ComEd Residential, Commercial and Industrial Saturation ... · ComEd Residential, Commercial and...

©2017 ITRON CONFIDENTIAL PROPRIETARY

Itron, Inc.

Dunsky Energy Consulting

Energy Resources Center, University of Illinois Chicago

Trend Tech Energy Services, LLC

February 6, 2020

ComEd Residential, Commercial and Industrial

Saturation/End-Use Research, Market

Penetration and Potential Study

©2017 ITRON CONFIDENTIAL PROPRIETARY 2

PRESENTATION OUTLINE

» Residential primary data collection methods and results (50 mins)

» Commercial primary data collection methods and results (50 mins)

» Industrial data collection methods and results (20 mins)


RESEARCH GOALS AND OBJECTIVESAs stated in the RFP…

1. Collect detailed baseline data for as

many market segments as possible

2. Develop distributions of end-use

energy consumption by segment

3. Develop trend analysis of EE market

penetration for key technologies

4. Estimate economic potential of EE

5. Characterize incremental cost-

effective potential that exists beyond

expected market trends

6. Provide a concise summary of key

findings

7. Deliver all primary and secondary

data in an easy-to-use format that

allows combination with other internal

and external datasets


PROJECT SCOPE & STATUSOverview of Task-Level Workflow and Interdependencies

Task 1: Research

and Data Collection

Task 3a: Energy

Utilization

Distributions

Task 3b: Economic

Potential

Task 4: Analysis of

ComEd EE Markets

Task 2: Market

Penetration Study

(Top-Down)

Task 2: Market

Penetration Study

(Bottom-Up)

Task 2: Market

Penetration Study

(Reconciled)


OVERVIEW OF ITRON TEAMComposition and Roles

▪ Prime bidder

▪ Overall project management

▪ Lead for:

• Task 1 (Primary Data Collection)

• Task 3a (Energy Utilization

Distributions)

• Task 4 (Analysis of ComEd EE

Markets)

▪ Subcontractor

▪ Lead for:

• Task 2 (Market Penetration Study)

• Task 3b (Economic Potential)

▪ Support for:

• Task 4 (Analysis of ComEd EE

Markets)

▪ Subcontractors

▪ Support for:

• Task 1 (Primary Data Collection)

Trend Tech Energy Services

RESIDENTIAL PRIMARY DATA COLLECTIONMethods and results


RESIDENTIAL PRIMARY DATA COLLECTIONOverview of Key Activities


RESIDENTIAL PRIMARY DATA COLLECTIONSample Frame Development

» Itron’s (formerly Silver Spring Networks’) Operations Optimizer already fully deployed at ComEd

» Operations Optimizer contains billing data (2007+), AMI data (2010+), up-to-date CIS, plus:

• Tax assessor data for most residential accounts (all of Cook County)

• Grid connectivity mapping


RESIDENTIAL PRIMARY DATA COLLECTIONSample Design

» Developed the following sample design variables in collaboration with ComEd:

• Building type (Single-family, Multi-family)

• Whole-home consumption (Low, Medium, High)

• Location (Chicago, Suburban, Other/Rural)

• Income Eligibility (Low income eligible, Non-low income eligible)

» 36 total sample strata:

• Population numbers in certain strata are small (e.g. all MF/Rural strata)


RESIDENTIAL PRIMARY DATA COLLECTIONMulti-Modal Survey Design


WHY USE A MULTI-MODAL SURVEY DESIGN?

» Several fundamental tradeoffs between sample size and depth/accuracy of data collection

• Standard web or telephone surveys: cheap (large sample sizes), but unreliable data on end-use equipment

characteristics (e.g. capacity, rated efficiency) due to lack of customer knowledge and/or user recall error

• On-site surveys: accurate data on equipment characteristics, but expensive (small sample sizes)

» Itron’s multi-modal approach designed to overcome these tradeoffs by:

• Using mass mailings of postcards to recruit customers into web survey ($25 Amazon.com gift card incentives)

• Optimizing the web survey for mobile devices

- Allows survey to leverage device mobility for room-by-room lighting inventories

- Allows survey to leverage technologies available on mobile devices (cameras, location services)

» Key features of mobile-optimized survey:

• “Self-report” questions limited to variables that customers know with a high degree of accuracy (e.g. home size,

occupancy, rent vs. own, etc.)

