Approach to Estimating Resource Potentials

Resources analyzed in this study differ with respect to several salient attributes, such as the typeof load impact (energy or capacity), availability, reliability, and applicability to various customerclasses and customer segments (business, dwelling, or facility types). They also requirefundamentally different approaches in program design, incentive structures, and deliverymechanisms for their deployment. Therefore, analysis of the potential for these resourcesrequires methods tailored to address the unique technical and market characteristics of eachresource. These methods, however, generally spring from a common conceptual framework, andtheir applications to various resources rely on similar analytic methodologies.

This general methodology is best described as a combination “top-down/bottom-up” approach.As illustrated in Figure 2, the top-down methodology component begins with the most currentutility load forecasts, decomposes them into their constituent customer sector, customer segment,and end-use components. The bottom-up component considers the potential technical impacts ofvarious demand-side and supplemental resource technologies, measures, and practices on eachend use, which are then estimated based on engineering calculations, taking into account fuelshares, current market saturations, technical feasibility, and costs. These unique impacts areaggregated to produce estimates of resource potential at the end-use, customer sector, and serviceterritory levels. In many ways, the approach is analogous to generating two alternative loadforecasts at the end-use level (one with and one without DSM and supplemental resources) andcalculating resource potential as the difference between the two forecasts.


Figure 2. General Methodology for Assessment of Demand-Side Resource Potential

Load ForecastLoad Forecast

Sector LoadsSector Loads

Technical Potential byMeasure/End-Use

Technical Potential byMeasure/End-Use

Technical PotentialTechnical Potential

• Customer count/• Sector sales

• Simulation models/• Secondary sources

BaselineEnd-Use Consumption (EUC)

BaselineEnd-Use Consumption (EUC)

• Measure savings• Measure applicability• Measure interactions• Fuel shares• Current saturations

Economic PotentialEconomic Potential

Achievable ResourcePortfolios

• Market constraints• Institutional constraints

• Measure costs• Avoided costs• Economic screens

CalibrationCalibration

Considering the Role of Uncertainty

Studies of energy-efficiency potential provide an important means of developing reliableestimates of the magnitude, costs, and availability (timing) of these resources, and are a criticalfirst step in a utility’s resource planning process. The results of these studies also help inform theutility’s work in developing energy-efficiency programs and products.

By their nature, these studies rely on large amounts of data and a number of pivotal assumptionsconcerning the future in calculating technical and economic potentials. For example, theassessment of the technical potential is inherently a static analysis and assumes “frozen”efficiencies for all baseline technologies. Advances in technologies (e.g., the emergence of newtechnologies and enhancements to existing ones) that reduce the energy intensity of electricalequipment and appliances change the potential for various end uses. Cost-reducing innovationsand increases in demand for energy efficient products and technologies, on the other hand, canbe expected to improve the potentials for measures that do not currently meet cost-effectivenesscriteria and, at the same time, to increase the adoption of measures which do. If new energycodes and standards for new buildings and equipment are adopted in the future, this would alsoreduce estimates of technical potential as they would elevate the baseline assumptions used incalculating technical potentials.


Economic potentials are similarly sensitive to changes in technology costs and in determinants ofthe utility’s avoided costs, particularly fuel prices.6 Clearly, fluctuations in avoided costs willdirectly affect the expected future value of conservation resources. However, the direction ofthese impacts depends on expectations of future movements in avoided costs. When avoidedcosts rise above forecast levels, the value of conservation resources increase. Conversely, lowerfuture avoided costs lower the amounts of economic potential and diminish the value ofconservation investments already made.

Another consideration when analyzing the economic potential estimates is the economic analysisis based on assumptions intended to reflect “average” or “typical” customers. This means, whilea measure might not pass the economic screen within the context of this study, there could beinstances where the measure would be cost-effective. For example, a premium central airconditioner may not be cost-effective in an average single-family home, but, in a larger homewith more occupants, it could pass the economic screen due to higher cooling consumption.

Assumptions concerning measure life represent an additional source of uncertainty in economicpotentials. Laboratory analyses of technological performance rely on assumptions of maximumuseful measure life. It is generally accepted that physical life in the field differs from laboratoryperformance. Unfortunately, measure life estimates based on laboratory results or optimum fieldconditions do not account for real-life variables such as the installation, operation, andmaintenance practices employed, and the potential effects of remodeling and renovations at thesite in which the measure is installed. On the other hand, there are also cases where individualmeasures and equipment might last well beyond their typical life expectancy.7

Estimates of economic potentials are also affected by future government policies, programs, andregulations. In general, any government action that internalizes the costs of greenhouse gas(GHG) emissions8 likely increases the avoided costs, therefore increasing the quantity ofeconomic potential. A 1998 study by the U.S. Energy Information Administration looking at theeffects of the Kyoto Treaty conditions estimated emissions limits to achieve the Kyoto Protocolswould lead to electricity price increases between 20% and 86%, reflecting mainly increased fuelcosts.9 Such a policy shift clearly would have profound ramifications in terms of cost-effectiveness of energy-efficiency and renewable resources.

6 Note projected avoided costs are developed by each utility based on their generation mix, expected fuel costs,and other economic considerations, and, as they are considered proprietary data, are not presented in this report.

7 Skumatz, L. and C. Hickman, “Measure Life Study: The Effect of Commercial Building Changes on EnergyUsing Equipment.” Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings, 1992, Vol.3:3.281-3.292.

8 A range of approaches to reduce GHG emissions has been proposed and implemented. Most but not allapproaches are directed at CO2 emissions specifically. The most probable approaches would be imposition of anemissions tax or implementation of a cap-and-trade market with specified and declining emissions limits. Onelikely effect of government efforts to reduce CO2 emissions would be to shift electricity generation to fuels orresources that produce less CO2 per kWh or to technologies that capture and keep the CO2 from entering theatmosphere.

9 This scenario is based on carbon price peaks early in the 2008-2012 period, reaching between $67 and $348 permetric ton ($18 to $95 per metric ton of CO2) in 2010, then declining as energy markets adjust and moreefficient, new technologies become available and gradually penetrate the market. See:http://www.eia.doe.gov/oiaf/kyoto/kyotobrf.html.


Adoption of renewable portfolio standard (RPS) policies, requiring electricity providers to obtaina minimum percentage of their power from renewable energy resources by a certain date, wouldalso affect viability of many energy-efficiency and renewable energy programs.10 The probableeffect of an RPS is an increase in avoided costs, which would make additional resources cost-effective.

Tax credits for energy efficiency or renewables is another area where government policy wouldaffect the potentials for energy efficiency and renewables. Tax credits have been increasinglypopular as a way to promote efficiency increases. EPACT 2005 established federal tax credits forconsumers purchasing and installing specific products, such as energy-efficient windows,insulation, doors, roofs, and heating and cooling equipment, up to $500 beginning in January2006. EPACT 2005 also provides a credit equal to 30% of qualifying expenditures for purchaseof qualified photovoltaic systems and for solar water heating equipment. State incentives in theform of personal and corporate tax breaks for specific energy-efficiency measures are alsobecoming prevalent throughout the country.

Deployment Strategy and Timing

The objectives of resource potential assessment are different from those of program design andproduct development in that they seek to provide estimates of technically feasible and cost-effective energy-efficiency opportunities. They are also useful in understanding not only theamounts of available opportunities, but the sectors and end uses where they might beconcentrated. Yet, they provide little information or guidance as to how and by what means theidentified resource potential might be deployed. The potential for many of the electricalequipment or building shell measures might be realized through utility incentives or legislativeaction to institute efficiency codes and standards. For example, approximately 35% of energy-efficiency potential in the residential sector derives from lighting measures, primarily theinstallation of compact fluorescent light bulbs. With the recent Energy Bill signed by PresidentGeorge W. Bush on December 19, 2007, much of the identified residential lighting potential maybe realized without financial intervention by utilities.11

Energy awareness campaigns, energy education, and training programs similar to those offeredby Iowa investor-owned utilities can also serve as effective means of creating new energy savingopportunities, increasing the adoption of available efficiency measures, or improving theirsavings through more effective operation and maintenance.

10 As of the end of 2006, 20 states plus the District of Columbia had RPS policies in place. Two additional states,Illinois and Vermont, had nonbinding goals for adopting renewables instead. The requirements varied from2.2% to 20% of the amount of electricity generated, with an average requirement around 15% within an averagetime horizon of 10 years from now. In the West, California and Nevada both have a 20% RPS requirement, andWashington and Montana have a requirement for 15% renewable generation.

11 Under the measure, all light bulbs must use 25% to 30% less energy than today's products by 2012 to 2014. Thephase-in will start with 100-watt bulbs in January 2012 and end with 40-watt bulbs in January 2014. By 2020,bulbs must be 70% more efficient. Note that the because the legislation was not signed until after a draft copy ofthis report had been produced the potential estimates do not account for these federal requirements.


The length of the planning horizon is also a major factor in the utility’s ability to deployidentified resources. Energy-efficiency potential studies in North America have generally used a10- or 20-year planning horizon. The shorter, 10-year planning period in this study would limitthe opportunity to capture the identified potentials associated with retrofit and normal equipmentturnover.

Market Acceptance

The economic potentials identified in this study represent energy-efficiency opportunitiesexpected to be technically feasible and cost-effective, according to the screening criteriaestablished in the Chapter 35 of the IA Administrative Rules.12 The actual levels of potentials,which may be expected to be “achievable,” are likely to be lower than economic potentialsprimarily for two reasons.

First, the application of economic screens in this study was based solely on incremental costs ofidentified measures, including only direct capital and installation labor costs. It did not consideradministrative expenses associated with program development, marketing, and ongoing programoperation. Administrative costs tend to vary depending on several factors, such as fuel (gasversus electricity), target market (customer sector), vintage (existing versus new construction),location (urban versus rural), and program structure, primarily incentive levels. Clearly, oncethese costs are explicitly accounted for, one should expect a decrease in economic potential ascertain measures would no longer meet the cost-effectiveness thresholds.

Administrative costs are also likely to vary in the future, depending on the program’s maturity.In some cases, one might expect these costs to increase as market barriers tend to become moredifficult to surmount over time due to the “early-adopter” effect: the notion that customers withan interest in energy efficiency tend to participate in utility programs in early years. Over time,more intensive – and more costly – marketing efforts will be required to penetrate the market.Second, significant market barriers exist in the new construction market, due to issuesconcerning the concept of economically favorable “windows of opportunity.” That is, unlike theretrofit market, new-construction energy-efficiency opportunities can be captured only as theybecome available. This, in turn, requires greater effort by the utility to maintain on-goingrelationship with the trade allies in the new construction market.

It is reasonable to expect marginal increases in marketing costs would be offset to some degreeby a decline in technology costs. In this study, however, costs for all technologies and measuresare represented in nominal value, implicitly accounting for lower future technologyimprovements.

12 Economic screening of measures in this study explicitly accounts for the effects of dual-fuel savings applicableto certain measures, such as insulation and weatherization. However, it does not account for other, non-energybenefits, such as improved comfort (in the residential sector), improved productivity (such as the effects ofbetter lighting in commercial settings), or industrial process improvements. The latter benefits, however, areunlikely to alter the results of economic screening as the majority of measures with such benefits are alreadycost-effective solely based on their energy benefits.


Estimating Achievable Potential and Establishing Targets

Outside the influence of these factors on economic potential are uncertainties concerning marketacceptance of energy-efficiency measures and programs offered by utilities. Surely, levels ofcost-effective, energy-efficiency potential that is realistically achievable depends on severalfactors, including customers’ willingness to participate in energy-efficiency programs (which ispartially a function of incentive levels), retail energy rates, and a host of market barriers whichhave historically impeded the adoption of energy-efficiency measures and practices byconsumers.13 These barriers tend to vary depending on customer sector, local energy marketconditions, and other, hard-to-quantify factors.

Projection of achievable potential, however, poses significant analytic challenges due touncertainties in the factors discussed above. A review of the energy-efficiency literature,particularly potential assessment and evaluations, indicates several approaches used to estimateachievable potential:

1. Benchmarking. Determination of achievable potentials may be based on either externalor internal benchmarks. “External” benchmarks involve relying on the experiences ofsimilar programs offered by other utilities. This approach provides valuable measures forapproximating market penetration potentials. However, due to differences across variousjurisdictions (such as customer mix, energy costs, etc.) and variations in program designparameters (such as intensity of marketing effort, marketing incentive structures, etc.),these data may not be transferable from one jurisdiction to another. “Internal”benchmarking, on the other hand, involves using the utility’s own past experience toestimate achievable potential. This method also will have to be used with caution for tworeasons: first, past experience is not necessarily a reliable indicator of future trends (e.g.,programs historically run may not have been well designed or implemented); and second,existing programs may have indeed saturated the market to the extent that they haveexhausted most of the expected potential.

2. Delphi Method. This method involves surveys of experts and energy-efficiency programmanagers to obtain their professional views on what might be expected to be achievable.

3. Customer Surveys. While benchmarking and surveys of experts might be usefulindicators of program participation, another approach is to elicit information onwillingness to participate directly from the utility customers.

13 Consumers’ apparent unwillingness to invest in energy efficiency has been attributed to the existence of certainmarket barriers for energy efficiency. A rich literature exists concerning what has become known as the “marketbarriers to energy efficiency” debate. Market barriers identified in the energy-efficiency literature fall into fivebroad classes of market imperfections thought to inhibit investments in energy efficiency: (1) misplaced or splitincentives; (2) high front costs and lack of access to financing; (3) lack of information and uncertaintyconcerning the benefits, costs, and risks of energy-efficiency investments; (4) investment decisions guided byconvention and custom: and (5) time and “hassle” factors. For an ample discussion of these barriers, seeWilliam H. Golove and Joseph H. Eto, “Market Barriers to Energy Efficiency: A Critical Reappraisal of theRationale for Public Policies to Promote Energy,” Lawrence Berkeley National Laboratory, University ofCalifornia, Berkeley, California, LBL-38059, March 1996.


A review of recent conservation potential assessment studies in North America indicates a widerange for achievable potentials, from 30% (New Jersey) to 75% (New England) of economicpotentials across all sectors.14 However, due to differences in methodology, underlyingassumptions (e.g., length of the planning horizon), variations in local market conditions (e.g.,customer mix, electric rates, and historical conservation efforts), it is difficult to estimate an“average” or “typical” figure from these studies. The calculated averages reported here shouldtherefore be interpreted in light of these limitations and be considered only as “indicative”measures of what might be achievable. The available data indicate, on the whole, an estimatedaverage market penetration of approximately 47% across all sectors. Assuming unlimitedfunding and no cost-effectiveness criteria, one study estimated a maximum penetration rate of80%.

The Power and Conservation Council in the Pacific Northwest, a region with a history ofconservation planning that began in the late 1970s, has historically assumed that 85% percent ofeconomic potential are likely to be achievable. Recent data from the Council indicates that whilethe region has indeed achieved significant portions of the expected economic potential since theearly 1980s, a large portion of these savings have been achieved through the implementation ofenergy codes and standards, particularly in Oregon and Washington.15

More rigorous attempts have been made by several utilities to develop realistic estimates ofachievable potentials. For example, a survey of about 30 national energy-efficiency expertsconducted for Tacoma Power in 2006 found between 30% to 48% of economic potentials arelikely to be achievable across all sectors for existing buildings, assuming a 50% incentive and a10-year planning horizon.

Arguably, many of these market barriers may be mitigated through program design features,particularly incentive levels, marketing efforts, and delivery mechanisms. Higher incentives,especially when they can be justified by incorporating non-energy benefits as allowed in somejurisdictions, can help increase customers’ willingness to participate in utility-sponsoredprograms. More aggressive marketing and establishing effective partnerships with local tradeallies can also improve market acceptance.

Resource potential studies are complex undertakings, requiring large amounts of technical andmarket data and relying on a number of pivotal assumptions concerning the future to calculatethe technical and economic potentials. Given the length of the planning horizon and the changingconditions in the market for energy efficiency, the results of this study will be subject to manyuncertainties. Planning is ultimately a dynamic process, reflecting changing market conditions.Therefore, it is important to consider the findings of this study as indicative, rather thanconclusive. Inevitably, much of this study’s data will have to be updated, and many of itsunderlying assumptions will need to be revisited periodically.

14 See Appendix H for a bibliography of the referenced studies.15 “Achievable Savings: A Retrospective Look at the Northwest Power and Conservation Planning Assumptions,”

Council Document 2007-7 May 2007, Northwest Power and Conservation Council.


Organization of this Report

This report is organized in three volumes. The present document, Volume I, presents themethodology and findings, and includes the following chapters:

Chapter 2, ““Data Development,” provides an overview of the methods and findings fromthe comprehensive primary and secondary data collection and analysis efforts

Chapter 3, “Energy Efficiency,” presents the technical and economic potential availablefrom energy-efficiency resources

Chapter 4, “Demand Response,” presents the technical and economic potential availablefrom demand response programs

Chapter 5, “Renewable Resources,” describes the various types of small scale renewableresources available and the technical and market potential of these resources

Supplemental technical information, assumptions, data, and other relevant details are presentedin Volume II as appendices. These include:

Appendix A: Energy Efficiency Measure Descriptions

Appendix B: Customer Surveys, including Survey Instruments and Summary of Results

Appendix C: Supplemental Material – Energy Efficiency, including Data & Assumptionsand Detailed Results

Appendix D: Supplemental Material – Demand Response

Appendix E: Supplemental Material – Renewables

Appendix F: Simulations

Appendix G: Attribution of Energy Savings: An Assessment of the Net-to-GrossRatio Issue

Appendix H: Bibliography of Referenced Studies

Finally, Volume III contains the results of the code compliance study. This stand-alone studywas initiated in late 2007 in order to allow a large enough inventory of homes that were requiredto meet the 2006 IECC, which was not fully enforced in Iowa until April 2007.


2. Data Collection

Primary Data Collection

The 2007 Assessment of Energy and Capacity Savings Potential data collection efforts includeda combined total of over 1,200 telephone and on-site surveys of residential and non-residentialcustomers, trade allies and contractors (Table 10 and Table 11). This primary research, and thepotential study overall, differed significantly from previous assessments which, to a large extent,relied primarily on secondary data.

This approach represented a concerted effort to ensure an accurate representation of the Iowamarket for use in modeling the energy and capacity savings potential. To maximize the values ofthe data collection efforts, certain measures that represent disproportionately large savingspotentials were given highest priority in the surveys. For these measures, the comprehensivesurvey effort collected three metrics critical to estimating efficiency potential, including:

Equipment saturation: The percent of customers that own specific equipment, efficientor standard efficiency (e.g., the percent of single-family homes with air-conditioning);

Efficiency penetration: The percent of the installed equipment stock considered efficient(e.g., the percent of installed central air-conditioners that exceed SEER 13); and

Market share: The percent of current sales of equipment that is considered efficient (e.g.,the percent of central air-conditioner sales in the last 12 months that exceeded SEER 13).

Primary data collection findings were validated through comparisons to available state andregional secondary data. The findings of the data collection efforts were also presented to theutilities, the Iowa Utility Association, the Iowa Office of Consumer Advocate, independent third-party consultants contracted by these groups, and interested stakeholders.

The following tables present each of the residential and non-residential data collection effortsthat were undertaken, the measures investigated, the sources of the samples, stratificationmethods, and the number of completed surveys. The survey instruments and the detailedtabulations of the results for each of these efforts are presented in Volume II, Appendix B.


Table 10. Residential Primary Data Collection Efforts

Data Collection Effort Method Measures Sources StratificationNumber ofSurveys/

VisitsResidential ApplianceSaturation Survey(RASS)

TelephoneSurvey

Residential Appliances andHousehold Characteristics

Iowa Single-FamilyHomeowners, Residents ofMulti-Family Buildings, Mobile/Manufactured HomeResidents, and Low-IncomeHouseholds Identified fromDatabase of Utility Customers

By Building Typeand Utility

405

Residential On-SiteValidation Effort

In-PersonOn-SiteAudits

Residential Appliances andHousehold Characteristics

Participants in RASS Agreeingto Site Visits

N/A 67

Residential On-SiteCFL Survey

In-PersonOn-SiteAudits.Concurrentwith UtilityProgramAudit

CFLs Iowa Single-FamilyHomeowners, Residents ofMulti-Family Buildings, Mobile/Manufactured HomeResidents, and Low-IncomeHouseholds

By Building Typeand Utility

277

Residential HVACTrade Ally Survey

TelephoneSurvey

Residential Central AirConditioners, Air and GroundSource Heat Pumps, GasFurnaces, Boilers, ElectricFurnaces

Residential HVAC Dealers andInstallers Identified ThroughYellow-Page Searches, Listsof Participating Trade Alliesfrom Utilities, and D&B Data.

N/A 30

Residential PlumberTrade Ally Survey

TelephoneSurvey

Water Heaters (Gas/Electric,Tankless/Storage Tank andHeat Pump Water Heaters)Showerheads and Faucets

Residential PlumbersIdentified Through Yellow-Page Searches, Lists ofParticipating Trade Allies fromUtilities, and D&B Data.

N/A 20

Retailer Survey TelephoneSurvey

Thermostats, Water Heaters(Gas, Electric, Storage andTankless), Clothes Washersand, Refrigerators, Freezers,Dishwashers, CFLs, RoomACs, Dehumidifiers, Lightingfixtures, Ceiling fans, AtticFans, Televisions, HDTVs,DVD Players, Set-TopReceivers, Monitors, Printers,Faucet Aerators,Showerheads, Windows,Doors

Retail Stores InstallersIdentified Through Yellow-Page Searches, and D&BData.

By Utility andMeasure Type

73

Residential HomeBuilder

TelephoneSurvey

Heating Equipment, CoolingEquipment, Ducts, WaterHeating, Windows, Lighting,Siding, Framing, Barriers,Insulation, Attic Fans,Showerheads, Faucets

Builders Identified ThroughYellow-Page Searches, Listsof Participating Builders fromUtilities, and D&B Data.

N/A 32

Total Residential Surveys 904


Table 11. Non-Residential Primary Data Collection Efforts

Data Collection Effort Method Measures Sources StratificationNumber ofSurveys/

VisitsNon-Residential EndUser

TelephoneSurvey

Heating and Cooling Systems,Controls, Refrigeration, WaterHeating, Commercial KitchenEquipment, Lighting andLighting Controls

Utility Provided Samples ofNon-Residential Customers

CustomerSegment/BuildingType

195

Non-Residential EndUser Site Visits

In-PersonOn-SiteAudits

Heating and Cooling Systems,Controls, Refrigeration, WaterHeating, Commercial KitchenEquipment, Lighting andLighting Controls

Participants in Non-Residential End UserTelephone Survey Agreeingto Site Visits

CustomerSegment/BuildingType

40

Non-ResidentialBuilders

TelephoneSurvey

Lighting and HVAC Controls,Sensors, Insulation CoolRoofs, Ducts, Lighting,Windows, Lighting Equipment

Builders Identified ThroughYellow-Page Searches, Listsof Participating Buildersfrom Utilities, and D&B Data.

