2013 california statewide critical peak pricing evaluation josh l. bode candice a. churchwell drmec...

2013 California Statewide Critical Peak Pricing

Evaluation

Josh L. BodeCandice A. Churchwell

DRMEC Spring 2014 Load Impacts Evaluation Workshop

San Francisco, CaliforniaMay 2014

2

Introduction and comparison of rates

PG&E Results

SCE Results

SDG&E Results

Appendix: Evaluation methodology

Presentation overview

3

PG&E called 8 events

SCE called 10 events

SDG&E called 4 events

Each utility calls event days based on their system conditions

SDG&E’s events last from 11 AM to 6 PM while PG&E’sand SCE’s last from 2 to 6 PM

Event days are different across the three utilities

System load patterns across utilities are not always coincidental, particularly for Northern and Southern California

Comparisons in impacts between the utilities should be made with caution

No event day was common to all utilities.

4

PG&E’s average load reduction per customer was 8.6% (22.4 kW).

SCE’s average load reduction per customer was 5.8% (14.2 kW).

SDGE’s average load reduction per customer was 6.9% (18.4 kW).

Average event day percent reductions by utility are in a similar range

5

2013 reductions were slightly larger than last year’s

Utility Year Number of Events Accts Temp

(°F)

Reference Load (MW)

Load Impact (MW)

Percent Impact (%)

PG&E

2010 9 1,650 90.2 592 23 3.9%

2011 9 1,750 88.1 473 28 5.9%

2012 9 1,627 86.5 437 30 6.9%

2013 8 1,717 90.8 448 38 8.6%

SCE

2010 12 4,100 84.7 1077 31 2.9%

2011 12 3,000 84.7 615 35 5.7%

2012 12 2,508 87.3 554 33 5.9%

2013 10 2,495 87.3 613 36 5.8%

SDG&E

2010 4 1,350 81.3 357 19 5.3%

2011 2 1,300 86.2 359 19 5.2%

2012 7 1,117 80.4 268 16 6.0%

2013 4 1,095 84.1 293 20 6.9%

PG&E and SDG&E average event conditions were hotter

PG&E showed the largest jump in reductions from 30 to 38 MW, due to both more participants and larger percent reductions

The customer mix has evolved substantially over time but response has been consistent

Even when enrollments seem similar, customers exit and join, leading to changes

6

PG&E Specific Results

7

PG&E’s average load reduction was 8.6%, or 38.4 MW, across the 8 events in 2013

8

PG&E detailed event load impacts

Avg. Customer Reference

Load

Avg. Customer

Load w/ DRImpact

Aggregate Impact

% Reduction

Avg. Temp.

Daily Maximum

Temp.

(kW) (kW) (kW) (MW) % °F °F

6/7/2013 Fri 1,707 254.4 227.6 26.8 45.7 10.5% 90.4 100.9

6/28/2013 Fri 1,710 270.4 243.4 27.0 46.2 10.0% 94.8 104.0

7/1/2013 Mon 1,713 268.0 243.2 24.8 42.5 9.3% 93.9 106.5

7/2/2013 Tue 1,713 264.2 245.0 19.2 32.8 7.3% 94.0 108.0

7/9/2013 Tue 1,714 260.1 237.4 22.7 38.9 8.7% 91.5 106.5

7/19/2013 Fri 1,719 243.8 226.2 17.5 30.2 7.2% 87.2 101.0

9/9/2013 Mon 1,730 280.0 255.9 24.0 41.6 8.6% 90.6 99.0

9/10/2013 Tue 1,730 265.4 248.6 16.9 29.2 6.4% 83.7 99.0

10/18/2013* Fri 1,730 234.0 219.9 14.1 24.4 6.0% 76.8 78.5

1,717 260.6 238.3 22.4 38.4 8.6% 90.8 102.7

Event DateDay of Week

Accounts

Avg. Event**

* Unofficial event** Avg. event estimates do not include the unofficial event

9

Manufacturing; Wholesale & Transport; and Agriculture, Mining, & Construction accounted for 41% of the load and over 75% of impacts