• Equipment characteristics collected by having customers take and upload photos of their equipment nameplates

» Bottom line: approach generates “on-site quality” baseline information at the cost and scale

of standard telephone surveys


RESIDENTIAL SURVEY BACK OFFICE WORKFLOW

Nameplate images

Self-reported data

Location validation

Make/model

lookup library• Capacity

• Efficiency

• Vintage

Image recognition

Complete site

information

Utility CIS,

Billing,

Tracking

Postcard

Incentive

OK to pay incentive

Merge final data


RESIDENTIAL PRIMARY DATA COLLECTIONSurvey Implementation

» Postcards mailed in two waves to allow for a nested sample of on-site verification surveys

» Objectives of on-site verification surveys were to:

• Verify accuracy of model numbers derived from nameplate images

• Verify accuracy of self-reported equipment ownership and lighting inventories (by type and shape)

» First wave = 5,000 postcards (mailed late March)

• Offered additional $50 Amazon.com gift card incentive to participate in on-site verifications

• >60 customers opted into on-site verification surveys

• Completed 46 on-site verifications

» Bottom line findings of on-site verifications:

• 97% of self-reported equipment ownership verified to be correct

• 94% of model numbers derived from nameplate images verified to be correct and at customer premise

» Second wave = 30,000 postcards (mailed mid-May)

Overall participation in web survey:• ~2,000 click-ins

• ~3,000 nameplate images submitted

• Analytic dataset varies from ~1,700 to ~800 (depending on

the end-use in question) due to attrition from beginning to

end of survey


SURVEY DISPOSITION BY STRATA CATEGORY

Presentation Title |

Strata Group N Survey % Survey % Pop

Housing TypeSingle Family 1,674 81% 74%

Multi Family 396 19% 26%

Geography

Chicago 674 33% 29%

Suburban 807 39% 69%

Other 589 28% 2%

Usage

High 701 34% 34%

Medium 779 38% 33%

Low 590 29% 33%

Income LevelNon-Low Income 1,364 66% 58%

Low Income 706 34% 42%


RESULTS OUTLINE…in case we’re already running behind schedule

» Household characteristics (3)

» Building envelope (2)

» Space cooling (11)

» Space heating (3)

» Thermostats (2)

» Water heating (2)

» Laundry appliances (4)

» Kitchen appliances (6)

» Interior lighting (3)

» Exterior lighting (1)

» Dehumidifiers & air purifiers (2)

» Water fixtures (1)

» Consumer electronics (2)

» Key findings (5)