N/A 21

Non-ResidentialArchitects &Engineering Firms

TelephoneSurvey

Lighting and HVAC Controls,Sensors, Insulation CoolRoofs, Ducts, Lighting,Windows, Lighting Equipment

A&E Firms IdentifiedThrough Yellow-PageSearches, and D&B Data.

N/A 17

Non-ResidentialLighting Vendors

TelephoneSurvey

Lighting Equipment andControls

Lighting Vendors IdentifiedThrough Yellow-PageSearches, Lists ofParticipating Trade Alliesfrom Utilities, and D&B Data.

N/A 12

Compressed AirVendors

TelephoneSurvey

Compressed Air Equipment,Motors and Drives

Compressed Air VendorsIdentified Through Yellow-Page Searches, Lists ofParticipating Trade Alliesfrom Utilities, and D&B Data.

N/A 12

MechanicalContractors

TelephoneSurvey

Heating and CoolingEquipment, Controls, Motorsand Drives

Mechanical ContractorsIdentified Through Yellow-Page Searches, Lists ofParticipating Trade Alliesfrom Utilities, and D&B Data.

N/A 12

RefrigerationSpecialists

TelephoneSurvey

Refrigeration Equipment,Motors, Drives, Lighting,Insulation Measures, andControls

Refrigeration SpecialistsIdentified Through Yellow-Page Searches, Lists ofParticipating Trade Alliesfrom Utilities, and D&B Data.

N/A 12

Total Non-Residential Surveys 321


Utility Database Mining

Saturation and penetration data for HVAC and shell measures were also examined in a separatedata collection effort. This separate effort consisted of mining utility databases of the auditprograms. Over the current program period (2004–2008), the utilities have conducted thousandsof audits of both residential and small non-residential customers. The program databases recordboth the recommended measures that can result in significant energy savings for the participantsand the existing equipment stock at the time of the audit. This valuable data is significantly moreextensive than the equivalent data collection effort that could have been conducted during theassessment; consequently, existing data was mined and the results incorporated into the study.

Secondary Data

As noted above, to limit the length of the surveys, thereby increasing participation in the surveyswhile not causing an undue burden to the utility customers, each effort was carefully designed tofocus on high impact measures that the respective survey respondent was best suited to discuss.As a result, the saturations and penetrations, some lower impact measures, and specialty itemsrelied more on secondary data such as findings from the national ENERGY STAR Program,studies conducted by other utilities and energy-efficiency agencies around the country, and third-party studies conducted by private research organizations, state and federal agencies.

Summary of Data Collection for High Priority Measures

The various data sources for each of the high priority measures are summarized in Table 12 andTable 13. The customer surveys provided the majority of the saturation data, and the utility auditdata and site visits provided much of the penetration data, while the trade ally and other“upstream” market actor surveys provided the market share information. Secondary data wasrelied upon to fill in data gaps or verify the primary data collection findings.

Table 12. Summary of Data Sources for Residential Sector MeasuresPrimary Data Secondary Data

Measure Type

UtilityAuditData

End-useCustomerTelephone

SurveysCustomerSite Visits

HVAC/Plumber

Trade AllySurveys

ApplianceRetailerSurvey

Home BuilderSurvey

TradeAssoc-iations

RetailerPartner

Sales Data

AdditionalStudies /Reports

HVAC EquipmentResidentialCentral AC Furnaces Geothermal/AirSource/Add-onHeat Pumps ProgrammableThermostats Other HeatingClothes Washers


Primary Data Secondary Data

Measure Type

UtilityAuditData

End-useCustomerTelephone

SurveysCustomerSite Visits

HVAC/Plumber

Trade AllySurveys

ApplianceRetailerSurvey

Home BuilderSurvey

TradeAssoc-iations

RetailerPartner

Sales Data


Water Heating Clothes Dryers Building Envelope

Windows Insulation OtherRefrigerators Appliances forDemandResponse CFLs


Table 13. Summary of Data Sources for Non-Residential Sector Measures

Primary Data Sources Secondary Data Sources

Measure Type

AuditData

Mining

TelephoneSurvey /

Site Visitswith Non-residentialCustomers

TradeAlly

Surveys

Builders /A&EFirm

Surveys

TradeAssoc-iations

(GAMA/IHPA/NE

MA)

National ESRetailer

Partner SalesData


HVAC EquipmentCentral AirConditioning Furnaces Geothermal/AirSource/Add onHeat Pump Boilers ProgrammableThermostats Building EnergyManagementSystems OccupancySensors Heat Recoveryfrom Exhaust Airto Water Heating Other Heating

Clothes washers Water Heating Clothes Dryers Building Envelope

Windows Insulation Other

Motors/ASDs Refrigerators Appliances forDemand ResponseProgram CFLs/T8Lighting/High BayLighting/LEDExit/Pulse StartMetal Halide


Business Segment Data

Quantec and Summit Blue Consulting, as part of the joint utility potential study, conducted aseparate task to better classify non-residential customers into specific business segments.Although the utility customer databases contain SIC codes, the data are missing or believed to beoutdated for a substantial number of customers. The goal of this task, therefore, was to merge athird-party source of customer firmographic information with the CSS.

To conduct this task, the study team purchased Dun & Bradstreet (D&B) firmographic data forthe entire State of Iowa. The data contained such fields as SIC, number of employees, annualrevenue, and square footage of facilities. Data were then merged with each of the utilitydatabases using a hierarchy approach, which started with a “hard” match for name and addressand eventually went down to a “fuzzy logic” approach, which looked for specific text strings(e.g., bakery) in the company name to assign SIC code.

The results of this task were used to characterize the non-residential customers into differentbusiness segments, identifying the number of customers, energy consumption, and potential foreach of these segments. Separate memos describing the details of this process were provided toeach of the utilities.

Additional Utility Data for Potential Analysis

Extensive data sets were also provided by each of the investor-owned utilities participating inthis study. These data, provided by sector where applicable, included:

Customer counts Electric and gas sales (consumption) System hourly load shapes Peak demand history Sales and demand forecasts Historical demand and efficiency achievements Avoided costs Line losses

Finally, there were many more additional data sources for both the energy-efficiency, demand,and renewable potential estimates, including measure costs and benefits. Additional sources anddata development specific to each of these tasks, therefore, are presented in the each of thefollowing chapters.


3. Energy Efficiency

Scope of Analysis

The main focus in assessing energy-efficiency resources was producing reasonable estimates ofsavings available in each utility’s service territory over the 10-year planning horizon to informthe creation of the next 5-year plan. Separate assessments of technical and economic potential forresidential, commercial, and industrial sectors were made for each utility, split by fuel type.Within each utility’s sector-level assessment, the study further distinguished among customersegments or facility types and their respective applicable end uses. Ten residential segments(existing and new construction for single-family, multifamily, manufactured, low income single-family, and low-income multifamily), 24 commercial segments (12 building types within theexisting and new construction), and 32 industrial segments (16 facility types, also within existingand new construction vintages) were analyzed.

The study includes a comprehensive set of energy-efficiency electric and natural gas measuresapplicable to Iowa’s climate and customer characteristics. This list includes both measuresanalyzed in the previous 5-year plan (which may be in current utility programs) and newmeasures that have become commercially available over the past five years. The analysis beganby assessing the technical potential for 304 unique electric and 152 unique gas energy-efficiencymeasures (Table 14).

Table 14. Energy-Efficiency Measure Counts (Base-Case Scenario)Sector, Potential Type Electric Measure Counts Gas Measure Counts

CommercialTechnical 146 unique, 3,296 permutations across

segments71 unique, 1,227 permutations acrosssegments

Economic 105 unique, 1,377 permutations acrosssegments

54 unique, 548 permutations across segments

ResidentialTechnical 142 unique, 2,004 permutations across

segments73 unique, 777 permutations across segments

Economic 80 unique, 872 permutations acrosssegments

44 unique, 371 permutations across segments

IndustrialTechnical 16 unique process improvements, 632

permutations across segments8 unique process improvements, 134permutations across segments

Economic 16 unique process improvements, 618permutations across segments

8 unique process improvements, 134permutations across segments

Considering all permutations of these measures across all customer sectors, customer segments,and fuels, customized data had to be compiled and analyzed for nearly 8,000 measures. Forelectricity, of the 304 unique measures, 191 meet the cost-effectiveness criterion for economicpotential, while 106 out of 152 gas measures were deemed cost-effective in at least onepermutation of the above distinguishing categories. This study did not fully address all plug load


measures, especially in the residential sector, thus underestimating the total potential in this enduse. These types of measures may more appropriately be characterized as part of a markettransformation effort pursued at the larger regional—if not national—level. A complete list ofenergy-efficiency measures analyzed is provided in Volume II, Appendix C.

The remainder of this section is divided into three parts: A brief description of the methodologyfor estimating technical and economic potential; summary resource potentials by fuel; andfinally, detailed sector-level results.

Methodology

The basic methodology for estimating energy-efficiency potential is consistent for all six sector-fuel combinations:

Develop baseline forecast: A baseline forecast is created based on end use consumptionestimates, calibrated to each utility’s base year sales and official forecast. This providesaccurate estimates of consumption by utility, fuel, sector, customer segment, end use, andyear.

Compile measure lists: All measures applicable to Iowa’s climate and customers wereanalyzed to accurately depict the potential for each utility over the 10-year planninghorizon. When expanded by utility, fuel, customer segment, end use, and vintage, this listtotaled nearly 8,000 measures (as discussed above).

Estimate technical potential: An alternate forecast was created where all technicallyfeasible measures were assumed to be installed. The difference between this forecast andthe baseline represents the technical potential in each year.

Estimate economic potential: A second alternate forecast was created where alltechnically feasible and cost-effective measures were assumed to be installed. Thedifference between this forecast and the baseline represents the technical potential in eachyear. As noted above, the application of economic screens in this study was based solelyon incremental costs of identified measures, including only direct capital and installationlabor costs, while benefits were derived from avoided electric and gas costs.16 Additionalbenefits, particularly those from non-energy benefits often “bundled” with energy-efficient products, were not considered.17

As noted above, a detailed discussion of the methodology for estimating energy-efficiencypotential is presented in Volume II, Appendix C.

16 Measures that could result in both electric and gas savings (e.g., shell measures) were screened based onexpected savings from both fuels (i.e., there were dual-fuel benefits from a single set of incremental costs).

17 Note that other jurisdictions have examined these non-energy benefits and considered their influence on thebenefit-to-cost ratios.


Summary of Resource Potential – Electricity

Table 15 and Table 16 show 2018 baseline sales and potential by utility and sector, respectively.As shown, the results of this study indicate 9,767 GWh of technically feasible electric energy-efficiency potential by 2018, the end of the 10-year planning horizon. Approximately6,800 GWh of these resources are cost-effective at an average levelized per-unit cost of3 cents/kWh. The identified economic potential amounts to 17% of forecast load in 2018 andover 1,500 MW of peak demand reduction.

These savings are based on forecasts of future consumption absent any utility program activities.While consumption forecasts account for the past savings each utility has acquired, the estimatedpotential is inclusive of—not in addition to—current or forecasted program savings.

As shown in Table 15, technical and economic potential are a function of baseline sales, but areroughly comparable when analyzing in percentage terms. Differences in technical potential as apercent of baseline sales are driven by differences in the distribution of customers by segmentand other utility-specific customer characteristics. In addition to these differences, the economicpotential varies due to differences in utility avoided costs.

Table 15. Technical and Economic Electric Energy-Efficiency Potential(GWh in 2018) by Utility

UtilityBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical

EconomicPotential

(MW)

AverageLevelized

Cost

Alliant 18,250 4,453 24% 3,304 18% 74% 662 $0.03

MidAm 21,329 5,314 25% 3,473 16% 65% 875 $0.03

Total 39,580 9,767 25% 6,777 17% 69% 1,537 $0.03

Each sector’s technical and economic potentials are provided in Table 16. The residential sectorrepresents the largest portion of both the technical and economic potential at 51% and 47%,respectively. The commercial sector is the second largest contributor to the technical potential,but because industrial improvements are highly cost-effective, it becomes the smallestcontributor to the economic potential at about 23% (Table 16).


Table 16. Technical and Economic Electric Energy-Efficiency Potential(GWh in 2018) by Sector (Alliant and MidAmerican)

SectorBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical

EconomicPotential

(MW)

AverageLevelized

Cost

Residential 10,819 4,937 46% 3,215 30% 65% 997 $0.04

Commercial 9,086 2,695 30% 1,563 17% 58% 270 $0.03

Industrial 19,675 2,136 11% 1,999 10% 94% 270 $0.01

Total 39,580 9,767 25% 6,777 17% 69% 1,537 $0.03

Table 17 shows the technical and economic potential by sector and resource type, which refers towhether the resources are discretionary or represent phased-in potential. Discretionary resourcesare opportunities existing in current building stock (retrofit opportunities in existingconstruction), while phased-in resources are those reliant on equipment burnout and newconstruction. In all sectors, these discretionary resources represent the vast majority of both thetechnical and economic electric potential. Overall, discretionary resources represent 91%(6,154 GWh) of the economic potential.

Table 17. Technical and Economic Energy-Efficiency Potential(GWh in 2018) by Sector and Resource Type

Technical Potential Economic PotentialSectorDiscretionary Phased-in Discretionary Phased-in

Residential 4,345 592 2,990 225

Commercial 2,479 216 1,485 78

Industrial 1,794 342 1,679 320

Total 8,618 1,150 6,154 623

The distinction between discretionary and phased-in resources becomes important in the contextof timing of resource availability and acquisition planning. Phased-in resources are timing-driven: when a piece of equipment fails, there is an opportunity to install a high-efficiency modelin its place. If standard equipment is installed in the absence of early replacement, the high-efficiency equipment could not be installed until the new equipment reaches the end of itsnormal life cycle. The same is true for new construction, where resource acquisitionopportunities become available only when a home or building is built. On the other hand,discretionary resources are not subject to the same timing constraints. Though program planningis outside the scope of this study, these considerations are vital for setting accurate annualprogram and portfolio goals.


Summary of Resource Potential – Natural Gas

Table 18 and Table 19 show 2018 baseline sales and potential by sector and utility, respectively.As shown, the results of this study indicate over 40,000,000 decatherms of technically feasiblegas energy-efficiency potential by 2018, the end of the 10-year planning horizon. Approximately28,500,000 decatherms of these resources are cost-effective at an average levelized per-unit costof 44 cents/therm. The identified economic potential amount to 27% of forecast load in 2018 andover 1,500 peak day decatherms.

As with electric potential, technical, and economic potential are a function of baseline sales, butthey are roughly comparable across utilities when analyzing in percentage terms. Differences areagain driven by utility customer characteristics and avoided costs.

Table 18. Technical and Economic Gas Energy-Efficiency Potential(Thousand decatherms in 2018) by Utility

UtilityBaseline



% of BaselineEconomic

Potential


% ofBaseline

Economicas % of

Technical


daydecatherms)

AverageLevelized

Cost

Alliant 27,484 10,600 39% 7,683 28% 72% 88,822 $0.45

Aquila 16,307 6,556 40% 4,842 30% 74% 58,990 $0.55

MidAm 61,704 23,497 38% 16,039 26% 68% 197,144 $0.40

Total 105,495 40,653 39% 28,564 27% 70% 344,855 $0.44

Each sector’s technical and economic potentials are provided in Table 19. As with electricpotential, the residential represents the largest portion of both the technical and economicpotential (about 65% of each). Almost all the remaining potential lies in the commercial sector,with a small portion (897,000 decatherms) in industrial (Table 19).

Table 19. Technical and Economic Gas Energy-Efficiency Potential(Thousand decatherms in 2018) by Sector (Alliant, Aquila, and MidAmerican)

SectorBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical


daydecatherms)

AverageLevelized

Cost

Residential 65,968 26,532 40% 18,654 28% 70% 248,713 $0.44

Commercial 34,475 13,224 38% 9,013 26% 68% 93,784 $0.48

Industrial 5,052 897 18% 897 18% 100% 2,459 $0.07

Total 105,495 40,653 39% 28,564 27% 70% 344,955 $0.44

Because of the opportunities available in high-efficiency space and water heating for natural gasequipment, phased-in resources represent a substantially larger portion of the gas potential than


the electric. As shown in Table 20, resources account for 17% of the technical and 16% of theeconomic (compared to 91% of electric potential) .

Table 20. Technical and Economic Gas Energy-Efficiency Potential(Thousand decatherms in 2018) by Sector and Resource Type

Technical Potential Economic PotentialSectorDiscretionary Phased-in Discretionary Phased-in

Residential 21,491 5,041 15,669 2,985

Commercial 11,505 1,719 7,571 1,442

Industrial 798 99 798 99

Total 33,794 6,859 24,039 4,525

Detailed Resource Potential

Residential Sector - Electric

Residential customers in Iowa account for about one-quarter of baseline electricity retail sales.The single-family, manufactured, multifamily, and low-income dwellings that comprise thissector present a variety of potential savings sources, including equipment efficiency upgrades(e.g., air conditioning, refrigerators), improvements to building shells (e.g., insulation, windows,air sealing), and increases in lighting efficiency (e.g., compact fluorescent light bulbs, LEDinterior lighting).

Based on resources included in this assessment, electric economic potential in the residentialsector is expected to be 3,215 GWh over 10 years, corresponding to a 30% reduction (32% forAlliant and 28% for MidAmerican) of 2018 residential consumption at an average levelized costof 4 cents/kWh (Table 21).

Table 21. Residential Sector Electric Energy-Efficiency Potential by Utility (GWh in 2018)

UtilityBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical

AverageLevelized

Cost

Alliant 4,441 2,043 46% 1,426 32% 70% $0.04

MidAmerican 6,379 2,894 45% 1,790 28% 62% $0.04

Total 10,819 4,937 46% 3,215 30% 65% $0.04

As shown in Figure 3, single-family homes represent 74% of the total economic residentialpotential, followed by low-income, multifamily, and manufactured homes. The main driver ofthese results is each home type’s proportion of baseline sales, but other factors, such as heating


fuel sources, play an important role in determining potential. For example, multifamily homestypically have more electric heating than other home types, which increases their relative shareof the potential. On the other hand, the lower use per customer for multifamily units serves todecrease this potential as some measures may not be cost-effective at lower consumption levels.A comprehensive list of the specific factors affecting the results are included in the segment-specific data, provided in Volume II, Appendix C.

Figure 3. Residential Sector Electric Economic Potential by Segment

Total: 3,215,358 MW h

Note: "Other" inc lu des:M u lti-fam ily: 5 .3% , M anu fac tu red: 3.9%

Single Family74%

Low Income17%

Other9%

The largest portion (35%) of economic potential by end use (Figure 4) in the residential sectorcomes from lighting measures, specifically compact fluorescent lighting.18 Air conditioning(central and room) accounts for the next largest slice (27%), followed by HVAC auxiliary(ventilation), and space heating. The remaining potential is in refrigerators, freezers, waterheating, plug load, and other appliances (see Table 22).

18 As noted earlier, the current energy bill (signed in December 2007) includes a provision that, by 2012 to 2014,all light bulbs must use 25% to 30% less energy than today's products. Note that because the legislation was notsigned until after a draft copy of this report had been produced, potential estimates do not account for thesefederal requirements.


Table 22. Residential Sector Electric Energy-Efficiency Potential by End Use(GWh in 2018)

End Use Baseline Sales Technical Potential Economic Potential

Central AC 1,451 1,141 796

Central Heat 428 159 114

Cooking Oven 316 34 0

Cooking Range 362 120 0

Dryer 537 16 0

Freezer 333 254 251

HVAC Auxiliary 657 426 257

Heat Pump 226 127 94

Lighting 1,947 1,658 1,140

Plug Load 2,756 208 81

Pool Pump 19 14 9

Refrigerator 643 298 218

Room AC 188 97 61

Room Heat 303 177 152

Water Heat 654 209 43

Total 10,819 4,937 3,215

Figure 4. Residential Sector Electric Economic Potential by End Use

Total: 3,215,358 MW h

Note: "Other" inc lu des:Heat Pu m p: 2.9% , P lu g Loads: 2.5% , W ater Heating: 1.3%

Light ing35%

Cooling27%

Appliances15%

HVAC Auxiliary8%

Heating8%

Other7%


Additional details regarding the savings associated with specific measures assessed within eachend use are provided in Volume II, Appendix C.

Residential Sector – Natural Gas

Based on resources included in this assessment, gas economic potential in the residential sectoris expected to be about 18,600,000 decatherms over 10 years, corresponding to a 28% reduction(30% for Alliant, 31% for Aquila, and 27% for MidAmerican) of 2018 residential consumptionat an average levelized cost of 44 cents/therm (Table 23).

Table 23. Residential Sector Gas Energy-Efficiency Potential by Utility (Thousanddecatherms in 2018)

UtilityBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical

AverageLevelized

Cost

Alliant 14,738 6,230 42% 4,411 30% 71% $0.44

Aquila 9,350 3,875 41% 2,889 31% 75% $0.56

MidAmerican 41,880 16,427 39% 11,354 27% 69% $0.41

Total 65,968 26,532 40% 18,654 28% 70% $0.44

As shown in Figure 5, single-family homes represent 72% of the total economic residentialpotential, followed by low-income, multifamily, and manufactured homes. These results areextremely similar to the electric potential, with manufactured homes representing a smallerpercentage due to lower saturations of gas heating equipment.


Figure 5. Residential Sector Gas Economic Potential by Segment

Total: 18 ,654,001 decatherm s

Note: "Other" inc lu des:M anu fac tu red: 4.5% , M u lti-fam ily: 4.0%

Single Family72%

Low Income19%

Other9%

Because there are far fewer gas-fired end uses than electric, the potential is mainly confined tospace (94%) and water heating (6%). A small amount of economic potential exists in gas dryers(Table 24 and Figure 6).

Table 24. Residential Sector Gas Energy-Efficiency Potential by End Use (Thousanddecatherms in 2018)


Central Heat - Boiler 2,620 978 773

Central Heat - Furnace 47,185 21,459 16,701

Cooking - Oven 625 68 0

Cooking - Range 766 0 0

Dryer 659 19 3

Other 3,584 0 0

Pool Heat 146 12 0

Water Heat 10,382 3,997 1,177

Total 65,968 26,532 18,654


Figure 6. Residential Sector Gas Economic Potential by Segment


Heating94%

Water Heat ing6%

Commercial Sector - Electricity

Based on resources included in this assessment, electric economic potential in the commercialsector is expected to be just over 1,500 GWh over 10 years, corresponding to a 17% reduction(19% for Alliant and 15% for MidAmerican) of forecasted 2018 commercial consumption at anaverage levelized cost of 3 cents/kWh (Table 25). The composition of the commercial sectorvaries more than the residential sector in terms of percent of customers and sales by segment,which partially accounts for the difference in technical and economic potential as a percent of2018 sales.