While the Offices, Hotels, Finance, Services sector had the most load, 36%, it accounted for only 16% of program impacts

PG&E’s demand reductions were concentrated in two industries

10

On a percentage basis, reductions were similar for large and smaller customers alike

Not surprisingly, the largest customers account for a large share of the demand reductions

Accounts Reference Loads (MW)

Impacts (MW)0%

20%

40%

60%

80%

100%

342.0

221.721.2342.0

95.67.8

345.0

67.84.2

343.0

44.1 3.7345.0

17.6 1.5

Smallest Fifth4thMiddle Fifth2nd Largest Fifth

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%

Percent Reductions

Avg.

11

Ex ante estimates relied on available historical data

1. Model loads absent DR as a function of temperature and month

2. Estimate loads absent DR for 1-in-2 and 1-in-10 conditions

5. Combine loads and percent reductions

3. Model historical percent impacts as a function of weather

4. Estimate percent impacts under 1-in-2 and 1-in-10 conditions

There is no robust empirical data about how medium customers will respond when default to CPP Percent impacts were based on the historical 2012–2013 industry specific percent load reductions

as a function of weather.

Medium reference loads were developed by using a representative sample of customers and estimated by LCA and industry.

The industry specific percent load reductions were then applied to medium customer loads.

12

Ex ante percent reductions are inline with historical percent reductions

0

10

20

30

0

10

20

30

0

10

20

30

65 70 75 80 80 85 90 95 65 70 75

80 85 90 95 65 70 75 80 70 75 80

75 80 85 90 75 80 85 90

Greater Bay Area Greater Fresno Humboldt

Kern Northern Coast Other

Sierra Stockton

Ex-Post Ex-Post Fitted Values Ex-Ante Ex-Ante Fitted Values

% R

educ

tion

over

eve

nt w

indo

w

Avg. Daily Temperature (F)

2012 and 2013 events used as basis for ex ante

Estimates were produced by LCA

Comparison is based on: Historical

customers

Same event window and historical events

Assumes percent reductions of new customers in LCA will be similar

13

Reference loads align with historical loads

0

100

200

300

400

0

100

200

300

400

0

100

200

300

400

60 70 80 90 60 70 80 90 100 50 60 70 80

60 70 80 90 100 50 60 70 80 50 60 70 80

50 60 70 80 90 60 70 80 90

Greater Bay Area Greater Fresno Humboldt

Kern North Coast and North Bay Other

Sierra Stockton

Ex-Post Ex-Post Fitted Values Ex-Ante

Re

fere

nce

Lo

ad (

2-6

pm

)


References loads were separately estimated for large and medium customers by LCA


customers


New large customers are assumed to be similar to old ones

14

Weather Year Year

Accounts Reference Loads (MW) Percent Reductions Aggregate Impacts (MW)

2012 Estimates

2013 Estimates

2012 Estimates

2013 Estimates

2012 Estimates

2013 Estimates

2012 Load Impact (MW)


1-in-10

2014 1,796 1,673 603.6 535.9 7.3% 7.7% 44.1 41.5

2015 1,815 2,657 609.7 847.1 7.3% 8.4% 44.6 70.9

2016 1,815 2,781 609.9 886.3 7.3% 8.3% 44.6 73.7

2017-2023 1,815 2,783 609.9 886.9 7.3% 8.3% 44.6 73.7

1-in-2

2014 1,796 1,673 622.6 515.5 8.0% 7.0% 49.7 36.3

2015 1,815 2,657 628.9 810.9 8.0% 7.6% 50.2 61.3

2016 1,815 2,781 629.0 848.8 8.0% 7.5% 50.2 63.8

2017-2023 1,815 2,783 629.0 849.4 8.0% 7.5% 50.2 63.9

Comparison of 2013 ex ante year estimates to prior year estimates

Differences are mostly due to changes in the enrollment forecasts

2014 ex ante impacts align well with the 2013 avg. event response

Over time, customers who reduce demand have tended to remain on CPP, while those less likely to respond have migrated elsewhere

15

Weather Year Year Enrolled

Accounts

Avg. Reference Load

(MW 1 to 6 PM)

Avg. Estimated Load w/ DR

(MW 1 to 6 PM)

Avg. Load impact

(MW 1 to 6 PM)

% Load Reduction

(%)

Weighted Temp.