» All slides = 47

» Highlighted slides = 36



HOUSEHOLD CHARACTERISTICSAverage home size


1,856

1,372

1,874

1,5491,594

1,844

2,024

1,741

1,460

1,757

0

250

500

750

1,000

1,250

1,500

1,750

2,000

2,250

SFD MFD Non-LI LI Chicago Surban/Other High Medium Low

Building Type Income Eligibility Location Whole-Home Consumption Total

Ave

rage

Ft2

/Ho

me


HOUSEHOLD CHARACTERISTICSHome ownership


96%

46%

92%

71%66%

90% 89%

81% 79%83%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



Shar

e o

f H

om

e O

wn

ersh

ip


HOUSEHOLD CHARACTERISTICSEV and PV penetration


0.9%

1.2%

0.5%

1.8%

1.4%

0.8%

1.5%

0.8%0.6%

1.0%

0.6%

0.3%

0.5%

0.5%

0.7%

0.4%

0.3%

0.6%

0.6%

0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%



Shar

e o

f H

om

esPHEV

EV

PV


BUILDING ENVELOPEPrevailing window type


19%

35%

19%29% 28%

20% 22% 23% 23% 23%

69%

52%

69%59% 57% 69% 66% 63%

66% 65%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



Shar

e o

f H

om

es

Mixture of single, double, triple pane

All or most are triple pane

All or most are double pane

All or most are single pane


BUILDING ENVELOPEShare of homes with recent insulation work


0%

2%

4%

6%

8%

10%

12%

14%



Shar

e o

f H

om

esAttic Floors Exterior Walls Foundation Walls Other


SPACE COOLINGSaturation of central cooling by system type


84%

43%

84%

61% 64%

82%78% 78%

71%76%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



Shar

e o

f H

om

es

Other

Ductless system

Ground source heat pump

Forced air split system


SPACE COOLINGDistribution of central cooling system capacity per home


n = 1,220 obsmean = 2.87 tons


SPACE COOLINGAverage central cooling system capacity per home


2.96

2.59

3.03

2.69 2.73

2.96

3.29

2.86

2.51

2.87

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5



Ave

rage

To

ns

Co

olin

g C

apac

ity

Per

Ho

me


SPACE COOLINGDistribution of central cooling system rated efficiency


n = 1,220 obsmean = 12.43 SEER


SPACE COOLINGCentral cooling systems maintenance


Within the past year36%

1-2 years ago34%

3-4 years ago10%

4-5 years ago3%

>5 years ago7%

Don't know9%


SPACE COOLINGRoom cooling saturation, average EER, and usage


10% 6% 8%

24%

14%20%

26%

33%29%

29%

28%28%

10%19% 14%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SFD MFD

Building Type Total

Shar

e o

f Ho

mes

wit

h R

AC

Always (7 days per week)

Often (5-6 days per week)

Sometimes (3-4 days per week)

Rarely (1-2 days per week)

Never

Segment Saturation

Average Rated

EER

Share of Units

<6 years old

SFD 10% 10.16 49%

MFD 40% 10.44 49%

Total 18% 10.32 49%


SPACE COOLINGSaturation of fans used for room cooling


79%

63%

79%

70% 70%

77% 78% 77%

69%

75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



Shar

e o

f H

om

esWhole-house or attic fan Ceiling fan Window fan Portable fan


SPACE COOLINGUtilization analysis

» Despite collecting data to characterize distribution of rated efficiency and capacity of space cooling

equipment, space cooling energy consumption also greatly impacted by building envelope

performance and customer behavior

• Result is that space cooling consumption varies more widely (at the customer-level) than any other

major electric end use

» In turn, analyzing cost-effectiveness of space cooling measures in aggregate (e.g. when estimating

economic potential) can introduce significant “aggregation bias”

• When point averages that indicate a given measure is not cost-effective for a given segment, when

in fact it is cost-effective for a significant portion of that segment

» ComEd expressed a strong interest in using data collection and analysis approaches designed to

minimize aggregation bias wherever possible

• For most major residential end uses, we can adequately characterize the distribution of end-use

energy consumption based on large samples of rated capacity and efficiency data

• For space cooling, we augmented our rich data set with a dedicated analysis designed to

characterize the distribution of space cooling energy consumption using two main analytic

frameworks – load disaggregation and cluster analysis




» Itron’s Operations Optimizer data analytics platform include a load disaggregation module

» OO combines statistical regressions of AMI and weather data with physics-based calculations that

take into building square footage and vintage (available from tax assessor data) to estimate load

associated with space cooling

• Enabled OO’s load disagg module on a one-time basis to generate space cooling estimates for

each of the customers that responded to our web survey

• Modified load disagg algorithm to use customer-specific CAC capacity and SEER values as inputs

» Used modified algorithm to generate customer-specific space cooling consumption estimates that

are calibrated to actual customer load, home size, home vintage, and CAC capacity and SEER

(1,277 customers total)

» Next objective was to identify the best way to group customers such that the aggregate results for

those groups adequately reflected the full distribution of space cooling consumption

» Used k-means cluster analysis to characterize distribution of space cooling into 3, 6, 9, and 12

clusters





3 Clusters 6 Clusters 12 Clusters



» Primary objective of cluster analysis was to

provide a data-driven basis to define segments

for potential modeling that minimized

aggregation bias for space cooling measures

» Analyzed characteristics of customers in each

cluster to identify variables in our sample frame

that best explain differences between clusters

• Building type (SFD, MFD)