Table 25. Commercial Sector Energy-Efficiency Potential by State (GWh in 2018)

UtilityBaseline


TechnicalPotential as %

of BaselineEconomicPotential

EconomicPotential

as % ofBaseline

Economicas % of

Technical

AverageLevelized

Cost

Alliant 4,940 1,454 29% 924 19% 64% $0.03

MidAmerican 4,146 1,240 30% 638 15% 51% $0.02

Total 9,086 2,695 30% 1,563 17% 58% $0.03

As shown in Figure 7, miscellaneous buildings and offices represent the largest shares (22% and20%, respectively) of economic potential in the commercial sector. The miscellaneous segmentis a combination of customers that do not fit into one of the other categories (e.g., agriculture)


and those that would, but did not have enough information to be classified. Considerable savingsopportunities are also expected in the commercial sector’s warehouse (14%), grocery (11%), andretail (10%) segments. Moderate savings amounts are expected to be available in education,health, restaurants, and lodging facilities.

Figure 7. Commercial Sector Economic Potential by Segment

Total: 1,562,568 MW h

Note: "Other" inc lu des:Restau rant: 3 .5% , Lodging: 3.1%

Miscellaneous22%

Office20%

Warehouse14%

Retail10%

Grocery11%

Education9%

Health8%

Other7%

As in the residential sector, lighting efficiency represents by far the largest portion of economicpotential in the commercial sector (65%), followed by HVAC auxiliary (8%), cooling (6%), andrefrigeration (6%), as shown in Table 26 and Figure 8. The large lighting potential includes bothbringing existing buildings to code and exceeding code in new and existing structures.

Table 26. Commercial Sector Electric Energy-Efficiency Potential by End Use(GWh in 2018)


Cooking 88 3 0

Cooling - Chillers 244 114 43

Cooling - DX 838 394 43

Dryer 274 0 0

Exterior Lighting 90 0 0

HVAC Auxiliary 1,083 375 124



Heat Pump 507 202 74

Lighting 3,729 1,189 1,017

Other 18 0 0

Plug Load 1,190 82 77

Refrigeration 511 149 91

Space Heat 402 149 69


Total 9,086 2,695 1,563

Figure 8. Commercial Sector Economic Potential by End Use

Commercial Sector – Natural Gas

The commercial sector represents about a third of both technical and economic gas energy-efficiency potential. The 9,000,000 decatherms of economic potential over 10 years, correspondsto a 26% reduction (28% for Alliant and Aquila and 24% for MidAmerican) of forecasted 2018commercial consumption at an average levelized cost of 48 cents/therm(Table 27).


Table 27. Commercial Sector Gas Energy-Efficiency Potential by Utility (Thousanddecatherms in 2018)

UtilityBaseline



% ofBaseline

EconomicPotential

Economic Potential as %of Baseline

Economicas % of

Technical

AverageLevelized

Cost

Alliant 10,007 3,892 39% 2,794 28% 72% $0.54

Aquila 6,733 2,639 39% 1,911 28% 72% $0.54

MidAmerican 17,735 6,693 38% 4,308 24% 64% $0.41

Total 34,475 13,224 38% 9,013 26% 68% $0.48

As in the residential sector, there are far fewer gas-fired end uses than electric. Both the technicaland economic potential are almost entirely heating (96% of the economic potential). Smallamounts of potential exist for water heating, cooking, and pool heating (Table 28 and Figure 9).

Table 28. Commercial Sector Gas Energy-Efficiency Potential by End Use(Thousand decatherms in 2018)


Cooking 1,688 178 164

Dryer 132 0 0

Other 28 0 0

Pool Heat 35 5 5

Space Heat - Boiler 9,358 5,199 3,724

Space Heat - Furnace 20,710 7,430 4,917

Space Heat - Other 1,747 0 0


Total 34,475 13,224 9,013


Figure 9. Commercial Sector Gas Economic Potential by End Use


Note: "Other" inc lu des:W ater Heating: 2.2% , Cook ing: 1.8%

Heating55%

Boiler41%

Other4%

Industrial Sector - Electricity

Technical and economic energy-efficiency potential were estimated for major end uses within16 major industrial sectors.19 Across all industries, economic potential totals approximately2,000 GWh over 10 years, corresponding to a 10% reduction (11% for Alliant and 10% forMidAmerican) of forecasted 2018 industrial consumption at an average levelized cost of1 cent/kWh (Table 29). Note that in the industrial sector, most of the technical potential iseconomic. Because of tight cost margins in the industrial sector, available measure data focuseson technologies that are currently cost-effective. As such, the universe of available measuresexamined is less than for the other sectors, possibly influencing the technical potentialdownward. Furthermore, the industrial potential estimates relied largely on energy audits thatprimarily examined individual measures and not on a systems approach; thus the actualeconomic potential is likely higher than that presented in this report. For a more completedescription of the methodology used, please see Volume II, Appendix C-1.

19 Industries analyzed varied by utility, based on customer and sales distributions


Table 29. Industrial Sector Energy-Efficiency Potential by State (GWh in 2018)

UtilityBaseline

Sales Technical Potential

TechnicalPotential as % of

BaselineEconomic

Potential


% ofBaseline

Economicas % of

Technical

AverageLevelized

Cost

Alliant 8,870 956 11% 954 11% 100% $0.01

MidAmerican 10,805 1,180 11% 1,045 10% 89% $0.01

Total 19,675 2,136 11% 1,999 10% 94% $0.01

In examining these aggregate results for the industrial sector, there should be some caution inassociating summary potential information for a particular facility type to individual utilities.While all residential and commercial customer segments were present for each utility, some ofthe facility types shows in Figure 10 applied to only one utility. For example, the electronicsindustry in MidAmerican’s service territory was not large enough to be modeled.

Figure 10. Industrial Sector Economic Potential by Segment

The majority of electric economic potential in the industrial sector (70%) are attributable toefficiency gains in process efficiency (heating, cooling, compressed air, etc.), followed byHVAC improvements (14%) and motor system improvements (mainly fans and pumps). A smallamount of additional potential exists for lighting and other facility improvements (Table 30 andFigure 11).


Table 30. Industrial Sector Electric Energy-Efficiency Potential by End Use (GWh in 2018)End Use Baseline Sales Technical Potential Economic Potential

Fans 1,180 60 52

HVAC 1,621 287 270

Indirect Boiler 146 6 6

Lighting 1,269 99 99

Motors - Other 4,019 202 172

Other 896 85 85

Process – Air Compressors 1,252 278 271

Process - Cooling 1,674 224 224

Process - Electro-Chemical 2,089 0 0

Process - Heating 2,598 620 557

Process - Other 85 14 12

Process - Refrigeration 1,004 168 167

Pumps 1,843 93 84

Total 19,675 2,136 1,999

Figure 11. Industrial Sector Electric Economic Potential by End Use

Total: 1,999,164 MW h

Note: "Other" inc lu des:Lighting: 4.9% , Other: 4.3%

Process70%

HVAC14%

Motors7%

Other9%


Industrial Sector – Natural Gas

Most industrial processes and end uses use electricity, and, therefore, the industrial sectorrepresents an extremely small portion of natural gas baseline sales and potential. Across allindustries, economic potential totals approximately 900,000 decatherms over 10 years,corresponding to an 18% reduction (17% for Alliant, 19% for Aquila, and 18% forMidAmerican) of forecasted 2018 industrial consumption at an average levelized cost of8 cents/therm (Table 31).

Table 31. Industrial Sector Gas Energy-Efficiency Potential by Utility (Thousanddecatherms in 2018)

UtilityBaseline



% ofBaseline

EconomicPotential


% ofBaseline

Economicas % of

Technical Average Levelized Cost

Alliant 2,740 478 17% 478 17% 100% $0.06

Aquila 224 42 19% 42 19% 100% $0.07

MidAmerican 2,088 377 18% 377 18% 100% $0.08

Total 5,052 897 18% 897 18% 100% $0.07

Due to the nature of industries using natural gas in Iowa, over 75% of the economic potential liesin chemical manufacturing (40%) and food processing (38%). As Figure 12 shows, there are alsosubstantial savings opportunities in minerals (7%) and machinery (6%, ).


Figure 12. Industrial Sector Gas Economic Potential by Segment

Total: 897,381 decatherm s

Note: "Other" inc lu des:M etals: 3 .4% , M iscellaneou s: 1.6% , Paper: 1.1% , Prin ting: 1.1% , T ransportation: 1.0%

Chemicals40%

Food38%

Minerals7%

Machinery6%

Other8%

Almost all (85%) of baseline consumption is in boilers and process heating, thus these end usesaccount for almost 90% of the economic potential. The remaining potentials are in HVACimprovements and other (non-heating) process improvements (Table 32 and Figure 13).)

Table 32. Industrial Sector Gas Energy-Efficiency Potential by End Use(Thousand decatherms in 2018)


HVAC 364 89 89

Indirect Boiler 2,406 195 195

Other 91 0 0

Process - Heat 1,960 603 603

Process - Other 231 11 11

Total 5,052 897 897


Figure 13. Industrial Sector Gas Economic Potential by End Use

Total: 897,381 decatherm s

Process68%

Boiler22%

HVAC10%


4. Demand Response

Scope of Analysis

Demand response (DR) or load reduction programs focused on reducing a utility’s capacityneeds are comprised of flexible, price-responsive loads, which may be curtailed or interruptedduring system emergencies or when wholesale market prices exceed the utility’s supply cost.These programs are designed to help reduce peak demand, promote improved system reliability,and, in some cases, may lead to the deferment of investments in delivery and generationinfrastructure. Objectives of DR may be met through a broad range of price-based (e.g., time-varying rates and interruptible tariffs) or incentive-based (e.g., direct load control) strategies. Inthis assessment, the following demand-response strategies were analyzed:

1. Direct Load Control (DLC) programs allow a utility to remotely interrupt or cycleelectrical equipment and appliances at a customer’s facility. In this study, the assessmentof DLC program potential is analyzed for central electric cooling programs (includingheat pumps) and for central cooling and electric water heating combination programs.Each of these programs are modeled for residential and small commercial customersseparately. Large commercial customer DLC is also modeled, which, using integrationwith existing energy management systems (EMS), have additional controls on lighting,HVAC, and plug loads.

2. Thermal Energy Storage (TES) programs are designed to reduce demand associated withcooling during on-peak periods through load-shifting. For most common TESapplications, ice is made during off-peak periods (unoccupied times at night) using theexisting cooling system. This ice is saved and used to cool the building during peakdemand periods, which mitigates customer high demand and energy charges during on-peak periods. This program is often targeted at large commercial customers with rooftopcooling units.

3. Interruptible Tariffs refer to contractual arrangements between the utility and itscustomers who agree to curtail or interrupt their loads in whole or part for apredetermined period when requested. In most cases, mandatory participation is requiredonce the customer enrolls in the program; however, these programs may includeprovisions for customers to exercise an economic buy-through of a curtailment event.Incentives are paid regardless of the quantity of events called each year (less anypenalties associated with an event buy-through). This analysis assumes such programstarget commercial and industrial (C&I) customers with average monthly loads greaterthan 200 kW.

4. Demand-Bidding or Demand Buy-Back programs offer payments to customers forvoluntarily reducing their demand when requested by the utility. The buyback amountgenerally depends on market prices published by the utility ahead of the event, coupledwith the customer’s ability to curtail use during the hours load curtailment is requested.The reduction level achieved is verified using an agreed-upon baseline usage levelspecific to the participating customer. As with interruptible tariffs, this analysis assumessuch programs target C&I customers with loads greater than 200 kW.


5. Time-of-Use (TOU) programs are generally based on two- or three-tiered time-differentiated tariff structures that charge fixed prices for usage during different blocks oftime (typically on- and off-peak prices by season). TOU rates are designed to moreclosely reflect the marginal cost of generating and delivering power. This study analyzesthe potential for TOU rates only for the residential sector; C&I TOU rates are typicallyconsidered a standard tariff and not a capacity-focused program option.

6. Critical Peak Pricing (CPP) or extreme-day pricing refers to programs aiming to reducesystem demand by encouraging customers to reduce their loads for a limited number ofhours during the year. During such events, customers have the option of curtailing theirusage or paying substantially higher-than-standard retail rates. CPP programs integrate apricing structure similar to TOU with the distinction of more extreme pricing signals forthe critical events. For residential and small commercial sectors, it is assumed enablingtechnology is installed (such as smart thermostats); for larger commercial customers(greater than 30kW), interval meters would be installed.

7. Real-Time Pricing (RTP) is a tariff structure for customers to pay electric rates tied tomarket prices. The prices are typically posted by the utility based on day-ahead hourlyprices. RTP price structures are most suitable for large C&I customers with flexibleschedules which may be adjusted on short notice. This analysis assumes an RTP tariffwould target large C&I customers (greater than 200 kW). Since MidAmerican does nothave the infrastructure to give customers a day’s notice for the price structure, thisprogram is only run for Alliant.

Program options listed above are based on a thorough review of literature, cataloging andclassifying DR strategies offered by utilities and regional transmission organizations across thecountry. For each program offering, data were collected on the offerings’ main features, such asobjectives, program periods, eligibility criteria, curtailment event triggers, incentive structures,and technology requirements. These program options are described in more detail later in thissection.

Estimate Demand Response Resource Potentials

The Quantec Team’s methodology for estimating DR potentials is based on a combined “top-down/bottom-up” approach. Quantec’s DRPro® Model provided the basic framework for thisanalysis. As shown schematically in Figure 14, the approach begins with the utility system loadsand disaggregates them into sector, segment, and applicable end uses. For each DR program (orprogram component), potential technical impacts are then calculated for all applicable end uses.The end-use load impacts are then aggregated to obtain estimates of technical potentials. Marketfactors such as probabilities of program and event participation are then applied to technicalpotentials to obtain estimates of market potentials. The methodology for calculating technicaland market potential are described in greater detail below.20

20 Note the study does not examine changes in energy use that may occur from demand response programs. Someprograms are expected to reduce energy use, while others may primarily lead to load shifting.


Figure 14. Schematic Overview of Demand Response Assessment Methodology

Estimating Demand Response Technical Potential

DR technical potentials are first estimated at the end-use level, then aggregated to marketsegment, sector, and system levels. This approach was implemented in four steps, as follows.

1. Define customer sectors, market segments, and applicable end uses. The first step in theprocess involved defining appropriate sectors, market segments, and end uses within eachsegment for each utility. The Quantec team used the following classification scheme fordemand response:

o Customer classes/sectors: residential, commercial, and industrial.o Market segments:

a. Residential: single-family, low-income single-family, multifamily, low-incomemultifamily, and manufactured homes.

b. Commercial: education, grocery, health, lodging, large offices, small offices,restaurants, large retail, small retail, warehouses, and other commercial.

c. Industrial: food manufacturing, primary metal manufacturing, papermanufacturing, plastics rubber manufacturing, chemical manufacturing,instruments, nonmetallic mineral products, industrial machinery, fabricated metalproducts, printing related support, transportation equipment manufacturing,


electronic equipment manufacturing, lumber wood manufacturing, furnituremanufacturing, and miscellaneous manufacturing.

o Large accounts: the largest C&I customers are researched for each utility and uniquesegments are created as necessary to appropriately account for their characteristics.

o End uses: cooling, water-heating, lighting, plug loads, process (industrial), pumping(agriculture), etc.

2. Screen customer segments and end uses for eligibility. This step involved screening enduses for applicability of specific DR strategies. For example, hot water loads in hospitalsor cooking loads in restaurants were excluded (if no backup generation is available).

3. Compile utility-specific sector/end-use loads. Reliable estimates of DR potential dependon the correct characterization of sector and end-use loads. Load profiles were developedfor each end use within various market segments of each utility. Contributions to systempeak for each end use were estimated based on end-use load shapes for each utilityjurisdiction.

4. Estimate technical potential. Technical potential for each DR program is assumed to be afunction of customer eligibility in each class, affected end uses in that class, and theexpected impact of the strategy on the targeted end uses. Analytically, technical potential(TP) for a demand-response program (s) is calculated as the sum of impacts at the end-use level (e), generated in customer class (c), by the program; that is:

sces TPTPand

secscssce LIEUSLETP

where,

LEcs (load eligibility) represents the percent of customer class loads that are eligible forstrategy sEUScse represents the share of end use e in customer class c eligible for DR strategiesLIse (load impact) is percent reduction in end-use load e resulting from programs

Load eligibility thresholds was established by calculating the percent of load by customer classand market segment that meets minimum (or maximum) load criterion for each program basedon program filings.

Estimate Demand Response Market Potential

As discussed above, estimates of expected load impacts resulting from various DR programs(LIse) are based on data available from evaluations of existing programs offered by Alliant andMidAmerican, as well as a comprehensive review and assessment of DR program impactsoffered by utilities throughout the United States. Program participation indicates the percent ofparticipating customers, while event participation summarizes the percent of programparticipation that will participate in any one event.


Develop Demand Response Resource Supply Curves

Market potentials were determined based on applicable program costs that generally fall into twocategories along with event participation and program participation:

To add additional perspective on market potentials, we developed supply curves by combiningmarket potentials for each DR program strategy with its per-unit resource costs to produce“cumulative” resource supply curves. The supply curves show price/quantity relationships foreach utility at the aggregate level. Interactive program impacts were not taken into consideration.

Program implementation costs were researched and documented by our senior engineering staff.All categories of costs were considered, generally falling into two categories:

Fixed program expenses such as program infrastructure, administration, maintenance,and communication.

Variable costs such as incentive payments to participants, customer-site hardware,customer specific marketing/recruiting, and metering.

Summary of Demand Response Resource Potential

Table 33 and Table 34 report estimated resource potential for all DR resources for the residential,commercial, and industrial sectors for Alliant and MidAmerican. Market potential is highest inthe industrial sector due to the interruptible program. As noted above, however, the analysis doesnot account for program interactions and overlap, and thus the total technical and marketpotential estimates are provided as examples only, but are not fully attainable.

Table 33. Alliant EnergyTechnical and Market Potential (MW in 2018)



MarketAcceptance as


Residential 988 590 71 9%Commercial 970 602 70 9%Industrial 1475 1195 262 21%Total 3434 2388 403 14%Note: Individual results may not sum to total due to rounding.Note: Interactions between programs has not been taken into account.Note: DLC RES AC has been eliminated from these potential results to account for complete overlap with DLC RES AC

and water heating.


Table 34. MidAmerican EnergyTechnical and Market Potential (MW in 2018)



MarketAcceptance as


Residential 1,367 809 97 9%Commercial 964 388 38 5%Industrial 1,516 868 159 13%Total 3,846 2,065 295 10%Note: Individual results may not sum to total due to roundingNote: Interactions between programs has not been taken into account

Resource Costs and Supply Curves

Costs for DR program options vary significantly by category and actual amounts. Applicableresource acquisition costs generally fall into two categories:

Fixed program expenses such as program infrastructure, administration,maintenance, and communication.

Variable costs such as incentive payments to participants, customer-site hardware,customer specific marketing/recruiting, and metering. Variable costs may vary basedon the number of customers (e.g., hardware) or kW (primarily incentives).

Where possible, costs estimates were developed for each program option based on data availablefrom Alliant and MidAmerican directly or comparable programs. In certain cases, this level ofspecificity was difficult to establish as many utilities did not track or report program costs withsufficient detail. For example, development of a new DR program can be a significant effort for autility, requiring enrollment, call centers, program management, load research, development ofevaluation protocols, changes to billing systems, and marketing. Background research on utilitiesacross the indicated large variations in direct program costs. Based on the experiences of Alliant,MidAmerican, and other utilities, this analysis assumed $400,000 as a “typical” first cost forprogram development. No first-cost ($0) was assumed in cases where preexisting programs orinfrastructure existed.

Marketing costs can also vary widely, largely based on the sector and level of utilityinvolvement. Based on interviews with program managers, this analysis assumes $30 for eachnew residential participant ($25 in the case of TOU) and $500 for each commercial or industrialparticipant based on the difficulty of getting large C&I sites to participate in these programs.

In developing estimates of per-unit costs, program expenses were allocated annually over theexpected program life cycle (10 years), then discounted by a real cost of capital to estimate thetotal discounted cost (actual Alliant and MidAmerican values were used). The ratio of this valueand the average annual kW reduction produces the levelized per-kW cost for each resource.Additionally, attrition rates are used to account for program turnover due to changes in electricservice (i.e., housing stock turnover) and program drop-outs. The basic assumption for thisanalysis is 3%, based on averaged values experienced by Alliant and MidAmerican. Attrition


requires reinvestment of new customer costs, including technology, installation, and marketing.In addition, the analysis assumes a measure life for the installed technology, and all costs areadjusted upward by 15% or $60,000 to account for administrative expenses.21 Table 35 displaysthe per-unit ($/kW-year) costs by service territory for the estimated market potential.

Real-time pricing and critical peak pricing (both C&I programs) are estimated to be the leastexpensive options, with a levelized cost of $11/kW-year for Alliant, while critical peak pricingand demand bidding are the least expensive options for MidAmerican, with a levelized cost of$19/kW-year and $17/kW-year, respectively (real-time pricing was not run for MidAmerican).

Table 35. Levelized Costs and Market Potential (MW in 2018)Alliant Energy MidAmerican Energy

Levelized Cost MarketPotential (MW)


MarketPotential (MW)


Direct Load Control (DLC)Residential (A/C only) 48 $55 66 $56Residential (A/C and WH) 53 $62 72 $63Small Commercial (A/C) 1 $96 1 $81Medium to LargeCommercial

1 $119 1 $169

Thermal Energy Storage(TES)

1 $135 1 $150

Interruptible Tariffs 291 $45 170 $26Demand Bidding 18 $14 15 $17TOU Rates 7 $38 10 $87Critical Peak Pricing (CPP)

Residential 11 $95 15 $95C&I 11 $11 9 $19

Real-Time Pricing (RTP) 9 $11 - - - - - -

Service territory supply curves are constructed from quantities of estimated market resourcepotential and per-unit costs of each resource option. The capacity-focused supply curves, shownin Figure 15 and Figure 16, represent the quantity of each resource (cumulative market MW) thatcan be achieved at or below the cost at any point. Cumulative MW is created by summing themarket potential along the horizontal axis sequentially, in the order of their levelized costs. ForAlliant, the CPP program for C&I has 11 MW available, and the second lowest cost of the DRresources. Its quantity, therefore, is added to the 9 MW of RTP program, showing that, in total,20 MW of resources are available at prices equal to or less than $11/kW-year. Programinteractions are not taken into account for this study. Measure’s highlighted in red are existingutility programs.

21 All resource classes in this study include a 15% or $60,000 administrative adder to account for ongoingprogram expenses.