(°F)

1-in-10 August

System Peak Day

2013 506 44.1 41.5 2.6 5.9% 97.6

2014 506 44.1 41.5 2.6 5.9% 97.6

2015 15,084 583.7 548.8 34.9 6.0% 97.3

2016 16,237 624.8 587.8 36.9 5.9% 96.8

2017 27,655 1062.0 1001.0 61.0 5.7% 95.8

2018 25,330 972.9 916.8 56.0 5.8% 95.9

2019 21,194 812.5 766.1 46.4 5.7% 95.6

1-in-2 August

System Peak Day

2013 506 41.2 38.9 2.3 5.5% 95.0

2014 506 41.2 38.9 2.3 5.5% 95.0

2015 15,084 543.2 513.1 30.1 5.5% 94.9

2016 16,237 582.9 551.0 31.9 5.5% 94.6

2017 27,655 996.7 943.8 52.9 5.3% 94.5

2018 25,330 912.4 863.9 48.6 5.3% 94.52019 21,194 763.5 723.2 40.2 5.3% 94.4

Due to limited empirical data, ex ante estimates for medium customers have a higher degree of uncertainty

16

SCE Specific Results

17

SCE’s average load reduction was 5.8%, or 35.5 MW, across the 10 event days in Jul-Sep 2013

18

SCE detailed event load impacts

19

Manufacturing accounts for roughly 27% of customers and load but provides 68% of the reductions

Wholesale and transport accounts for 17% of the customers and load but provides 12% of the reductions

Two industry groups account for 87% of the demand reductions

20

At SCE, larger customers not only have more load but also deliver larger percent reductions


Impacts (MW)0%

20%

40%

60%

80%

100%

503.0

309.421.8496.0

115.4

6.7

502.0

86.5

3.7

496.0

64.12.0

496.0

30.5 0.7


0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%

Percent Reductions

Average

21

Ex ante percent reductions line up with historical percent reductions


Estimates were produced by area


customers


Assumes percent reductions of new customers by area will be similar

0

5

10

15

0

5

10

15

70 75 80 85 70 75 80 85

70 75 80 85

Orange County Other

South of Lugo

Ex-Post Ex-Post Fitted Values Ex-Ante Ex-Ante Fitted Values

% R

educ

tion

over

eve

nt w

indo

w


Graphs by planarea

22


2013 estimates reflect the evolution of SCE’s default CPP customers and more recent historical data

2014 ex ante impacts align well with the 2013 avg. event response, 35.5 MW

Over time, customers who reduce demand have tended to remain on CPP, while those less likely to respond have migrated elsewhere

Weather Year Year

2012 Reference

Load

2013 Reference

Load

2012 Percent

Load Impact

2013 Percent

Load Impact

2012 Accounts

2013 Accounts

2012 Load

Impact (MW)

2013 Load

Impact (MW)

1-in-10 2014 222.0 263.8 4.2% 5.7% 3,099 2,512 28.8 37.7

1-in-10 2015 222.0 263.8 4.2% 5.7% 3,130 2,473 29.1 37.1

1-in-10 2016-2023 222.0 263.8 4.2% 5.7% 3,141 2,473 29.2 37.1

1-in-2 2014 217.6 257.6 4.4% 5.6% 3,099 2,512 29.6 36.0

1-in-2 2015 217.6 257.6 4.4% 5.6% 3,130 2,473 29.9 35.5

1-in-2 2016-2023 217.6 257.6 4.4% 5.6% 3,141 2,473 30.0 35.5

23

SDG&E Specific Results

24

SDG&E’s average weekday load impact was 6.9%, 19.6 MW

25

SDG&E detailed event load impacts

26

Offices made up 46% of the load at SDG&E (versus 36% and 23% at PG&E and SCE). They also reduced demand more.