• Whole-home consumption (H, M, L)

• Home size (<2,000 ft2, >=2,000 ft2)

• Low-income program eligibility

» Collapsed several segments with very low

customer populations, similar average space

cooling consumption, and/or similar market

adoption barriers

» Final segments for potential modeling (9):


Segment Avg CAC kWh/yr

01.SFD-H-<2Kft2-nonLI 3,054

02.SFD-H->2Kft2-nonLI 2,862

03.SFD-M-<2Kft2-nonLI 2,089

04.SFD-M->2Kft2-nonLI 1,799

05.SFD-L-nonLI 1,190

06.MFD-H-nonLI 2,208

07.MFD-M-nonLI 921

08.MFD-L-nonLI 1,037

09.SFD-<2Kft2-LI 2,077

10.SFD->2Kft2-LI 2,508

11.MFD-LI 2,475

Total 2,127


SPACE HEATINGSaturation of central heating by technology and fuel


78%

44%

70%

8%

13%

9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SFD MFD

Building Type Total

Shar

e o

f H

om

es

Other

Portable space heater

Ground-source heat pump

Ductless heat pump

Air-source central heat pump

Electric baseboard heater

Propane boiler

Electric boiler

Gas boiler

Propane furnace

Electric furnace

Gas furnace


SPACE HEATINGCentral heating systems maintenance


Within the past year26%

1-2 years ago22%

3-4 years ago7%

4-5 years ago3%

>5 years ago7%

Don't know35%


SPACE HEATINGPortable space heating ownership and usage


7% 7% 7%

24%13%

21%

23%

27%

24%

26% 40% 30%

21%13%

19%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SFD MFD

Building Type Total

Shar

e o

f H

om

es w

ith

Ele

ctri

c R

oo

m H

eate

rs

Always (7 days per week)

Often (5-6 days per week)

Sometimes (3-4 days per week)

Rarely (1-2 days per week)

Never

Segment Saturation

SFD 12%

MFD 17%

Total 14%


THERMOSTATSSaturation of thermostats by type


25%16%

24%

43%

24%

39%

31%

52%

35%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SFD MFD

Building Type Total

Shar

e o

f Ho

mes

No thermostat

Standard thermostat

Programmable thermostat

Communicating thermostat


THERMOSTATSFrequency of overriding programmed thermostat settings


Never Programmed

24%

Frequently Override7%

Regularly Override14%

Occasionally Override33%

Rarely Override40%

Never Override7%

Programmed76%


WATER HEATINGFuel shares of water heating


Natural gas85%

Propane2%

Fuel oil0.1% Solar

0.1%

Other fuel0.1%

Don't know4%

Conventional storage7%

Heat pump storage

0.3%

Tankless0.5%

Tankless coil

0.1%

Don't know1.0%

Electricity9%


WATER HEATINGSaturation of insulation measures for electric water heating systems


Yes7%

No67%

Don't know26%

Share with Tank Wrap

Yes17%

No50%

Don't know33%

Share with Pipe Wrap


LAUNDRY APPLIANCESSaturation of clothes washers


68%

45%

63%

30%

17%

27%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SFD MFD

Building Type Total

Shar

e o

f Ho

mes

No clothes washer

Front-loading clothes washer

Top-loading clothes washer


LAUNDRY APPLIANCESDistribution of clothes washers rated efficiency (Modified Energy Factor)


n = 989 obsmean = 1.89 MEF


LAUNDRY APPLIANCESSaturation of clothes dryers


17%28% 25%

56%

59%57%

27%

11%17%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Chicago Suburban/Other