Figure 15. Alliant Energy Territory Supply Curve(Cumulative MW in 2018)

Figure 16. MidAmerican Energy Territory Supply Curve(Cumulative MW in 2018)

DLC - Res - AC

DLC-Small Com -AC

Time Of Use Rates

DLC - Com

TES

Interruptible Loads

Demand Bidding

CPP - Res

CPP - C&I

DLC - RES - AC andWater Heat

$0

$20

$40

$60

$80

$100

$120

$140

$160

$180

- 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00

Cumulative Savings (MW)

Lev

eliz

edC

ost

($/k

W-y

ear

)

RTPCPP - C&I

Demand Bidding

TOU InterruptibleLoads

DLC - Res - ACDLC - Res - AC &

WH

DLC - Small Com -AC

DLC - ComTES

CPP- RES

$0

$20

$40

$60

$80

$100

$120

$140

$160

- 100.00 200.00 300.00 400.00 500.00

Cumulative Savings (MW)

Levelized Cost ($/kW-year)


Resource Acquisition Schedule

Each program option has its own ramping rate; the general logic is that it requires three years togrow a new program from inception to full potential, and the first few years have relatively slowgrowth. After year three, the program levels increase at the rate of sales growth, by sector. Forcurrent program offerings from Alliant or MidAmerican (e.g., residential DLC, interruptibletariffs, etc.), ramping begins with the existing program quantity under contract and increaseswith load growth. The assumption, therefore, is that mature programs – in the absence of anysubstantial changes in incentive structures – are unlikely to see any changes in impacts other thanfrom increases in load growth.

Demand Response Resource Results by Program Option

Direct Load Control Results

DLC programs are designed to interrupt specific end-use loads at customer facilities throughutility-directed control. When deemed necessary, the utility is authorized to cycle or shut offparticipating appliances or equipment for a limited number of hours on a limited number ofoccasions. Customers do not have to pay for the equipment or installation of control systems andare given incentives that are usually paid through monthly credits on their utility bills. For thistype of program, receiver systems are installed on the customer equipment to enablecommunications from the utility and to execute controls. Historically, DLC programs have beenmandatory once a customer elects to participate; however, voluntary participation is now anoption for some programs with more intelligent control systems and override capabilities at thecustomer facility.22

Recently, DLC of air-conditioning has emerged as the most common load management programtype. A recent FERC report indicates, as of August 2006, 234 entities offer DLC programs, andmost of these offer residential air-conditioning load control.23 In addition to reviewing meta-studies on DLC, the research team conducted in-depth interviews and researched many keyutility programs, including those from Alliant and MidAmerican, as well as those sponsored byutilities such as Florida Power and Light, Nevada Power, Sacramento Municipal Utility District,Southern California Edison, Pacific Gas and Electric, Puget Sound Energy, Austin Energy,Consolidated Edison, Long Island Power Authority, Idaho Power, Xcel-MN, and WisconsinPublic Service.24,25

This analysis covers residential and commercial DLC programs and reviewed multiple types ofavailable end uses, with four program options:

22 Typically, penalties are associated with non-compliance or opt-outs23 FERC, Assessment of Demand Response and Advanced Metering, August 200624 DOE, Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them, Report

to Congress, February 200625 E Source, EDRP-F-8, Best Practices in Residential Direct Load Control Programs, November 2006.


Residential air-conditioning only

Residential air-conditioning with water heating

Small commercial air-conditioning only

Medium to large commercial programs

Values used in modeling have been standardized based on DR program research or, whereappropriate, have been averaged between or taken directly from the MidAmerican and AlliantDLC programs.

Air-Conditioning Only (Residential). Currently, MidAmerican reports approximately 50 MW ofsavings from its SummerSaver program, which targets only residential customers with eligibilityspecific to central cooling systems (including heat pumps).26 On average, MidAmerican callsbetween eight to ten events per season, which is consistent with most of the researched utilityprograms mentioned above. Based on the number of residential customers with central air-conditioning, as well as a 50% cycling strategy, technical potential is estimated as 231 MW forAlliant and 316 MW for MidAmerican.

Market potential, however, depends largely on the expected rate of program sign-up. Across thecountry, participation rates vary widely, from as little as 1% to 40% of residential customers.MidAmerican had a program participation rate of 19%, while Alliant had a slightly higherparticipation rate of 23%. An average of 21% is used in the analysis.

Table 36 shows the technical and market results for Alliant and MidAmerican territories, bycustomer class. The difference in potential is largely attributed to MidAmerican having moreresidential customers with central air-conditioning. Alliant and MidAmerican have a levelizedcost of $55/kW-year and $56/kW-year, respectively.

Table 36. Residential DLC Air-Conditioning: Technical and Market Potential(MW in 2018)

Alliant Energy MidAmerican EnergySector Technical

PotentialMarket

Potential27Market as % of

2018 PeakTechnicalPotential

MarketPotential

Market as % of2018 Peak

Residential 231 49 6% 316 66 6%

In terms of costs, technology selection is one of the most historically important factors:thermostats (which raise the temperature set-point) versus switches (which utilize a duty cyclingstrategy). While equipment costs can vary considerably, the price differences between two-waythermostats and simple one-way switches are diminishing as technology costs fall, although

26 The Alliant residential DLC program also includes the option for electric water heat, and thus is discussedbelow.

27 An impact evaluation of the Alliant residential DLC program found that a significant number (nearly 50%) ofthe load control receivers (LCRs) were not properly functioning. The program is currently installing newerLCRs, however, so this failure rate is not expected to continue going forward.


installation costs remain significantly different. A range of other issues emerge in choosing theappropriate technology for a given program, including selection of a communications medium(FM, paging, other) and the length of the control period. Installed hardware costs range fromabout $150 for a simple switch to $450 for a two-way thermostat. This study assumes a one-wayswitch (similar to the current Alliant and MidAmerican programs) at a cost of $175.

Utility incentives for residential DLC programs can also vary widely, from only the freeprogrammable thermostat, to a set incentive amount per month, to a 15% discount on customers’summer electric bills, which can sum to $50-$60 annually for many participants. Currently,MidAmerican pays $40 for the first year of participation and $30 each subsequent year;incentives for Alliant’s DLC program are set at $32/year for only air conditioner cycling, with anadditional $8/year for including water heater cycling. Incentives for this analysis are set at$32/year for residential A/C cycling. Additional costs are assessed for this program, including:$30 per new customer of marketing (based on Alliant data); $7 for each existing customer forcommunications, replacement of technology every ten years; $400,000 for program start-up, andan attrition rate (requiring reinvestment of new-customer costs) of 3% based on an average of1.5% from MidAmerican and 4% from Alliant.28 Detailed assumptions are provided in Table 37.

28 Other programs researched indicate a 7% attrition rate is common, which is based on a 5% rate of electricservice turnover plus 2% program removals.


Table 37. Assumptions for DLC Residential Air-Conditioning PotentialProgram Concept Assumptions

Customer Sectors Eligible All ResidentialEnd Uses Eligible for Program Central Cooling or Air-Source Heat PumpCustomer Size Requirements, if any N/ASummer Load Basis Top 40 Summer HoursWinter Load Basis No Winter

Inputs Model Values Model AssumptionsAnnual Attrition (%) 3% 1.5% (MidAm) and 4% (Alliant) were determined based on data

available from the two utilities. Other studies have found 7%(composed of 5% change of service and 2% removals) fromutilities, including RMP, Xcel, Eon US, SMUD, PSE&G, FP&L(removals range from 1%–3%).

Per Customer Impacts (kW) 0.85 Alliant reports 0.8 for A/C controls and 0.2 for WH, while MidAm'ssavings are calculated to be 0.89 (total reduction of load dividedby total number of customers). For consistency, the averagevalue is used in the model.

Total kW reduction per program 50,660 MidAm DataAnnual Administrative Costs (% of First-yearCost)

15% An administrative adder of 15% was typically assumed for allprogram strategies (assuming that since 15% will be taken from afirst cost of $400,000, the annual administrative cost will be$60,000).

Technology Cost $175 Alliant reports $190 (from EE plan), while $175 is indicated in theCEC report from 2004 (for the installed cost of ratio frequencyload control devices). WH controls will require another switch andresult in doubling this cost.

Marketing Cost $30 Marketing costs are set at $30 based on data available fromAlliant.

Incentive (annual costs) $32 MidAm reports $40 for first year and $30 each year after. Alliantreports $32 for A/C only, and up to $40 including WH. Forconsistency, the average value is used in the model.

Communication Costs (per Customer PerYear)

$7 This value accounts for annual per-customer communication of aone-way transmission system.

Overhead: First Costs $0 MidAm and Alliant both currently offer DLC programs; thereforeno first costs are necessary.

Per Customer First Cost $205 MidAm and Alliant both currently offer DLC programs; therefore,no first costs are necessary beyond technology and marketing upfront, per participant.

Per Customer Ongoing $57 Ongoing costs are calculated from summing annual customerincentives, annual communication costs, and 10% of Technologycosts for repair and/or replacement of equipment.

Eligible Load (%) 100% of theCooling Load

Eligible load is the percentage of customers with this specific enduse.

Technical Potential (as % of Gross) 100% The assumption is made that all central AC units can be retrofit.Program Participation (%) 21% MidAm participation is set at 19% and is calculated from the

number of 2006 participants divided by the total number ofeligible participants. Alliant participation is set at 23% of totalresidential customers.

Event Participation (%) 100% It is assumed all customers signed up for the program will becalled on during an event.

Average # Events per Season 8 MidAm indicates eight events per season, and Alliant indicatessix to eight events.

Cycling Strategy 50% Both MidAm and Alliant indicate 50% cycling strategies.


Residential Air-Conditioning and Water Heating. There are several program examples ofutilities combining residential air-conditioning DLC programs with other end uses. The mostcommon program option (with examples from Alliant, as well as other utilities such as XcelEnergy, E.ON US, and Florida Power & Light) is to offer customers the opportunity to add hotwater heating control to their air-conditioning control program. This study estimates the potentialfor this combination using the same air-conditioning assumptions shown in the cooling-onlyprogram, but adding water heat as an option and including a separate incentive level of $8/year(consistent with the current Alliant program). The installed costs for water heat control areassumed to be the same as the air-conditioning control, as a similar one-way switch would beused. The technical potential for the water heating portion includes only customers with bothcentral cooling and electric hot water heating.29

The results in Table 38 show that adding hot water heating leads to a slight increase in theamount of market potential (additional 4 MW for Alliant and additional 6 MW forMidAmerican), yet raises the average levelized cost of the program (as the hot water heatershave the same costs as air-conditioning, but provide less per-unit demand reductions). Alliant’slevelized costs increases from $55/kW-year to $62/kW-year, while MidAmerican increased from$56/kW-year to $63/kW-year. This is particularly true in the summer, when the value of capacityis highest for Alliant and MidAmerican.

Table 38. DLC Air-Conditioning and Water Heating: Technical and Market Potential(MW in 2018)


PotentialMarket

PotentialMarket as %of 2018 Peak

TechnicalPotential

MarketPotential

Market as %of 2018 Peak

Residential 266 53 6% 364 72 7%

Detailed assumptions providing values and sources that derived the potential and levelized costsare shown in Table 39.

29 DLC of electric hot water only is generally not considered cost effective, so is only analyzed as an add-on to thecentral air-conditioning DLC program.


Table 39. Assumptions for DLC Residential Air-Conditioning and Water Heating PotentialProgram Concept Assumptions

Customer Sectors Eligible All ResidentialEnd Uses Eligible for Program Central Cooling (including Heat Pump) and Electric Hot

Water HeatingCustomer Size Requirements, if any N/ASummer Load Basis Top 40 Summer Hours

Inputs Model Values Model AssumptionsAnnual Attrition (%) 3% 1.5% (MidAm) and 4% (Alliant) were determined based on data available from

the two utilities. Other studies have found 7% (composed of 5% change ofservice and 2% removals) from utilities, including RMP, Xcel, Eon US, SMUD,PSE&G, FP&L (removals range from 1%–3%).

Per Customer Impacts (kW) A/C = 0.85kWand WH = 0.2kWRes

Alliant reports 0.8 for A/C controls and 0.2 for WH, while MidAm's savings arecalculated to be 0.89 (total reduction of load divided by total number ofcustomers). For consistency, the average value is used in the model.

Total kW reduction per program 24,430 Alliant DataAnnual Administrative Costs 15% An administrative adder of 15% was typically assumed for all program

strategies (assuming that since 15% will be taken from a first cost of$400,000, the annual administrative cost will be $60,000).

Technology Costs $175 for A/Ccontrol switch,$175 for WHcontrol switch

Alliant reports $190 (from EE plan), while $175 is indicated in the CEC reportfrom 2004 (for the installed cost of ratio frequency load control devices). WHcontrols will require another switch and result in doubling this cost.

Marketing Cost (Recruitment Cost) $30 Marketing costs are set at $30 based on data available from Alliant.Incentive (annual cost) $32 for Central

AC; $8 for WaterHeater

MidAm reports $40 for first year and $30 each year after. Alliant reports $32for A/C only, and up to $40 including WH.

Communication Costs (per CustomerPer Year)

$7 This value accounts for annual per-customer communication of a one-waytransmission system.

Overhead: First Costs $0 MidAm and Alliant both currently offer DLC programs; therefore, no first costsare necessary.

Per Customer First Costs $205 for A/C and$175 for WH

MidAm and Alliant both currently offer DLC programs; therefore, no first costsare necessary beyond technology and marketing up front, per participant.

Per Customer Ongoing Costs $57 for CentralAC; $33 forWater Heating

Ongoing costs are calculated from summing annual customer incentives,annual communication costs, and 10% of Technology costs for repair and/orreplacement of equipment.

Eligible Load (%) 100% Eligible load is the percentage of customers with this specific end use.Technical Potential(as % of Load Basis)

100% The assumption is made that all central AC units can be retrofit.

Program Participation (%) 21% for CentralAC; 12% forWater Heating

It is assumed 21% of the participating cooling load will sign up for the program(which is the same as for DLC AC only program). By subsector, programparticipation is assumed to be the same rate of program sign-up, but accountsfor the saturation of electric hot water heating of customers with central AC. Itis calculated as the percent of customers with electric hot water and centralcooling, divided by the percent of customers with electric hot water heating,which is multiplied by the participation rate of central AC customers. Thesaturations of end uses are taken from RASS survey results.

Event Participation (%) 100% It is assumed all customers signed up for the program will be called on duringan event.

Average # Events per Season 8 MidAm indicates eight events per season, and Alliant indicates six to eightevents.

Cycling Strategy 50% for A/C;100% for WH

Both MidAm and Alliant indicate 50% AC cycling strategies. We also assumethe water heating tank can be shut off during the event.


Air-Conditioning Only (Small Commercial). Some DLC programs around the nation are alsoincluding small commercial customers (less than 30 kW) into their existing residential DLCprograms (PacifiCorp–Utah Cool keeper program is one example). Many of the cost assumptions(e.g., switches) are the same as the residential DLC program. Some key differences are anincrease in the kW/per customer impact due to larger-sized units, as well as significantly lowercustomer participation rate due to concerns about impacts on business activity (typically about5% for commercial compared to approximately 21% for residential).

Table 40 below shows the technical and market results for Alliant and MidAmerican territoriesfor a small commercial air-conditioning DLC program. The market potential for air-conditioningin either territory is around 1 MW (<1% of 2018 territory peak). Alliant and MidAmerican havea levelized cost of $96/kW-year and $81/kW-year, respectively.30

Table 40. DLC Air-Conditioning: Technical and Market Potential(MW in 2018)


PotentialMarket


TechnicalPotential

MarketPotential


Small Commercial 17 1 <1% 23 1 <1%


30 Note that some cost factors, including administration first costs and ongoing costs, would not be considered ifthe program were to be run as an extension of a current residential DLC project.


Table 41. Assumptions for DLC Small Commercial Air-Conditioning PotentialProgram Concept Assumptions

Customer Sectors Eligible Small Commercial (<30kW) market segmentsEnd Uses Eligible for Program Central Cooling or Air-Source Heat PumpCustomer Size Requirements, if any N/ASummer Load Basis Top 40 Summer HoursWinter Load Basis No Winter


available from the two utilities. Other studies have found 7% (composedof 5% change of service and 2% removals) from utilities, including RMP,Xcel, Eon US, SMUD, PSE&G, FP&L (removals range from 1%–3%).

Per Customer Impacts (kW) 1.5 We assume an increased impact for small commercial customers(calculated by: 5/3 * 0.89 kW). The calculation accounts for thedifference in demand for an average-sized unit (e.g.. ratio of aresidential 3 ton unit and a small commercial 5 ton unit).

Total kW reduction per program N/AAnnual Administrative Costs (% of First-year Cost)

15% An administrative adder of 15% was typically assumed for all programstrategies (assuming that since 15% will be taken from a first cost of$400,000, the annual administrative cost will be $60,000).

Technology Cost $175 Alliant reports $190 (from EE plan), while $175 is indicated in the CECreport from 2004 (for the installed cost of ratio frequency load controldevices). WH controls will require another switch and result in doublingthis cost.

Marketing Cost $30 Marketing costs are set at $30 based on data available from Alliant.Incentive (annual costs) $48 While commercial customers will have a larger impact than residential,

they will also be more difficult to get to participate in the program.Therefore, we assume the $48 for small commercial customerincentives, calculated from the residential incentive (1.5 * $32).


$7 This value accounts for annual per-customer communication of a one-way transmission system.

Overhead: First Costs $0 MidAm and Alliant both currently offer DLC programs; therefore, no firstcosts are necessary.

Per Customer First Cost $205 MidAm and Alliant both currently offer DLC programs; therefore, no firstcosts are necessary beyond technology and marketing up front, perparticipant.

Per Customer Ongoing $73 Ongoing costs are calculated from summing annual customerincentives, annual communication costs, and 10% of Technology costsfor repair and/or replacement of equipment.

Eligible Load (%) Varies bySector

Eligible load is the percentage of customers with this specific end use.

Technical Potential (as % of Gross) 50% The assumption is made that all central AC units can be retrofit.Program Participation (%) 5% We assume commercial participation to be approximately 25% of

residential participation.Event Participation (%) 100% It is assumed all customers signed up for the program will be called on

during an event.Average # Events per Season 8 MidAm indicates eight events per season, and Alliant indicates six to

eight events.Cycling Strategy 50% Both MidAm and Alliant indicate 50% cycling strategies.


Large Commercial DLC Programs. Direct control of commercial customers is an enticingoption for utilities due to the large size of loads and the reliability of direct control. Yet, thisoption requires significant technological investment in coordination with the existing EMS and isgenerally not favored by customers.

Recently, the International Energy Agency (IEA) released a study that included a survey of40 major utilities with capacity-focused programs, revealing that fewer than one-quarter of thosesurveyed offered DLC programs to their commercial customers. Participation rates wereextremely low, and the majority of these were offered to small commercial customers (ascovered in the air-conditioning program above). Utilities offering programs to large C&Icustomers include: Florida Power and Light, Xcel Energy, Otter Tail Power and Light, MadisonGas and Electric, Wisconsin Electric, and Wisconsin Public Service.

Although the program history is limited, this study estimates potential for large commercialcustomers, requiring a size threshold of 200 kW to increase likelihood of existing EMS systems.The following end uses are assessed by customer segment: cooling, hot water, lighting, plugload, and refrigeration. It is assumed this program option would be called at similar frequency tothe air-conditioning program: approximately 40 hours per summer.

Technically, only a small portion of the total end-use loads could be curtailed (Table 42). Toestimate the market, the most uncertain factor is program participation. Findings from the IEAsurvey indicated C&I DLC program participation rates are generally quite low (less than 1% ofload), excepting Xcel Energy and Otter Tail Power, which achieved participation rates greaterthan 10% at a cost of about $250/kW. This study assumes a program participation rate of 2%.Event participation is assumed at 90% based on other national programs. As shown in Table 42,although approximately 83 MW and 53 MW are technically available for the Alliant andMidAmerican territories, respectively, there is essentially no market potential for this programoption due to a lack of interest among customers.

Table 42. DLC Large Commercial: Technical and Market Potential(MW in 2018)


PotentialMarket


TechnicalPotential

MarketPotential


Commercial 83 1 <1% 53 1 <1%

In terms of costs, the analysis estimates interfacing with existing EMS controls for each end use,reflecting a hierarchy of measures: (1) cooling, (2) lighting, (3) hot water, (4) process, and(5) plug load. Controls are assumed to last 10 years. Customer incentives are assumed at $6/kWper month ($72/kW-year) based on the need to pay customers relatively high incentives to havedirect control over loads.



Table 43. Assumptions for DLC Large Commercial PotentialProgram Concept Assumptions

Customer Sectors Eligible All Commercial subsectorsEnd Uses Eligible for Program Cooling, hot water, lighting, plug load,

refrigerationCustomer Size Requirements, if any Loads greater than 200 kW due to EMS

system requirementsSummer Load Basis Top 40 SummerWinter Load Basis No Winter


available from the two utilities. Other studies have found 7%(composed of 5% change of service and 2% removals) from utilitiesincluding RMP, Xcel, Eon US, SMUD, PSE&G, FP&L (removalsrange from 1%–3%).

Per Customer Impacts (kW) Varies by Sector This value is a product of technical potential and average kW ofeligible customers.

Total kW reduction per program N/AAnnual Administrative Costs 15% An administrative adder of 15% was typically assumed for all

program strategies (assuming that since 15% will be taken from afirst cost of $400,000, the annual administrative cost will be$60,000).

Technology Cost Varies by Sector Cost estimates assume the sites have centralized EMS systemsand are based on costs Nexant has reviewed for participants inPG&E's Auto Critical Peak Pricing Program. These costs reflect ahierarchy of DR measures that goes: (1) Cooling; (2) Lighting;(3)Hot Water; (4) Process; and (5) Plug load. DLC projects requirea costly interface with existing EMS controls. It is assumed thesecontrols will be linked to facilitate cooling DR measures initially withadditional measures, most often lighting, added on once the systemis connected (i.e., lighting measures cannot be implemented at thelower cost without first incurring the costs associated with coolingmeasures).

Marketing Cost (per new participant) $500 Alliant reports $500 per customer for marketing (based upon 10hours of effort by program staff at $50/hr).

Incentive (annual cost per participant) $72/kW annually We have observed $6/kW per month based upon other studies. Wearrive at $72/kW annually through multiplying the $6/kWassumption by 12 months.


N/A

Overhead: First Costs $400,000 We assume $400,000 overhead as a standard programdevelopment assumption, which includes costs for internal labor,research, and IT/billing system changes ($200,000 for labor and$200,000 for IT).


Inputs Model Values Model AssumptionsPer Customer First Cost Varies by Sector Our cost estimate assumes each site has a centralized EMS

system and is based on costs Nexant has reviewed for participantsin PG&E's Auto Critical Peak Pricing Program. These costs reflect ahierarchy of DR measures that goes: (1) Cooling; (2) Lighting;(3)Hot Water; (4) Process; and (5) Plug load. DLC projects requirea costly interface with existing EMS controls. It is assumed thesecontrols will be linked to facilitate Cooling DR measures initially withadditional measures, most often lighting, added on once the systemis connected (i.e., lighting measures cannot be implemented at thelower cost without first incurring the costs associated with coolingmeasures).

Per Customer Ongoing Varies Ongoing costs are calculated from summing annual customerincentives and 5% of technology costs for repair and/orreplacement of equipment.