Institutional and Wholesale & Transport segments still performed the best and accounted for a substantial share of impacts.

SDG&E had more customers and load in the offices sector

27

At SDG&E, larger customers also accounted for a large amount of the demand reductions


Impacts (MW)0%

20%

40%

60%

80%

100%

222.0

163.5

14.4217.0

59.4

1.1

219.0

40.4 3.3

219.0

21.9 0.8216.0

5.9 0.5


0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0%10.0%

Percent Reductions

Average

On a percentage basis, there is no discernable pattern of responsiveness by size

28

Ex ante percent reductions are inline with 2012–2013 historical percent reductions



customers


0

1

2

3

4

5

6

7

8

9

10

% R

ed

uct

ion

ove

r e

ven

t w

ind

ow

70 75 80Avg. Daily Temperature (F)

Ex Post Ex-Post Fitted ValuesExAnte

29

Ex ante reference loads align with historical loads

0

100

200

300

400

Re

fere

nce

Lo

ad

(2

-6p

m)

60 65 70 75 80 85Avg. Daily Temperature (F)

Ex-Post Ex-Post Fitted Values Ex-Ante

30


Differences are due to small changes in the estimated percent reductions

The 2013 estimates incorporate more historical data (2012 and 2013 events v. 2012 alone)

Weather Year Year

2012 Reference

Load

2013 Reference

Load

2012 Percent

Load Impact

2013 Percent

Load Impact

2012 Accounts

2013 Accounts



1-in-10

2014 269.4 274.3 5.9% 6.7% 1,097 1,146 17.4 21.1

2015 269.4 274.3 5.9% 6.7% 1,114 1,164 17.7 21.4

2016 269.4 274.3 5.9% 6.7% 1,128 1,193 17.9 22.0

1-in-2

2014 260.5 261.1 5.9% 6.2% 1,097 1,146 16.9 18.5

2015 260.5 261.1 5.9% 6.2% 1,114 1,164 17.2 18.8

2016 260.5 261.1 5.9% 6.2% 1,128 1,193 17.4 19.2

31

Due to limited empirical data, ex ante estimates for medium customers have a higher degree of uncertainty

Weather Year Year Enrolled Accounts

Avg. Reference

Load

Avg. Estimated

Load w/ DR

Avg. Load Impact

% Load Reduction

Weighted Temp.

(11 AM - 6 PM MW)

(11 AM - 6 PM MW)

(11 AM - 6 PM MW) (%) (°F)

1-in-10 August

System Peak Day

2013 0 - - - - -2014 0 - - - - -2015 9,025 448.6 428.4 20.1 4.5% 84.32016 7,387 365.0 345.6 19.3 5.3% 84.32017 6,640 327.2 307.5 19.7 6.0% 84.3

1-in-2 August System Peak

Day

2013 0 - - - - -2014 0 - - - - -2015 9,025 428.7 410.9 17.8 4.2% 81.92016 7,387 348.9 331.9 17.0 4.9% 81.92017 6,640 312.9 295.5 17.3 5.5% 81.9

Impacts are based on large default CPP, but adjusted for differences in the industry mix and size of medium customers

For comments or questions, contact:

Josh L. Bode, M.P.P.

[email protected]

Candice A. Churchwell, M.S.