Location Total

Shar

e o

f Ho

me

s

No clothes dryer

Bottled gas clothes dryer

Natural gas clothes dryer

Electric clothes dryer


LAUNDRY APPLIANCESDistribution of clothes dryer rated efficiency (Combined Energy Factor)


n = 244 obsmean = 3.28 CEF


KITCHEN APPLIANCESSaturation of refrigerators


75%

91%

76%83% 84%

76% 72%79%

85%79%

21%

7%

21%14% 12%

21%24%

18%12%

18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



Shar

e o

f H

om

es

None

3 or more units

2 units

1 unit


KITCHEN APPLIANCESDistribution of refrigerator rated annual consumption (kWh/yr)


n = 934 obsmean = 567 kWh/yr


KITCHEN APPLIANCESSaturation of stand-alone freezers


22%

11%

20% 19%

15%

22%

25%

19%

14%

20%

1%

1%

1% 1%

1%

1%

1%

2%

0%

1%

0%

5%

10%

15%

20%

25%

30%



Shar

e o

f H

om

es3 or more units

2 units

1 unit


KITCHEN APPLIANCESDistribution of stand-alone freezer rated annual consumption (kWh/yr)




KITCHEN APPLIANCESSaturation of dishwashers


67%

39%

73%

40%45%

69%65% 65%

52%

62%

0%

10%

20%

30%

40%

50%

60%

70%

80%



Shar

e o

f H

om

es

3 or more units

2 units

1 unit


KITCHEN APPLIANCESDistribution of dishwasher rated annual consumption (kWh/yr)




INTERIOR LIGHTINGAverage number of sockets by technology


22.7

11.9

19.9

4.7

2.9

4.2

8.4

2.3

6.8

0

5

10

15

20

25

30

35

40

45

SFD MFD

Building Type Total

Ave

rage

So

cket

s p

er H

om

e

Don't know

Other

Infrared

Halogen

Fluorescent Tube

Incandescent

CFL

LED


INTERIOR LIGHTINGAverage number of sockets by technology


22.6

16.119.9

4.9

3.2

4.2

8.8

4.1

6.8

0

5

10

15

20

25

30

35

40

45

Non-LI LI

Income Eligibility Total

Ave

rage

So

cket

s p

er H

om

e

Don't know

Other

Infrared

Halogen

Fluorescent Tube

Incandescent

CFL

LED


INTERIOR LIGHTINGAverage number of sockets by technology and lamp shape


2.3

0.2

1.1

0.10

2

4

6

8

10

12

14

16

18

20

LED CFL Incandescent Halogen

Ave

rage

So

cke

ts p

er

Ho

me

Specialty

Reflector

Standard


INTERIOR LIGHTINGReplace on burnout plans


62%54%

61%

12%

34%42%

24%

3% 4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Incandescentburnout

Halogen burnout CFL burnout

Shar

e o

f Res

po

nd

ents

Replace with incandescent

Replace with halogen

Replace with CFL

Replace with LED


EXTERIOR LIGHTINGAverage number of sockets by technology and control type


22%

29%

23%

20%

28%

20%

0%

5%

10%

15%

20%

25%

30%

35%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

SFD MFD

Building Type Total

Share

of In

cand

esce

nts/H

alogen

s Co

ntro

lled

Ave

rage

So

cke

ts p

er

Ho

me

LED or CFL

Incandescent or Halogen

…by Timers

…by Motion Sensors


DEHUMIDIFIERS & AIR PURIFIERSSaturation of air purifiers, dehumidifiers, and humidifiers


0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

SFD MFD

Building Type Total

Shar

e o

f Ho

mes

Air purifier Dehumidifier Humidifier


DEHUMIDIFIERS & AIR PURIFIERSUsage of air purifiers and dehumidifiers


4% 6%

12%20%

12%

26%14%

10%

58%

38%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Air Purifier Dehumidifier

Shar

e o

f Ho

mes

wit

h A

ir P

uri

fier

s/D

ehu

mid

ifie

rs

>11 months

9-11 months

7-9 months

4-6 months

<=3 months


WATER FIXTURESAverage number of water fixtures and saturation of water efficiency measures