Eligible Load (%) Varies by Sector We assume full eligibility of loads greater than 200 kW.Technical Potential (as % of Load Basis) Varies by Sector These assumptions are based on detailed engineering audits of DR

potential of C&I customers throughout California by Nexant, withthird-party verification of results. Findings are amalgamated bysector and end-use category and supported by senior engineeringanalysis.

Program Participation (%) 2% Survey results indicate zero market potential when combined withother programs (10% is the high stand-alone potential). We areassuming participation is more likely 2% (a range of participationlevels are observed nationally (0.1% to 30.5% - Xcel, Otter TailPower).

Event Participation (%) 90% This assumption is based on Xcel Energy Peak Controlled Ratesand is consistent with other similar programs.

Average # Events per Season Varies by Sector This value is a product of technical potential and average kW ofcustomers greater than 200 kW.

Cycling Strategy N/A

Thermal Energy Storage

For C&I customers, it is possible to use TES systems for cooling; these systems produce iceduring off-peak periods, which is then used during on-peak periods to cool buildings during pre-specified times (typically six hours per day, from April to October).31

Few investor-owned utilities currently offer TES programs to their customers. Information onthree such programs was obtained as a result of discussions with a major manufacturer of TESequipment. PG&E and SCE initiated RFP processes for TES programs in early 2007, and verylittle information is available about the status of these programs. Xcel Energy (Minnesota) hasoffered incentives for TES systems in one form or another for about 20 years, currently doing soas part of its Custom Solutions energy-efficiency program, with modest program results (one ortwo installations each year).

31 At this time, there is a commercialized application for small commercial applications (5–20 tons of cooling), forwhich two pilot programs exist in the country.


TES systems require rooftop cooling units, typically found on medium to large commercial sites.Therefore, this analysis assumes only commercial sector customers with greater than 30 kW intotal site demand would be eligible for participation, and the technical feasibility of participatingis reduced to account for only customers with DX cooling units. Program participation isassumed to be quite low, about 1.5% of eligible load, based on the experience of existingprograms.

Table 44 displays the results for TES potential for the Alliant and MidAmerican territories.Technically, the Alliant territory has 89 MW of potential, but, due to low participation rates, it islikely only 1 MW is available (representing less than 1% of the 2018 territory peak). Similarly,the MidAmerican territory has 67 MW of potential but only 1 MW of market potential.

Table 44. Thermal Energy Storage: Technical and Market Potential(MW in 2018)


PotentialMarket


TechnicalPotential

MarketPotential


Commercial 89 1 <1% 67 1 <1%



Table 45. Assumptions for TES PotentialProgram Name Assumptions

Customer Sectors Eligible All Commercial Market SegmentsEnd Uses Eligible for Program Electric Cooling LoadsCustomer Size Requirements, if any All Commercial Customers with Load >30kWSummer Load Basis Average On-Peak SummerWinter Load Basis No Winter

Inputs Model Value Model AssumptionAnnual Attrition (%) 3% 1.5% (MidAm) and 4% (Alliant) were determined based on data

available from the two utilities. Other studies have found 7%(composed of 5% change of service and 2% removals) fromutilities, including RMP, Xcel, Eon US, SMUD, PSE&G, FP&L(removals range from 1%–3%).


Total kW reduction per program N/AAnnual Administrative Costs (% ofoverheadfirst cost)

15% An administrative adder of 15% was typically assumed for allprogram strategies (assuming that since 15% will be taken from afirst cost of $400,000, the annual administrative cost will be$60,000).

Technology Cost $798/kW We assume 1.33 kW/ton A/C units at $600 per ton will result in$798/kW. Cost estimates assume a cost of $600/ton of coolingoffset, which is less than an estimate from the TES programmanager at a rural electric utility in N. California ($1,000/ton).

Marketing Cost $500 Alliant reports $500 per customer for marketing (based upon 10hours of effort by program staff, at $50/hr).

Incentive (annual costs) N/A The incentive in this program is given with free technology. Mostcustomers would also participate in some type of price structureprogram to realize savings associated with shifting cooling load tooff -peak hours.


N/A

Overhead: First Costs $200,000 We assume half of the standard assumption ($400,000) since therewill be no changes to the billing system.

Per Customer First Cost $500Per Customer Ongoing $0 In this case, since the incentive is the cost of technology, ongoing

costs will not fall on the utility, therefore are not considered.Eligible Load (%) Varies by Sector We assume all commercial customers can participate that have

loads greater than 30kW.Technical Potential (as % of Gross) Varies by Sector These assumptions are based on the saturation of DX cooling by

commercial sub-sector.Program Participation (%) 1.5% This assumption is scaled down from 2.5%, based on information

from Alliant conversation.Event Participation (%) 100% Full participation is expected given the highly reliable scheduling of

pre-cooling.Average # Events per Season N/ACycling Strategy 100%


Interruptible Program

Interruptible programs refer to contractual arrangements between the utility and its customers,typically C&I customers which agree to curtail or interrupt their operations, in whole or part, fora predetermined period when requested by the utility. In most cases, mandatory participation orliquidated damage agreements are required once the customer enrolls in the program; however,the number of curtailment requests, both in total and on a daily basis, is limited by the terms ofthe contracts.

Customers are generally not paid for individual events, but are compensated in the form of afixed monthly amount per kW of pledged interruptible load or through a rate discount. Typically,contracts require customers to curtail their connected load by the greater of a set percentage (e.g.,15%–20%) or a predetermined level (e.g., 100 kW). Both Alliant and MidAmerican programsare set up for customers to curtail a predetermined level. These programs often involve long-termcontracts and have non-compliance penalties ranging from simply dropping the customer fromthe program to more punitive actions, such as requiring the customer to repay the utility for thecommitted (but not curtailed) energy at market rates.

The IEA survey of 40 utilities’ DR programs revealed that slightly more than half of utilitiessurveyed offer curtailable or interruptible rate programs to their C&I customers. Utilities offeringprograms included almost all the major utilities in California, Illinois, Indiana, Iowa, Minnesota,and Wisconsin, as well as a variety of other utilities, including Allegheny Energy, ColoradoSprings Utilities, Hydro Quebec, and Kansas City Power and Light. Most utilities requireminimum demand reductions to be eligible for the programs, ranging from 50 kW for XcelEnergy, up to the more typical level of 250 kW for MidAmerican.

In this study, it is assumed C&I customers with a monthly demand of at least 200 kW would beeligible for such a program. Technical potential is estimated by customer segment. One keyaspect to the potential savings associated with the interruptible program is backup generators.Since these participants can turn on a back up generator during these critical peak times, theburden on a customer who has a backup generator is minimal.

Since both utilities currently have a successful interruptible program, many assumptions for thisprogram were modeled to resemble the current programs for each utility.

Table 46 shows the Alliant territory has 785 MW of technical potential in the C&I sectors and290 MW of market potential, totaling 14% of the Alliant territory’s 2018 commercial andindustrial peak loads. The MidAmerican territory has 662 MW of technical potential in the C&Isectors resulting in 170 MW of market potential and representing 9% of the MidAmericanterritory’s 2018 commercial and industrial peak load. In many utility programs (excluding thosein California), customers are allowed to use backup generators to meet curtailment requirements.MidAmerican and Alliant interruptible programs already take into account the use of standbygenerators to help ease the burden of these interruptible events.


Table 46. Interruptible Program: Technical and Market Potential(MW in 2018)


PotentialMarket


TechnicalPotential

MarketPotential


Residential - - - - - - - - - - - - - - - - - -Commercial 153 57 7% 109 28 4%Industrial 633 234 18% 553 142 12%Total 786 291 14% 662 170 9%

One key difference between MidAmerican and Alliant is seen in the incentive offered toparticipating customers. MidAmerican offers an incentive level of $35/kW-year, while Alliantindicates an incentive (starting in 2009) of $62/kW-year. This major difference in incentive levelhas a significant impact on the levelized cost; Alliant shows a levelized cost of $45/kW, whileMidAmerican shows a much lower levelized cost of $26/kW.



Table 47. Assumptions for Interruptible C&I PotentialProgram Name Assumptions

Customer Sectors Eligible Non-Residential (Large C/I)End Uses Eligible for Program N/ACustomer Size Requirements, if any Customers >200kWSummer Load Basis Top 40 Summer HoursWinter Load Basis Top 40 Winter Hours


available from the two utilities. Other studies have found 7%(composed of 5% change of service and 2% removals) from utilities,including RMP, Xcel, Eon US, SMUD, PSE&G, FP&L (removalsrange from 1%–3%).


Total kW reduction per program N/AAnnual Administrative Costs 15% An administrative adder of 15% was typically assumed for all program


Technology Cost $1,400 Technology costs include communication, connectivity and meters, ifnecessary; these are based on California spending $32 M for 23,000large C&I hardware after the energy crisis.

Marketing Cost $500 Alliant reports $500 per customer for marketing (based upon 10 hoursof effort by program staff at $50/hr).

Incentive $35/kW$62/kW

These values ($35/kW for MidAm, $62/kW for Alliant) weredetermined based on data available from the two utilities.


N/A

Overhead: First Costs $0 MidAm and Alliant both currently offer the program; therefore, no firstcosts are necessary.

Per Customer First Cost $1,900 MidAm and Alliant both currently offer DLC programs; therefore, nofirst costs are necessary beyond technology and marketing up front,per participant.

Per Customer Ongoing $400 + Techrepair/replacement

($70)

Based on information from Alliant, ongoing customer costs includetime spent renewing contracts ($100/hr for 4 hours), in addition toannual customer incentives and 5% of technology costs for repairand/or replacement of equipment.

Eligible Load (%) Varies by Sector We assume full eligibility of loads greater than 200 kW.Technical Potential (as % of Gross) 25% commercial;

50% industrialThese assumptions are based on detailed engineering audits of DRpotential of C&I customers throughout California by Nexant, withthird-party verification of results.

Program Participation (%) 40% Alliant; 30%MidAm

These assumptions are based on information available from theutilities.

Event Participation (%) 86% MidAm; 50%Alliant

Figures based on average participation from recent program years.

Average # Events per Season N/ACycling Strategy N/A


Demand Buyback

Under DBB or demand bidding arrangements, the utility offers payments to customers forreducing demand when requested by the utility. Under these programs, the customer remains ona standard rate but is presented with options to bid or propose load reductions in response toutility requests. The buyback amount generally depends on market prices published by the utilityahead of the curtailment event, and the reduction level is verified against an agreed-uponbaseline usage level.

DBB is a mechanism enabling consumers to actively participate in electricity trading by offeringto undertake changes in their normal consumption patterns. Participation requires the flexibilityto make changes to their normal electricity demand profile, install the necessary control andmonitoring technology to execute the bids, and demonstrate bid delivery. One of severalInternet-based programs is generally used to disseminate information on buyback rates topotential customers, who can then take the appropriate actions to manage their peak loads duringrequested events. The program option in this analysis targets the large C&I customers (>200kW),consistent with national programs.

Unlike curtailment programs, customers have the option to curtail power requirements on anevent-by-event basis. Incentives are paid to participants for energy reduced during each event,based primarily on the difference between market prices and utility rates. DBB products arecommon in the United States and are being offered by many major utilities. The use of DBBofferings as a means of mitigating price volatility in power markets is especially common amongindependent system operators (ISOs), including ISOs, in California (CAISO), New York(NYISO), and New England (ISO-NE). However, DBB options are not currently being exercisedregularly due to relatively low power prices. The IEA survey of 40 utilities’ DR programsrevealed that about half of the utilities surveyed offered DBB programs to their C&I customers.Investor-owned utilities offering programs include almost all of the major utilities in California,Illinois, Indiana, Minnesota, and Wisconsin, as well as a variety of other utilities, includingAllegheny Energy, KCP&L, and Portland General Electric.

Six utilities that reported larger DBB program impacts as part of the previous IEA survey werereinterviewed. Utilities generally restrict eligibility for DBB programs to large customers whocan reduce their loads by at least 500 kW – 1,000 kW during peak periods. Of the six utilitiesinterviewed, only Commonwealth Edison has a low minimum load reduction criterion of 10 kW.Program participation has also been significantly influenced by the minimum load reductionrequired, and Commonwealth Edison consequently has 3,700 participants.

Some utilities, however, have captured significant demand reduction potential from just a fewprogram participants. Minnesota Power estimates that it could realize about 100 MW of demandreduction – about 9% of their C&I peak demand – from their five participants in this program ifspot market prices again reach the heights of 1999–2000. Commonwealth Edison claims thesecond largest peak reduction potential of the utilities interviewed, at about 5% of their C&I peakdemand. The other utilities’ estimated their potential peak demand reduction impacts from thisprogram at 0%–2% of their C&I peak demands. These programs have not resulted in large peakdemand impacts for utilities in the past five years due to the relatively low level of spot marketprices during this period.


Table 48 shows that in the Alliant territory, of more than 378 MW of technical potential, anaverage of 18 MW can be expected during any one event. In the MidAmerican territory,308 MW of technical potential results in an average of 15 MW expected during any one event.

Table 48. Demand Buyback: Technical and Market Potential (MW in 2018)Alliant Energy MidAmerican Energy

Sector TechnicalPotential

MarketPotential


TechnicalPotential

MarketPotential


Residential - - - - - - - - - - - - - - - - - -Commercial 123 5 <1% 87 4 <1%Industrial 255 13 <1% 221 11 <1%Total 378 18 <1% 308 15 <1%

Because participants are paid based on market energy rates, the cost of this program is relativelylow and passes all economic screens. The levelized cost shows the resulting $14/kW-year and$17/kW-year in Alliant and MidAmerican territories, respectively. New customer costs includehardware ($1,400 for communications, connectivity, and any necessary metering), marketing($500), and program development ($400,000). New participant costs must be reinvested due to3% annual attrition rates (based on averaged value for Alliant and MidAmerican) and a hardwarelife of 20 years.



Table 49. Assumptions for DBB PotentialProgram Name Assumptions

Customer Sectors Eligible All C&I Market SegmentsEnd Uses Eligible for Program Total Load of All End UsesCustomer Size Requirements, if any Customers >200kWSummer Load Basis Top 40 Summer HoursWinter Load Basis Top 40 Winter Hours

Inputs Model Value Model AssumptionsAnnual Attrition (%) 3% 1.5% (MidAm) and 4% (Alliant) were determined based on data


Per Customer Impacts (kW) Varies bySector

This value is a product of technical potential and average kW ofeligible customers.

Total kW reduction per program N/AAnnual Administrative Costs 15% (60000

per year)An administrative adder of 15% was typically assumed for allprogram strategies (assuming that since 15% will be taken from afirst cost of $400,000, the annual administrative cost will be $60,000).

Technology Cost $1,400 Technology costs include communication, connectivity, and meters, ifnecessary; these are based on California spending $32 M for 23,000large C&I hardware after the energy crisis.

Marketing Cost $500 Alliant reports $500 per customer for marketing (based upon 10hours of effort by program staff at $50/hr).

Incentive $10/kW We assume the estimate of $10 per kW, which is taken from 2000–2002 Demand Exchange Program, based on average market pricesof $100/MWh.


N/A

Overhead: First Costs $400,000 We assume $400,000 overhead as a standard program developmentassumption, which includes costs for internal labor, research andIT/billing system changes ($200,000 for labor and $200,000 for IT).

Per Customer First Cost $1,900 This value is calculated from the technology cost and the marketingcost per new participant.

Per Customer Ongoing $10/kW + $70 Ongoing costs are calculated from summing annual customerincentives and 5% of technology costs for repair and/or replacementof equipment.


We assume full eligibility of loads greater than 200 kW.

Technical Potential (as % of Gross) 20% These assumptions are based on detailed engineering audits of DRpotential of C&I customers throughout California by Nexant, withthird-party verification of results.

Program Participation (%) Varies bySector

This assumption is based on internal survey results, with an averageof 20% participation.

Event Participation (%) 25% Event participation is based on 2006 PacifiCorp results of 25% eventparticipation (based on average price of $130/MWh at 12 MW perevent).



Residential Time of Use Rates

Information on TOU rates was obtained from tariffs from 60 U.S. utilities, promotional materialsused by utilities offering new TOU (or TOU with CPP) programs during the past five years, andseveral interviews with utility staff members.32 TOU rates have been offered by U.S. utilitiessince at least the 1970s, but the historic impacts have been quite low.

The TOU rates developed in recent years typically differ from those of the past in severalimportant ways. First, most new TOU rates contain three price tiers as opposed to the two-tierrates common in many long-standing TOU programs, including those offered by Alliant andMidAmerican. This allows utilities to set high prices during their highest peak periods and offerexceptionally low off-peak prices overnight when the cost is at its lowest and supply is plentiful.The majority of hours are assigned a “mid-peak” price that is typically a slightly discountedversion of the standard rate. Another change is that the duration of the peak period is typicallyshorter than in the past.

Finally, the price differentials between peak and off-peak prices tend to be greater than in thepast to encourage load shifting away from the peak period. For long-standing TOU rates, thisdifferential averaged about 7.6 cents/kWh, whereas newer programs tend to have a differential ofgreater than 10 cents/kWh.

TOU rates are assumed to be available only to the residential customer segments, and thepotential is based on the total load rather than individual end uses. The technically feasibleportion of the load basis expected to be reduced during peak hours is 5% based on results fromCalifornia and Puget Sound Energy.33 The participation rate of the top ten highest-enrolled TOUprograms in the country34 is on average 16%, yet these programs do not represent the experienceof all national programs, many of which have participation rates of <1%. For this analysis,program participation will be modeled at 8%, the approximate average of all programs.

Table 50 shows there is 92 MW of technical potential and 7 MW of market potential in theAllaint territory. In the MidAmerican territory, there is 127 MW of technical potential and10 MW of market potential, both representing less than 1% of 2018 territory peak.

32 Includes: Gulf Power, Alabama Power, Ameren, Pacific Gas and Electric, Southern California Edison, SanDiego Gas and Electric, and Teco Energy. Interviews with utility staff: Arizona Public Service, Salt RiverProject, and Florida Power and Light.

33 Charles River Associates, “Impact Evaluation of the California Statewide Pricing Pilot, Final Report,” March16, 2005. See also, Piette, Mary Ann and David S. Watson, “Participation through Automation: FullyAutomated Critical Peak Pricing in Commercial Buildings,” 2006, Lawrence Berkeley National Laboratory.Linkugel, Eric, Proceedings of the 2006 ACEEE Summer Study on Energy Efficiency in Buildings, PacificGrove, CA, August 2006.

34 FERC, 2006 and R. Gunn, “North American Demand Response Survey Results” (Association of EnergyServices Professionals, Phoenix, AZ, February 2006).


Table 50. Time of Use Rates: Technical and Market Potential (MW in 2018)Alliant Energy MidAmerican Energy


MarketPotential


TechnicalPotential

MarketPotential


Residential 92 7 <1% 127 10 <1%

The difference in levelized cost between Alliant and MidAmerican is attributable to theprograms’ first costs. Since Alliant currently has a TOU program, the overhead first costs havebeen removed, while MidAmerican has a program first cost of $400,000. Alliant has a levelizedcost of $38/kW, while MidAmerican has a significantly higher levelized cost of $87/kW.Because of the low participation rate for this program (8%), the overhead first cost has asubstantially higher impact on the total levelized cost for the program than most successfulprograms.



Table 51. Assumptions for Residential TOU PotentialProgram Concepts Assumptions

Customer Sectors Eligible All Residential Market SegmentsEnd Uses Eligible for Program Total Load of All End UsesCustomer Size Requirements, if any ResidentialSummer Load Basis Average On-Peak SummerWinter Load Basis Average On-Peak Winter



Per Customer Impacts (kW) 0.60 This value is a product of technical potential and average kW ofeligible customers.

Total kW reduction per program N/AAnnual Administrative Costs 15% ($60000) An administrative adder of 15% was typically assumed for all program


Technology Cost $300 This value reflects the cost of meter ($200) and installation ($100).Marketing Cost $25 This cost assumes the same marketing price for DLC residential

($30) with the absence of the $5 mailer.Incentive (annual costs) N/A Bill savings may accrue for some customers, equating to lost

revenues for the utility. This analysis assumes revenue neutrality forthe utility.

Communication Costs (per Customer perYear)

N/A

Overhead: First Costs $0$400,000

Alliant currently has a TOU program, so no overhead first costs aremodeled. MidAmerican currently does not have a TOU program,therefore the standard program development assumption of $400,000is used, which includes costs for internal labor, research, andIT/billing system changes ($200,000 for labor and $200,000 for IT).

Per Customer First Cost $325 This value is calculated from the technology cost and the marketingcost per new participant.

Per Customer Ongoing $15 Ongoing costs are calculated based on 5% of technology costs forrepair and/or replacement of equipment (no customer incentives forTOU).

Eligible Load (%) 100% All residential customers are eligible.Technical Potential (as % of Gross) 15% This value is based on results from California residential pricing

programs (CA SPP); fixed TOU shows 15% average peak demandreduced (Charles River Associates, 2005). These results are similarto those from the now discontinued Puget Sound Energy TOUprogram.

Program Participation (%) 8% Alliant indicates 6% to 8% for program participation.Event Participation (%) 100% There are no "events" with TOU rates. Participation can be viewed as

100%.Average # Events per Season N/ACycling Strategy N/A


Critical Peak Pricing

Under a CPP program, customers receive a discount on their normal retail rates during non-critical peak periods in exchange for paying premium prices during critical peak events.However, the peak price is determined in advance, providing customers with some degree ofcertainty about the participation costs. The basic rate structure is a TOU tariff where the rate hasfixed prices for usage during different blocks of time (typically on- and off-peak prices byseason, occasionally including a mid-peak price). During CPP events, the normal peak priceunder a TOU rate structure is replaced with a much higher price, generally set to reflect theutility’s avoided cost of supply during peak periods.

CPP rates only take effect a limited number of times during the year, with a cap typically set onthe number of CPP event hours that can be implemented. In times of emergency or high marketprices, the utility can invoke a critical peak event, where customers are notified and rates becomemuch higher than normal, encouraging customers to shed or shift load. Most CPP programsprovide advance notice along with event criteria, such as a threshold for forecasted weathertemperatures, to help customers plan their operations. One of the attractive features of the CPPprogram is the absence of a mandatory curtailment requirement; however, both incentives andpenalties lie within the pricing structure.

The benefit of a CPP rate over a standard TOU rate is an extreme price signal can be sent tocustomers for a limited number of events. Utilities have found that demand reductions duringthese events are typically greater than during TOU peak periods for several reasons:

Customers under CPP rates are often equipped with automated controls triggered by asignal from the utility.

The higher CPP rate serves as an incentive for customers to shift load away from theCPP event period.

The relative rarity of CPP events may encourage short-term behavioral changes,resulting in reduced consumption during the events.

Since the CPP rate only applies on select days, it raises a number of questions about when autility can call an event, for how long, and how often. The rules governing utility dispatch ofCPP events varies widely by utility and by program, with some utilities reserving the right to callan event any time, while others must provide notice one day prior to the event.