[email protected]

Nexant, Inc.101 Montgomery St., 15th Floor

San Francisco, CA 94104415-777-0707

33

Appendix Evaluation Methodology

and Validation

34

CPP rates introduce two changes: Higher prices on peaks hours of critical days (CPP adder) designed to

encourage customers to reduce demand Rate discounts during non-event days to offset CPP adder

The impact of the rate discount on non-event days is not estimated for three reasons: Focus for planning and operations is on the dispatchable demand

reductions that can be attained The pre-enrollment data needed to quantify non-event day impacts is

too distant (four or five years prior) Most non-event day impacts, if any, are now embedded in system

load forecasts (and not incremental) Analyses in 2010 and 2011 did not find statistically significant impacts

due to the rate discount

The focus on the evaluation was on the dispatchable event day response

35

For industrial customers (and commercial customers without a successful match), impacts were estimated using customer-specific regressions.

Electricity usage patterns on non-event days are used to estimate what customers would have done if an event had not been called (a within-subjects method).

Approach works: For very large customers (where a valid

control group may not be possible) Customers with low weather sensitivity When non-event day conditions are

similar to event days (often not the case)

For commercial customers, the estimates rely on difference-in-differences panel regressions.

Observe how the control and participant groups behave during both event and non-event days.

Method is less likely to be an artifact of the model selected. It better captures behavior during event days without comparable weather conditions.

Approach works best with: Ample control group candidates There are many observable variables Non-event or pre-enrollment data A small number of customer does not

dominate the load and/or reductions

These use an external control group and non-event day data.

This approach was used for weather-sensitive commercial customers: institutional/governmental industries, offices, hotels, finance, services, and retail stores.

This approach was used for less weather-sensitive industrial customers, and for those commercial customers that could not be matched with a suitable control customer.

The ex post evaluation used the best available method for commercial and industrial customers

36

PG&E example using raw aggregated data for summer weekdays without any modeling

Some of the noise is explained through day of week, seasonal effects and customer specific weather

Generally customer specific regression are better suited for industrial customers

Individual Regressions – some 2013 events lacked comparable non-event days

200

220

240

260

280

300

60 70 80 90 100 60 70 80 90 100

Commercial Industrial

Non-event days Event days

Avg

. kW

pe

r cu

sto

me

r (2

-6 p

m)

Avg. Customer Daily Max Temp (F)

37

Difference-in-differences

Difference-in-differences uses information from a control group and information for hot non-event days

Hourly loads for a well-matched control group nearly mirror the loads of the CPP population on event-like days.

These small differences are subtracted from the difference between control and CPP population loads on actual event days – the difference-in-differences.

05

01

00

150

200

250

Avg

. C

usto

me

r kW

0 5 10 15 20 25Hour Ending

Control Group

CPP participants

Difference

Proxy Event Days

05

01

00

150

200

250

Avg

. C

usto

me

r kW

0 5 10 15 20 25Hour Ending

Control Group

CPP participants

Difference

Control Adjusted

Diff-in-Diff

Actual Event Days

38

The non-event control days were selected to match event conditions as closely as possible

We matched non-event days to historical events based on system loads, temperature, day of week and program year.

Comparable proxy days are not available for some days with very extreme weather.

150

0016

000

170

0018

000

190

00S

yste

m P

eak

for

Day

85 90 95 100 105Daily Maximum(Fahrenheit)

Actual Events Proxy Events

PG&E

120

0014

000

160

0018

000

200

0022

000

Sys

tem

Pea

k fo

r D

ay



SCE

360

038

00

400

042

00

440

046

00

Sys

tem

Pea

k fo

r D

ay



SDG&E

39

The validation tests show that the hybrid method out-performs other alternatives

Impacts are estimated for the proxy event days, using the same models and process used for the ex post evaluation. If a method is accurate, it produces impact estimates for the average event that center on zero and are insignificant because, in fact, there is no event.

The impacts when CPP event day prices were not in effect are near zero and the reference loads estimated via the control group match the CPP participant loads.

0

50

100

150

200

250

300

0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24

PG&E SDG&E SCE

Reference Load Actual Load Estimated Impact

Avg

. C

usto

me

r kW

Hour Ending

2013 california statewide critical peak pricing evaluation josh l. bode candice a. churchwell drmec...

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