49%

38%

47%

61%

51%

60%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

SFD MFD

Building Type Total

Share o

f Fixtures

Nu

mb

er o

f Fi

xtu

res

Per

Ho

me

Number of faucets

Number of showerheads

% Faucets with aerators

% Low-flow showerheads


CONSUMER ELECTRONICSSaturation of smart power strips and systems controlled in those homes


30%

7%

89%

60%

10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Tier 1 Tier 2 TV/Cable Computer Other

Smart Strip Penetration(Share of Homes)

Systems Controlled(Share of Homes with Smart Strips)


CONSUMER ELECTRONICSSaturation of voice-activated devices and systems controlled in those homes


10%

42%

27%

43%

13%

59%

11% 12%

0%

10%

20%

30%

40%

50%

60%

70%

TVs

Mob

ile d

evic

es

Smar

t sp

eake

rs

Ente

rtai

nm

ent

Ligh

tin

g

Mob

ile d

evic

e

Ther

mo

stat

Oth

er

Voice-Activated Device Penetration(Share of Homes)

Systems Controlled(Share of Homes with Voice-Activated Devices)


KEY FINDINGSLEDs have become the dominant residential lighting technology


End-Use/Technology Metric 2012 Baseline Study 2019 Baseline Study

All SFD MFD All SFD MFD

Lighting

Incandescent Share of all sockets 65% 66% 60% 19% 20% 11%

CFL Share of all sockets 20% 19% 24% 12% 11% 14%

Linear Fluorescent Share of all sockets 8% 9% 5% 5% 5% 6%

Halogen Share of all sockets 6% 5% 11% 3% 3% 3%

LED Share of all sockets 1% 1% 0% 55% 54% 58%


KEY FINDINGSSmall increase in CAC efficiency, but little evidence of fuel switching away from gas




Space Cooling

Central air conditioner Share of all homes 73% 87% 46% 80% 87% 48%

…<14 SEER Share of all CAC systems 93% - - 87%

Space Heating

Primary electric space heating Share of all homes 10% 4% 24% 9% 8% 13%

…Heat pump Share of all homes 2% - - 2% 2% 1%

Water Heating

Electric water heating Share of all homes 8% 6% 13% 9% 8% 11%

Water heater tank wrap Share of all homes 3% - - 7%

Hot water pipe wrap Share of all homes 9% - - 17%

Faucet aerators Share of all homes 62% - - 47% 49% 38%

Low-flow showerheads Share of all homes 40% - - 60% 61% 51%


KEY FINDINGSStrong evidence of impact of ComEd’s appliance recycling programs




Laundry Appliances

Clothes washers Share of all homes 80% 98% 47% 90% 98% 62%

Electric clothes dryers Share of all homes 25% 26% 23% 25% 27% 17%

Kitchen Appliances

Refrigerator Share of all homes 100% 100% 100% 99% 99% 100%

Secondary refrigerator Share of all homes 30% 42% 7% 20% 24% 9%

Standalone freezer Share of all homes 31% 40% 13% 21% 23% 21%

Dishwasher Share of all homes 67% 75% 54% 63% 68% 41%

Room HVAC

Window air conditioner Share of all homes 30% 18% 52% 18% 10% 40%

Air purifier/humidifier Share of all homes 36% 31% 27% 16% 18% 12%

Dehumidifier Share of all homes 23% 34% 5% 12% 16% 3%


KEY FINDINGSLaundry appliance stock turnover likely to be highest in near-term (compared to other appliances)


n = 244 obsmean = 3.28 CEF



n = 989 obsmean = 1.89 MEF

1994

1994

2001

2010


KEY FINDINGSPenetration of “new” consumer technologies is already significant

» Penetration of the newest consumer technologies which control or have the potential to

control aspects of residential energy consumption is already significant

• Communicating thermostats = 24% (commercially available since 2011)

• Smart power strips = 37% (commercially available since 2011)

• Voice-activated smart speakers = 30% (commercially available since 2016)

» These markets are highly dynamic and should be considered in stark contrast to those

of the other major residential end uses

» These devices currently control only a very small portion of residential load, but the

potential (and probability) for these technologies to influence a much larger percentage

of residential load in the very near future is significant


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