Currently, peak pricing is being offered through experimental pilots or full-scale programs byseveral organizations in the United States,35 notably Southern Company (Georgia Power), GulfPower, Niagara Mohawk, California utilities (SCE, PG&E, SDG&E), PJM Interconnection, andNew York ISO (NYISO). Adoption of CPP has not been as widespread in the Western states as ithas been in the East.

Residential CPP. The most common national CPP programs are offered to the residentialcustomer class. Recently, significant literature has shown the value of a technology-enabled CPPprogram, which essentially provides customers with smart thermostats that can be programmedto change temperature settings and even control other end uses, such as lighting and waterheating, depending on the pricing period (e.g., critical peak period, on-peak, or off-peak).36 Thiscombination of pricing and technology has shown to be an effective combination in improvingper-customer load impacts.

More recently, process-oriented appliances, such as dishwashers and washing machines, haveincorporated technologies to respond to external CPP signals. During critical events when a rateincrease occurs, these “energy-managed appliances” receive notification on the applianceinterface, giving customers direct notification and the option of delaying usage of the appliance.These appliances also have the capability to temporarily reduce their energy consumption duringmoments of grid instability. For example, a clothes dryer with this technology will reduce powerupon receipt of a remote signal from the utility, then correct for the momentary reductionthrough extending the drying time. In both situations of signal response, the customer has theability to override the signaled reduction.

35 See Wolak, Frank, “Residential Customer Response to Real-Time Pricing: The Anaheim Critical-Peak Pricing”September 2006. See FERC, Assessment of Demand Response and Advanced Metering, August 2006. SeeEnergy & Environmental Economics, A Survey of Time-of-Use Pricing and Demand-Response Programs, July2006. See Charles River Associates, “Impact Evaluation of the California Statewide Pricing Pilot, FinalReport,” March 16, 2005. See also, Piette, Mary Ann and David S. Watson “Participation through Automation:Fully Automated Critical Peak Pricing in Commercial Buildings,” 2006, Lawrence Berkeley NationalLaboratory. Linkugel, Eric Proceedings of the 2006 ACEEE Summer Study on Energy Efficiency in Buildings,Pacific Grove, CA, August 2006. See staff CPUC testimony athttp://www.sdge.com/regulatory/tariff/A_05_03_015_%20Gaines_redline.pdf.

36 Dynamic Pricing, Advanced Metering and Demand Response in Electricity Markets, Severin Borenstein,Michael Jaske, and Arthur Rosenfeld, October 2002, FERC. DOE


Technically, national studies have shown that 13% – 40%37 of peak demand can be reduced forparticipating customers; this study assumes a 27% result for the California pricing pilot.38 In2006, Gulf Power’s CPP program had 2.5% of customers and a goal of reaching 10%penetration. Event participation is estimated to be 95%, based on opt-outs being typically lessthan 5% now that utilities require customers to use the Internet or the call center to opt out of aCPP event.

Table 52 shows that technically, 232 MW and 318 MW are available for Alliant andMidAmerican territories, respectively. These figures are reduced by the program and eventparticipation rates, resulting in 11 MW (Alliant) and 15 MW (MidAmerican).

Table 52. Residential CPP : Technical and Market Potential(MW in 2018)


PotentialMarket


TechnicalPotential

MarketPotential


Residential 232 11 1% 318 15 1%

The levelized cost calculated at a rate of $95/kW for both Alliant and MidAmerican. Detailedassumptions providing values and sources that derived the potential and levelized costs areshown in Table 53.

37 Charles River Associates (CRA), Impact Evaluation of the California Statewide Pricing Pilot, March 16, 2005;California Energy Commission (CEC), Statewide Pricing Pilot load reduction data forZone 4 (desert and inland climate), provided in MS Excel by Pat McAuliffe, CEC staff, via e-mail November 3,2006; Demand Response Research Center (DRRC), Ameren Critical Peak Pricing Pilot, Presentation by RickVoytas, Manager of Corporate Analysis at Ameren Services, at the Demand Response Town Hall Meeting,Berkeley, CA, June 26, 2006; International Energy Agency, Demand-Side Management Programme, Task XI:Time of Use Pricing and Energy Use for Demand Management Delivery, Subtask 2: Time of Use Pricing forDemand Management Delivery, April 2005. Rocky Mountain Institute, Automated Demand Response SystemPilot, Final Report Volume 1: Introduction and Executive Summary, March 2006. Summit Blue Consulting,Interim Report for the myPower Pricing Segment Evaluation, prepared for PSEG, December 27, 2006.University of California Energy Institute (UCEI), Dynamic Pricing, Advanced Metering and Demand Responsein Electricity Markets, S. Borenstein et al., October 2002.

38 See Charles River Associates, 2005.


Table 53. Assumptions for Residential CPP PotentialProgram Concepts Assumptions

Customer Sectors Eligible All Residential Market SegmentsEnd Uses Eligible for Program Total Load of All End UsesCustomer Size Requirements, if any AllSummer Load Basis Top 40 Summer Hours

Inputs Model Value Model AssumptionsAnnual Attrition (%) 3% 1.5% (MidAm) and 4% (Alliant) were determined based on data available

from the two utilities. Other studies have found 7% (composed of 5%change of service and 2% removals) from utilities, including RMP, Xcel,Eon US, SMUD, PSE&G, FP&L (removals range from 1%–3%).

Per Customer Impacts (kW) 0.60 This value is a product of technical potential and average kW of eligiblecustomers.

Total kW reduction per programAnnual Administrative Costs 15% An administrative adder equivalent to 15% of program cost was typically

assumed for all program strategies (assuming that, since 15% would betaken from a first cost of $400,000, the annual administrative cost will be$60,000).

Technology Cost $300 This value reflects the cost of meter ($200) and installation ($100).Marketing Cost $35 This cost assumes an increase from the TOU marketing cost.Incentive (annual costs) N/ACommunication Costs (per CustomerPer Year)

$7 This value accounts for annual per-customer communication of a one-way transmission system.

Overhead: First Costs $200,000 Since the setup of CPP will build upon the existing TOU residentialprogram, the first costs will be 50% of the standard assumption of$400,000.

Per Customer First Cost $335 This value is calculated from the technology cost and the marketing costper new participant.

Per Customer Ongoing $22 Ongoing costs are calculated from summing annual customer incentivesand 5% of technology costs for repair and/or replacement of equipment.

Eligible Load (%) 100% All residential customers are eligible.Technical Potential (as % of Gross) 27% The assumption is based on results from California residential pilot CPP

programs for statewide average (Charles River Associates, 2005).Program Participation (%) 5% Gulf Power has the only full-scale residential CPP program. The

company reported 8,500 participants as of October 2006, out of 350,000residential customers (2.4%). (Sources: Jim Thompson presentation toPURC Energy Policy Roundtable, October 31, 2006; and FERC Form861 data, 2005.) They expect to reach at least 10% penetration. (Source:Dynamic Pricing, Advanced Metering and Demand Response inElectricity Markets, Severin Borenstein, Michael Jaske, and ArthurRosenfeld, October 2002.)

Event Participation (%) 95% Opt-outs are typically less than 5% now that utilities are requiringcustomers to use the Internet or call center to opt out of a CPP event.(Source: Conversation with Tom Van Denover, VP Comverge March2007.) With 2-way communications (through AMI or Zigbee gateway, forexample) utilities can identify and replace malfunctioning thermostats, soevent participation is much higher than in older one-way, switch-basedDLC programs.



Commercial and Industrial CPP. There have been very few C&I CPP programs for medium-to-large customers, and the pilots tested in California have typically linked the CPP rate with“enhanced automation” technologies that facilitate load curtailment. This implies the CPP rateitself and the price incentive it creates may not be the main driver of load reductions.

In FERC’s 2006 survey of utilities offering DR programs, roughly 25 entities reported offering atleast one CPP tariff. However, many of the tariffs were only pilot programs, and almost all the11,000 participants were residential customers. The top five utilities (by number of participantsenrolled) accounted for 96% of the total number of participants reported to be on CPP rates. GulfPower had the largest number (about 8,000 participants), which were entirely residential. CassCountry Electric Cooperative came in next at nearly 3,000 residential-only participants. Theother three in the “top five” were the three major California investor-owned utilities. Of those,only SCE included commercial customers in its pilot, and it had 270 commercial participants.The lack of commercial CPP programs is supported by a 2006 survey of pricing and DRprograms commissioned by the U.S. Environmental Protection Agency, which found only fourlarge-customer CPP programs, all of them in California.39

Table 54 shows there is 166 MW of technical potential in the Alliant territory, with 11 MWmarket potential (representing nearly 1% of 2018 territory peak). The MidAmerican territory has143 MW of technical potential and 9 MW of market potential. The majority of market potentialis in the industrial sector.

Table 54. C&I CPP: Technical and Market Potential (MW in 2018)Alliant Energy MidAmerican Energy


MarketPotential


TechnicalPotential

MarketPotential


Residential - - - - - - - - - - - - - - - - - -Commercial 61 4 <1% 49 3 <1%Industrial 105 7 <1% 93 6 <1%Total 166 11 <1% 143 9 <1%

The levelized cost was calculated at a rate of $11/kW for Alliant and $19/kW for MidAmerican.Detailed assumptions providing values and sources that derived the potential and levelized costsare shown in Table 55.

39 See “Participation through Automation: Fully Automated Critical Peak Pricing in Commercial Buildings,”Mary Ann Piette, David S. Watson, Naoya Motegi, Sila Kiliccote, Lawrence Berkeley National Laboratory,Eric Linkugel, Pacific Gas and Electric Company, Proceedings of the 2006 ACEEE Summer Study on EnergyEfficiency in Buildings, Pacific Grove, CA, August 13-18, 2006. See also, Charles River Associates, ImpactEvaluation of the California Statewide Pricing Pilot, Final Report, March 16, 2005.


Table 55. Assumptions Used for C&I CPPProgram Concept Assumptions

Customer Sectors Eligible All C&I Market SegmentsEnd Uses Eligible for Program Total Load of All End UsesCustomer Size Requirements, if any C&I greater than 30kWSummer Load Basis Top 40 Summer Hours




Total kW reduction per program N/AAnnual Administrative Costs 15% An administrative adder of 15% was typically assumed for all

program strategies (assuming that since 15% will be taken from afirst cost of $400,000, the annual administrative cost will be $60,000).


Marketing Cost $500 Alliant reports $500 per customer for marketing (based upon 10hours of effort by program staff at $50/hr).

Incentive (annual costs) N/A There are no customer incentives, but the utility may not design therate to be revenue neutral, which could prove to be a cost in terms oflost revenues.


N/A

Overhead: First Costs $200,000 Since the setup of CPP will build upon the existing TOU program, thefirst costs will be 50% of the standard assumption of $400,000.


Per Customer Ongoing $70 Ongoing costs are calculated from summing annual customerincentives and 5% of technology costs for repair and/or replacementof equipment.

Eligible Load (%) Varies by Sector We assume all commercial customers can participate that have loadsthat are greater than 30kW.

Technical Potential (as % of Gross) 8% These assumptions are based on detailed engineering audits of DRpotential of C&I customers throughout California by Nexant, withthird-party verification of results. Studies of CPP results show that 8%was saved on average (LBNL Fully Automated CPP study, 2006).

Program Participation (%) 12% This assumption is based on internal survey results (excludingparticipation for Health and Lodging building types).

Event Participation (%) 56% The assumption is based on the results of the 2006 California C&I forpilot CPP programs.



Real-Time Pricing

Commercial and Industrial RTP. Under RTP programs, electricity prices vary each houraccording to the expected marginal cost of supply and are typically established one day ahead ofthe time the prices are in effect. Where CPP utilizes pre-set pricing, RTP utilizes electricitywholesale prices, which change throughout the day. Programs vary from day-ahead to hour-ahead notification. Notification occurs via the Internet or technology-enabled devices (Internet-or radio-based devices).

At least 24 utilities offer RTP programs for commercial customers, although 13 are pilotprograms. In states where the wholesale market is run by an Independent System Operator (e.g.,MISO, PJM, ISO-NE, NYISO), prices typically reflect the hourly spot market price, either on aday-ahead or closer to a true real-time basis. For vertically integrated utilities such as GeorgiaPower, which has an RTP rate program, prices are set by the marginal cost of generation.

The most commonly cited reason for introducing RTP is to build customer satisfaction andloyalty by providing an opportunity for customers to realize bill savings. A two-part rate, wherea Customer Baseline Load (CBL) is established and compared to actual loads, is used by NiagaraMohawk and Georgia Power, and was common in early program designs. Only the difference inactual versus expected usage is subject to real-time prices. Many newer programs haveunbundled the electricity commodity from transmission and distribution services, and theelectricity component is priced according to hourly energy prices. Additionally, Georgia Poweroffers Price Protection Products that enable RTP customers to manage their exposure to volatileprices.

One important thing to note in C&I RTP programs is, while a few programs have been verysuccessful, it can be difficult to attract participants. A survey conducted by Lawrence BerkeleyNational Laboratory of 40 of 42 voluntary C&I RTP programs found just three programs hadmore than 100 customers enrolled in 2003, which accounted for the majority of all non-residential RTP participants identified in the survey. For example, half of the programs in thestudy had fewer than ten customers enrolled, and one-third had no participants.

The program modeled in this analysis requires a minimum threshold of 200 kW, which isconsistent with other programs nationally.

Table 56 shows there is 279 MW of technical potential for the Alliant territory, with 9 MW ofmarket potential. The MidAmerican is not part of MISO and currently does not have thecapability to receive advanced notice of electricity price structure in time to relay to customersand therefore this study did not evaluate RTP for MidAmerican.

40 Barbose, Galen et al., A Survey of Utility Experience with Real Time Pricing, LBNL, December 2004.See also Neenan Associates, “Customer Adaptation to RTP as Standard Offer Electric Service: A Case Study ofNiagara Mohawk's Large Customer RTP Tariff,” LBNL 2004.


Table 56. RTP: Technical and Market Potential (MW in 2018)Alliant Energy MidAmerican Energy


MarketPotential


TechnicalPotential

MarketPotential


Residential - - - - - - - - - - - - - - - - - -Commercial 76 1 <1% - - - - - - - - -Industrial 203 8 <1% - - - - - - - - -Total 279 9 <1% - - - - - - - - -Note: RTP – C&I has not been modeled for MidAmerican.

The levelized cost calculated at a rate of $11/kW for Alliant. Detailed assumptions providingvalues and sources that derived the potential and levelized costs are shown in Table 57.


Table 57. Assumptions for C&I RTPProgram Concept Assumptions

Customer Sectors Eligible All C&I Market SegmentsEnd Uses Eligible for Program Total Load of All End UsesCustomer Size Requirements, if any Greater than 200kWSummer Load Basis Average On-Peak Summer

Inputs Model Value Model AssumptionsAnnual Attrition (%) 3% 1.5% (MidAm) and 4% (Alliant) were determined based on data

available from the two utilities. Other studies have found 7%(composed of 5% change of service and 2% removals) from utilities,including RMP, Xcel, Eon US, SMUD, PSE&G, FP&L (removals rangefrom 1%–3%).

Per Customer Impacts (kW) Varies bySector

This value is a product of technical potential and average kW of eligiblecustomers.

Total kW reduction per program N/AAnnual Administrative Costs 15% ($60000) An administrative adder of 15% was typically assumed for all program



Marketing Cost $500 Alliant reports $500 per customer for marketing (based upon 10 hoursof effort by program staff at $50/hr).

Incentive (annual costs) N/A There are no customer incentives, but the utility may not design therate to be revenue neutral, which could prove to be a cost in terms oflost revenues.


N/A

Overhead: First Costs $400,000 We assume $400,000 overhead as a standard program developmentassumption, which includes costs for internal labor, research andIT/billing system changes ($200,000 for labor and $200,000 for IT).


Per Customer Ongoing $70 Ongoing costs are calculated from summing annual customerincentives and 5% of technology costs for repair and/or replacement ofequipment.


We assume full eligibility of loads greater than200 kW.

Technical Potential (as % of Gross) Varies bySector

These assumptions are based on detailed engineering audits of DRpotential of C&I customers throughout California by Nexant, with third-party verification of results. Studies of CPP results show 8% was savedon average (LBNL Fully Automated CPP study, 2006), which iscomparable to taking this technical potential and the event participationcombined.

Program Participation (%) Varies bySector

This assumption is based on internal survey results.

Event Participation (%) 100%Average # Events per Season N/ACycling Strategy N/A


Residential Real-Time Pricing. At this point, residential RTP is not widespread, but interest isreported to be increasing.41 In Illinois in 2006, after a four-year pilot program run by theCommunity Energy Cooperative in Chicago with ComEd customers, the General Assemblyunanimously passed legislation requiring large investor-owned utilities to offer residential RTPto all customers. New programs offered by ComEd and Ameren launched in Spring 2007. In thisstudy, however, the program history was judged to be inadequate to provide a reliable basis foreffectively modeling this program option.

41 Anthony Star presentation, “Why Residential Real-Time Pricing is the Real Deal!” presented at Innovations inRetail Pricing, Association of Energy Services Professionals, May 18, 2006.


5. Renewable Resources

Scope

In addition to traditional energy-efficiency resources, this report includes an analysis of twoclasses of renewable resources: active (dispersed generation) and passive (energy-efficiency)resources. Active resources, loosely defined as “dispersed generation” (DG), include energy-based resources of biomass and three “clean generation” (non-combustion) resources: buildingphotovoltaics (on-site solar), small hydro, and small wind. Passive resources fall into two broadcategories: passive solar building design and renewable efficiency measures.

Active Resources

Active (or DG) resources are used to produce electricity and offset electric loads. As such, theywere only considered for the two electricity-delivering utilities, Alliant and MidAmericanEnergy. For clean generation resources, only resources less than the net metering limit (500 kW)were considered. There is, therefore, additional potential from clean renewable resources over500 kW, but, as these are considered supply-side options, they are not considered in this study.Larger installations over 500 kW, however, are included for biomass since they often areoperated in large industrial facilities that consume all electricity produced. The analysisexamined four active resources:

Biomass Energy refers to energy generated from any plant- or animal-based material.Biomass can be directly combusted (i.e., industrial biomass) or fed into an anaerobicdigester to produce biogas, which can then be combusted to produce electricity.Although biomass energy is based on a renewable resource, this combustion processis not considered “clean” as it does produce combustion products (e.g., carbondioxide, NOx, etc.).

Building Photovoltaics are from rooftop-based photovoltaic (PV) panels that convertsunlight to electricity.

Small Hydro is sometimes known as run-of-river hydroelectric power generation asdams need not be built to regulate water flow. Four basic types of installations areincluded in this study: small hydro, micro hydro, low-power conventional, and low-power unconventional.

Small Wind encompasses small, electricity-generating wind turbines, installed at thecustomer site.

Passive Efficiency Resources

Passive efficiency resources offset gas or electric requirements similar to other energy-efficiencyresources. Passive efficiency measures include: solar water heaters, solar attic fans, corn pelletstoves, and passive solar design techniques (e.g., eaves on south facing windows, Trombe walls[thermal walls], tree planting, and smart siting).


Methodology

The overall methodology used to calculate the potential from renewable resources included threesteps:

Technical potential was calculated separately for each resource categories, using thefollowing key data inputs:

o Biomass Energy: utility’s industrial customer database for size and count ofbiomass-producing industrial facilities and service territory demographics forbiogas-producing (anaerobic digester) facilities.

o Building PV: customer counts and building square footage assumptions.o Small Hydro: potential river sites for turbines from Hydro Prospector42 by county

and installation type, including stream flow data from representative streams todetermine availability factors.

o Small Wind: energy output estimated using the Wind Turbine Output Calculatorprovided by the Iowa Energy Center,43 factoring wind power density, populationdensity, proximity to airports, and sensitive land areas.

o Passive Efficiency Resources: technical feasibility factors, similar to otherenergy-efficiency resources.

Various technology costs were calculated based on literature searches, availabledatabases, and other states’ programs. Installed costs included capital costs, planning,installation, and other adders.

Market potential was determined for each resource class based on otherprogrammatic successes. Note that not all market potential is economic and,therefore, may not be achievable. For passive efficiency resources, market potentialgiven was equivalent to the economic potential, calculated similarly to other energy-efficiency resources.

Summary of Findings

This section presents a summary of the key findings for renewable potentials. More detailregarding each resource follows these highlights.

Resource Potential

To correctly estimate the quantity of potential in the market, it is essential to know the currentpenetration of renewable technologies currently found in the marketplace. The installedrenewable nameplate capacity, presented in Table 58, was obtained from existingdatabases,44 ,45,46 net metering data, and the Wind Energy Manual (published by the Iowa Energy

42 http://hydropower.id.doe.gov/prospector/index.shtml43 http://www.energy.iastate.edu/renewable/wind/maps-index.html44 http://www.eea-inc.com/chpdata/index.html


Center).47 This capacity excluded large “central-station” generation facilities and the largeutility-owned generation facilities (e.g., wind farms). A full list of each site is provided inVolume II, Appendix E. Insufficient data existed for an accurate assessment of installed capacityof passive efficiency resources.

Table 58. Installed DG Renewable Capacity by Resource (2006)Resource Capacity (MW)

Biomass Energy 58.4Building Photovoltaics 0.02Small Hydro 7.3Small Wind 6.0Total 71.7

Technical Potential

The technical potential from active DG resources (Biomass Energy, PV, Small Hydro and SmallWind), not including existing capacity, is 62,886 GWh in 2018 (Table 59). More than half of thetechnical potential for DG renewables comes from PV (52%), followed by small wind (30%),and biomass energy (16%). It should be recognized that technical potential for the DG resourcesis significantly higher than what can be achieved, primarily due to high upfront costs required forthese resources and, particularly for small wind, feasibility constraints.

Passive resources, which offset energy usage for cooling, space heating, or water heating, canaccount for an additional 469 GWh of electric savings, plus 732,466 decatherms of savings(Table 60), not including estimates for currently installed measures. In total, technical electricpotential from DG and passive renewable resources in 2018 is 63,355 GWh.

Table 59. Technical Potential for DG Renewable Resources (2018)Resource GWh Percent

Biomass Energy 10,134 16%Building Photovoltaics 32,888 52%Small Hydro 1,156 2%Small Wind 18,708 30%Total 62,886 100%

45 http://www.epa.gov/lmop/proj/index.htm gives waste-in-place data for eligible landfills. If waste-in-place is notspecified, a 500 kW generation potential is assumed.

46 http://www.chpcenternw.org/ and http://www.intermountainchp.org/47 Available at http://www.energy.iastate.edu/renewable/wind/wem-index.html.


Table 60. Technical Potential for Passive Renewable Resources (2018)Resource Potential


Market and Economic Potential

For DG resources, market potential represents the portion of technical potential that mightactually be installed. It should be realized that not all these resources are economic, but,nonetheless, may be installed by customers willing to accept long payback times. For passiveefficiency measures, the economic potential is provided, as determined for other energy-efficiency resources.

Note that the market potential also considered current incentives for these resources. Other thanincentives shown in Volume II, Appendix E for wind and PV, no direct rebates are currentlyoffered to Iowa customers (i.e., no $/W rebates is offered). Despite the lack of direct rebates, theFederal Production Tax Credit48 is available to commercial and industrial projects, and theFederal Renewable Energy Production Incentive49 is available to non-taxable entities (e.g.,municipal projects) for clean energy options.

The market potential for all renewable resources is shown in Table 61 for DG renewableresources savings and in Table 62 for passive resources. Compared to the technical potential ofDG resources (Table 59), this potential is significantly less due to economic considerations, lowawareness of technologies, and other permitting or interconnection concerns (details are providedin the results sections, below).

Among the DG resources, biomass energy composes the largest percentage of market potential(155 GWh), followed by small wind (103 GWh), and PV (25 GWh). The percentage of technicalpotential economic for passive efficiency resources is 97% of electric (453 GWh) and 88% ofgas (644 thousand DTh).

Table 61. Market Potential for DG Renewable Resources (2018)Resource Potential (GWh) Percent

Biomass Energy 155 54%Building Photovoltaics 25 8%Small Hydro 7 2%Small Wind 103 36%Total 290 100%

48 Production Tax Credit is 1.9 cents/kWh available through December 31, 2008 and applies to the first 10 yearsof production (http://www.dsireusa.org).

49 Renewable Energy Production Incentive is 1.5 cents per kWh (indexed for inflation) with a 10 year term.(http://www.dsireusa.org).


Table 62. Economic Potential for Passive Renewable Resources by Fuel (2018)Resource Potential


Figure 17 presents the cumulative supply curve for all DG resources. Biomass Energy is brokeninto potential from Industrial Biomass (direct combustion) and Anaerobic Digesters (biogascombustion). Further details on these and all renewable potentials are discussed below.

Figure 17. Cumulative Supply Curve for Dispersed Generation Renewable Resources(2018)

Ind Biomass

PV

Wind

HydroAnaerobicDigesters

$-

$0.10

$0.20

$0.30

$0.40

$0.50

$0.60

$0.70

0 50,000 100,000 150,000 200,000 250,000 300,000

Cummulative MWh

Leve

lized

Co

st($

/kW

h)

Biomass Energy

Biomass is combusted within an on-site generator at a customer’s facility. Generally, powergenerated through these technologies is expected to contribute to the utility’s base load resourcesrather than peak load requirements. Peak load reduction with an on-site generator or dispatchablestandby generation is not addressed here.50 In addition, a program by the Iowa Department ofNatural Resources (DNR) promotes community-scale digesters.51 This specialized application is

50 Dispatchable standby generation is generally considered a demand response resource.51 For more information on these community-scale digesters, please see “Anaerobics for Iowa Communities;”

BioCycle, July 2007.


not considered here as it is large scale and requires coordination between multiple parties,including city or county governments.

The three primary generator technologies available in the market are: (1) reciprocating engines(either spark-ignition or compression-ignition); (2) turbines (gas or steam for larger capacity[>1 MW] or microturbines for smaller capacity [<1 MW]); and (3) fuel cells, primarily thoseusing phosphoric acid (PAFC) or molten carbonate (MCFC) as the electrolyte, although othertypes of fuel cells are now becoming commercially viable.52

The source of biomass can be either industrial-produced biomass or biogas fuel produced fromanaerobic digesters. This biomass can be consumed in one of the above generators for on-siteelectricity usage. Industrial biomass includes waste product from industries such as chemicalplants (including ethanol production) or pulp and paper manufacturing, which is combusted inplace of natural gas or other fuel to produce steam for use in a steam turbine. Industrial biomasssystems are generally large scale, usually greater than 1 MW.

Anaerobic digesters create methane gas (biogas fuel) by breaking down liquid or solid biologicalwaste. The waste used can be derived from farm manure, landfills, or wastewater treatmentfacilities. Anaerobic digesters for farms, smaller landfills, and wastewater treatments facilitiesare coupled with smaller-scale generators, such as reciprocating engines, microturbines, or fuelcells. Larger landfills, with capacities over 1 MW, can use either reciprocating engines or gasturbines

As anaerobic digesters require high temperatures to operate, a combined heat and power unit(CHP) is usually used instead of a standard generator. A CHP unit includes a standard electricalgenerator, such as a reciprocating engine, but the generator’s waste heat is captured and used forother processes. For example, a typical spark-ignition engine has an electrical efficiency of aboutonly 35%; the “lost” energy is primarily waste heat. A CHP unit will capture much of this wasteheat and use it for maintaining the temperature of an anaerobic digester.53

Biomass fuels from the agricultural sector (e.g., crop waste such as bagasse from sugar, ricehulls, and rice straw) are not considered in this study. Due to high moisture content and varyingavailability, crop residues are not a viable fuel alternative for most generation applications.54 Inaddition, the prime energy-producing crops (sugar cane and rice) are largely not present in Iowa.

This study only considers on-site biomass generation primarily used for building energy and heatneeds. Large “central-station” generation facilities operating to sell the majority (or all) of theirpower to the grid are outside the scope of this work.

52 Note that not all types of fuel cells available operate at a high enough temperature to be applicable for ananaerobic digester. Only those types that are viable are considered here.

53 The waste heat can also be used to create steam and run a steam turbine, commonly referred to as cogeneration,or a combined cycle turbine with HSRG. These are not considered here as they requires large gas turbines notfrequently needed for the “behind-the-meter” resource recovery market.

54 “Combined Heat & Power Market for Opportunity Fuels,” Resource Dynamics Corp, 2004.


Biomass Energy Generation Background Data

The primary resource for the installed cost of CHP technologies is the California’s Self-Generation Incentive Program (SGIP).55 This program, funded by the main investor-ownedutilities of California, provides varying levels of incentives for individual customers to installvarious dispersed generation technologies, including CHP, with a maximum capacity of 5 MW.The program has been in effect since 2001 and has a publicly available database of allinstallations, including generation technology, capacity, fuel, and total cost. For this assessment,nameplate capacity is based on the average of the units installed through California’s SGIP foranaerobic digesters.

Typical nameplate capacities for steam turbines vary widely. Although larger or smaller capacityunits can be installed for any of these technologies, a 4.8 MW unit is used as a proxy based on astudy for the Energy Trust of Oregon.56 . Different-sized units would have the same measure lifeand capacity factors, but they may have different costs. Generally, smaller units are moreexpensive on a $/kW basis. These values are summarized in Table 63. Note that no fuel costs areused as it is assumed that fuel is generated and combusted on site. The measure life and capacityfactors were obtained from the literature.57 These values are assumed to be equivalent for Alliantand MidAmerican.

Table 63. Biomass Energy Prototypical Generating Units

Technology Capacity(kW)

Measure Life(years)

CapacityFactor

Anaerobic DigestersReciprocating Engine 662 20 0.9Microturbine 206 15 0.9Fuel Cell 420 10 0.95Gas Turbine 3,174 20 0.95Industrial BiomassSteam Turbine 4,800 20 0.9

With these prototypical generating units, associated costs are determined from the SGIP databaseor, for industrial biomass, literature searches.56 Installed costs include: planning and feasibility,engineering and design, permitting, generator equipment costs, waste heat recovery costs,construction, installation, interconnection, and service contracts. SGIP database costs werereduced by 17% to remove included sales tax (7%) as well a 10% reduction based on highercosts typical of the California market.58

55 http://www.cpuc.ca.gov/static/energy/electric/051005_sgip.htm56 “Sizing and Characterizing the Market for Oregon Biopower Projects,” prepared for Energy Trust of Oregon, by

CH2MHill, 2005.57 “Gas-Fired Distributed Energy Resource Technology Characterization,” National Renewable Energy

Laboratory, NREL-TP-620-34783, 2003.58 Based on general cost comparisons given in RS Means, 2007.


It should be noted that, for generators used with anaerobic digesters, any of the four CHPtechnologies could be used; thus, costs can vary widely. These costs are reported in Table 64. Anominally constant installed cost is assumed, and no administration costs are included in totalcost. Together with the discount rate, this allows a full life-cycle cost analysis of the resource.

Table 64. Costs for Technologies Considered (2007 Dollars)

Technology Installed Cost($/kW)

Annual O&MCosts ($/kW/yr)

Anaerobic DigestersReciprocating Engine (RE) $1,761 $79Microturbine (MT) $3,402 $71Fuel Cell (FC) $6,829 $17Gas Turbine (GT) $2,022 $58Industrial BiomassSteam Turbine (ST) $1,800 $39

Biomass Energy Technical Potential

The technical potential for biomass and biogas assumes all technologies will be adopted in allavailable customer sites to meet their average annual electric demands, regardless of cost orother market barriers. This applies to all sites that may use anaerobic digesters and all industrialbiomass-producing facilities. These two sectors, however, need to be treated separately. Toderive this potential, each utility’s 2006 customer database was used; as such, the 2018 technicalpotential given was ramped up from the first-year load. Details on the technical inputs (cost,capacity factors, etc.) can be seen in Table 63 and Table 64. Technical potential by resourcecategory at generation is given in Table 65.

Anaerobic Digesters. The best candidates for anaerobic digesters include animal farms (dairy orswine), landfills, and wastewater treatment facilities. For farms, the amount of biogas that can begenerated is directly related to the number and type of animals on site. Based on typicalcollection systems, a study by the Environmental Protection Agency (EPA) assumes that onecow will generate 2.5 kWh/day and that one pig will generate 0.25 kWh/day.59 Given sizeconstraints, it is likely that only farms with more than 500 head of cattle or 2,000 head of swinewill install a generator. Based on the number and average size of farms within each utility’sterritory, overall potential is calculated.60,61

For wastewater treatment facilities, the population served by a particular facility will determinethe expected generation output. The EPA maintains a database of all wastewater treatmentfacilities and their current and future design flows. A study by Federal Energy ManagementProgram assumes approximately 1 million gallons of waste per day (1 MGD) can produce about

59 “Market Opportunities for Biogas Recovery,” EPA-430-8-06-004, http://www.epa.gov/agstar60 http://www.nass.usda.gov/Census_of_Agriculture/index.asp61 “Sizing and Characterizing the Market for Oregon Biopower Projects,” CH2MHill for Energy Trust of Oregon,

2005.


35 kW of energy; as such, generally 3 MGD is the minimum waste flow before an anaerobicdigester will be installed.62 Thus, only wastewater treatment facilities with at least 3 MGD flow(current or future) are included in the potential.

Finally, for landfills, the EPA Landfill Methane Outreach Program (LMOP) encourages theimplementation of generators at landfills. As part of this program, a database of participating andcandidate landfills, based on waste-in-place and throughput, is available by state (with zip coderesolution).45

Industrial Biomass. Industrial biomass potential is based on customers in the four key biomass-producing industries: wood products, food, paper, and chemical manufacturing. The utilitycustomer database was used to determine the overall load associated with these industries. Forsimplicity, the electric load for each customer was grouped into a bin (e.g., 200 kW – 499 kW or500 kW – 999 kW average annual electric load). For buildings with a load between 1 MW and5 MW, an average load of 2.5 MW is assumed; for those with average annual load larger than5 MW, the actual customer load was taken from the customer database.

Table 65. Biomass Energy Technical Potential (GWh in 2018)by Resource Category

Technology Alliant MidAmerican TotalAnaerobic Digesters 2,874 2,420 5,294Industrial Biomass 3,190 1,650 4,840Total 6,064 4,070 10,134

Biomass Energy Market Potential

The market potential provides an analysis of what the market may accept, but not all of this isnecessarily economically feasible. Market potential is based on adoption rates within otherprograms. This analysis is fairly independent of technical potential, but produces reasonableresults based on adoption rates through other programs.

Anaerobic digesters. The market potential for anaerobic digesters was based on the adoption ratewithin SGIP, approximately 1% overall of program implementation.63 Separate adoption rateswere calculated for the different generation technologies. For small generators (<1 MW,reciprocating engines, microturbines, and fuel cells) the adoption rate is assumed to be the sameas that seen in California, while for large generation (>1 MW) the adoption rate is assumed to beequal between reciprocating engines and gas turbines. The estimated total market potential foranaerobic digesters is about 44 GWh in 2018.

62 http://www1.eere.energy.gov/femp/pdfs/bamf_wastewater.pdf63 Note this is less than the ~4% of technical potential (after 20 years) that was estimated in “CHP Market

Potential in the Western United States,” Energy and Environmental Analysis, Inc, ORNL Report:B-REP-05-5427-013, 2005. However, this adoption includes non-renewable CHP and large-scale generators(>20 MW), which would increase the overall percentage.


Industrial Biomass. This potential as a fraction of technical (2%) is expected to be about twicethat as for anaerobic digesters, based on greater familiarity, lower cost, and ease ofimplementation of industrial biomass. The projected growth in U.S. electricity generation fromindustrial biomass was used as the basis for growth in generation by biomass within eachutility’s industrial sector.64 The industrial biomass growth is normalized by the ratio of theutility’s industrial electrical load to the U.S. industrial load. The potential is based on the fourkey biomass-producing industries (lumber, food, pulp and paper, and chemical manufacturing).The estimated total market potential for industrial biomass is about 110 GWh in 2018.

Resource Potentials

The results of this analysis indicate a cumulative market potential of 155 GWh from all biomassand biogas by 2018, with slightly higher potential for Alliant (81 GWh) compared toMidAmerican (74 GWh). Levelized costs ($/kWh) are shown in Table 66 for each technology,calculated using costs from Table 64 and a nominal discount rate of 4.81%. As evident by theirlevelized costs, not all these technologies are necessarily cost effective. Total market potential isaround 1.5% of the technical potential.

Table 66. Biomass Energy Market Potential (GWh) by Sector in 2018IndustrialBiomass Anaerobic Digesters

UtilitySteam

TurbineGas

TurbinesRecip.Engine

Micro-turbine Fuel Cell

Total

Alliant 55 0.3 10 13 3.2 81Mid-American 55 0.0 7 9 2.3 74Total 110 0.3 16 22 5.6 155% of 2018 IUA electric sales 0.26% 0.00% 0.04% 0.05% 0.01% 0.36%Levelized Cost ($/kWh) $0.02 $0.03 $0.03 $0.05 $0.11Individual results may not sum to total due to rounding

Clean Energy

Clean energy consists of energy generation options that do not consume a hydrocarbon-basedfuel. Namely, photovoltaics, small hydro, and small wind. Each resource is unique and,consequently, the technical and market potentials are calculated differently.

Clean Energy Background Data

The installed costs and operation and maintenance costs (O&M) for the three clean energytechnologies are shown in Table 67. Also included are expected measure life and capacityfactors. Capacity factors are an indication of the percentage of the year energy will be produced.Further details for each technology are given below.

64 From Energy Information Administration (EIA) 2007 Annual Energy Outlook.


Table 67. Costs, Measure Life and Capacity Factor for Clean Energy Resources

Technology Installed Cost($/kW)

O&M Cost($/kW/yr) Measure Life Capacity Factor

Building PV $9,000 $100 25 0.14 ( res)0.12 (com)

Small Hydro65 $4,862 $457 40 0.5Small Wind 66 $3,700 $20 25 0.21

Building PV

On-site PVs consist of solar electricity-generation from building-mounted photovoltaic panels.PV systems are weather-dependent and rely on the sun to generate electricity. This study focuseson renewable-electricity generation potential from rooftop residential and commercial buildings.PV systems include an array of solar electric modules, an inverter (DC to AC), and a balance ofsystems. These systems do not have battery back-up equipment and are completely connected tothe utility (grid-tied). PV generation is a whole-building electricity generation resource andtypically only offsets a portion of baseline loads. In most cases, PV is considered a secondarysource of a building’s energy needs. When excess PV electricity is generated (more than thebuilding’s loads), it is fed back into the grid. This depends heavily on the PV system size andcurrent weather and, for residential and commercial customers, generally occurs when thebuilding is not occupied.

Three primary PV technologies considered are: (1) mono-crystalline (single crystalline cell);(2) poly-crystalline (multi-crystalline cell); and (3) amorphous thin-film. These threetechnologies currently dominate the solar market.67 Efficiencies of these technologies areimproving annually and are accounted for in this study. This study does not include large PVgeneration facilities that operate to sell the majority (or all) of their power to the grid andemerging PV technologies.

The PV Watts performance calculator, developed by the National Renewable Energy Laboratory,is used to determine the capacity factor.68 The amount of solar insolation (i.e., the measure ofsolar energy received on a given surface area in a given time), based on weather stations,determines the performance potential for the region. All commercial and multifamily buildingsare fixed with 0.0° array tilt (flat roof), while single-family and manufactured homes are fixed at18.5° tilt (4/12 pitch). With this variance in array tilt, two resulting capacity factors result. Forthe commercial sector, the capacity factor is 0.12, while it is 0.14 for the residential sector.

PV Energy Costs. The primary and secondary resources for PV installed costs are from theCalifornia Energy Commission (CEC), the Energy Trust of Oregon (ETO), the U.S. Departmentof Energy (DOE), and other on-line sources. Cost analysis for PV installation of other programs

65 Average cost. See Appendix E for detailed information.66 Average cost. See Appendix E for source information.67 EIA, based on photovoltaic cell and module shipments by type, 2005.68 http://rredc.nrel.gov/solar/codes_algs/PVWATTS/


results in an average installation cost in 2006 of $9/W, which is assumed in this analysis.69 Othertechnical data have been acquired from multiple primary and secondary resources to determinemeasure life, and O&M costs. A PV system has a measure life of 25 years.70 O&M costs includeinverter replacement every ten years and seasonal module washing.71

Small Hydro

Hydraulic power can be captured wherever a flow of water falls from a higher level to a lowerlevel. This may occur where a stream runs down a hillside, a river passes over a waterfall orman-made weir, or where a reservoir discharges water back into the main river. The vertical fallof the water is known as the “head,” and this, along with the flow rate, determines the poweroutput.72 The primary resource used in this study to evaluate potential sites for hydrodevelopment was the Virtual Hydropower Prospector (VHP), which is available through theIdaho National Laboratory.73 This is a GIS-based tool that allows users to identify existing smallhydro sites and additional potential. The focus of this analysis was on Low-Power Hydro, up tothe net metering limit of 500 kW.

One of the main differences between small/micro hydro (< 500 kW) and larger systems is thatsmall hydro is almost always run-of-river. Run-of-river hydro plants do not require dams. Thewater flowing in the stream is channeled into pipes (or a penstock) and then into a turbine, whichgenerates electricity. The water is then returned to the stream downstream from the turbine.

The environmental footprints of run-of-river facilities are much lower than those of larger hydroplants, which require large storage reservoirs. No land needs to be flooded to create a reservoirfor the plant, but a small weir may be installed to help regulate flow.

The benefits of small hydro are many and include:

High efficiency (70% – 90%).

A high capacity factor (typically 50%).

A high level of predictability, varying with annual rainfall patterns.

Slow rate of change for output power, which varies only gradually from day to day (notfrom minute to minute).

A good correlation with summer cooling demand.

69 “Solar Trends: California Energy Commission” by SunPower Consulting LLC provided cost analysis, August2006, ETO, and DOE.

70 Data was averaged from the following sources: NREL, NW Power, and Conservation Council, and typicalwarranty periods.

71 NREL, “A Review of PV Inverter Technology Cost and Performance Projections”, 2006.72 Further data on the power calculation is given in Appendix E.73 http://hydropower.inl.gov/prospector


A long-lasting and robust technology; systems can be engineered to last for 50 years ormore.

Environmentally benign; fish and other wildlife are not affected by the installation.

Hydro Energy Costs

Installing a hydro system includes the following costs: Penstock, Intake, Powerhouse, GeneratingEquipment, Access Road, Switchyard, and Transmission Line. In addition, a percentage of theseis included for Engineering (20%) and Contingency (30%).

Costs vary considerably according to the size of the system installed, with the cost per kW goingdown as the system size increases. For this study, costs were taken from a study prepared forBC Hydro that included all of the costs listed above.74 Data from sites less than 500 kW incapacity and with less than three miles of transmission required to be installed were used.Estimated installed costs were $4,862/kW, with additional O&M costs of $457/kW per year(calculated as 9.4% of installed cost). Details on the cost analysis are provided in Volume II,Appendix E.

Small Wind

Wind energy is converted to mechanical or electrical energy through the use of a wind turbine.Wind energy is an intermittent resource, meaning that the energy output varies and isunpredictable. Despite the intermittency of the wind, the wind energy industry is growing.

The total installed capacity of small wind (<100 kW) in the U.S. is estimated to be about 62 MWas of 2006.75 According to the American Wind Energy Association (AWEA), Iowa is rankedtenth in the U.S. for wind energy potential. In terms of installed wind capacity, Iowa is the thirdstate, behind Texas and California, with a total installed wind capacity (including utility-scalewind farms) of 931 MW as of the end of 2006.76

Small wind turbines are generally defined as having an installed capacity of up to 100 kW. Asnoted above, however, small wind turbines for this analysis were assumed to have an installedcapacity from 1 kW to 500 kW. In addition to the small wind turbines used in this analysis, manynew small turbines (around 1 kW) have been developed in recent years. Southwest Windpower’sSkystream 3.7™, with an installed capacity of 1.8 kW, is an all-inclusive wind generator idealfor homes and small businesses.77 Another small turbine, the Swift Rooftop Wind EnergySystem™ by Renewable Devices, is a 1.5 kW generator that produces up to 3,000 kWh of

74 Green Energy Study for British Columbia Phase 2: Mainland; Small Hydro, October 2002, Prepared for BCHydro by Sigma Engineering Ltd.

75 Compiled from American Wind Energy Association. Home and Farm Wind Energy Systems: Reaching theNext Level. AWEA. June 2005. and American Wind Energy Association. AWEA Small Wind Turbine GlobalMarket Study 2007. AWEA. July 2007.

76 Wiser, R. and M. Bolinger (LBNL). Annual Report on U.S. Wind Power Installation, Cost and PerformanceTrends: 2006. U.S. Department of Energy- Energy Efficiency and Renewable Energy. May 2007.

77 http://www.skystreamenergy.com/skystream/


electricity a year.78 It should be noted that small wind rooftop systems can cause issues withvibrations and turbulence.

The AWEA Small Wind Turbine Global Market Study 2007 conducted a survey with manyplayers in the small wind industry, including researchers, component vendors, manufacturers,engineers, consultants, utilities, local government offices, and dealers/distributors/installers. Thesurvey found that the top market barriers to installing small wind turbines were economics/coststo the customer. Additional key barriers included restrictive zoning and permitting rules, costs,and lack of financial incentives. Respondents were also asked about state priorities and the toptwo areas of improvement for each state. For Iowa, respondents listed rebates as the first priorityand interconnection as the second. The study also found that the fastest growing market segmentis grid-connected residential scale turbines (1 kW – 10 kW).79

Multiple incentives are currently available to owners of wind energy systems in Iowa, includingtax credits, tax exemptions, and loan programs. Volume II, Appendix E provides a listing of thecurrent incentives available.

Small Wind Energy Costs

The cost for a wind turbine varies by the size of the system installed. In general, as the installedcapacity of wind turbines increases, the installed cost per kW decreases. Costs are assumed to benominally constant. However, it should be recognized that costs may increase due to tighter steelsupplies. Costs were taken primarily from turbine manufacturer and distributor websites ordiscussions with manufacturers. Details on the cost analysis and distribution of turbinesaccording to size and customer sector are also provided in Volume II, Appendix E.

Clean Energy Technical Potential

The technical potential for all clean energy resources is shown in Table 68. Below are details onthe derivation for this technical potential for each of these technologies.

Table 68. Technical Potential of Clean Energy Resources by Technology(GWh in 2018)

Technology Alliant MidAmerican TotalBuilding PV 17,259 15,629 32,888Small Hydro 504 652 1,156Small Wind 9,490 9,218 18,708Total 27,250 25,499 52,749

78 http://www.renewabledevices.com/swift/79 American Wind Energy Association. AWEA Small Wind Turbine Global Market Study 2007. AWEA.

July 2007.


Building PV

Analysis of this technical potential is based on rooftop applications only. This provides aconservative estimate since other applications such as ground or pole mounted PV, awnings, andcar ports are not considered. This estimate of technical potential considers the physicallimitations due to roof area, shading, orientation, and expected building growth. The PVmethodology is diagrammatically displayed in Figure 18, showing how different inputs are usedto estimate technical potential. Each input will be described in detail below, with further detailsavailable in Volume II, Appendix E.

Figure 18. PV Potential Methodology

Existing Stock and Forecasting. Available square footage of roof area is based on site visits,surveys, and data mining results performed as part of this study for commercial and residentialbuildings in Iowa. The load forecast is used to estimate the growth in the building stock.

PV Commercial Assumptions. The following assumptions are comparable to and consistent withother studies:

All commercial rooftops are considered flat (0° pitch).

30% of all roofs are unavailable (20% due to obstructions and equipment, 10% spacelost due shading from the equipment).

All building types are equally distributed across all zip codes.

PV Residential Assumptions. The following assumptions are based on field experience andremain consistent with other studies:


Single-family and manufactured households typically have 4/12 (18.5o) pitch roofs.

Multifamily structures have flat roofs (0o pitch).

25% of roofs face south.

81% of roof areas are unavailable due to shading.

All building types are equally distributed across all zip codes.

PV Power Density Assumptions. PV cell technology evolves over time, and efficiencycontinually improves. According to the DOE, cell efficiency is projected to improve at anaverage rate of roughly 2.1% a year across all three classes of technologies. This assumption iscomparable with other studies. Conversely, there is also a performance degradation of 1%efficiency per year. Both of these assumptions are included in this analysis.

This analysis also takes into account market shares of competing solar cell technologies: mono-crystalline, poly-crystalline, and amorphous ‘thin-film,’ from which a weighted average iscalculated to determine an overall efficiency. In addition, it is important to account for the spacebetween modules needed for racking materials and installation requirements for the entire array,increasing the overall footprint. To adjust for this, the power density (W/sq.ft.) is reduced by20% to give the total system array efficiency. This result is applied to the projected increase incell efficiency to determine the power density annually.

The system power density multiplied by the useable square footage for each building type resultsin the total name plate capacity (kW) or the total DC kW installed.

PV Watts Performance Calculator. As noted earlier, the PV Watts performance calculator isused to determine the capacity factor.80 The amount of solar insolation available is based onDes Moines’ weather station, which is equivalent to that used in the energy-efficiency buildingsimulation models. The weighted average capacity factor by commercial and residentialbuildings and utility was calculated at 0.13.

Small Hydro

The technical potential for small hydro was calculated based on the potential sites listed in theHydropower Prospector. Data were downloaded for all suitable potential small hydro sites inIowa. These data included capacity, county, and other information, such as head and streamflow. They were then analyzed to derive hydro potential by county, adding up the potential forall four types of installations.

Projects were deemed to be feasible and included in the inventory of potential sites if theyfulfilled the following criteria:

80 Developed by the National Renewable Energy Laboratory, the PV Watts Performance Calculator uses hourlyTypical Meteorological Year (TMY) weather data and a PV performance model based on Sandia NationalLaboratories' PVFORM to estimate monthly and annual AC energy production (kWh).


Hydropower potential≥10 kW.

Did not lie within a zone in which development was excluded by federal law orpolicy.

Did not lie within a zone making development highly unlikely because of land-usedesignations.

Did not coincide with an existing hydroelectric plant.

Was within 1 mile of a road.

Was within 1 mile of part of the power infrastructure (power plant, power line, orsubstation) or within a typical distance from a populated area for plants of the samepower class in the region.

The potential power output for each site was calculated using the following assumptions:

Project location: optimal based on hydraulic head capture.

Penstock length: optimal based on capturing 90% of hydraulic head captured withlongest, typical penstock length, based on existing low-power or small hydro plants inthe region.

Flow rate: lesser of the following: half the stream reach flow rate or flow raterequired to produce an annual average power of 26,280 MWh using hydraulic headcorresponding to optimal small hydro penstock.

There are several assumptions in this study that indicate that actual potential may be higher thanthe study is predicting. Specifically:

The assumption of using half the stream reach flow rate is very conservative. Forexample, a small hydro potential study produced for BC Hydro estimates 90% ofstream flow is useable, deeming that only 10% of flow needs to be retained to protectfish. Therefore, the actual potential at each site could be as much as 80% higher thanthe potential given in the Hydro Prospector.81

The study did not include potential for hydrokinetic technologies in cases where thereis little head available but sufficient velocity and stream depth to support suchhydrokinetic technologies.

The study did not include sites with less than 10 kW of capacity as they are notincluded in the Hydropower Prospector. There could be many more small sites withpotential for development not covered in this study.

81 Details of the study that produced the site statistics available in the Hydro Prospector are given in the reportFeasibility Assessment of the Water Energy Resources of the United States for New Low Power and SmallHydro Classes of Hydroelectric Plants, January 2006, Prepared for the DOE, Office of Energy Efficiency andRenewable Energy by Idaho National Laboratory.


Potential from Hydro Prospector

The data for all potential projects in the state of Iowa were taken from the HydropowerProspector on-line tool. Sites were included that had more potential than the maximum allowablesize for a behind-the-meter system, but capacity was set to 500 kW, assuming part of thepotential could be utilized. The table below shows the number of sites and the capacity range foreach site category.

Table 69. Potential Hydro SitesCapacity (kW)Technology No. Sites

Minimum Maximum AverageMicro Hydro 1,941 20 200 60Small Hydro 66 500 500 500Low Power Conventional 131 202 500 377Low Power Unconventional 260 200 500 374

The total amount of potential by technology class is provided below:

Table 70. Technical Potential by Technology Class (GWh in 2018)

Microhydro Low PowerConventional

Low PowerUnconventional Small Hydro

Total Feasiblefor Applicable

Counties

Total AdjustedTechnicalPotential

513 217 426 145 1,300 1,156

The potential for each utility was then calculated by multiplying the potential for each county bythe percentage of that county within the services territories of Alliant and MidAmerican. Thisreduced the total potential from 1,300 GWh to 1,156 GWh, accounting for some counties havingareas neither in MidAmerican’s nor Alliant’s service areas.

It should be noted that these percentages may not agree with the distribution of potential hydrosites within a county as the exact location of the utilities’ operating areas within each county wasnot known. Therefore, maps of each county have been provided in Volume II, Appendix E thatshow the locations of potential sites for each county. A detailed comparison of each utility’soperating areas with these maps would determine exactly how much potential exists for eachutility in each county.

To calculate generation per month, stream flow data were taken from the U.S. GeologicalService website.82 These data show the stream flow for each month for different streams in eachcounty and were used to estimate the proportion of total annual generation for each month in theyear by first calculating the percentage of annual stream flow in each month for the sample

82 http://waterdata.usgs.gov/ia


stream in that county, then applying that percentage to the annual generation for the wholecounty.83

This analysis showed the share of annual generation is distributed differently depending on inwhich part of the state the county lies. In addition, the total potential is much lower in winterthan in summer, reaching a low in January of 19% of the peak output occurring in June. Furtherdetails are given in Volume II, Appendix E.

Small Wind

The technical potential for small wind assumes all technologies will be installed at all availablecustomer sites, regardless of cost or other market barriers. This applies to all sites under thefollowing characteristics:

Sites with a wind resource greater than Class 2.

Sites with a population density less than 100 persons per square mile.

Sites greater than 2 miles from an airport.

Sites not located on National Park lands or wetlands.

All exclusions are subtracted from 100% to gain the percent inclusion for each county. Thispercentage is applied to both utilities to gain an estimate of technical potential. Because of thehigh level of this study, exclusions for each county apply to both utilities because the high-exclusion counties primarily belong to one utility or the other (e.g., the majority of Polk Countyis under MidAmerican’s service territory). All exclusions are intended to provide a conservativeestimate of technical potential.

Wind Resource. Wind resource maps provide valuable data for assessing wind power density fora given area. The Iowa Wind Energy Institute (IWEI) has completed a wind energy assessmentfor Iowa. Data were collected around the state from 1996 – 2000. Maps of Iowa’s wind resourcewere generated as part of an Iowa Energy Center grant.84 The map of the estimated averageannual wind speeds is shown in Figure 19.

83 The calculation can be represented as: Monthly generation (kWh) = kW potential x 8760 hours/year xpercentage of annual stream flow in the month.

84 Grant No. 93-04-02.


Figure 19. Estimated Average Annual Wind Speeds for Iowa

A wind resource of at least DOE Class 2 (corresponding to wind speeds of between5.6 meters/sec and 6.4 meters/sec at 50 meters above ground) is needed to operate today’sturbines. The majority of Iowa has at least Class 2 wind speeds; however, some parts ofnortheastern Iowa have lower-than-needed wind speeds (corresponding to purple shades on themap). Therefore, exclusions have been made for the following counties:

Allamakee (90%) Winneshiek (20%)

Clayton (90%) Fayette (10%)

Dubuque (50%) Delaware (5%)

Jackson (40%) Jones (5%)

These exclusion percentages are an estimation of the area of each county with a wind resourceless than Class 2. This map shows average wind speeds, but it should be recognized that windenergy is an intermittent resource, and, thus, the estimated output varies by month. Wind energyoutput in Iowa is greatest in the spring months (March and April) and lowest in the summermonths (July and August), based on wind resource from the Wind Turbine Output Calculator.

Population Density. Small wind turbines are not a viable option for heavily populated regionsdue to the lack of land available for the turbines and the interruption of air flow by tallbuildings.85 Population density was found using the 2000 Population Density by Township andPlace within Counties in Iowa map (see Volume II, Appendix E) from the Office of Social and

85 Building integrated turbines are gaining greater acceptance in Europe and may be deemed a viable option in theUS in the future, but have not been included in the analysis here due to insufficient level of acceptance in theUS and insufficient availability of data.


Economic Trend Analysis (SETA).86 Regions within each county that contained more than 100persons per square mile were excluded from the technical potential. For most on-site windturbines, an area of one acre is needed. Therefore, excluding any land area including more than100 persons per square mile provides a conservative estimate (100 persons per square mile equalto about 0.2 persons per acre). Because these exclusions were estimated by land area and themajority of utility customers likely reside in the densely populated areas, a multiplier of 1.2 wasadded to the exclusion estimate to be conservative. Counties with the greatest population densityclusters and most affected by this exclusion included (in order of high to low exclusion):

Polk County, Des Moines city (75%)

Scott County, Davenport city (50%)

Black Hawk County, Waterloo and Cedar Falls cities (30%)

Linn County, Cedar Rapids city (20%)

Johnson County, Iowa city (20%)

Pottawattamie County, Council Bluffs city (20%)

Woodbury County, Sioux city (15%)

Dubuque County, Dubuque city (15%)

These exclusion percentages estimate of the area of each county with a population densitygreater 100 persons per square mile.

Proximity to Airports. Wind turbine sites within 2 miles of an airport may be subject to towerheight regulations by the Federal Aviation Administration (FAA).87 Small wind turbines areunlikely to be affected by these height restrictions, but this assumption has been made to ensure aconservative resource estimate. Therefore, some exclusions have been made for land surroundingairports in Iowa. Because the average county size in Iowa is about 570 square miles, an exclusionof 3% is added to any county with an airport in its boundaries.

Sensitive Areas. Sensitive areas include National Park land and wetlands. Two National ParkService areas are located in the state of Iowa: Effigy Mounds National Monument – HarpersFerry, IA (Allamakee County) and Herbert Hoover National Historic Site – West Branch, IA(Cedar County). To account for these sensitive areas and possible buildings located therein,exclusions were applied to each county based on National Park land area to county area:88

Allamakee County (0.6%) and Cedar County (0.05%). Because this study pertains only tobehind-the-meter applications, no exclusions were made for wetlands under the assumption nobuildings reside on land that consists of 100% wetlands.

86 http://www.seta.iastate.edu/87 AWEA. http://www.awea.org/smallwind/toolbox2/factsheet_visual_impact.html88 Effigy Mounds National Monument approximately 2,500 acres; Allamakee County approximately 640 square

miles (410,000 acres); Herbert Hoover National Historic Site approximately 190 acres; Cedar Countyapproximately 580 square miles (371,000 acres).


Clean Energy Market Potential

Market potential by technology is given in Table 71. The total potential from all resources acrossIUA territory is 135 GWh. Note none of the clean energy options are likely to be cost effective,and the current market potential is purely from customers willing to accept long payback times.Details on derivation of this market potential are given below for each technology.

Table 71. Clean Energy Market Potential (GWh) by Sector in 2018Building PV Small Hydro Small WindSector

Alliant MidAm Alliant MidAm Alliant MidAmResidential 5.1 3.7 0.7 0.9 22.0 21.0Commercial 7.8 8.1 2.0 2.6 26.0 26.0Industrial - - - - - - 0.3 0.4 3.7 3.5Total 12.9 11.8 3.0 3.9 52.0 51.0% of 2018 IUA sales 0.06% 0.02% 0.24%Levelized Cost ($/kWh) $0.62 $0.16 $0.15Individual results may not sum to total due to rounding

All clean energy options are intermittent resources. For Small Hydro, peak power generationoccurs in late spring; Small Wind has its peak during the winter months; and PV peaks in thesummer. As such, PV and hydro have good coincidence during system peak periods. Thevariation in market potential over the year for each technology is shown in Figure 20.

Figure 20. Clean Energy Average Monthly Market Potential (2018)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MW

h

Hydro

Wind

PV


Although none of the clean energy resources are likely to be considered cost effective, changesfrom other factors may affect the payback period, even without the resource becoming economic.These factors may include government incentives, technological breakthroughs that reduce costs,and future energy costs. It is difficult to quantify the effect of payback period on adoption, butdecreasing the payback period to less than ten years can have as much as a two- to three-foldincrease in market potential.

Building PV

Market potential for PV is based on solar programs around the country. The following sourceswere used to determine the adoption rate of implementing PV installations within their regions:

New Jersey’s Clean Energy ProgramTM

Connecticut Clean Energy Fund

Energy Trust of Oregon

Florida Energy Office’s Solar Energy Systems Incentives Program

Massachusetts Technology Collaborative’s Small Renewables Initiative

California Energy Commission’s Renewable Energy Program with San Diego Gas &Electric89

A program’s success is, in part, dependent on the current incentives available. Incentives can beprovided by one or more of the following: federal tax incentives, state tax incentives, utility buy-downs, production-based incentives, and other rebates. Volume II, Appendix E lists several stateprograms from around the country offering PV incentives.90 Incentives have become critical inpromoting and creating a successful PV program. Depending on the type and size of incentive, itcan affect the adoption rate. In most instances, the total incentive is roughly 50% of the installedcost for the residential market and 75% for the commercial sector. The market potential is basedon existing programs implementing these incentive levels and is calculated from their adoptionrates. The resulting market potential is less than 1% of the technical potential. Iowa has a marketpotential of 0.009% of the technical potential.91

It should be noted that the market potential percentage may vary by specific regional areas, asthere are varying degrees of acceptance and political climate. The adoption rate heavily dependsexistence of current programs, “green” culture, understanding of technology and meteorologicalconsiderations as well as other economic factors.

The resulting market potential is 25 GWh. The levelized cost for PV is $0.62 /kWh.

89 “Technical Potential for Rooftop Photovoltaic in the San Diego Region,” by Scott Anders of the Energy PolicyInitiatives Center, University of San Diego School of Law and Tom Bialek of San Diego Gas & Electric, 2005.

90 Database of State Incentives for Renewables and Energy Efficiency (DSIRE) www.dsireusa.org.91 Similar to Oregon and Connecticut’s market potential.


Small Hydro

The market potential for small hydro is difficult to analyze because very few utility or stateprograms exist promoting hydro as a customer-based renewable resource. Currently in NorthAmerica, the Energy Trust of Oregon, BC Hydro, and Holy Cross Energy (Colorado) all havesome sort of program promoting small hydro.92 However, data available on program installationsand potential are sparse, and thus could not be used for this assessment. Instead, a similar marketto technical potential percentage as wind was used (0.6%), applied equivalently across theservice territories. This resulted in a total market potential of 6.9 GWh.

Small Wind

The market potential estimates were based primarily on the two California programs offeringrebates for small wind technologies: the SGIP and the Emerging Renewables Program (ERP).New Jersey’s Customer On-Site Renewables Program (CORE) was also reviewed to aid in themarket potential estimates. The California ERP began in 1998 and the SGIP began in 2001.Since their inceptions, the research team estimated the California programs have funded about0.35% of the small wind technical potential in that state. Because this study is a ten-yearpotential study, the technology is likely to see substantial cost decreases and improvements, andpermitting acceptance could also improve during that time. Therefore, this study assumes themarket potential for 2018 is double the market to technical potential percentage for California’sprevious programs, or 0.6% of the estimated technical potential. This value was applied to allcounties in Iowa for both Alliant and MidAmerican.

Passive Efficiency Resources

Passive energy resources were evaluated equivalently to other energy-efficiency resources. For afull description of methodology, please see Volume II, Appendix C-1. Detailed descriptions ofthese measures are provided in Volume II, Appendix A. These passive efficiency measures areapplicable to the residential and commercial sectors, and provide water heating (solar waterheating, residential only) or HVAC savings. Except for deciduous trees, the passive solarmeasures are only considered for new construction.

Table 72. Passive Efficiency Potentials by Sector (GWh in 2018)

Segment 2018 BaselineSales

TechnicalPotential

EconomicPotential

Economic As% of Baseline

SalesResidential 10,819 368 354 3.3%Commercial 9,086 101 99 1.1%Total 19,905 469 453 2.3%Note: Results may not sum to total due to rounding.

92 Holy Cross Energy restricts incentives to installations with capacities < 25 kW.


Table 73. Passive Efficiency Potentials by Sector (1000 DTh in 2018)

Segment 2018 BaselineSales

TechnicalPotential

EconomicPotential

Economic As% of Baseline

SalesResidential 65,968 680 595 0.9%Commercial 34,475 53 49 0.1%Total 100,443 732 644 0.6%Note: Results may not sum to total due to rounding.

Residential Sector

The measures and levelized costs (averaged across all customer segments and vintages) areprovided in Table 74. Solar attic fans, window overhangs and deciduous trees do not offer anyheating savings; thus, their levelized costs are not given.

Table 74. Levelized Costs of Passive Efficiency Measures in Residential Sector

Measure Levelized Cost($/kWh)

Levelized Cost($/therm)

Solar Water Heater $0.67 $10.42Solar Attic Fan $0.72 - - -Pellet (corn) Stoves $0.00 $0.00Window Overhangs $0.76 - - -Trombe Walls $0.14 $2.37Smart Siting $0.33 $5.45Deciduous Trees $0.05 - - -

The economic potential of passive efficiency measures in the residential sector is expected to be354 GWh and 595 thousand DTh over ten years, corresponding to a 3.3% reduction of 2018electrical residential consumption (Figure 21) and a 0.9% reduction in 2018 gas consumption. Ofthe total economic potential, 149 GWh and 139,312 DTh are within Alliant’s service territory,205 GWh and 367,148 DTh are within Mid American’s territory, and 88,574 DTh are within theAquila service territory.

Potentials, as split by end use for the residential sector, are shown in Figure 21 for electric. Alleconomic gas potential is in heating, so no pie chart is given. Additional details regardingsavings associated with specific measures assessed within each end use are provided in VolumeII, Appendix E.


Figure 21. Residential Sector Passive Renewable Resources:Economic Electric Potential by End Use

Cooling72%

Heating26%

Heat Pump2%

Commercial Sector

The measures and levelized costs (averaged across all customer segments and vintages) areshown in Table 75. As window overhangs and deciduous trees do not offer any heating savings,there are no associated levelized costs.

Table 75. Levelized Costs of Passive Efficiency Measures in Commercial Sector

Measure LevelizedCost ($/kWh)

LevelizedCost ($/therm)

Window Overhangs $0.10 - - -Trombe Walls $0.01 $0.19Smart Siting $0.38 $3.87Deciduous Trees $0.01 - - -

All passive energy measures in the commercial sector are under the auspices of passive solardesign techniques and impact HVAC usage. The economic potential of passive efficiencymeasures in the commercial sector is expected to be 99 GWh and 49 thousand DTh over tenyears, corresponding to an 1.1% reduction of 2018 electrical commercial consumption (Figure22) and 0.2% reduction in 2018 gas consumption (Figure 23). Of the total economic potential,49 GWh and 6,384 DTh are within Alliant’s service territory, 50 GWh and 29,640 DTh arewithin Mid American’s territory, and 12,747 DTh are within the Aquila service territory.

Potential, split by end use, is shown in Figure 22 and Figure 23 for the commercial sector,electric and gas fuels, respectively. Additional details regarding the savings associated withspecific measures assessed within each end use are provided in Volume II, Appendix E.


Figure 22. Commercial Sector Passive Renewable Resources:Economic Electric Potential by End Use

Cooling85%

Heat Pump13%

Heating2%

Figure 23. Commercial Sector Passive Renewable Resources:Economic Gas Potential by End Use93

Heating67%

Boiler33%

Emission Reductions

Emissions reduction occur due to clean energy and passive efficiency measure installations. Anestimated emissions savings potential based on the market potential is shown in Table 76. If theentire market potential of 453 GWh from clean energy and passive efficiency measures wasrealized, about 440,463 tons of CO2, 1,462 tons of SO2, 861 tons of NOx, and 0.01 tons ofmercury (Hg) would be abated annually.

93 The “Heating” end use is savings for which a furnace is the primary space heating equipment.


Table 76. Estimated Emissions Savings Potential94

EmissionIowa Emissions

Factor(lbs/MWh)

EmissionsSavings

(Short tons)

EmissionsSavings(Tonnes)

CO2 1,943.0 440,463.0 399,581.0SO2 6.45 1,462.0 1,326.0NOx 3.80 861.0 781.0Hg 0.0001 0.01 0.01

94 Values estimated for Iowa from eGrid 2006 v2.1 State File (Year 2004 Data). Available at:http://www.epa.gov/cleanenergy/egrid/index.htm.

assessment of energy and capacity savings potential in iowa · 2020. 9. 28. · terry fry, sam...

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